Instruction: What field is the article from?

Prompt:
Title: DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM
Abstract: In the burgeoning field of natural language processing, Neural Topic Models
(NTMs) and Large Language Models (LLMs) have emerged as areas of significant
research interest. Despite this, NTMs primarily utilize contextual embeddings
from LLMs, which are not optimal for clustering or capable for topic
generation. Our study addresses this gap by introducing a novel framework named
Diffusion-Enhanced Topic Modeling using Encoder-Decoder-based LLMs (DeTiME).
DeTiME leverages ncoder-Decoder-based LLMs to produce highly clusterable
embeddings that could generate topics that exhibit both superior clusterability
and enhanced semantic coherence compared to existing methods. Additionally, by
exploiting the power of diffusion, our framework also provides the capability
to generate content relevant to the identified topics. This dual functionality
allows users to efficiently produce highly clustered topics and related content
simultaneously. DeTiME's potential extends to generating clustered embeddings
as well. Notably, our proposed framework proves to be efficient to train and
exhibits high adaptability, demonstrating its potential for a wide array of
applications.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Generative Powers of Ten
Abstract: We present a method that uses a text-to-image model to generate consistent
content across multiple image scales, enabling extreme semantic zooms into a
scene, e.g., ranging from a wide-angle landscape view of a forest to a macro
shot of an insect sitting on one of the tree branches. We achieve this through
a joint multi-scale diffusion sampling approach that encourages consistency
across different scales while preserving the integrity of each individual
sampling process. Since each generated scale is guided by a different text
prompt, our method enables deeper levels of zoom than traditional
super-resolution methods that may struggle to create new contextual structure
at vastly different scales. We compare our method qualitatively with
alternative techniques in image super-resolution and outpainting, and show that
our method is most effective at generating consistent multi-scale content.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: LM-Cocktail: Resilient Tuning of Language Models via Model Merging
Abstract: The pre-trained language models are continually fine-tuned to better support
downstream applications. However, this operation may result in significant
performance degeneration on general tasks beyond the targeted domain. To
overcome this problem, we propose LM-Cocktail which enables the fine-tuned
model to stay resilient in general perspectives. Our method is conducted in the
form of model merging, where the fine-tuned language model is merged with the
pre-trained base model or the peer models from other domains through weighted
average. Despite simplicity, LM-Cocktail is surprisingly effective: the
resulted model is able to achieve a strong empirical performance in the whole
scope of general tasks while preserving a superior capacity in its targeted
domain. We conduct comprehensive experiments with LLama and BGE model on
popular benchmarks, including FLAN, MMLU, MTEB, whose results validate the
efficacy of our proposed method. The code and checkpoints are available at
https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: TigerBot: An Open Multilingual Multitask LLM
Abstract: We release and introduce the TigerBot family of large language models (LLMs),
consisting of base and chat models, sized from 7, 13, 70 and 180 billion
parameters. We develop our models embarking from Llama-2 and BLOOM, and push
the boundary further in data, training algorithm, infrastructure, and
application tools. Our models yield meaningful performance gain over SOTA
open-source models, e.g., Llama-2, specifically 6% gain in English and 20% gain
in Chinese. TigerBot model family also achieves leading performance in major
academic and industrial benchmarks and leaderboards. We believe that TigerBot
represents just a snapshot of lightning-fast progression in LLM open-source
community. Therefore, we are thrilled to give back by publicly releasing our
models and reporting our approach behind, with additional emphases on building
SOTA LLMs in a democratized way and making LLMs of use in real-world
applications.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
Abstract: With the increasing availability of Foundation Models, federated tuning has
garnered attention in the field of federated learning, utilizing data and
computation resources from multiple clients to collaboratively fine-tune
foundation models. However, in real-world federated scenarios, there often
exist a multitude of heterogeneous clients with varying computation and
communication resources, rendering them incapable of supporting the entire
model fine-tuning process. In response to this challenge, we propose a novel
federated tuning algorithm, FedRA. The implementation of FedRA is
straightforward and can be seamlessly integrated into any transformer-based
model without the need for further modification to the original model.
Specifically, in each communication round, FedRA randomly generates an
allocation matrix. For resource-constrained clients, it reorganizes a small
number of layers from the original model based on the allocation matrix and
fine-tunes using LoRA. Subsequently, the server aggregates the updated LoRA
parameters from the clients according to the current allocation matrix into the
corresponding layers of the original model. It is worth noting that FedRA also
supports scenarios where none of the clients can support the entire global
model, which is an impressive advantage. We conduct experiments on two
large-scale image datasets, DomainNet and NICO++, under various non-iid
settings. The results demonstrate that FedRA outperforms the compared methods
significantly. The source code is available at
\url{https://github.com/leondada/FedRA}.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: DanZero+: Dominating the GuanDan Game through Reinforcement Learning
Abstract: The utilization of artificial intelligence (AI) in card games has been a
well-explored subject within AI research for an extensive period. Recent
advancements have propelled AI programs to showcase expertise in intricate card
games such as Mahjong, DouDizhu, and Texas Hold'em. In this work, we aim to
develop an AI program for an exceptionally complex and popular card game called
GuanDan. This game involves four players engaging in both competitive and
cooperative play throughout a long process to upgrade their level, posing great
challenges for AI due to its expansive state and action space, long episode
length, and complex rules. Employing reinforcement learning techniques,
specifically Deep Monte Carlo (DMC), and a distributed training framework, we
first put forward an AI program named DanZero for this game. Evaluation against
baseline AI programs based on heuristic rules highlights the outstanding
performance of our bot. Besides, in order to further enhance the AI's
capabilities, we apply policy-based reinforcement learning algorithm to
GuanDan. To address the challenges arising from the huge action space, which
will significantly impact the performance of policy-based algorithms, we adopt
the pre-trained model to facilitate the training process and the achieved AI
program manages to achieve a superior performance.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Adversarial Attacks and Defenses in Large Language Models: Old and New Threats
Abstract: Over the past decade, there has been extensive research aimed at enhancing
the robustness of neural networks, yet this problem remains vastly unsolved.
