Instruction: What field is the article from?

Prompt:
Title: MAP's not dead yet: Uncovering true language model modes by conditioning away degeneracy
Abstract: It has been widely observed that exact or approximate MAP (mode-seeking)
decoding from natural language generation (NLG) models consistently leads to
degenerate outputs (Stahlberg and Byrne, 2019, Holtzman et al., 2019). This has
generally been attributed to either a fundamental inadequacy of modes in models
or weaknesses in language modeling. Contrastingly in this work, we emphasize
that degenerate modes can even occur in the absence of any model error, due to
contamination of the training data. Specifically, we show that mixing even a
tiny amount of low-entropy noise with a population text distribution can cause
the data distribution's mode to become degenerate, implying that any models
trained on it will be as well. As the unconditional mode of NLG models will
often be degenerate, we therefore propose to apply MAP decoding to the model's
distribution conditional on avoiding specific degeneracies. Using exact-search,
we empirically verify that the length-conditional modes of machine translation
models and language models are indeed more fluent and topical than their
unconditional modes. For the first time, we also share many examples of exact
modal sequences from these models, and from several variants of the LLaMA-7B
model. Notably, the modes of the LLaMA models are still degenerate, showing
that improvements in modeling have not fixed this issue. Because of the cost of
exact mode finding algorithms, we develop an approximate mode finding approach,
ACBS, which finds sequences that are both high-likelihood and high-quality. We
apply this approach to LLaMA-7B, a model which was not trained for instruction
following, and find that we are able to elicit reasonable outputs without any
finetuning.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation
Abstract: Image-to-text radiology report generation aims to automatically produce
radiology reports that describe the findings in medical images. Most existing
methods focus solely on the image data, disregarding the other patient
information accessible to radiologists. In this paper, we present a novel
multi-modal deep neural network framework for generating chest X-rays reports
by integrating structured patient data, such as vital signs and symptoms,
alongside unstructured clinical notes.We introduce a conditioned
cross-multi-head attention module to fuse these heterogeneous data modalities,
bridging the semantic gap between visual and textual data. Experiments
demonstrate substantial improvements from using additional modalities compared
to relying on images alone. Notably, our model achieves the highest reported
performance on the ROUGE-L metric compared to relevant state-of-the-art models
in the literature. Furthermore, we employed both human evaluation and clinical
semantic similarity measurement alongside word-overlap metrics to improve the
depth of quantitative analysis. A human evaluation, conducted by a
board-certified radiologist, confirms the model's accuracy in identifying
high-level findings, however, it also highlights that more improvement is
needed to capture nuanced details and clinical context.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Causal Discovery Under Local Privacy
Abstract: Differential privacy is a widely adopted framework designed to safeguard the
sensitive information of data providers within a data set. It is based on the
application of controlled noise at the interface between the server that stores
and processes the data, and the data consumers. Local differential privacy is a
variant that allows data providers to apply the privatization mechanism
themselves on their data individually. Therefore it provides protection also in
contexts in which the server, or even the data collector, cannot be trusted.
The introduction of noise, however, inevitably affects the utility of the data,
particularly by distorting the correlations between individual data components.
This distortion can prove detrimental to tasks such as causal discovery. In
this paper, we consider various well-known locally differentially private
mechanisms and compare the trade-off between the privacy they provide, and the
accuracy of the causal structure produced by algorithms for causal learning
when applied to data obfuscated by these mechanisms. Our analysis yields
valuable insights for selecting appropriate local differentially private
protocols for causal discovery tasks. We foresee that our findings will aid
researchers and practitioners in conducting locally private causal discovery.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Plug-and-Play, Dense-Label-Free Extraction of Open-Vocabulary Semantic Segmentation from Vision-Language Models
Abstract: From an enormous amount of image-text pairs, large-scale vision-language
models (VLMs) learn to implicitly associate image regions with words, which is
vital for tasks such as image captioning and visual question answering.
However, leveraging such pre-trained models for open-vocabulary semantic
segmentation remains a challenge. In this paper, we propose a simple, yet
extremely effective, training-free technique, Plug-and-Play Open-Vocabulary
Semantic Segmentation (PnP-OVSS) for this task. PnP-OVSS leverages a VLM with
direct text-to-image cross-attention and an image-text matching loss to produce
semantic segmentation. However, cross-attention alone tends to over-segment,
whereas cross-attention plus GradCAM tend to under-segment. To alleviate this
issue, we introduce Salience Dropout; by iteratively dropping patches that the
model is most attentive to, we are able to better resolve the entire extent of
the segmentation mask. Compared to existing techniques, the proposed method
does not require any neural network training and performs hyperparameter tuning
without the need for any segmentation annotations, even for a validation set.
PnP-OVSS demonstrates substantial improvements over a comparable baseline
(+29.4% mIoU on Pascal VOC, +13.2% mIoU on Pascal Context, +14.0% mIoU on MS
COCO, +2.4% mIoU on COCO Stuff) and even outperforms most baselines that
conduct additional network training on top of pretrained VLMs.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Evaluating General-Purpose AI with Psychometrics
Abstract: Artificial intelligence (AI) has witnessed an evolution from task-specific to
general-purpose systems that trend toward human versatility. As AI systems
begin to play pivotal roles in society, it is important to ensure that they are
adequately evaluated. Current AI benchmarks typically assess performance on
collections of specific tasks. This has drawbacks when used for assessing
general-purpose AI systems. First, it is difficult to predict whether AI
systems could complete a new task it has never seen or that did not previously
exist. Second, these benchmarks often focus on overall performance metrics,
potentially overlooking the finer details crucial for making informed
decisions. Lastly, there are growing concerns about the reliability of existing
benchmarks and questions about what is being measured. To solve these
challenges, this paper suggests that psychometrics, the science of
psychological measurement, should be placed at the core of evaluating
general-purpose AI. Psychometrics provides a rigorous methodology for
identifying and measuring the latent constructs that underlie performance
across multiple tasks. We discuss its merits, warn against potential pitfalls,
and propose a framework for putting it into practice. Finally, we explore
future opportunities to integrate psychometrics with AI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Training Chain-of-Thought via Latent-Variable Inference
Abstract: Large language models (LLMs) solve problems more accurately and interpretably
when instructed to work out the answer step by step using a
``chain-of-thought'' (CoT) prompt. One can also improve LLMs' performance on a
specific task by supervised fine-tuning, i.e., by using gradient ascent on some
tunable parameters to maximize the average log-likelihood of correct answers
from a labeled training set. Naively combining CoT with supervised tuning
requires supervision not just of the correct answers, but also of detailed
rationales that lead to those answers; these rationales are expensive to
produce by hand. Instead, we propose a fine-tuning strategy that tries to
maximize the \emph{marginal} log-likelihood of generating a correct answer
using CoT prompting, approximately averaging over all possible rationales. The
core challenge is sampling from the posterior over rationales conditioned on
the correct answer; we address it using a simple Markov-chain Monte Carlo
(MCMC) expectation-maximization (EM) algorithm inspired by the self-taught
reasoner (STaR), memoized wake-sleep, Markovian score climbing, and persistent
contrastive divergence. This algorithm also admits a novel control-variate
technique that drives the variance of our gradient estimates to zero as the
model improves. Applying our technique to GSM8K and the tasks in BIG-Bench
Hard, we find that this MCMC-EM fine-tuning technique typically improves the
model's accuracy on held-out examples more than STaR or prompt-tuning with or
without CoT.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Towards a Standardized Reinforcement Learning Framework for AAM Contingency Management
Abstract: Advanced Air Mobility (AAM) is the next generation of air transportation that
includes new entrants such as electric vertical takeoff and landing (eVTOL)
aircraft, increasingly autonomous flight operations, and small UAS package
delivery. With these new vehicles and operational concepts comes a desire to
increase densities far beyond what occurs today in and around urban areas, to
utilize new battery technology, and to move toward more autonomously-piloted
aircraft. To achieve these goals, it becomes essential to introduce new safety
management system capabilities that can rapidly assess risk as it evolves
across a span of complex hazards and, if necessary, mitigate risk by executing
appropriate contingencies via supervised or automated decision-making during
flights. Recently, reinforcement learning has shown promise for real-time
decision making across a wide variety of applications including contingency
management. In this work, we formulate the contingency management problem as a
Markov Decision Process (MDP) and integrate the contingency management MDP into
the AAM-Gym simulation framework. This enables rapid prototyping of
reinforcement learning algorithms and evaluation of existing systems, thus
providing a community benchmark for future algorithm development. We report
baseline statistical information for the environment and provide example
performance metrics.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SBTRec- A Transformer Framework for Personalized Tour Recommendation Problem with Sentiment Analysis
Abstract: When traveling to an unfamiliar city for holidays, tourists often rely on
guidebooks, travel websites, or recommendation systems to plan their daily
itineraries and explore popular points of interest (POIs). However, these
approaches may lack optimization in terms of time feasibility, localities, and
user preferences. In this paper, we propose the SBTRec algorithm: a BERT-based
Trajectory Recommendation with sentiment analysis, for recommending
personalized sequences of POIs as itineraries. The key contributions of this
work include analyzing users' check-ins and uploaded photos to understand the
relationship between POI visits and distance. We introduce SBTRec, which
encompasses sentiment analysis to improve recommendation accuracy by
understanding users' preferences and satisfaction levels from reviews and
comments about different POIs. Our proposed algorithms are evaluated against
other sequence prediction methods using datasets from 8 cities. The results
demonstrate that SBTRec achieves an average F1 score of 61.45%, outperforming
baseline algorithms.