Here, one major impediment has been the overestimation of the robustness of new
defense approaches due to faulty defense evaluations. Flawed robustness
evaluations necessitate rectifications in subsequent works, dangerously slowing
down the research and providing a false sense of security. In this context, we
will face substantial challenges associated with an impending adversarial arms
race in natural language processing, specifically with closed-source Large
Language Models (LLMs), such as ChatGPT, Google Bard, or Anthropic's Claude. We
provide a first set of prerequisites to improve the robustness assessment of
new approaches and reduce the amount of faulty evaluations. Additionally, we
identify embedding space attacks on LLMs as another viable threat model for the
purposes of generating malicious content in open-sourced models. Finally, we
demonstrate on a recently proposed defense that, without LLM-specific best
practices in place, it is easy to overestimate the robustness of a new
approach.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Leveraging AI for Natural Disaster Management : Takeaways From The Moroccan Earthquake
Abstract: The devastating 6.8-magnitude earthquake in Al Haouz, Morocco in 2023
prompted critical reflections on global disaster management strategies,
resulting in a post-disaster hackathon, using artificial intelligence (AI) to
improve disaster preparedness, response, and recovery. This paper provides (i)
a comprehensive literature review, (ii) an overview of winning projects, (iii)
key insights and challenges, namely real-time open-source data, data scarcity,
and interdisciplinary collaboration barriers, and (iv) a community-call for
further action.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Chain of Images for Intuitively Reasoning
Abstract: The human brain is naturally equipped to comprehend and interpret visual
information rapidly. When confronted with complex problems or concepts, we use
flowcharts, sketches, and diagrams to aid our thought process. Leveraging this
inherent ability can significantly enhance logical reasoning. However, current
Large Language Models (LLMs) do not utilize such visual intuition to help their
thinking. Even the most advanced version language models (e.g., GPT-4V and
LLaVA) merely align images into textual space, which means their reasoning
processes remain purely verbal. To mitigate such limitations, we present a
Chain of Images (CoI) approach, which can convert complex language reasoning
problems to simple pattern recognition by generating a series of images as
intermediate representations. Furthermore, we have developed a CoI evaluation
dataset encompassing 15 distinct domains where images can intuitively aid
problem-solving. Based on this dataset, we aim to construct a benchmark to
assess the capability of future multimodal large-scale models to leverage
images for reasoning. In supporting our CoI reasoning, we introduce a symbolic
multimodal large language model (SyMLLM) that generates images strictly based
on language instructions and accepts both text and image as input. Experiments
on Geometry, Chess and Common Sense tasks sourced from the CoI evaluation
dataset show that CoI improves performance significantly over the pure-language
Chain of Thoughts (CoT) baselines. The code is available at
https://github.com/GraphPKU/CoI.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Defending Against Transfer Attacks From Public Models
Abstract: Adversarial attacks have been a looming and unaddressed threat in the
industry. However, through a decade-long history of the robustness evaluation
literature, we have learned that mounting a strong or optimal attack is
challenging. It requires both machine learning and domain expertise. In other
words, the white-box threat model, religiously assumed by a large majority of
the past literature, is unrealistic. In this paper, we propose a new practical
threat model where the adversary relies on transfer attacks through publicly
available surrogate models. We argue that this setting will become the most
prevalent for security-sensitive applications in the future. We evaluate the
transfer attacks in this setting and propose a specialized defense method based
on a game-theoretic perspective. The defenses are evaluated under 24 public
models and 11 attack algorithms across three datasets (CIFAR-10, CIFAR-100, and
ImageNet). Under this threat model, our defense, PubDef, outperforms the
state-of-the-art white-box adversarial training by a large margin with almost
no loss in the normal accuracy. For instance, on ImageNet, our defense achieves
62% accuracy under the strongest transfer attack vs only 36% of the best
adversarially trained model. Its accuracy when not under attack is only 2%
lower than that of an undefended model (78% vs 80%). We release our code at
https://github.com/wagner-group/pubdef.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning
Abstract: Battery Electric Vehicles (BEVs) are increasingly significant in modern
cities due to their potential to reduce air pollution. Precise and real-time
estimation of energy consumption for them is imperative for effective itinerary
planning and optimizing vehicle systems, which can reduce driving range anxiety
and decrease energy costs. As public awareness of data privacy increases,
adopting approaches that safeguard data privacy in the context of BEV energy
consumption modeling is crucial. Federated Learning (FL) is a promising
solution mitigating the risk of exposing sensitive information to third parties
by allowing local data to remain on devices and only sharing model updates with
a central server. Our work investigates the potential of using FL methods, such
as FedAvg, and FedPer, to improve BEV energy consumption prediction while
maintaining user privacy. We conducted experiments using data from 10 BEVs
under simulated real-world driving conditions. Our results demonstrate that the
FedAvg-LSTM model achieved a reduction of up to 67.84\% in the MAE value of the
prediction results. Furthermore, we explored various real-world scenarios and
discussed how FL methods can be employed in those cases. Our findings show that
FL methods can effectively improve the performance of BEV energy consumption
prediction while maintaining user privacy.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Questioning Biases in Case Judgment Summaries: Legal Datasets or Large Language Models?
Abstract: The evolution of legal datasets and the advent of large language models
(LLMs) have significantly transformed the legal field, particularly in the
generation of case judgment summaries. However, a critical concern arises
regarding the potential biases embedded within these summaries. This study
scrutinizes the biases present in case judgment summaries produced by legal
datasets and large language models. The research aims to analyze the impact of
biases on legal decision making. By interrogating the accuracy, fairness, and
implications of biases in these summaries, this study contributes to a better
understanding of the role of technology in legal contexts and the implications
for justice systems worldwide. In this study, we investigate biases wrt
Gender-related keywords, Race-related keywords, Keywords related to crime
against women, Country names and religious keywords. The study shows
interesting evidences of biases in the outputs generated by the large language
models and pre-trained abstractive summarization models. The reasoning behind
these biases needs further studies.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation
Abstract: In this study, we leverage a deep learning-based method for the automatic
diagnosis of schizophrenia using EEG brain recordings. This approach utilizes
generative data augmentation, a powerful technique that enhances the accuracy
of the diagnosis. To enable the utilization of time-frequency features,
spectrograms were extracted from the raw signals. After exploring several
neural network architectural setups, a proper convolutional neural network
(CNN) was used for the initial diagnosis. Subsequently, using Wasserstein GAN
with Gradient Penalty (WGAN-GP) and Variational Autoencoder (VAE), two
different synthetic datasets were generated in order to augment the initial
dataset and address the over-fitting issue. The augmented dataset using VAE
achieved a 3.0\% improvement in accuracy reaching up to 99.0\% and yielded a
lower loss value as well as a faster convergence. Finally, we addressed the
lack of trust in black-box models using the Local Interpretable Model-agnostic
Explanations (LIME) algorithm to determine the most important superpixels
(frequencies) in the diagnosis process.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Label Smoothing for Enhanced Text Sentiment Classification
Abstract: Label smoothing is a widely used technique in various domains, such as image
classification and speech recognition, known for effectively combating model
overfitting. However, there is few research on its application to text
sentiment classification. To fill in the gap, this study investigates the
implementation of label smoothing for sentiment classification by utilizing
different levels of smoothing. The primary objective is to enhance sentiment
classification accuracy by transforming discrete labels into smoothed label
distributions. Through extensive experiments, we demonstrate the superior
performance of label smoothing in text sentiment classification tasks across
eight diverse datasets and deep learning architectures: TextCNN, BERT, and
RoBERTa, under two learning schemes: training from scratch and fine-tuning.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Bitformer: An efficient Transformer with bitwise operation-based attention for Big Data Analytics at low-cost low-precision devices
Abstract: In the current landscape of large models, the Transformer stands as a
cornerstone, playing a pivotal role in shaping the trajectory of modern models.
However, its application encounters challenges attributed to the substantial
computational intricacies intrinsic to its attention mechanism. Moreover, its
reliance on high-precision floating-point operations presents specific hurdles,
particularly evident in computation-intensive scenarios such as edge computing
environments. These environments, characterized by resource-constrained devices
and a preference for lower precision, necessitate innovative solutions.
  To tackle the exacting data processing demands posed by edge devices, we
introduce the Bitformer model, an inventive extension of the Transformer
paradigm. Central to this innovation is a novel attention mechanism that
adeptly replaces conventional floating-point matrix multiplication with bitwise
operations. This strategic substitution yields dual advantages. Not only does
it maintain the attention mechanism's prowess in capturing intricate long-range
information dependencies, but it also orchestrates a profound reduction in the
computational complexity inherent in the attention operation. The transition
from an $O(n^2d)$ complexity, typical of floating-point operations, to an
$O(n^2T)$ complexity characterizing bitwise operations, substantiates this
advantage. Notably, in this context, the parameter $T$ remains markedly smaller
than the conventional dimensionality parameter $d$.
  The Bitformer model in essence endeavors to reconcile the indomitable
requirements of modern computing landscapes with the constraints posed by edge
computing scenarios. By forging this innovative path, we bridge the gap between
high-performing models and resource-scarce environments, thus unveiling a
promising trajectory for further advancements in the field.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Raising the ClaSS of Streaming Time Series Segmentation
Abstract: Ubiquitous sensors today emit high frequency streams of numerical
measurements that reflect properties of human, animal, industrial, commercial,
and natural processes. Shifts in such processes, e.g. caused by external events
or internal state changes, manifest as changes in the recorded signals. The
task of streaming time series segmentation (STSS) is to partition the stream
into consecutive variable-sized segments that correspond to states of the
observed processes or entities. The partition operation itself must in
performance be able to cope with the input frequency of the signals. We
introduce ClaSS, a novel, efficient, and highly accurate algorithm for STSS.