  The paper further discusses the flexibility of the SBTRec algorithm, its
ability to adapt to different scenarios and cities without modification, and
its potential for extension by incorporating additional information for more
reliable predictions. Overall, SBTRec provides personalized and relevant POI
recommendations, enhancing tourists' overall trip experiences. Future work
includes fine-tuning personalized embeddings for users, with evaluation of
users' comments on POIs,~to further enhance prediction accuracy.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Next-gen traffic surveillance: AI-assisted mobile traffic violation detection system
Abstract: Road traffic accidents pose a significant global public health concern,
leading to injuries, fatalities, and vehicle damage. Approximately 1,3 million
people lose their lives daily due to traffic accidents [World Health
Organization, 2022]. Addressing this issue requires accurate traffic law
violation detection systems to ensure adherence to regulations. The integration
of Artificial Intelligence algorithms, leveraging machine learning and computer
vision, has facilitated the development of precise traffic rule enforcement.
This paper illustrates how computer vision and machine learning enable the
creation of robust algorithms for detecting various traffic violations. Our
model, capable of identifying six common traffic infractions, detects red light
violations, illegal use of breakdown lanes, violations of vehicle following
distance, breaches of marked crosswalk laws, illegal parking, and parking on
marked crosswalks. Utilizing online traffic footage and a self-mounted on-dash
camera, we apply the YOLOv5 algorithm's detection module to identify traffic
agents such as cars, pedestrians, and traffic signs, and the strongSORT
algorithm for continuous interframe tracking. Six discrete algorithms analyze
agents' behavior and trajectory to detect violations. Subsequently, an
Identification Module extracts vehicle ID information, such as the license
plate, to generate violation notices sent to relevant authorities.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains
Abstract: This paper addresses privacy concerns in multi-agent reinforcement learning
(MARL), specifically within the context of supply chains where individual
strategic data must remain confidential. Organizations within the supply chain
are modeled as agents, each seeking to optimize their own objectives while
interacting with others. As each organization's strategy is contingent on
neighboring strategies, maintaining privacy of state and action-related
information is crucial. To tackle this challenge, we propose a game-theoretic,
privacy-preserving mechanism, utilizing a secure multi-party computation (MPC)
framework in MARL settings. Our major contribution is the successful
implementation of a secure MPC framework, SecFloat on EzPC, to solve this
problem. However, simply implementing policy gradient methods such as MADDPG
operations using SecFloat, while conceptually feasible, would be
programmatically intractable. To overcome this hurdle, we devise a novel
approach that breaks down the forward and backward pass of the neural network
into elementary operations compatible with SecFloat , creating efficient and
secure versions of the MADDPG algorithm. Furthermore, we present a learning
mechanism that carries out floating point operations in a privacy-preserving
manner, an important feature for successful learning in MARL framework.
Experiments reveal that there is on average 68.19% less supply chain wastage in
2 PC compared to no data share, while also giving on average 42.27% better
average cumulative revenue for each player. This work paves the way for
practical, privacy-preserving MARL, promising significant improvements in
secure computation within supply chain contexts and broadly.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis
Abstract: Big Data empowers the farming community with the information needed to
optimize resource usage, increase productivity, and enhance the sustainability
of agricultural practices. The use of Big Data in farming requires the
collection and analysis of data from various sources such as sensors,
satellites, and farmer surveys. While Big Data can provide the farming
community with valuable insights and improve efficiency, there is significant
concern regarding the security of this data as well as the privacy of the
participants. Privacy regulations, such as the EU GDPR, the EU Code of Conduct
on agricultural data sharing by contractual agreement, and the proposed EU AI
law, have been created to address the issue of data privacy and provide
specific guidelines on when and how data can be shared between organizations.
To make confidential agricultural data widely available for Big Data analysis
without violating the privacy of the data subjects, we consider
privacy-preserving methods of data sharing in agriculture. Deep learning-based
synthetic data generation has been proposed for privacy-preserving data
sharing. However, there is a lack of compliance with documented data privacy
policies in such privacy-preserving efforts. In this study, we propose a novel
framework for enforcing privacy policy rules in privacy-preserving data
generation algorithms. We explore several available agricultural codes of
conduct, extract knowledge related to the privacy constraints in data, and use
the extracted knowledge to define privacy bounds in a privacy-preserving
generative model. We use our framework to generate synthetic agricultural data
and present experimental results that demonstrate the utility of the synthetic
dataset in downstream tasks. We also show that our framework can evade
potential threats and secure data based on applicable regulatory policy rules.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Multi-Step Dialogue Workflow Action Prediction
Abstract: In task-oriented dialogue, a system often needs to follow a sequence of
actions, called a workflow, that complies with a set of guidelines in order to
complete a task. In this paper, we propose the novel problem of multi-step
workflow action prediction, in which the system predicts multiple future
workflow actions. Accurate prediction of multiple steps allows for multi-turn
automation, which can free up time to focus on more complex tasks. We propose
three modeling approaches that are simple to implement yet lead to more action
automation: 1) fine-tuning on a training dataset, 2) few-shot in-context
learning leveraging retrieval and large language model prompting, and 3)
zero-shot graph traversal, which aggregates historical action sequences into a
graph for prediction. We show that multi-step action prediction produces
features that improve accuracy on downstream dialogue tasks like predicting
task success, and can increase automation of steps by 20% without requiring as
much feedback from a human overseeing the system.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Batched Low-Rank Adaptation of Foundation Models
Abstract: Low-Rank Adaptation (LoRA) has recently gained attention for fine-tuning
foundation models by incorporating trainable low-rank matrices, thereby
reducing the number of trainable parameters. While LoRA offers numerous
advantages, its applicability for real-time serving to a diverse and global
user base is constrained by its incapability to handle multiple task-specific
adapters efficiently. This imposes a performance bottleneck in scenarios
requiring personalized, task-specific adaptations for each incoming request. To
mitigate this constraint, we introduce Fast LoRA (FLoRA), a framework in which
each input example in a minibatch can be associated with its unique low-rank
adaptation weights, allowing for efficient batching of heterogeneous requests.
We empirically demonstrate that FLoRA retains the performance merits of LoRA,
showcasing competitive results on the MultiPL-E code generation benchmark
spanning over 8 languages and a multilingual speech recognition task across 6
languages.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SoK: Pitfalls in Evaluating Black-Box Attacks
Abstract: Numerous works study black-box attacks on image classifiers. However, these
works make different assumptions on the adversary's knowledge and current
literature lacks a cohesive organization centered around the threat model. To
systematize knowledge in this area, we propose a taxonomy over the threat space
spanning the axes of feedback granularity, the access of interactive queries,
and the quality and quantity of the auxiliary data available to the attacker.