ClaSS assesses the homogeneity of potential partitions using self-supervised
time series classification and applies statistical tests to detect significant
change points (CPs). In our experimental evaluation using two large benchmarks
and six real-world data archives, we found ClaSS to be significantly more
precise than eight state-of-the-art competitors. Its space and time complexity
is independent of segment sizes and linear only in the sliding window size. We
also provide ClaSS as a window operator with an average throughput of 538 data
points per second for the Apache Flink streaming engine.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Making Harmful Behaviors Unlearnable for Large Language Models
Abstract: Large language models (LLMs) have shown great potential as general-purpose AI
assistants in various domains. To meet the requirements of different
applications, LLMs are often customized by further fine-tuning. However, the
powerful learning ability of LLMs not only enables them to acquire new tasks
but also makes them susceptible to learning undesired behaviors. For example,
even safety-aligned LLMs can be easily fine-tuned into harmful assistants as
the fine-tuning data often contains implicit or explicit harmful content. Can
we train LLMs on harmful data without learning harmful behaviors? This paper
proposes a controllable training framework that makes harmful behaviors
unlearnable during the fine-tuning process. Specifically, we introduce
``security vectors'', a few new parameters that can be separated from the LLM,
to ensure LLM's responses are consistent with the harmful behavior. Security
vectors are activated during fine-tuning, the consistent behavior makes LLM
believe that such behavior has already been learned, there is no need to
further optimize for harmful data. During inference, we can deactivate security
vectors to restore the LLM's normal behavior. The experimental results show
that the security vectors generated by 100 harmful samples are enough to
prevent LLM from learning 1000 harmful samples, while preserving the ability to
learn other useful information.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Diffusion Weighted Graph Framework for New Intent Discovery
Abstract: New Intent Discovery (NID) aims to recognize both new and known intents from
unlabeled data with the aid of limited labeled data containing only known
intents. Without considering structure relationships between samples, previous
methods generate noisy supervisory signals which cannot strike a balance
between quantity and quality, hindering the formation of new intent clusters
and effective transfer of the pre-training knowledge. To mitigate this
limitation, we propose a novel Diffusion Weighted Graph Framework (DWGF) to
capture both semantic similarities and structure relationships inherent in
data, enabling more sufficient and reliable supervisory signals. Specifically,
for each sample, we diffuse neighborhood relationships along semantic paths
guided by the nearest neighbors for multiple hops to characterize its local
structure discriminately. Then, we sample its positive keys and weigh them
based on semantic similarities and local structures for contrastive learning.
During inference, we further propose Graph Smoothing Filter (GSF) to explicitly
utilize the structure relationships to filter high-frequency noise embodied in
semantically ambiguous samples on the cluster boundary. Extensive experiments
show that our method outperforms state-of-the-art models on all evaluation
metrics across multiple benchmark datasets. Code and data are available at
https://github.com/yibai-shi/DWGF.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: DA-STC: Domain Adaptive Video Semantic Segmentation via Spatio-Temporal Consistency
Abstract: Video semantic segmentation is a pivotal aspect of video representation
learning. However, significant domain shifts present a challenge in effectively
learning invariant spatio-temporal features across the labeled source domain
and unlabeled target domain for video semantic segmentation. To solve the
challenge, we propose a novel DA-STC method for domain adaptive video semantic
segmentation, which incorporates a bidirectional multi-level spatio-temporal
fusion module and a category-aware spatio-temporal feature alignment module to
facilitate consistent learning for domain-invariant features. Firstly, we
perform bidirectional spatio-temporal fusion at the image sequence level and
shallow feature level, leading to the construction of two fused intermediate
video domains. This prompts the video semantic segmentation model to
consistently learn spatio-temporal features of shared patch sequences which are
influenced by domain-specific contexts, thereby mitigating the feature gap
between the source and target domain. Secondly, we propose a category-aware
feature alignment module to promote the consistency of spatio-temporal
features, facilitating adaptation to the target domain. Specifically, we
adaptively aggregate the domain-specific deep features of each category along
spatio-temporal dimensions, which are further constrained to achieve
cross-domain intra-class feature alignment and inter-class feature separation.
Extensive experiments demonstrate the effectiveness of our method, which
achieves state-of-the-art mIOUs on multiple challenging benchmarks.
Furthermore, we extend the proposed DA-STC to the image domain, where it also
exhibits superior performance for domain adaptive semantic segmentation. The
source code and models will be made available at
\url{https://github.com/ZHE-SAPI/DA-STC}.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers
Abstract: Abstracts derived from biomedical literature possess distinct domain-specific
characteristics, including specialised writing styles and biomedical
terminologies, which necessitate a deep understanding of the related
literature. As a result, existing language models struggle to generate
technical summaries that are on par with those produced by biomedical experts,
given the absence of domain-specific background knowledge. This paper aims to
enhance the performance of language models in biomedical abstractive
summarisation by aggregating knowledge from external papers cited within the
source article. We propose a novel attention-based citation aggregation model
that integrates domain-specific knowledge from citation papers, allowing neural
networks to generate summaries by leveraging both the paper content and
relevant knowledge from citation papers. Furthermore, we construct and release
a large-scale biomedical summarisation dataset that serves as a foundation for
our research. Extensive experiments demonstrate that our model outperforms
state-of-the-art approaches and achieves substantial improvements in
abstractive biomedical text summarisation.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder
Abstract: In recent years, the field of 3D self-supervised learning has witnessed
significant progress, resulting in the emergence of Multi-Modality Masked
AutoEncoders (MAE) methods that leverage both 2D images and 3D point clouds for
pre-training. However, a notable limitation of these approaches is that they do
not fully utilize the multi-view attributes inherent in 3D point clouds, which
is crucial for a deeper understanding of 3D structures. Building upon this
insight, we introduce a novel approach employing a 3D to multi-view masked
autoencoder to fully harness the multi-modal attributes of 3D point clouds. To
be specific, our method uses the encoded tokens from 3D masked point clouds to
generate original point clouds and multi-view depth images across various
poses. This approach not only enriches the model's comprehension of geometric
structures but also leverages the inherent multi-modal properties of point
clouds. Our experiments illustrate the effectiveness of the proposed method for
different tasks and under different settings. Remarkably, our method
outperforms state-of-the-art counterparts by a large margin in a variety of
downstream tasks, including 3D object classification, few-shot learning, part
segmentation, and 3D object detection. Code will be available at:
https://github.com/Zhimin-C/Multiview-MAE

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: 3DiFACE: Diffusion-based Speech-driven 3D Facial Animation and Editing
Abstract: We present 3DiFACE, a novel method for personalized speech-driven 3D facial
animation and editing. While existing methods deterministically predict facial
animations from speech, they overlook the inherent one-to-many relationship
between speech and facial expressions, i.e., there are multiple reasonable
facial expression animations matching an audio input. It is especially
important in content creation to be able to modify generated motion or to
specify keyframes. To enable stochasticity as well as motion editing, we
propose a lightweight audio-conditioned diffusion model for 3D facial motion.
This diffusion model can be trained on a small 3D motion dataset, maintaining
expressive lip motion output. In addition, it can be finetuned for specific
subjects, requiring only a short video of the person. Through quantitative and
qualitative evaluations, we show that our method outperforms existing
state-of-the-art techniques and yields speech-driven animations with greater
fidelity and diversity.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification
Abstract: Numerous studies have proved their effective strength in detecting Control
Area Network (CAN) attacks. In the realm of understanding the human semantic
space, transformer-based models have demonstrated remarkable effectiveness.
Leveraging pre-trained transformers has become a common strategy in various
language-related tasks, enabling these models to grasp human semantics more
comprehensively. To delve into the adaptability evaluation on pre-trained
models for CAN intrusion detection, we have developed two distinct models:
CAN-SecureBERT and CAN-LLAMA2. Notably, our CAN-LLAMA2 model surpasses the
state-of-the-art models by achieving an exceptional performance 0.999993 in
terms of balanced accuracy, precision detection rate, F1 score, and a
remarkably low false alarm rate of 3.10e-6. Impressively, the false alarm rate
is 52 times smaller than that of the leading model, MTH-IDS (Multitiered Hybrid
Intrusion Detection System). Our study underscores the promise of employing a
Large Language Model as the foundational model, while incorporating adapters
for other cybersecurity-related tasks and maintaining the model's inherent
language-related capabilities.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Teaching Unknown Objects by Leveraging Human Gaze and Augmented Reality in Human-Robot Interaction
Abstract: Robots are becoming increasingly popular in a wide range of environments due
to their exceptional work capacity, precision, efficiency, and scalability.
This development has been further encouraged by advances in Artificial
Intelligence, particularly Machine Learning. By employing sophisticated neural
networks, robots are given the ability to detect and interact with objects in
their vicinity. However, a significant drawback arises from the underlying
dependency on extensive datasets and the availability of substantial amounts of
training data for these object detection models. This issue becomes
particularly problematic when the specific deployment location of the robot and
the surroundings, are not known in advance. The vast and ever-expanding array
of objects makes it virtually impossible to comprehensively cover the entire
spectrum of existing objects using preexisting datasets alone. The goal of this
dissertation was to teach a robot unknown objects in the context of Human-Robot
Interaction (HRI) in order to liberate it from its data dependency, unleashing
it from predefined scenarios. In this context, the combination of eye tracking
and Augmented Reality created a powerful synergy that empowered the human
teacher to communicate with the robot and effortlessly point out objects by
means of human gaze. This holistic approach led to the development of a
multimodal HRI system that enabled the robot to identify and visually segment
the Objects of Interest in 3D space. Through the class information provided by
the human, the robot was able to learn the objects and redetect them at a later
stage. Due to the knowledge gained from this HRI based teaching, the robot's
object detection capabilities exhibited comparable performance to
state-of-the-art object detectors trained on extensive datasets, without being
restricted to predefined classes, showcasing its versatility and adaptability.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: ConSequence: Synthesizing Logically Constrained Sequences for Electronic Health Record Generation
Abstract: Generative models can produce synthetic patient records for analytical tasks
when real data is unavailable or limited. However, current methods struggle
with adhering to domain-specific knowledge and removing invalid data. We
present ConSequence, an effective approach to integrating domain knowledge into
sequential generative neural network outputs. Our rule-based formulation
includes temporal aggregation and antecedent evaluation modules, ensured by an
efficient matrix multiplication formulation, to satisfy hard and soft logical
constraints across time steps. Existing constraint methods often fail to
guarantee constraint satisfaction, lack the ability to handle temporal
constraints, and hinder the learning and computational efficiency of the model.