Our new taxonomy provides three key insights. 1) Despite extensive literature,
numerous under-explored threat spaces exist, which cannot be trivially solved
by adapting techniques from well-explored settings. We demonstrate this by
establishing a new state-of-the-art in the less-studied setting of access to
top-k confidence scores by adapting techniques from well-explored settings of
accessing the complete confidence vector, but show how it still falls short of
the more restrictive setting that only obtains the prediction label,
highlighting the need for more research. 2) Identification the threat model of
different attacks uncovers stronger baselines that challenge prior
state-of-the-art claims. We demonstrate this by enhancing an initially weaker
baseline (under interactive query access) via surrogate models, effectively
overturning claims in the respective paper. 3) Our taxonomy reveals
interactions between attacker knowledge that connect well to related areas,
such as model inversion and extraction attacks. We discuss how advances in
other areas can enable potentially stronger black-box attacks. Finally, we
emphasize the need for a more realistic assessment of attack success by
factoring in local attack runtime. This approach reveals the potential for
certain attacks to achieve notably higher success rates and the need to
evaluate attacks in diverse and harder settings, highlighting the need for
better selection criteria.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Compute at Scale: A Broad Investigation into the Data Center Industry
Abstract: This report characterizes the data center industry and its importance for AI
development. Data centers are industrial facilities that efficiently provide
compute at scale and thus constitute the engine rooms of today's digital
economy. As large-scale AI training and inference become increasingly
computationally expensive, they are dominantly executed from this designated
infrastructure. Key features of data centers include large-scale compute
clusters that require extensive cooling and consume large amounts of power, the
need for fast connectivity both within the data center and to the internet, and
an emphasis on security and reliability. The global industry is valued at
approximately $250B and is expected to double over the next seven years. There
are likely about 500 large (above 10 MW) data centers globally, with the US,
Europe, and China constituting the most important markets. The report further
covers important actors, business models, main inputs, and typical locations of
data centers.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions
Abstract: Intensive Care Units (ICU) require comprehensive patient data integration for
enhanced clinical outcome predictions, crucial for assessing patient
conditions. Recent deep learning advances have utilized patient time series
data, and fusion models have incorporated unstructured clinical reports,
improving predictive performance. However, integrating established medical
knowledge into these models has not yet been explored. The medical domain's
data, rich in structural relationships, can be harnessed through knowledge
graphs derived from clinical ontologies like the Unified Medical Language
System (UMLS) for better predictions. Our proposed methodology integrates this
knowledge with ICU data, improving clinical decision modeling. It combines
graph representations with vital signs and clinical reports, enhancing
performance, especially when data is missing. Additionally, our model includes
an interpretability component to understand how knowledge graph nodes affect
predictions.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Interpretable by Design: Wrapper Boxes Combine Neural Performance with Faithful Explanations
Abstract: Can we preserve the accuracy of neural models while also providing faithful
explanations? We present wrapper boxes, a general approach to generate
faithful, example-based explanations for model predictions while maintaining
predictive performance. After training a neural model as usual, its learned
feature representation is input to a classic, interpretable model to perform
the actual prediction. This simple strategy is surprisingly effective, with
results largely comparable to those of the original neural model, as shown
across three large pre-trained language models, two datasets of varying scale,
four classic models, and four evaluation metrics. Moreover, because these
classic models are interpretable by design, the subset of training examples
that determine classic model predictions can be shown directly to users.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Are These the Same Apple? Comparing Images Based on Object Intrinsics
Abstract: The human visual system can effortlessly recognize an object under different
extrinsic factors such as lighting, object poses, and background, yet current
computer vision systems often struggle with these variations. An important step
to understanding and improving artificial vision systems is to measure image
similarity purely based on intrinsic object properties that define object
identity. This problem has been studied in the computer vision literature as
re-identification, though mostly restricted to specific object categories such
as people and cars. We propose to extend it to general object categories,
exploring an image similarity metric based on object intrinsics. To benchmark
such measurements, we collect the Common paired objects Under differenT
Extrinsics (CUTE) dataset of $18,000$ images of $180$ objects under different
extrinsic factors such as lighting, poses, and imaging conditions. While
existing methods such as LPIPS and CLIP scores do not measure object intrinsics
well, we find that combining deep features learned from contrastive
self-supervised learning with foreground filtering is a simple yet effective
approach to approximating the similarity. We conduct an extensive survey of
pre-trained features and foreground extraction methods to arrive at a strong
baseline that best measures intrinsic object-centric image similarity among
current methods. Finally, we demonstrate that our approach can aid in
downstream applications such as acting as an analog for human subjects and
improving generalizable re-identification. Please see our project website at
https://s-tian.github.io/projects/cute/ for visualizations of the data and
demos of our metric.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Generalization Analogies: A Testbed for Generalizing AI Oversight to Hard-To-Measure Domains
Abstract: As AI systems become more intelligent and their behavior becomes more
challenging to assess, they may learn to game the flaws of human feedback
instead of genuinely striving to follow instructions; however, this risk can be
mitigated by controlling how LLMs generalize human feedback to situations where
it is unreliable. To better understand how reward models generalize, we craft
69 distribution shifts spanning 8 categories. We find that reward models do not
learn to evaluate `instruction-following' by default and instead favor personas
that resemble internet text. Techniques for interpreting reward models'
internal representations achieve better generalization than standard
fine-tuning, but still frequently fail to distinguish instruction-following
from conflated behaviors. We consolidate the 15 most challenging distribution
shifts into the GENeralization analogIES (GENIES) benchmark, which we hope will
enable progress toward controlling reward model generalization.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Can CLIP Help Sound Source Localization?
Abstract: Large-scale pre-trained image-text models demonstrate remarkable versatility
across diverse tasks, benefiting from their robust representational
capabilities and effective multimodal alignment. We extend the application of
these models, specifically CLIP, to the domain of sound source localization.
Unlike conventional approaches, we employ the pre-trained CLIP model without
explicit text input, relying solely on the audio-visual correspondence. To this
end, we introduce a framework that translates audio signals into tokens
compatible with CLIP's text encoder, yielding audio-driven embeddings. By
directly using these embeddings, our method generates audio-grounded masks for
the provided audio, extracts audio-grounded image features from the highlighted
regions, and aligns them with the audio-driven embeddings using the
audio-visual correspondence objective. Our findings suggest that utilizing
pre-trained image-text models enable our model to generate more complete and
compact localization maps for the sounding objects. Extensive experiments show
that our method outperforms state-of-the-art approaches by a significant
margin.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Faithful Path Language Modelling for Explainable Recommendation over Knowledge Graph
Abstract: Path reasoning methods over knowledge graphs have gained popularity for their
potential to improve transparency in recommender systems. However, the
resulting models still rely on pre-trained knowledge graph embeddings, fail to
fully exploit the interdependence between entities and relations in the KG for
recommendation, and may generate inaccurate explanations. In this paper, we
introduce PEARLM, a novel approach that efficiently captures user behaviour and
product-side knowledge through language modelling. With our approach, knowledge
graph embeddings are directly learned from paths over the KG by the language
model, which also unifies entities and relations in the same optimisation
space. Constraints on the sequence decoding additionally guarantee path
faithfulness with respect to the KG. Experiments on two datasets show the
effectiveness of our approach compared to state-of-the-art baselines. Source
code and datasets: AVAILABLE AFTER GETTING ACCEPTED.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Context-dependent Instruction Tuning for Dialogue Response Generation
Abstract: Recent language models have achieved impressive performance in natural
language tasks by incorporating instructions with task input during
fine-tuning. Since all samples in the same natural language task can be
explained with the same task instructions, many instruction datasets only
provide a few instructions for the entire task, without considering the input
of each example in the task. However, this approach becomes ineffective in
complex multi-turn dialogue generation tasks, where the input varies highly
with each turn as the dialogue context changes, so that simple task
instructions cannot improve the generation performance. To address this
limitation, we introduce a context-based instruction fine-tuning framework for
each multi-turn dialogue which generates both responses and instructions based
on the previous context as input. During the evaluation, the model generates
instructions based on the previous context to self-guide the response. The
proposed framework produces comparable or even outstanding results compared to
the baselines by aligning instructions to the input during fine-tuning with the
instructions in quantitative evaluations on dialogue benchmark datasets with
reduced computation budget.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Reward Shaping for Improved Learning in Real-time Strategy Game Play
Abstract: We investigate the effect of reward shaping in improving the performance of
reinforcement learning in the context of the real-time strategy,
capture-the-flag game. The game is characterized by sparse rewards that are
associated with infrequently occurring events such as grabbing or capturing the
flag, or tagging the opposing player. We show that appropriately designed
reward shaping functions applied to different game events can significantly
improve the player's performance and training times of the player's learning
algorithm. We have validated our reward shaping functions within a simulated
environment for playing a marine capture-the-flag game between two players. Our
experimental results demonstrate that reward shaping can be used as an
effective means to understand the importance of different sub-tasks during
game-play towards winning the game, to encode a secondary objective functions
such as energy efficiency into a player's game-playing behavior, and, to
improve learning generalizable policies that can perform well against different
skill levels of the opponent.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition
Abstract: Illumination variation has been a long-term challenge in real-world facial
expression recognition(FER). Under uncontrolled or non-visible light
conditions, Near-infrared (NIR) can provide a simple and alternative solution
to obtain high-quality images and supplement the geometric and texture details
that are missing in the visible domain. Due to the lack of existing large-scale
NIR facial expression datasets, directly extending VIS FER methods to the NIR
spectrum may be ineffective. Additionally, previous heterogeneous image
synthesis methods are restricted by low controllability without prior task
knowledge. To tackle these issues, we present the first approach, called for
NIR-FER Stochastic Differential Equations (NFER-SDE), that transforms face
expression appearance between heterogeneous modalities to the overfitting
problem on small-scale NIR data. NFER-SDE is able to take the whole VIS source
image as input and, together with domain-specific knowledge, guide the
preservation of modality-invariant information in the high-frequency content of
the image. Extensive experiments and ablation studies show that NFER-SDE
significantly improves the performance of NIR FER and achieves state-of-the-art
results on the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Finetuning Text-to-Image Diffusion Models for Fairness
Abstract: The rapid adoption of text-to-image diffusion models in society underscores
an urgent need to address their biases. Without interventions, these biases
could propagate a distorted worldview and limit opportunities for minority
groups. In this work, we frame fairness as a distributional alignment problem.
Our solution consists of two main technical contributions: (1) a distributional
alignment loss that steers specific characteristics of the generated images
towards a user-defined target distribution, and (2) biased direct finetuning of
diffusion model's sampling process, which leverages a biased gradient to more
effectively optimize losses defined on the generated images. Empirically, our
method markedly reduces gender, racial, and their intersectional biases for
occupational prompts. Gender bias is significantly reduced even when finetuning
just five soft tokens. Crucially, our method supports diverse perspectives of
fairness beyond absolute equality, which is demonstrated by controlling age to
a $75\%$ young and $25\%$ old distribution while simultaneously debiasing
gender and race. Finally, our method is scalable: it can debias multiple
concepts at once by simply including these prompts in the finetuning data. We
hope our work facilitates the social alignment of T2I generative AI. We will
share code and various debiased diffusion model adaptors.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Adversarial Prompt Tuning for Vision-Language Models
Abstract: With the rapid advancement of multimodal learning, pre-trained
Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable
capacities in bridging the gap between visual and language modalities. However,
these models remain vulnerable to adversarial attacks, particularly in the
image modality, presenting considerable security risks. This paper introduces
Adversarial Prompt Tuning (AdvPT), a novel technique to enhance the adversarial
robustness of image encoders in VLMs. AdvPT innovatively leverages learnable
text prompts and aligns them with adversarial image embeddings, to address the
vulnerabilities inherent in VLMs without the need for extensive parameter
training or modification of the model architecture. We demonstrate that AdvPT
improves resistance against white-box and black-box adversarial attacks and
exhibits a synergistic effect when combined with existing
image-processing-based defense techniques, further boosting defensive
capabilities. Comprehensive experimental analyses provide insights into
adversarial prompt tuning, a novel paradigm devoted to improving resistance to
adversarial images through textual input modifications, paving the way for
future robust multimodal learning research. These findings open up new
possibilities for enhancing the security of VLMs. Our code will be available
upon publication of the paper.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Supervised structure learning
Abstract: This paper concerns structure learning or discovery of discrete generative
models. It focuses on Bayesian model selection and the assimilation of training
data or content, with a special emphasis on the order in which data are
ingested. A key move - in the ensuing schemes - is to place priors on the
selection of models, based upon expected free energy. In this setting, expected
free energy reduces to a constrained mutual information, where the constraints
inherit from priors over outcomes (i.e., preferred outcomes). The resulting
scheme is first used to perform image classification on the MNIST dataset to
illustrate the basic idea, and then tested on a more challenging problem of
discovering models with dynamics, using a simple sprite-based visual
disentanglement paradigm and the Tower of Hanoi (cf., blocks world) problem. In
these examples, generative models are constructed autodidactically to recover
(i.e., disentangle) the factorial structure of latent states - and their
characteristic paths or dynamics.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization
Abstract: Dynamic graph neural networks (DGNNs) are increasingly pervasive in
exploiting spatio-temporal patterns on dynamic graphs. However, existing works
fail to generalize under distribution shifts, which are common in real-world
scenarios. As the generation of dynamic graphs is heavily influenced by latent
environments, investigating their impacts on the out-of-distribution (OOD)
generalization is critical. However, it remains unexplored with the following
two major challenges: (1) How to properly model and infer the complex
environments on dynamic graphs with distribution shifts? (2) How to discover
invariant patterns given inferred spatio-temporal environments? To solve these
challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE)
framework for OOD generalization by modeling complex coupled environments and
exploiting spatio-temporal invariant patterns. Specifically, we first design
the environment-aware EA-DGNN to model environments by multi-channel
environments disentangling. Then, we propose an environment instantiation
mechanism for environment diversification with inferred distributions. Finally,
we discriminate spatio-temporal invariant patterns for out-of-distribution
prediction by the invariant pattern recognition mechanism and perform
fine-grained causal interventions node-wisely with a mixture of instantiated
environment samples. Experiments on real-world and synthetic dynamic graph
datasets demonstrate the superiority of our method against state-of-the-art
baselines under distribution shifts. To the best of our knowledge, we are the
first to study OOD generalization on dynamic graphs from the environment
learning perspective.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: The Logic of Doxastic Strategies
Abstract: In many real-world situations, there is often not enough information to know
that a certain strategy will succeed in achieving the goal, but there is a good
reason to believe that it will. The paper introduces the term ``doxastic'' for
such strategies.
  The main technical contribution is a sound and complete logical system that
describes the interplay between doxastic strategy and belief modalities.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Exploring Large Language Models for Code Explanation
Abstract: Automating code documentation through explanatory text can prove highly
beneficial in code understanding. Large Language Models (LLMs) have made
remarkable strides in Natural Language Processing, especially within software
engineering tasks such as code generation and code summarization. This study
specifically delves into the task of generating natural-language summaries for
code snippets, using various LLMs. The findings indicate that Code LLMs
outperform their generic counterparts, and zero-shot methods yield superior
results when dealing with datasets with dissimilar distributions between
training and testing sets.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: ASPEN: High-Throughput LoRA Fine-Tuning of Large Language Models with a Single GPU
Abstract: Transformer-based large language models (LLMs) have demonstrated outstanding
performance across diverse domains, particularly when fine-turned for specific
domains. Recent studies suggest that the resources required for fine-tuning
LLMs can be economized through parameter-efficient methods such as Low-Rank
Adaptation (LoRA). While LoRA effectively reduces computational burdens and
resource demands, it currently supports only a single-job fine-tuning setup.
  In this paper, we present ASPEN, a high-throughput framework for fine-tuning
LLMs. ASPEN efficiently trains multiple jobs on a single GPU using the LoRA
method, leveraging shared pre-trained model and adaptive scheduling. ASPEN is
compatible with transformer-based language models like LLaMA and ChatGLM, etc.
Experiments show that ASPEN saves 53% of GPU memory when training multiple
LLaMA-7B models on NVIDIA A100 80GB GPU and boosts training throughput by about
17% compared to existing methods when training with various pre-trained models
on different GPUs. The adaptive scheduling algorithm reduces turnaround time by
24%, end-to-end training latency by 12%, prioritizing jobs and preventing
out-of-memory issues.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: ReIDTracker Sea: the technical report of BoaTrack and SeaDronesSee-MOT challenge at MaCVi of WACV24
Abstract: Multi-Object Tracking is one of the most important technologies in maritime
computer vision. Our solution tries to explore Multi-Object Tracking in
maritime Unmanned Aerial vehicles (UAVs) and Unmanned Surface Vehicles (USVs)
usage scenarios. Most of the current Multi-Object Tracking algorithms require
complex association strategies and association information (2D location and
motion, 3D motion, 3D depth, 2D appearance) to achieve better performance,
which makes the entire tracking system extremely complex and heavy. At the same
time, most of the current Multi-Object Tracking algorithms still require video
annotation data which is costly to obtain for training. Our solution tries to
explore Multi-Object Tracking in a completely unsupervised way. The scheme
accomplishes instance representation learning by using self-supervision on
ImageNet. Then, by cooperating with high-quality detectors, the multi-target
tracking task can be completed simply and efficiently. The scheme achieved top
3 performance on both UAV-based Multi-Object Tracking with Reidentification and
USV-based Multi-Object Tracking benchmarks and the solution won the
championship in many multiple Multi-Object Tracking competitions. such as
BDD100K MOT,MOTS, Waymo 2D MOT

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operator
Abstract: Offline reinforcement learning (RL) faces a significant challenge of
distribution shift. Model-free offline RL penalizes the Q value for
out-of-distribution (OOD) data or constrains the policy closed to the behavior
policy to tackle this problem, but this inhibits the exploration of the OOD
region. Model-based offline RL, which uses the trained environment model to
generate more OOD data and performs conservative policy optimization within
that model, has become an effective method for this problem. However, the
current model-based algorithms rarely consider agent robustness when
incorporating conservatism into policy. Therefore, the new model-based offline
algorithm with a conservative Bellman operator (MICRO) is proposed. This method
trades off performance and robustness via introducing the robust Bellman
operator into the algorithm. Compared with previous model-based algorithms with
robust adversarial models, MICRO can significantly reduce the computation cost
by only choosing the minimal Q value in the state uncertainty set. Extensive
experiments demonstrate that MICRO outperforms prior RL algorithms in offline
RL benchmark and is considerably robust to adversarial perturbations.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Towards a Feminist Metaethics of AI
Abstract: The proliferation of Artificial Intelligence (AI) has sparked an overwhelming
number of AI ethics guidelines, boards and codes of conduct. These outputs
primarily analyse competing theories, principles and values for AI development
and deployment. However, as a series of recent problematic incidents about AI
ethics/ethicists demonstrate, this orientation is insufficient. Before
proceeding to evaluate other professions, AI ethicists should critically
evaluate their own; yet, such an evaluation should be more explicitly and
systematically undertaken in the literature. I argue that these insufficiencies
could be mitigated by developing a research agenda for a feminist metaethics of
AI. Contrary to traditional metaethics, which reflects on the nature of
morality and moral judgements in a non-normative way, feminist metaethics
expands its scope to ask not only what ethics is but also what our engagement
with it should be like. Applying this perspective to the context of AI, I
suggest that a feminist metaethics of AI would examine: (i) the continuity
between theory and action in AI ethics; (ii) the real-life effects of AI
ethics; (iii) the role and profile of those involved in AI ethics; and (iv) the
effects of AI on power relations through methods that pay attention to context,
emotions and narrative.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Lesion Search with Self-supervised Learning
Abstract: Content-based image retrieval (CBIR) with self-supervised learning (SSL)
accelerates clinicians' interpretation of similar images without manual
annotations. We develop a CBIR from the contrastive learning SimCLR and
incorporate a generalized-mean (GeM) pooling followed by L2 normalization to
classify lesion types and retrieve similar images before clinicians' analysis.