In contrast, our approach efficiently handles all types of constraints with
guaranteed logical coherence. We demonstrate ConSequence's effectiveness in
generating electronic health records, outperforming competitors in achieving
complete temporal and spatial constraint satisfaction without compromising
runtime performance or generative quality. Specifically, ConSequence
successfully prevents all rule violations while improving the model quality in
reducing its test perplexity by 5% and incurring less than a 13% slowdown in
generation speed compared to an unconstrained model.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Sample as You Infer: Predictive Coding With Langevin Dynamics
Abstract: We present a novel algorithm for parameter learning in generic deep
generative models that builds upon the predictive coding (PC) framework of
computational neuroscience. Our approach modifies the standard PC algorithm to
bring performance on-par and exceeding that obtained from standard variational
auto-encoder (VAE) training. By injecting Gaussian noise into the PC inference
procedure we re-envision it as an overdamped Langevin sampling, which
facilitates optimisation with respect to a tight evidence lower bound (ELBO).
We improve the resultant encoder-free training method by incorporating an
encoder network to provide an amortised warm-start to our Langevin sampling and
test three different objectives for doing so. Finally, to increase robustness
to the sampling step size and reduce sensitivity to curvature, we validate a
lightweight and easily computable form of preconditioning, inspired by Riemann
Manifold Langevin and adaptive optimizers from the SGD literature. We compare
against VAEs by training like-for-like generative models using our technique
against those trained with standard reparameterisation-trick-based ELBOs. We
observe our method out-performs or matches performance across a number of
metrics, including sample quality, while converging in a fraction of the number
of SGD training iterations.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors
Abstract: Most 3D generation research focuses on up-projecting 2D foundation models
into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS)
loss or fine-tuning on multi-view datasets. Without explicit 3D priors, these
methods often lead to geometric anomalies and multi-view inconsistency.
Recently, researchers have attempted to improve the genuineness of 3D objects
by directly training on 3D datasets, albeit at the cost of low-quality texture
generation due to the limited texture diversity in 3D datasets. To harness the
advantages of both approaches, we propose Bidirectional Diffusion(BiDiff), a
unified framework that incorporates both a 3D and a 2D diffusion process, to
preserve both 3D fidelity and 2D texture richness, respectively. Moreover, as a
simple combination may yield inconsistent generation results, we further bridge
them with novel bidirectional guidance. In addition, our method can be used as
an initialization of optimization-based models to further improve the quality
of 3D model and efficiency of optimization, reducing the generation process
from 3.4 hours to 20 minutes. Experimental results have shown that our model
achieves high-quality, diverse, and scalable 3D generation. Project website:
https://bidiff.github.io/.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition
Abstract: The use of deep learning for radio modulation recognition has become
prevalent in recent years. This approach automatically extracts
high-dimensional features from large datasets, facilitating the accurate
classification of modulation schemes. However, in real-world scenarios, it may
not be feasible to gather sufficient training data in advance. Data
augmentation is a method used to increase the diversity and quantity of
training dataset and to reduce data sparsity and imbalance. In this paper, we
propose data augmentation methods that involve replacing detail coefficients
decomposed by discrete wavelet transform for reconstructing to generate new
samples and expand the training set. Different generation methods are used to
generate replacement sequences. Simulation results indicate that our proposed
methods significantly outperform the other augmentation methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: On a Functional Definition of Intelligence
Abstract: Without an agreed-upon definition of intelligence, asking "is this system
intelligent?"" is an untestable question. This lack of consensus hinders
research, and public perception, on Artificial Intelligence (AI), particularly
since the rise of generative- and large-language models. Most work on precisely
capturing what we mean by "intelligence" has come from the fields of
philosophy, psychology, and cognitive science. Because these perspectives are
intrinsically linked to intelligence as it is demonstrated by natural
creatures, we argue such fields cannot, and will not, provide a sufficiently
rigorous definition that can be applied to artificial means. Thus, we present
an argument for a purely functional, black-box definition of intelligence,
distinct from how that intelligence is actually achieved; focusing on the
"what", rather than the "how". To achieve this, we first distinguish other
related concepts (sentience, sensation, agency, etc.) from the notion of
intelligence, particularly identifying how these concepts pertain to artificial
intelligent systems. As a result, we achieve a formal definition of
intelligence that is conceptually testable from only external observation, that
suggests intelligence is a continuous variable. We conclude by identifying
challenges that still remain towards quantifiable measurement. This work
provides a useful perspective for both the development of AI, and for public
perception of the capabilities and risks of AI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Context-aware feature attribution through argumentation
Abstract: Feature attribution is a fundamental task in both machine learning and data
analysis, which involves determining the contribution of individual features or
variables to a model's output. This process helps identify the most important
features for predicting an outcome. The history of feature attribution methods
can be traced back to General Additive Models (GAMs), which extend linear
regression models by incorporating non-linear relationships between dependent
and independent variables. In recent years, gradient-based methods and
surrogate models have been applied to unravel complex Artificial Intelligence
(AI) systems, but these methods have limitations. GAMs tend to achieve lower
accuracy, gradient-based methods can be difficult to interpret, and surrogate
models often suffer from stability and fidelity issues. Furthermore, most
existing methods do not consider users' contexts, which can significantly
influence their preferences. To address these limitations and advance the
current state-of-the-art, we define a novel feature attribution framework
called Context-Aware Feature Attribution Through Argumentation (CA-FATA). Our
framework harnesses the power of argumentation by treating each feature as an
argument that can either support, attack or neutralize a prediction.
Additionally, CA-FATA formulates feature attribution as an argumentation
procedure, and each computation has explicit semantics, which makes it
inherently interpretable. CA-FATA also easily integrates side information, such
as users' contexts, resulting in more accurate predictions.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models
Abstract: Current datasets for unwanted social bias auditing are limited to studying
protected demographic features such as race and gender. In this work, we
introduce a comprehensive benchmark that is meant to capture the amplification
of social bias, via stigmas, in generative language models. Taking inspiration
from social science research, we start with a documented list of 93 US-centric
stigmas and curate a question-answering (QA) dataset which involves simple
social situations. Our benchmark, SocialStigmaQA, contains roughly 10K prompts,
with a variety of prompt styles, carefully constructed to systematically test
for both social bias and model robustness. We present results for
SocialStigmaQA with two open source generative language models and we find that
the proportion of socially biased output ranges from 45% to 59% across a
variety of decoding strategies and prompting styles. We demonstrate that the
deliberate design of the templates in our benchmark (e.g., adding biasing text
to the prompt or using different verbs that change the answer that indicates
bias) impacts the model tendencies to generate socially biased output.
Additionally, through manual evaluation, we discover problematic patterns in
the generated chain-of-thought output that range from subtle bias to lack of
reasoning.
  Warning: This paper contains examples of text which are toxic, biased, and
potentially harmful.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Style Transfer to Calvin and Hobbes comics using Stable Diffusion
Abstract: This project report summarizes our journey to perform stable diffusion
fine-tuning on a dataset containing Calvin and Hobbes comics. The purpose is to
convert any given input image into the comic style of Calvin and Hobbes,
essentially performing style transfer. We train stable-diffusion-v1.5 using Low
Rank Adaptation (LoRA) to efficiently speed up the fine-tuning process. The
diffusion itself is handled by a Variational Autoencoder (VAE), which is a
U-net. Our results were visually appealing for the amount of training time and
the quality of input data that went into training.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Towards Causal Representations of Climate Model Data
Abstract: Climate models, such as Earth system models (ESMs), are crucial for
simulating future climate change based on projected Shared Socioeconomic
Pathways (SSP) greenhouse gas emissions scenarios. While ESMs are sophisticated
and invaluable, machine learning-based emulators trained on existing simulation
data can project additional climate scenarios much faster and are
computationally efficient. However, they often lack generalizability and
interpretability. This work delves into the potential of causal representation
learning, specifically the \emph{Causal Discovery with Single-parent Decoding}
(CDSD) method, which could render climate model emulation efficient
\textit{and} interpretable. We evaluate CDSD on multiple climate datasets,
focusing on emissions, temperature, and precipitation. Our findings shed light
on the challenges, limitations, and promise of using CDSD as a stepping stone
towards more interpretable and robust climate model emulation.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment
Abstract: Optimal decision-making presents a significant challenge for autonomous
systems operating in uncertain, stochastic and time-varying environments.