Results have shown improved performance. We additionally build an open-source
application for image analysis and retrieval. The application is easy to
integrate, relieving manual efforts and suggesting the potential to support
clinicians' everyday activities.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Intelligent Stress Assessment for e-Coaching
Abstract: This paper considers the adaptation of the e-coaching concept at times of
emergencies and disasters, through aiding the e-coaching with intelligent tools
for monitoring humans' affective state. The states such as anxiety, panic,
avoidance, and stress, if properly detected, can be mitigated using the
e-coaching tactic and strategy. In this work, we focus on a stress monitoring
assistant tool developed on machine learning techniques. We provide the results
of an experimental study using the proposed method.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution
Abstract: The ongoing challenges in time series anomaly detection (TSAD), notably the
scarcity of anomaly labels and the variability in anomaly lengths and shapes,
have led to the need for a more efficient solution. As limited anomaly labels
hinder traditional supervised models in TSAD, various SOTA deep learning
techniques, such as self-supervised learning, have been introduced to tackle
this issue. However, they encounter difficulties handling variations in anomaly
lengths and shapes, limiting their adaptability to diverse anomalies.
Additionally, many benchmark datasets suffer from the problem of having
explicit anomalies that even random functions can detect. This problem is
exacerbated by ill-posed evaluation metrics, known as point adjustment (PA),
which can result in inflated model performance. In this context, we propose a
novel self-supervised learning based Tri-domain Anomaly Detector (TriAD), which
addresses these challenges by modeling features across three data domains -
temporal, frequency, and residual domains - without relying on anomaly labels.
Unlike traditional contrastive learning methods, TriAD employs both
inter-domain and intra-domain contrastive loss to learn common attributes among
normal data and differentiate them from anomalies. Additionally, our approach
can detect anomalies of varying lengths by integrating with a discord discovery
algorithm. It is worth noting that this study is the first to reevaluate the
deep learning potential in TSAD, utilizing both rigorously designed datasets
(i.e., UCR Archive) and evaluation metrics (i.e., PA%K and affiliation).
Through experimental results on the UCR dataset, TriAD achieves an impressive
three-fold increase in PA%K based F1 scores over SOTA deep learning models, and
50% increase of accuracy as compared to SOTA discord discovery algorithms.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation
Abstract: The acquisition of accurate rainfall distribution in space is an important
task in hydrological analysis and natural disaster pre-warning. However, it is
impossible to install rain gauges on every corner. Spatial interpolation is a
common way to infer rainfall distribution based on available raingauge data.
However, the existing works rely on some unrealistic pre-settings to capture
spatial correlations, which limits their performance in real scenarios. To
tackle this issue, we propose the SSIN, which is a novel data-driven
self-supervised learning framework for rainfall spatial interpolation by mining
latent spatial patterns from historical observation data. Inspired by the Cloze
task and BERT, we fully consider the characteristics of spatial interpolation
and design the SpaFormer model based on the Transformer architecture as the
core of SSIN. Our main idea is: by constructing rich self-supervision signals
via random masking, SpaFormer can learn informative embeddings for raw data and
then adaptively model spatial correlations based on rainfall spatial context.
Extensive experiments on two real-world raingauge datasets show that our method
outperforms the state-of-the-art solutions. In addition, we take traffic
spatial interpolation as another use case to further explore the performance of
our method, and SpaFormer achieves the best performance on one large real-world
traffic dataset, which further confirms the effectiveness and generality of our
method.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Survey of Large Language Models in Medicine: Principles, Applications, and Challenges
Abstract: Large language models (LLMs), such as ChatGPT, have received substantial
attention due to their impressive human language understanding and generation
capabilities. Therefore, the application of LLMs in medicine to assist
physicians and patient care emerges as a promising research direction in both
artificial intelligence and clinical medicine. To reflect this trend, this
survey provides a comprehensive overview of the principles, applications, and
challenges faced by LLMs in medicine. Specifically, we aim to address the
following questions: 1) How can medical LLMs be built? 2) What are the
downstream performances of medical LLMs? 3) How can medical LLMs be utilized in
real-world clinical practice? 4) What challenges arise from the use of medical
LLMs? and 5) How can we better construct and utilize medical LLMs? As a result,
this survey aims to provide insights into the opportunities and challenges of
LLMs in medicine and serve as a valuable resource for constructing practical
and effective medical LLMs. A regularly updated list of practical guides on
medical LLMs can be found at
https://github.com/AI-in-Health/MedLLMsPracticalGuide.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Steering Llama 2 via Contrastive Activation Addition
Abstract: We introduce Contrastive Activation Addition (CAA), an innovative method for
steering language models by modifying activations during their forward passes.
CAA computes ``steering vectors'' by averaging the difference in residual
stream activations between pairs of positive and negative examples of a
particular behavior such as factual versus hallucinatory responses. During
inference, these steering vectors are added at all token positions after the
user's prompt with either a positive or negative coefficient, allowing precise
control over the degree of the targeted behavior. We evaluate CAA's
effectiveness on Llama 2 Chat using both multiple-choice behavioral question
datasets and open-ended generation tasks. We demonstrate that CAA significantly
alters model behavior, outperforms traditional methods like finetuning and
few-shot prompting, and minimally reduces capabilities. Moreover, by employing
various activation space interpretation methods, we gain deeper insights into
CAA's mechanisms. CAA both accurately steers model outputs and also sheds light
on how high-level concepts are represented in Large Language Models (LLMs).

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering
Abstract: Large language models (LLMs) enable zero-shot approaches in open-domain
question answering (ODQA), yet with limited advancements as the reader is
compared to the retriever. This study aims at the feasibility of a zero-shot
reader that addresses the challenges of computational cost and the need for
labeled data. We find that LLMs are distracted due to irrelevant documents in
the retrieved set and the overconfidence of the generated answers when they are
exploited as zero-shot readers. To tackle these problems, we mitigate the
impact of such documents via Distraction-aware Answer Selection (DAS) with a
negation-based instruction and score adjustment for proper answer selection.
Experimental results show that our approach successfully handles distraction
across diverse scenarios, enhancing the performance of zero-shot readers.
Furthermore, unlike supervised readers struggling with unseen data, zero-shot
readers demonstrate outstanding transferability without any training.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy
Abstract: Deep Learning models have achieved remarkable performance in tasks such as
image classification or generation, often surpassing human accuracy. However,
they can struggle to learn new tasks and update their knowledge without access
to previous data, leading to a significant loss of accuracy known as
Catastrophic Forgetting (CF). This phenomenon was first observed by McCloskey
and Cohen in 1989 and remains an active research topic. Incremental learning
without forgetting is widely recognized as a crucial aspect in building better
AI systems, as it allows models to adapt to new tasks without losing the
ability to perform previously learned ones. This article surveys recent studies
that tackle CF in modern Deep Learning models that use gradient descent as
their learning algorithm. Although several solutions have been proposed, a
definitive solution or consensus on assessing CF is yet to be established. The
article provides a comprehensive review of recent solutions, proposes a
taxonomy to organize them, and identifies research gaps in this area.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation
Abstract: Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet
challenges persist in achieving model interpretability and optimizing
performance. We propose a novel transformer-based model, FragXsiteDTI, that
aims to address these challenges in DTI prediction. Notably, FragXsiteDTI is
the first DTI model to simultaneously leverage drug molecule fragments and
protein pockets. Our information-rich representations for both proteins and
drugs offer a detailed perspective on their interaction. Inspired by the
Perceiver IO framework, our model features a learnable latent array, initially
interacting with protein binding site embeddings using cross-attention and
later refined through self-attention and used as a query to the drug fragments
in the drug's cross-attention transformer block. This learnable query array
serves as a mediator and enables seamless information translation, preserving
critical nuances in drug-protein interactions. Our computational results on
three benchmarking datasets demonstrate the superior predictive power of our
model over several state-of-the-art models. We also show the interpretability
of our model in terms of the critical components of both target proteins and
drug molecules within drug-target pairs.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: FREDSum: A Dialogue Summarization Corpus for French Political Debates
Abstract: Recent advances in deep learning, and especially the invention of
encoder-decoder architectures, has significantly improved the performance of
abstractive summarization systems. The majority of research has focused on
written documents, however, neglecting the problem of multi-party dialogue
summarization. In this paper, we present a dataset of French political debates
for the purpose of enhancing resources for multi-lingual dialogue
summarization. Our dataset consists of manually transcribed and annotated
political debates, covering a range of topics and perspectives. We highlight
the importance of high quality transcription and annotations for training
accurate and effective dialogue summarization models, and emphasize the need
for multilingual resources to support dialogue summarization in non-English
languages. We also provide baseline experiments using state-of-the-art methods,
and encourage further research in this area to advance the field of dialogue
summarization. Our dataset will be made publicly available for use by the
research community.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches
Abstract: Electrical Impedance Tomography (EIT) is a powerful imaging technique with
diverse applications, e.g., medical diagnosis, industrial monitoring, and
environmental studies. The EIT inverse problem is about inferring the internal
conductivity distribution of an object from measurements taken on its boundary.