Environmental variability over time can significantly impact the system's
optimal decision making strategy for mission completion. To model such
environments, our work combines the previous notion of Time-Varying Markov
Decision Processes (TVMDP) with partial observability and introduces
Time-Varying Partially Observable Markov Decision Processes (TV-POMDP). We
propose a two-pronged approach to accurately estimate and plan within the
TV-POMDP: 1) Memory Prioritized State Estimation (MPSE), which leverages
weighted memory to provide more accurate time-varying transition estimates; and
2) an MPSE-integrated planning strategy that optimizes long-term rewards while
accounting for temporal constraint. We validate the proposed framework and
algorithms using simulations and hardware, with robots exploring a partially
observable, time-varying environments. Our results demonstrate superior
performance over standard methods, highlighting the framework's effectiveness
in stochastic, uncertain, time-varying domains.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks
Abstract: Recent advancements in Optical Character Recognition (OCR) have been driven
by transformer-based models. OCR systems are critical in numerous high-stakes
domains, yet their vulnerability to adversarial attack remains largely
uncharted territory, raising concerns about security and compliance with
emerging AI regulations. In this work we present a novel framework to assess
the resilience of Transformer-based OCR (TrOCR) models. We develop and assess
algorithms for both targeted and untargeted attacks. For the untargeted case,
we measure the Character Error Rate (CER), while for the targeted case we use
the success ratio. We find that TrOCR is highly vulnerable to untargeted
attacks and somewhat less vulnerable to targeted attacks. On a benchmark
handwriting data set, untargeted attacks can cause a CER of more than 1 without
being noticeable to the eye. With a similar perturbation size, targeted attacks
can lead to success rates of around $25\%$ -- here we attacked single tokens,
requiring TrOCR to output the tenth most likely token from a large vocabulary.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Extending Answer Set Programming with Rational Numbers
Abstract: Answer Set Programming (ASP) is a widely used declarative programming
paradigm that has shown great potential in solving complex computational
problems. However, the inability to natively support non-integer arithmetic has
been highlighted as a major drawback in real-world applications. This feature
is crucial to accurately model and manage real-world data and information as
emerged in various contexts, such as the smooth movement of video game
characters, the 3D movement of mechanical arms, and data streamed by sensors.
Nevertheless, extending ASP in this direction, without affecting its
declarative nature and its well-defined semantics, poses non-trivial
challenges; thus, no ASP system is able to reason natively with non-integer
domains. Indeed, the widespread floating-point arithmetic is not applicable to
the ASP case, as the reproducibility of results cannot be guaranteed and the
semantics of an ASP program would not be uniquely and declaratively determined,
regardless of the employed machine or solver. To overcome such limitations and
in the realm of pure ASP, this paper proposes an extension of ASP in which
non-integers are approximated to rational numbers, fully granting
reproducibility and declarativity. We provide a well-defined semantics for the
ASP-Core-2 standard extended with rational numbers and an implementation
thereof. We hope this work could serve as a stepping stone towards a more
expressive and versatile ASP language that can handle a broader range of
real-world problems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning
Abstract: We present MM-Narrator, a novel system leveraging GPT-4 with multimodal
in-context learning for the generation of audio descriptions (AD). Unlike
previous methods that primarily focused on downstream fine-tuning with short
video clips, MM-Narrator excels in generating precise audio descriptions for
videos of extensive lengths, even beyond hours, in an autoregressive manner.
This capability is made possible by the proposed memory-augmented generation
process, which effectively utilizes both the short-term textual context and
long-term visual memory through an efficient register-and-recall mechanism.
These contextual memories compile pertinent past information, including
storylines and character identities, ensuring an accurate tracking and
depicting of story-coherent and character-centric audio descriptions.
Maintaining the training-free design of MM-Narrator, we further propose a
complexity-based demonstration selection strategy to largely enhance its
multi-step reasoning capability via few-shot multimodal in-context learning
(MM-ICL). Experimental results on MAD-eval dataset demonstrate that MM-Narrator
consistently outperforms both the existing fine-tuning-based approaches and
LLM-based approaches in most scenarios, as measured by standard evaluation
metrics. Additionally, we introduce the first segment-based evaluator for
recurrent text generation. Empowered by GPT-4, this evaluator comprehensively
reasons and marks AD generation performance in various extendable dimensions.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Align before Adapt: Leveraging Entity-to-Region Alignments for Generalizable Video Action Recognition
Abstract: Large-scale visual-language pre-trained models have achieved significant
success in various video tasks. However, most existing methods follow an "adapt
then align" paradigm, which adapts pre-trained image encoders to model
video-level representations and utilizes one-hot or text embedding of the
action labels for supervision. This paradigm overlooks the challenge of mapping
from static images to complicated activity concepts. In this paper, we propose
a novel "Align before Adapt" (ALT) paradigm. Prior to adapting to video
representation learning, we exploit the entity-to-region alignments for each
frame. The alignments are fulfilled by matching the region-aware image
embeddings to an offline-constructed text corpus. With the aligned entities, we
feed their text embeddings to a transformer-based video adapter as the queries,
which can help extract the semantics of the most important entities from a
video to a vector. This paradigm reuses the visual-language alignment of VLP
during adaptation and tries to explain an action by the underlying entities.
This helps understand actions by bridging the gap with complex activity
semantics, particularly when facing unfamiliar or unseen categories. ALT
achieves competitive performance and superior generalizability while requiring
significantly low computational costs. In fully supervised scenarios, it
achieves 88.1% top-1 accuracy on Kinetics-400 with only 4947 GFLOPs. In 2-shot
experiments, ALT outperforms the previous state-of-the-art by 7.1% and 9.2% on
HMDB-51 and UCF-101, respectively.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Performance Prediction of Data-Driven Knowledge summarization of High Entropy Alloys (HEAs) literature implementing Natural Language Processing algorithms
Abstract: The ability to interpret spoken language is connected to natural language
processing. It involves teaching the AI how words relate to one another, how
they are meant to be used, and in what settings. The goal of natural language
processing (NLP) is to get a machine intelligence to process words the same way
a human brain does. This enables machine intelligence to interpret, arrange,
and comprehend textual data by processing the natural language. The technology
can comprehend what is communicated, whether it be through speech or writing
because AI pro-cesses language more quickly than humans can. In the present
study, five NLP algorithms, namely, Geneism, Sumy, Luhn, Latent Semantic
Analysis (LSA), and Kull-back-Liebler (KL) al-gorithm, are implemented for the
first time for the knowledge summarization purpose of the High Entropy Alloys
(HEAs). The performance prediction of these algorithms is made by using the
BLEU score and ROUGE score. The results showed that the Luhn algorithm has the
highest accuracy score for the knowledge summarization tasks compared to the
other used algorithms.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Evaluating Large Language Models: A Comprehensive Survey
Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across
a broad spectrum of tasks. They have attracted significant attention and been
deployed in numerous downstream applications. Nevertheless, akin to a
double-edged sword, LLMs also present potential risks. They could suffer from
private data leaks or yield inappropriate, harmful, or misleading content.
Additionally, the rapid progress of LLMs raises concerns about the potential
emergence of superintelligent systems without adequate safeguards. To
effectively capitalize on LLM capacities as well as ensure their safe and
beneficial development, it is critical to conduct a rigorous and comprehensive
evaluation of LLMs.
  This survey endeavors to offer a panoramic perspective on the evaluation of
LLMs. We categorize the evaluation of LLMs into three major groups: knowledge
and capability evaluation, alignment evaluation and safety evaluation. In
addition to the comprehensive review on the evaluation methodologies and
benchmarks on these three aspects, we collate a compendium of evaluations
pertaining to LLMs' performance in specialized domains, and discuss the
construction of comprehensive evaluation platforms that cover LLM evaluations
on capabilities, alignment, safety, and applicability.