It is severely ill-posed, necessitating advanced computational methods for
accurate image reconstructions. Recent years have witnessed significant
progress, driven by innovations in analytic-based approaches and deep learning.
This review explores techniques for solving the EIT inverse problem, focusing
on the interplay between contemporary deep learning-based strategies and
classical analytic-based methods. Four state-of-the-art deep learning
algorithms are rigorously examined, harnessing the representational
capabilities of deep neural networks to reconstruct intricate conductivity
distributions. In parallel, two analytic-based methods, rooted in mathematical
formulations and regularisation techniques, are dissected for their strengths
and limitations. These methodologies are evaluated through various numerical
experiments, encompassing diverse scenarios that reflect real-world
complexities. A suite of performance metrics is employed to assess the efficacy
of these methods. These metrics collectively provide a nuanced understanding of
the methods' ability to capture essential features and delineate complex
conductivity patterns. One novel feature of the study is the incorporation of
variable conductivity scenarios, introducing a level of heterogeneity that
mimics textured inclusions. This departure from uniform conductivity
assumptions mimics realistic scenarios where tissues or materials exhibit
spatially varying electrical properties. Exploring how each method responds to
such variable conductivity scenarios opens avenues for understanding their
robustness and adaptability.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: DeepThought: An Architecture for Autonomous Self-motivated Systems
Abstract: The ability of large language models (LLMs) to engage in credible dialogues
with humans, taking into account the training data and the context of the
conversation, has raised discussions about their ability to exhibit intrinsic
motivations, agency, or even some degree of consciousness. We argue that the
internal architecture of LLMs and their finite and volatile state cannot
support any of these properties. By combining insights from complementary
learning systems, global neuronal workspace, and attention schema theories, we
propose to integrate LLMs and other deep learning systems into an architecture
for cognitive language agents able to exhibit properties akin to agency,
self-motivation, even some features of meta-cognition.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: PG-Video-LLaVA: Pixel Grounding Large Video-Language Models
Abstract: Extending image-based Large Multimodal Models (LMMs) to videos is challenging
due to the inherent complexity of video data. The recent approaches extending
image-based LMMs to videos either lack the grounding capabilities (e.g.,
VideoChat, Video-ChatGPT, Video-LLaMA) or do not utilize the audio-signals for
better video understanding (e.g., Video-ChatGPT). Addressing these gaps, we
propose PG-Video-LLaVA, the first LMM with pixel-level grounding capability,
integrating audio cues by transcribing them into text to enrich video-context
understanding. Our framework uses an off-the-shelf tracker and a novel
grounding module, enabling it to spatially localize objects in videos following
user instructions. We evaluate PG-Video-LLaVA using video-based generative and
question-answering benchmarks and introduce new benchmarks specifically
designed to measure prompt-based object grounding performance in videos.
Further, we propose the use of Vicuna over GPT-3.5, as utilized in
Video-ChatGPT, for video-based conversation benchmarking, ensuring
reproducibility of results which is a concern with the proprietary nature of
GPT-3.5. Our framework builds on SoTA image-based LLaVA model and extends its
advantages to the video domain, delivering promising gains on video-based
conversation and grounding tasks. Project Page:
https://github.com/mbzuai-oryx/Video-LLaVA

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: LEDITS++: Limitless Image Editing using Text-to-Image Models
Abstract: Text-to-image diffusion models have recently received increasing interest for
their astonishing ability to produce high-fidelity images from solely text
inputs. Subsequent research efforts aim to exploit and apply their capabilities
to real image editing. However, existing image-to-image methods are often
inefficient, imprecise, and of limited versatility. They either require
time-consuming fine-tuning, deviate unnecessarily strongly from the input
image, and/or lack support for multiple, simultaneous edits. To address these
issues, we introduce LEDITS++, an efficient yet versatile and precise textual
image manipulation technique. LEDITS++'s novel inversion approach requires no
tuning nor optimization and produces high-fidelity results with a few diffusion
steps. Second, our methodology supports multiple simultaneous edits and is
architecture-agnostic. Third, we use a novel implicit masking technique that
limits changes to relevant image regions. We propose the novel TEdBench++
benchmark as part of our exhaustive evaluation. Our results demonstrate the
capabilities of LEDITS++ and its improvements over previous methods. The
project page is available at https://leditsplusplus-project.static.hf.space .

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation
Abstract: The advent of Industry 4.0 has precipitated the incorporation of Artificial
Intelligence (AI) methods within industrial contexts, aiming to realize
intelligent manufacturing, operation as well as maintenance, also known as
industrial intelligence. However, intricate industrial milieus, particularly
those relating to energy exploration and production, frequently encompass data
characterized by long-tailed class distribution, sample imbalance, and domain
shift. These attributes pose noteworthy challenges to data-centric Deep
Learning (DL) techniques, crucial for the realization of industrial
intelligence. The present study centers on the intricate and distinctive
industrial scenarios of Nuclear Power Generation (NPG), meticulously
scrutinizing the application of DL techniques under the constraints of finite
data samples. Initially, the paper expounds on potential employment scenarios
for AI across the full life-cycle of NPG. Subsequently, we delve into an
evaluative exposition of DL's advancement, grounded in the finite sample
perspective. This encompasses aspects such as small-sample learning, few-shot
learning, zero-shot learning, and open-set recognition, also referring to the
unique data characteristics of NPG. The paper then proceeds to present two
specific case studies. The first revolves around the automatic recognition of
zirconium alloy metallography, while the second pertains to open-set
recognition for signal diagnosis of machinery sensors. These cases, spanning
the entirety of NPG's life-cycle, are accompanied by constructive outcomes and
insightful deliberations. By exploring and applying DL methodologies within the
constraints of finite sample availability, this paper not only furnishes a
robust technical foundation but also introduces a fresh perspective toward the
secure and efficient advancement and exploitation of this advanced energy
source.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?
Abstract: Evaluating the accuracy of outputs generated by Large Language Models (LLMs)
is especially important in the climate science and policy domain. We introduce
the Expert Confidence in Climate Statements (ClimateX) dataset, a novel,
curated, expert-labeled dataset consisting of 8094 climate statements collected
from the latest Intergovernmental Panel on Climate Change (IPCC) reports,
labeled with their associated confidence levels. Using this dataset, we show
that recent LLMs can classify human expert confidence in climate-related
statements, especially in a few-shot learning setting, but with limited (up to
47%) accuracy. Overall, models exhibit consistent and significant
over-confidence on low and medium confidence statements. We highlight
implications of our results for climate communication, LLMs evaluation
strategies, and the use of LLMs in information retrieval systems.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Understanding Your Agent: Leveraging Large Language Models for Behavior Explanation
Abstract: Intelligent agents such as robots are increasingly deployed in real-world,
safety-critical settings. It is vital that these agents are able to explain the
reasoning behind their decisions to human counterparts; however, their behavior
is often produced by uninterpretable models such as deep neural networks. We
propose an approach to generate natural language explanations for an agent's
behavior based only on observations of states and actions, thus making our
method independent from the underlying model's representation. For such models,
we first learn a behavior representation and subsequently use it to produce
plausible explanations with minimal hallucination while affording user
interaction with a pre-trained large language model. We evaluate our method in
a multi-agent search-and-rescue environment and demonstrate the effectiveness
of our explanations for agents executing various behaviors. Through user
studies and empirical experiments, we show that our approach generates
explanations as helpful as those produced by a human domain expert while
enabling beneficial interactions such as clarification and counterfactual
queries.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
Abstract: Machine Learning (ML) has been instrumental in enabling joint transceiver
optimization by merging all physical layer blocks of the end-to-end wireless
communication systems. Although there have been a number of adversarial attacks
on ML-based wireless systems, the existing methods do not provide a
comprehensive view including multi-modality of the source data, common physical
layer components, and wireless domain constraints. This paper proposes Magmaw,
the first black-box attack methodology capable of generating universal
adversarial perturbations for any multimodal signal transmitted over a wireless
channel. We further introduce new objectives for adversarial attacks on
ML-based downstream applications. The resilience of the attack to the existing
widely used defense methods of adversarial training and perturbation signal
subtraction is experimentally verified. For proof-of-concept evaluation, we
build a real-time wireless attack platform using a software-defined radio
system. Experimental results demonstrate that Magmaw causes significant
performance degradation even in the presence of the defense mechanisms.