  We hope that this comprehensive overview will stimulate further research
interests in the evaluation of LLMs, with the ultimate goal of making
evaluation serve as a cornerstone in guiding the responsible development of
LLMs. We envision that this will channel their evolution into a direction that
maximizes societal benefit while minimizing potential risks. A curated list of
related papers has been publicly available at
https://github.com/tjunlp-lab/Awesome-LLMs-Evaluation-Papers.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: TabMT: Generating tabular data with masked transformers
Abstract: Autoregressive and Masked Transformers are incredibly effective as generative
models and classifiers. While these models are most prevalent in NLP, they also
exhibit strong performance in other domains, such as vision. This work
contributes to the exploration of transformer-based models in synthetic data
generation for diverse application domains. In this paper, we present TabMT, a
novel Masked Transformer design for generating synthetic tabular data. TabMT
effectively addresses the unique challenges posed by heterogeneous data fields
and is natively able to handle missing data. Our design leverages improved
masking techniques to allow for generation and demonstrates state-of-the-art
performance from extremely small to extremely large tabular datasets. We
evaluate TabMT for privacy-focused applications and find that it is able to
generate high quality data with superior privacy tradeoffs.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Understanding Grokking Through A Robustness Viewpoint
Abstract: Recently, an unusual phenomenon called grokking has gained much attention,
where sometimes a neural network generalizes long after it perfectly fits the
training data. We try to understand this seemingly strange phenomenon using the
robustness of the neural network. Using a robustness viewpoint, we show that
the popular $l_2$ weight norm (metric) of the neural network is actually a
sufficient condition for grokking. As we also empirically find that $l_2$ norm
correlates with grokking on the test data not in a timely way, we propose new
metrics based on robustness and information theory and find that our new
metrics correlate well with the grokking phenomenon. Based on the previous
observations, we propose methods to speed up the generalization process. In
addition, we examine the standard training process on modulo addition dataset
and find that it hardly learns other basic group operations before grokking,
including the commutative law. Interestingly, the speed up of generalization
when using our proposed method can be partially explained by learning the
commutative law, a necessary condition when the model groks on test dataset.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Directly Attention Loss Adjusted Prioritized Experience Replay
Abstract: Prioritized Experience Replay (PER) enables the model to learn more about
relatively important samples by artificially changing their accessed
frequencies. However, this non-uniform sampling method shifts the state-action
distribution that is originally used to estimate Q-value functions, which
brings about the estimation deviation. In this article, an novel off policy
reinforcement learning training framework called Directly Attention Loss
Adjusted Prioritized Experience Replay (DALAP) is proposed, which can directly
quantify the changed extent of the shifted distribution through Parallel
Self-Attention network, so as to accurately compensate the error. In addition,
a Priority-Encouragement mechanism is designed simultaneously to optimize the
sample screening criterion, and further improve the training efficiency. In
order to verify the effectiveness and generality of DALAP, we integrate it with
the value-function based, the policy-gradient based and multi-agent
reinforcement learning algorithm, respectively. The multiple groups of
comparative experiments show that DALAP has the significant advantages of both
improving the convergence rate and reducing the training variance.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Procedural Fairness Through Decoupling Objectionable Data Generating Components
Abstract: We reveal and address the frequently overlooked yet important issue of
disguised procedural unfairness, namely, the potentially inadvertent
alterations on the behavior of neutral (i.e., not problematic) aspects of data
generating process, and/or the lack of procedural assurance of the greatest
benefit of the least advantaged individuals. Inspired by John Rawls's advocacy
for pure procedural justice, we view automated decision-making as a microcosm
of social institutions, and consider how the data generating process itself can
satisfy the requirements of procedural fairness. We propose a framework that
decouples the objectionable data generating components from the neutral ones by
utilizing reference points and the associated value instantiation rule. Our
findings highlight the necessity of preventing disguised procedural unfairness,
drawing attention not only to the objectionable data generating components that
we aim to mitigate, but also more importantly, to the neutral components that
we intend to keep unaffected.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Retail Analytics in the New Normal: The Influence of Artificial Intelligence and the Covid-19 Pandemic
Abstract: The COVID-19 pandemic has severely disrupted the retail landscape and has
accelerated the adoption of innovative technologies. A striking example relates
to the proliferation of online grocery orders and the technology deployed to
facilitate such logistics. In fact, for many retailers, this disruption was a
wake-up call after which they started recognizing the power of data analytics
and artificial intelligence (AI). In this article, we discuss the opportunities
that AI can offer to retailers in the new normal retail landscape. Some of the
techniques described have been applied at scale to adapt previously deployed AI
models, whereas in other instances, fresh solutions needed to be developed to
help retailers cope with recent disruptions, such as unexpected panic buying,
retraining predictive models, and leveraging online-offline synergies.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Auto deep learning for bioacoustic signals
Abstract: This study investigates the potential of automated deep learning to enhance
the accuracy and efficiency of multi-class classification of bird
vocalizations, compared against traditional manually-designed deep learning
models. Using the Western Mediterranean Wetland Birds dataset, we investigated
the use of AutoKeras, an automated machine learning framework, to automate
neural architecture search and hyperparameter tuning. Comparative analysis
validates our hypothesis that the AutoKeras-derived model consistently
outperforms traditional models like MobileNet, ResNet50 and VGG16. Our approach
and findings underscore the transformative potential of automated deep learning
for advancing bioacoustics research and models. In fact, the automated
techniques eliminate the need for manual feature engineering and model design
while improving performance. This study illuminates best practices in sampling,
evaluation and reporting to enhance reproducibility in this nascent field. All
the code used is available at https:
//github.com/giuliotosato/AutoKeras-bioacustic
  Keywords: AutoKeras; automated deep learning; audio classification; Wetlands
Bird dataset; comparative analysis; bioacoustics; validation dataset;
multi-class classification; spectrograms.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction
Abstract: Human albumin is essential for indicating the body's overall health.
Accurately predicting plasma albumin levels and determining appropriate doses
are urgent clinical challenges, particularly in critically ill patients, to
maintain optimal blood levels. However, human albumin prediction is non-trivial
that has to leverage the dynamics of biochemical markers as well as the
experience of treating patients. Moreover, the problem of distribution shift is
often encountered in real clinical data, which may lead to a decline in the
model prediction performance and reduce the reliability of the model's
application. In this paper, we propose a framework named Out-of-Distribution
Generalized Dynamic Graph Neural Network for Human Albumin Prediction
(DyG-HAP), which is able to provide accurate albumin predictions for Intensity
Care Unit (ICU) patients during hospitalization. We first model human albumin
prediction as a dynamic graph regression problem to model the dynamics and
patient relationship. Then, we propose a disentangled dynamic graph attention
mechanism to capture and disentangle the patterns whose relationship to labels
under distribution shifts is invariant and variant respectively. Last, we
propose an invariant dynamic graph regression method to encourage the model to
rely on invariant patterns to make predictions. Moreover, we propose a dataset
named Albumin level testing and nutritional dosing data for Intensive Care
(ANIC) for evaluation. Extensive experiments demonstrate the superiority of our
method compared to several baseline methods in human albumin prediction.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Sparse Variational Student-t Processes
Abstract: The theory of Bayesian learning incorporates the use of Student-t Processes
to model heavy-tailed distributions and datasets with outliers. However,
despite Student-t Processes having a similar computational complexity as
Gaussian Processes, there has been limited emphasis on the sparse
representation of this model. This is mainly due to the increased difficulty in
modeling and computation compared to previous sparse Gaussian Processes. Our
motivation is to address the need for a sparse representation framework that
reduces computational complexity, allowing Student-t Processes to be more
flexible for real-world datasets. To achieve this, we leverage the conditional
distribution of Student-t Processes to introduce sparse inducing points.
Bayesian methods and variational inference are then utilized to derive a
well-defined lower bound, facilitating more efficient optimization of our model
through stochastic gradient descent. We propose two methods for computing the
variational lower bound, one utilizing Monte Carlo sampling and the other
employing Jensen's inequality to compute the KL regularization term in the loss
function. We propose adopting these approaches as viable alternatives to
Gaussian processes when the data might contain outliers or exhibit heavy-tailed
behavior, and we provide specific recommendations for their applicability. We
evaluate the two proposed approaches on various synthetic and real-world
datasets from UCI and Kaggle, demonstrating their effectiveness compared to
baseline methods in terms of computational complexity and accuracy, as well as
their robustness to outliers.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: How Far Have We Gone in Vulnerability Detection Using Large Language Models
Abstract: As software becomes increasingly complex and prone to vulnerabilities,
automated vulnerability detection is critically important, yet challenging.
Given the significant successes of Large Language Models (LLMs) in various
tasks, there is growing anticipation of their efficacy in vulnerability
detection. However, a quantitative understanding of their potential in
vulnerability detection is still missing. To bridge this gap, we introduce a
comprehensive vulnerability benchmark VulBench. This benchmark aggregates
high-quality data from a wide range of CTF (Capture-the-Flag) challenges and
real-world applications, with annotations for each vulnerable function
detailing the vulnerability type and its root cause. Through our experiments
encompassing 16 LLMs and 6 state-of-the-art (SOTA) deep learning-based models
and static analyzers, we find that several LLMs outperform traditional deep
learning approaches in vulnerability detection, revealing an untapped potential
in LLMs. This work contributes to the understanding and utilization of LLMs for
enhanced software security.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Abstract: The "pretrain-then-finetune" paradigm is commonly adopted in the deployment
of large language models. Low-Rank Adaptation (LoRA), a parameter-efficient
fine-tuning method, is often employed to adapt a base model to a multitude of
tasks, resulting in a substantial collection of LoRA adapters derived from one
base model. We observe that this paradigm presents significant opportunities
for batched inference during serving. To capitalize on these opportunities, we
present S-LoRA, a system designed for the scalable serving of many LoRA
adapters. S-LoRA stores all adapters in the main memory and fetches the
adapters used by the currently running queries to the GPU memory. To
efficiently use the GPU memory and reduce fragmentation, S-LoRA proposes
Unified Paging. Unified Paging uses a unified memory pool to manage dynamic
adapter weights with different ranks and KV cache tensors with varying sequence
lengths. Additionally, S-LoRA employs a novel tensor parallelism strategy and
highly optimized custom CUDA kernels for heterogeneous batching of LoRA
computation. Collectively, these features enable S-LoRA to serve thousands of
LoRA adapters on a single GPU or across multiple GPUs with a small overhead.
Compared to state-of-the-art libraries such as HuggingFace PEFT and vLLM (with
naive support of LoRA serving), S-LoRA can improve the throughput by up to 4
times and increase the number of served adapters by several orders of
magnitude. As a result, S-LoRA enables scalable serving of many task-specific
fine-tuned models and offers the potential for large-scale customized
fine-tuning services. The code is available at https://github.com/S-LoRA/S-LoRA

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning
Abstract: Self-supervised learning has been actively studied in time series domain
recently, especially for masked reconstruction. Most of these methods follow
the "Pre-training + Fine-tuning" paradigm in which a new decoder replaces the
pre-trained decoder to fit for a specific downstream task, leading to
inconsistency of upstream and downstream tasks. In this paper, we first point
out that the unification of task objectives and adaptation for task difficulty
are critical for bridging the gap between time series masked reconstruction and
forecasting. By reserving the pre-trained mask token during fine-tuning stage,
the forecasting task can be taken as a special case of masked reconstruction,
where the future values are masked and reconstructed based on history values.
It guarantees the consistency of task objectives but there is still a gap in
task difficulty. Because masked reconstruction can utilize contextual
information while forecasting can only use historical information to
reconstruct. To further mitigate the existed gap, we propose a simple yet
effective prompt token tuning (PT-Tuning) paradigm, in which all pre-trained
parameters are frozen and only a few trainable prompt tokens are added to
extended mask tokens in element-wise manner. Extensive experiments on
real-world datasets demonstrate the superiority of our proposed paradigm with
state-of-the-art performance compared to representation learning and end-to-end
supervised forecasting methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Analyze the Robustness of Classifiers under Label Noise
Abstract: This study explores the robustness of label noise classifiers, aiming to
enhance model resilience against noisy data in complex real-world scenarios.
Label noise in supervised learning, characterized by erroneous or imprecise
labels, significantly impairs model performance. This research focuses on the
increasingly pertinent issue of label noise's impact on practical applications.
Addressing the prevalent challenge of inaccurate training data labels, we
integrate adversarial machine learning (AML) and importance reweighting
techniques. Our approach involves employing convolutional neural networks (CNN)
as the foundational model, with an emphasis on parameter adjustment for
individual training samples. This strategy is designed to heighten the model's
focus on samples critically influencing performance.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2
Abstract: Over the last decade there has been an increasing frequency and intensity of
wildfires across the globe, posing significant threats to human and animal
lives, ecosystems, and socio-economic stability. Therefore urgent action is
required to mitigate their devastating impact and safeguard Earth's natural
resources. Robust Machine Learning methods combined with the abundance of
high-resolution satellite imagery can provide accurate and timely mappings of
the affected area in order to assess the scale of the event, identify the
impacted assets and prioritize and allocate resources effectively for the
proper restoration of the damaged region. In this work, we create and introduce
a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations
for the Greek Area). This dataset is unique as it comprises of satellite
imagery acquired before and after a wildfire event, it contains information
from Sentinel-2 and MODIS modalities with variable spatial and spectral
resolution, and contains a large number of events where the corresponding burnt
area ground truth has been annotated by domain experts. FLOGA covers the wider
region of Greece, which is characterized by a Mediterranean landscape and
climatic conditions. We use FLOGA to provide a thorough comparison of multiple
Machine Learning and Deep Learning algorithms for the automatic extraction of
burnt areas, approached as a change detection task. We also compare the results
to those obtained using standard specialized spectral indices for burnt area
mapping. Finally, we propose a novel Deep Learning model, namely BAM-CD. Our
benchmark results demonstrate the efficacy of the proposed technique in the
automatic extraction of burnt areas, outperforming all other methods in terms
of accuracy and robustness. Our dataset and code are publicly available at:
https://github.com/Orion-AI-Lab/FLOGA.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: DACBERT: Leveraging Dependency Agreement for Cost-Efficient Bert Pretraining
Abstract: Building on the cost-efficient pretraining advancements brought about by
Crammed BERT, we enhance its performance and interpretability further by
introducing a novel pretrained model Dependency Agreement Crammed BERT
(DACBERT) and its two-stage pretraining framework - Dependency Agreement
Pretraining. This framework, grounded by linguistic theories, seamlessly weaves
syntax and semantic information into the pretraining process. The first stage
employs four dedicated submodels to capture representative dependency
agreements at the chunk level, effectively converting these agreements into
embeddings. The second stage uses these refined embeddings, in tandem with
conventional BERT embeddings, to guide the pretraining of the rest of the
model. Evaluated on the GLUE benchmark, our DACBERT demonstrates notable
improvement across various tasks, surpassing Crammed BERT by 3.13% in the RTE
task and by 2.26% in the MRPC task. Furthermore, our method boosts the average
GLUE score by 0.83%, underscoring its significant potential. The pretraining
process can be efficiently executed on a single GPU within a 24-hour cycle,
necessitating no supplementary computational resources or extending the
pretraining duration compared with the Crammed BERT. Extensive studies further
illuminate our approach's instrumental role in bolstering the interpretability
of pretrained language models for natural language understanding tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Dataset Distillation via the Wasserstein Metric
Abstract: Dataset distillation (DD) offers a compelling approach in computer vision,
with the goal of condensing extensive datasets into smaller synthetic versions
without sacrificing much of the model performance. In this paper, we continue
to study the methods for DD, by addressing its conceptually core objective: how
to capture the essential representation of extensive datasets in smaller,
synthetic forms.
  We propose a novel approach utilizing the Wasserstein distance, a metric
rooted in optimal transport theory, to enhance distribution matching in DD. Our
method leverages the Wasserstein barycenter, offering a geometrically
meaningful way to quantify distribution differences and effectively capture the
centroid of a set of distributions. Our approach retains the computational
benefits of distribution matching-based methods while achieving new
state-of-the-art performance on several benchmarks.
  To provide useful prior for learning the images, we embed the synthetic data
into the feature space of pretrained classification models to conduct
distribution matching. Extensive testing on various high-resolution datasets
confirms the effectiveness and adaptability of our method, indicating the
promising yet unexplored capabilities of Wasserstein metrics in dataset
distillation.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Knowledge Trees: Gradient Boosting Decision Trees on Knowledge Neurons as Probing Classifier
Abstract: To understand how well a large language model captures certain semantic or
syntactic features, researchers typically apply probing classifiers. However,
the accuracy of these classifiers is critical for the correct interpretation of
the results. If a probing classifier exhibits low accuracy, this may be due
either to the fact that the language model does not capture the property under
investigation, or to shortcomings in the classifier itself, which is unable to
adequately capture the characteristics encoded in the internal representations
of the model. Consequently, for more effective diagnosis, it is necessary to
use the most accurate classifiers possible for a particular type of task.