Surprisingly, Magmaw is also effective against encrypted communication channels
and conventional communications.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Analysis of Information Propagation in Ethereum Network Using Combined Graph Attention Network and Reinforcement Learning to Optimize Network Efficiency and Scalability
Abstract: Blockchain technology has revolutionized the way information is propagated in
decentralized networks. Ethereum plays a pivotal role in facilitating smart
contracts and decentralized applications. Understanding information propagation
dynamics in Ethereum is crucial for ensuring network efficiency, security, and
scalability. In this study, we propose an innovative approach that utilizes
Graph Convolutional Networks (GCNs) to analyze the information propagation
patterns in the Ethereum network. The first phase of our research involves data
collection from the Ethereum blockchain, consisting of blocks, transactions,
and node degrees. We construct a transaction graph representation using
adjacency matrices to capture the node embeddings; while our major contribution
is to develop a combined Graph Attention Network (GAT) and Reinforcement
Learning (RL) model to optimize the network efficiency and scalability. It
learns the best actions to take in various network states, ultimately leading
to improved network efficiency, throughput, and optimize gas limits for block
processing. In the experimental evaluation, we analyze the performance of our
model on a large-scale Ethereum dataset. We investigate effectively aggregating
information from neighboring nodes capturing graph structure and updating node
embeddings using GCN with the objective of transaction pattern prediction,
accounting for varying network loads and number of blocks. Not only we design a
gas limit optimization model and provide the algorithm, but also to address
scalability, we demonstrate the use and implementation of sparse matrices in
GraphConv, GraphSAGE, and GAT. The results indicate that our designed GAT-RL
model achieves superior results compared to other GCN models in terms of
performance. It effectively propagates information across the network,
optimizing gas limits for block processing and improving network efficiency.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: PMMTalk: Speech-Driven 3D Facial Animation from Complementary Pseudo Multi-modal Features
Abstract: Speech-driven 3D facial animation has improved a lot recently while most
related works only utilize acoustic modality and neglect the influence of
visual and textual cues, leading to unsatisfactory results in terms of
precision and coherence. We argue that visual and textual cues are not trivial
information. Therefore, we present a novel framework, namely PMMTalk, using
complementary Pseudo Multi-Modal features for improving the accuracy of facial
animation. The framework entails three modules: PMMTalk encoder, cross-modal
alignment module, and PMMTalk decoder. Specifically, the PMMTalk encoder
employs the off-the-shelf talking head generation architecture and speech
recognition technology to extract visual and textual information from speech,
respectively. Subsequently, the cross-modal alignment module aligns the
audio-image-text features at temporal and semantic levels. Then PMMTalk decoder
is employed to predict lip-syncing facial blendshape coefficients. Contrary to
prior methods, PMMTalk only requires an additional random reference face image
but yields more accurate results. Additionally, it is artist-friendly as it
seamlessly integrates into standard animation production workflows by
introducing facial blendshape coefficients. Finally, given the scarcity of 3D
talking face datasets, we introduce a large-scale 3D Chinese Audio-Visual
Facial Animation (3D-CAVFA) dataset. Extensive experiments and user studies
show that our approach outperforms the state of the art. We recommend watching
the supplementary video.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: math-PVS: A Large Language Model Framework to Map Scientific Publications to PVS Theories
Abstract: As artificial intelligence (AI) gains greater adoption in a wide variety of
applications, it has immense potential to contribute to mathematical discovery,
by guiding conjecture generation, constructing counterexamples, assisting in
formalizing mathematics, and discovering connections between different
mathematical areas, to name a few.
  While prior work has leveraged computers for exhaustive mathematical proof
search, recent efforts based on large language models (LLMs) aspire to position
computing platforms as co-contributors in the mathematical research process.
Despite their current limitations in logic and mathematical tasks, there is
growing interest in melding theorem proving systems with foundation models.
This work investigates the applicability of LLMs in formalizing advanced
mathematical concepts and proposes a framework that can critically review and
check mathematical reasoning in research papers. Given the noted reasoning
shortcomings of LLMs, our approach synergizes the capabilities of proof
assistants, specifically PVS, with LLMs, enabling a bridge between textual
descriptions in academic papers and formal specifications in PVS. By harnessing
the PVS environment, coupled with data ingestion and conversion mechanisms, we
envision an automated process, called \emph{math-PVS}, to extract and formalize
mathematical theorems from research papers, offering an innovative tool for
academic review and discovery.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
Abstract: We present a novel edge-level ego-network encoding for learning on graphs
that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing
additional node and edge features or extending message-passing formats. The
proposed encoding is sufficient to distinguish Strongly Regular Graphs, a
family of challenging 3-WL equivalent graphs. We show theoretically that such
encoding is more expressive than node-based sub-graph MP-GNNs. In an empirical
evaluation on four benchmarks with 10 graph datasets, our results match or
improve previous baselines on expressivity, graph classification, graph
regression, and proximity tasks -- while reducing memory usage by 18.1x in
certain real-world settings.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Attribute Based Interpretable Evaluation Metrics for Generative Models
Abstract: When the training dataset comprises a 1:1 proportion of dogs to cats, a
generative model that produces 1:1 dogs and cats better resembles the training
species distribution than another model with 3:1 dogs and cats. Can we capture
this phenomenon using existing metrics? Unfortunately, we cannot, because these
metrics do not provide any interpretability beyond "diversity". In this
context, we propose a new evaluation protocol that measures the divergence of a
set of generated images from the training set regarding the distribution of
attribute strengths as follows. Single-attribute Divergence (SaD) measures the
divergence regarding PDFs of a single attribute. Paired-attribute Divergence
(PaD) measures the divergence regarding joint PDFs of a pair of attributes.
They provide which attributes the models struggle. For measuring the attribute
strengths of an image, we propose Heterogeneous CLIPScore (HCS) which measures
the cosine similarity between image and text vectors with heterogeneous initial
points. With SaD and PaD, we reveal the following about existing generative
models. ProjectedGAN generates implausible attribute relationships such as a
baby with a beard even though it has competitive scores of existing metrics.
Diffusion models struggle to capture diverse colors in the datasets. The larger
sampling timesteps of latent diffusion model generate the more minor objects
including earrings and necklaces. Stable Diffusion v1.5 better captures the
attributes than v2.1. Our metrics lay a foundation for explainable evaluations
of generative models.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Legal Requirements Analysis
Abstract: Modern software has been an integral part of everyday activities in many
disciplines and application contexts. Introducing intelligent automation by
leveraging artificial intelligence (AI) led to break-throughs in many fields.
The effectiveness of AI can be attributed to several factors, among which is
the increasing availability of data. Regulations such as the general data
protection regulation (GDPR) in the European Union (EU) are introduced to
ensure the protection of personal data. Software systems that collect, process,
or share personal data are subject to compliance with such regulations.
Developing compliant software depends heavily on addressing legal requirements
stipulated in applicable regulations, a central activity in the requirements
engineering (RE) phase of the software development process. RE is concerned
with specifying and maintaining requirements of a system-to-be, including legal
requirements. Legal agreements which describe the policies organizations
implement for processing personal data can provide an additional source to
regulations for eliciting legal requirements. In this chapter, we explore a
variety of methods for analyzing legal requirements and exemplify them on GDPR.
Specifically, we describe possible alternatives for creating machine-analyzable
representations from regulations, survey the existing automated means for
enabling compliance verification against regulations, and further reflect on
the current challenges of legal requirements analysis.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: ASPIRO: Any-shot Structured Parsing-error-Induced ReprOmpting for Consistent Data-to-Text Generation
Abstract: We present ASPIRO, an approach for structured data verbalisation into short
template sentences in zero to few-shot settings. Unlike previous methods, our
approach prompts large language models (LLMs) to directly produce
entity-agnostic templates, rather than relying on LLMs to faithfully copy the
given example entities, or validating/crafting the templates manually. We
incorporate LLM re-prompting, triggered by algorithmic parsing checks, as well
as the PARENT metric induced consistency validation to identify and rectify
template generation problems in real-time. ASPIRO, compared to direct LLM
output, averages 66\% parsing error rate reduction in generated verbalisations
of RDF triples on the DART dataset. Our best 5-shot text-davinci-003 setup,
scoring BLEU of 50.62, METEOR of 45.16, BLEURT of 0.82, NUBIA of 0.87, and
PARENT of 0.8962 on the Rel2Text dataset, competes effectively with recent
fine-tuned pre-trained language models.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology
Abstract: Artificial Intelligence (AI) based image analysis has an immense potential to
support diagnostic histopathology, including cancer diagnostics. However,
developing supervised AI methods requires large-scale annotated datasets. A
potentially powerful solution is to augment training data with synthetic data.
Latent diffusion models, which can generate high-quality, diverse synthetic
images, are promising. However, the most common implementations rely on
detailed textual descriptions, which are not generally available in this
domain. This work proposes a method that constructs structured textual prompts
from automatically extracted image features. We experiment with the PCam
dataset, composed of tissue patches only loosely annotated as healthy or
cancerous. We show that including image-derived features in the prompt, as
opposed to only healthy and cancerous labels, improves the Fr\'echet Inception
Distance (FID) from 178.8 to 90.2. We also show that pathologists find it
challenging to detect synthetic images, with a median sensitivity/specificity
of 0.55/0.55. Finally, we show that synthetic data effectively trains AI
models.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly
Abstract: Large Language Models (LLMs), such as GPT-3 and BERT, have revolutionized
natural language understanding and generation. They possess deep language
comprehension, human-like text generation capabilities, contextual awareness,
and robust problem-solving skills, making them invaluable in various domains
(e.g., search engines, customer support, translation). In the meantime, LLMs
have also gained traction in the security community, revealing security
vulnerabilities and showcasing their potential in security-related tasks. This
paper explores the intersection of LLMs with security and privacy.
Specifically, we investigate how LLMs positively impact security and privacy,
potential risks and threats associated with their use, and inherent
vulnerabilities within LLMs. Through a comprehensive literature review, the
paper categorizes findings into "The Good" (beneficial LLM applications), "The
Bad" (offensive applications), and "The Ugly" (vulnerabilities and their
defenses). We have some interesting findings. For example, LLMs have proven to
enhance code and data security, outperforming traditional methods. However,
they can also be harnessed for various attacks (particularly user-level
attacks) due to their human-like reasoning abilities. We have identified areas
that require further research efforts. For example, research on model and
parameter extraction attacks is limited and often theoretical, hindered by LLM
parameter scale and confidentiality. Safe instruction tuning, a recent
development, requires more exploration. We hope that our work can shed light on
the LLMs' potential to both bolster and jeopardize cybersecurity.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Releasing the CRaQAn (Coreference Resolution in Question-Answering): An open-source dataset and dataset creation methodology using instruction-following models
Abstract: Instruction-following language models demand robust methodologies for
information retrieval to augment instructions for question-answering
applications. A primary challenge is the resolution of coreferences in the
context of chunking strategies for long documents. The critical barrier to
experimentation of handling coreferences is a lack of open source datasets,
specifically in question-answering tasks that require coreference resolution.
In this work we present our Coreference Resolution in Question-Answering
(CRaQAn) dataset, an open-source dataset that caters to the nuanced information
retrieval requirements of coreference resolution in question-answering tasks by
providing over 250 question-answer pairs containing coreferences. To develop
this dataset, we developed a novel approach for creating high-quality datasets
using an instruction-following model (GPT-4) and a Recursive Criticism and
Improvement Loop.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Detecting Contextual Network Anomalies with Graph Neural Networks
Abstract: Detecting anomalies on network traffic is a complex task due to the massive
amount of traffic flows in today's networks, as well as the highly-dynamic
nature of traffic over time. In this paper, we propose the use of Graph Neural
Networks (GNN) for network traffic anomaly detection. We formulate the problem
as contextual anomaly detection on network traffic measurements, and propose a
custom GNN-based solution that detects traffic anomalies on origin-destination
flows. In our evaluation, we use real-world data from Abilene (6 months), and
make a comparison with other widely used methods for the same task (PCA, EWMA,
RNN). The results show that the anomalies detected by our solution are quite
complementary to those captured by the baselines (with a max. of 36.33%
overlapping anomalies for PCA). Moreover, we manually inspect the anomalies
detected by our method, and find that a large portion of them can be visually
validated by a network expert (64% with high confidence, 18% with mid
confidence, 18% normal traffic). Lastly, we analyze the characteristics of the
anomalies through two paradigmatic cases that are quite representative of the
bulk of anomalies.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
Abstract: Large Language Models (LLMs) have transformed the landscape of artificial
intelligence, while their enormous size presents significant challenges in
terms of computational costs. We introduce LoRAShear, a novel efficient
approach to structurally prune LLMs and recover knowledge. Given general LLMs,
LoRAShear at first creates the dependency graphs over LoRA modules to discover
minimally removal structures and analyze the knowledge distribution. It then
proceeds progressive structured pruning on LoRA adaptors and enables inherent
knowledge transfer to better preserve the information in the redundant
structures. To recover the lost knowledge during pruning, LoRAShear
meticulously studies and proposes a dynamic fine-tuning schemes with dynamic
data adaptors to effectively narrow down the performance gap to the full
models. Numerical results demonstrate that by only using one GPU within a
couple of GPU days, LoRAShear effectively reduced footprint of LLMs by 20% with
only 1.0% performance degradation and significantly outperforms
state-of-the-arts. The source code will be available at
https://github.com/microsoft/lorashear.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Understanding Path Planning Explanations
Abstract: Navigation is a must-have skill for any mobile robot. A core challenge in
navigation is the need to account for an ample number of possible
configurations of environment and navigation contexts. We claim that a mobile
robot should be able to explain its navigational choices making its decisions
understandable to humans. In this paper, we briefly present our approach to
explaining navigational decisions of a robot through visual and textual
explanations. We propose a user study to test the understandability and
simplicity of the robot explanations and outline our further research agenda.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Yin Yang Convolutional Nets: Image Manifold Extraction by the Analysis of Opposites
Abstract: Computer vision in general presented several advances such as training
optimizations, new architectures (pure attention, efficient block, vision
language models, generative models, among others). This have improved
performance in several tasks such as classification, and others. However, the
majority of these models focus on modifications that are taking distance from
realistic neuroscientific approaches related to the brain. In this work, we
adopt a more bio-inspired approach and present the Yin Yang Convolutional
Network, an architecture that extracts visual manifold, its blocks are intended
to separate analysis of colors and forms at its initial layers, simulating
occipital lobe's operations. Our results shows that our architecture provides
State-of-the-Art efficiency among low parameter architectures in the dataset
CIFAR-10. Our first model reached 93.32\% test accuracy, 0.8\% more than the
older SOTA in this category, while having 150k less parameters (726k in total).
Our second model uses 52k parameters, losing only 3.86\% test accuracy. We also
performed an analysis on ImageNet, where we reached 66.49\% validation accuracy
with 1.6M parameters. We make the code publicly available at:
https://github.com/NoSavedDATA/YinYang_CNN.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Supermind Ideator: Exploring generative AI to support creative problem-solving
Abstract: Previous efforts to support creative problem-solving have included (a)
techniques (such as brainstorming and design thinking) to stimulate creative
ideas, and (b) software tools to record and share these ideas. Now, generative
AI technologies can suggest new ideas that might never have occurred to the
users, and users can then select from these ideas or use them to stimulate even
more ideas. Here, we describe such a system, Supermind Ideator. The system uses
a large language model (GPT 3.5) and adds prompting, fine tuning, and a user
interface specifically designed to help people use creative problem-solving
techniques. Some of these techniques can be applied to any problem; others are
specifically intended to help generate innovative ideas about how to design
groups of people and/or computers ("superminds"). We also describe our early
experiences with using this system and suggest ways it could be extended to
support additional techniques for other specific problem-solving domains.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Can Language Model Moderators Improve the Health of Online Discourse?
Abstract: Human moderation of online conversation is essential to maintaining civility
and focus in a dialogue, but is challenging to scale and harmful to moderators.
The inclusion of sophisticated natural language generation modules as a force
multiplier aid moderators is a tantalizing prospect, but adequate evaluation
approaches have so far been elusive. In this paper, we establish a systematic
definition of conversational moderation effectiveness through a
multidisciplinary lens that incorporates insights from social science. We then
propose a comprehensive evaluation framework that uses this definition to asses
models' moderation capabilities independently of human intervention. With our
framework, we conduct the first known study of conversational dialogue models
as moderators, finding that appropriately prompted models can provide specific
and fair feedback on toxic behavior but struggle to influence users to increase
their levels of respect and cooperation.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness
Abstract: Vision Transformers (ViTs) achieve superior performance on various tasks
compared to convolutional neural networks (CNNs), but ViTs are also vulnerable
to adversarial attacks. Adversarial training is one of the most successful
methods to build robust CNN models. Thus, recent works explored new
methodologies for adversarial training of ViTs based on the differences between
ViTs and CNNs, such as better training strategies, preventing attention from
focusing on a single block, or discarding low-attention embeddings. However,
these methods still follow the design of traditional supervised adversarial
training, limiting the potential of adversarial training on ViTs. This paper
proposes a novel defense method, MIMIR, which aims to build a different
adversarial training methodology by utilizing Masked Image Modeling at
pre-training. We create an autoencoder that accepts adversarial examples as
input but takes the clean examples as the modeling target. Then, we create a
mutual information (MI) penalty following the idea of the Information
Bottleneck. Among the two information source inputs and corresponding
adversarial perturbation, the perturbation information is eliminated due to the
constraint of the modeling target. Next, we provide a theoretical analysis of
MIMIR using the bounds of the MI penalty. We also design two adaptive attacks
when the adversary is aware of the MIMIR defense and show that MIMIR still
performs well. The experimental results show that MIMIR improves (natural and
adversarial) accuracy on average by 4.19\% on CIFAR-10 and 5.52\% on
ImageNet-1K, compared to baselines. On Tiny-ImageNet, we obtained improved
natural accuracy of 2.99\% on average and comparable adversarial accuracy. Our
code and trained models are publicly
available\footnote{\url{https://anonymous.4open.science/r/MIMIR-5444/README.md}}.

Output: Computer Vision