Logistic regression on the output representation of the transformer neural
network layer is most often used to probing the syntactic properties of the
language model.
  We show that using gradient boosting decision trees at the Knowledge Neuron
layer, i.e., at the hidden layer of the feed-forward network of the transformer
as a probing classifier for recognizing parts of a sentence is more
advantageous than using logistic regression on the output representations of
the transformer layer. This approach is also preferable to many other methods.
The gain in error rate, depending on the preset, ranges from 9-54%

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Identification for Tree-shaped Structural Causal Models in Polynomial Time
Abstract: Linear structural causal models (SCMs) are used to express and analyse the
relationships between random variables. Direct causal effects are represented
as directed edges and confounding factors as bidirected edges. Identifying the
causal parameters from correlations between the nodes is an open problem in
artificial intelligence. In this paper, we study SCMs whose directed component
forms a tree. Van der Zander et al. (AISTATS'22, PLMR 151, pp. 6770--6792,
2022) give a PSPACE-algorithm for the identification problem in this case,
which is a significant improvement over the general Gr\"obner basis approach,
which has doubly-exponential time complexity in the number of structural
parameters. In this work, we present a randomized polynomial-time algorithm,
which solves the identification problem for tree-shaped SCMs. For every
structural parameter, our algorithms decides whether it is generically
identifiable, generically 2-identifiable, or generically unidentifiable. (No
other cases can occur.) In the first two cases, it provides one or two
fractional affine square root terms of polynomials (FASTPs) for the
corresponding parameter, respectively.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: BanditPAM++: Faster $k$-medoids Clustering
Abstract: Clustering is a fundamental task in data science with wide-ranging
applications. In $k$-medoids clustering, cluster centers must be actual
datapoints and arbitrary distance metrics may be used; these features allow for
greater interpretability of the cluster centers and the clustering of exotic
objects in $k$-medoids clustering, respectively. $k$-medoids clustering has
recently grown in popularity due to the discovery of more efficient $k$-medoids
algorithms. In particular, recent research has proposed BanditPAM, a randomized
$k$-medoids algorithm with state-of-the-art complexity and clustering accuracy.
In this paper, we present BanditPAM++, which accelerates BanditPAM via two
algorithmic improvements, and is $O(k)$ faster than BanditPAM in complexity and
substantially faster than BanditPAM in wall-clock runtime. First, we
demonstrate that BanditPAM has a special structure that allows the reuse of
clustering information $\textit{within}$ each iteration. Second, we demonstrate
that BanditPAM has additional structure that permits the reuse of information
$\textit{across}$ different iterations. These observations inspire our proposed
algorithm, BanditPAM++, which returns the same clustering solutions as
BanditPAM but often several times faster. For example, on the CIFAR10 dataset,
BanditPAM++ returns the same results as BanditPAM but runs over 10$\times$
faster. Finally, we provide a high-performance C++ implementation of
BanditPAM++, callable from Python and R, that may be of interest to
practitioners at https://github.com/motiwari/BanditPAM. Auxiliary code to
reproduce all of our experiments via a one-line script is available at
https://github.com/ThrunGroup/BanditPAM_plusplus_experiments.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation
Abstract: Large Language Models (LLMs) have demonstrated remarkable performance on
coding related tasks, particularly on assisting humans in programming and
facilitating programming automation. However, existing benchmarks for
evaluating the code understanding and generation capacities of LLMs suffer from
severe limitations. First, most benchmarks are deficient as they focus on a
narrow range of popular programming languages and specific tasks, whereas the
real-world software development scenarios show dire need to implement systems
with multilingual programming environments to satisfy diverse requirements.
Practical programming practices also strongly expect multi-task settings for
testing coding capabilities of LLMs comprehensively and robustly. Second, most
benchmarks also fail to consider the actual executability and the consistency
of execution results of the generated code. To bridge these gaps between
existing benchmarks and expectations from practical applications, we introduce
CodeScope, an execution-based, multilingual, multi-task, multi-dimensional
evaluation benchmark for comprehensively gauging LLM capabilities on coding
tasks. CodeScope covers 43 programming languages and 8 coding tasks. It
evaluates the coding performance of LLMs from three dimensions (perspectives):
difficulty, efficiency, and length. To facilitate execution-based evaluations
of code generation, we develop MultiCodeEngine, an automated code execution
engine that supports 14 programming languages. Finally, we systematically
evaluate and analyze 8 mainstream LLMs on CodeScope tasks and demonstrate the
superior breadth and challenges of CodeScope for evaluating LLMs on code
understanding and generation tasks compared to other benchmarks. The CodeScope
benchmark and datasets are publicly available at
https://github.com/WeixiangYAN/CodeScope.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics Cycle
Abstract: Generative artificial intelligence (GenAI), exemplified by ChatGPT,
Midjourney, and other state-of-the-art large language models and diffusion
models, holds significant potential for transforming education and enhancing
human productivity. While the prevalence of GenAI in education has motivated
numerous research initiatives, integrating these technologies within the
learning analytics (LA) cycle and their implications for practical
interventions remain underexplored. This paper delves into the prospective
opportunities and challenges GenAI poses for advancing LA. We present a concise
overview of the current GenAI landscape and contextualise its potential roles
within Clow's generic framework of the LA cycle. We posit that GenAI can play
pivotal roles in analysing unstructured data, generating synthetic learner
data, enriching multimodal learner interactions, advancing interactive and
explanatory analytics, and facilitating personalisation and adaptive
interventions. As the lines blur between learners and GenAI tools, a renewed
understanding of learners is needed. Future research can delve deep into
frameworks and methodologies that advocate for human-AI collaboration. The LA
community can play a pivotal role in capturing data about human and AI
contributions and exploring how they can collaborate most effectively. As LA
advances, it is essential to consider the pedagogical implications and broader
socioeconomic impact of GenAI for ensuring an inclusive future.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Sam-Guided Enhanced Fine-Grained Encoding with Mixed Semantic Learning for Medical Image Captioning
Abstract: With the development of multimodality and large language models, the deep
learning-based technique for medical image captioning holds the potential to
offer valuable diagnostic recommendations. However, current generic text and
image pre-trained models do not yield satisfactory results when it comes to
describing intricate details within medical images. In this paper, we present a
novel medical image captioning method guided by the segment anything model
(SAM) to enable enhanced encoding with both general and detailed feature
extraction. In addition, our approach employs a distinctive pre-training
strategy with mixed semantic learning to simultaneously capture both the
overall information and finer details within medical images. We demonstrate the
effectiveness of this approach, as it outperforms the pre-trained BLIP2 model
on various evaluation metrics for generating descriptions of medical images.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Hypergraph-Based Approach to Recommend Online Resources in a Library
Abstract: When users in a digital library read or browse online resources, it generates
an immense amount of data. If the underlying system can recommend items, such
as books and journals, to the users, it will help them to find the related
items. This research analyzes a digital library's usage data to recommend items
to its users, and it uses different clustering algorithms to design the
recommender system. We have used content-based clustering, including
hierarchical, expectation maximization (EM), K-mean, FarthestFirst, and
density-based clustering algorithms, and user access pattern-based clustering,
which uses a hypergraph-based approach to generate the clusters. This research
shows that the recommender system designed using the hypergraph algorithm
generates the most accurate recommendation model compared to those designed
using the content-based clustering approaches.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Teenagers and Artificial Intelligence: Bootcamp Experience and Lessons Learned
Abstract: Artificial intelligence (AI) stands out as a game-changer in today's
technology landscape. However, the integration of AI education in classroom
curricula currently lags behind, leaving teenagers inadequately prepared for an
imminent AI-driven future.
  In this pilot study, we designed a three-day bootcamp offered in the summer
of 2023 to a cohort of 60 high school students. The curriculum was delivered in
person through animated video content, easy-to-follow slides, interactive
playgrounds, and quizzes. These were packaged in the early version of an online
learning platform we are developing. Results from the post-bootcamp survey
conveyed a 91.4% overall satisfaction. Despite the short bootcamp duration,
88.5% and 71.4% of teenagers responded that they had an improved understanding
of AI concepts and programming, respectively.
  Overall, we found that employing diverse modalities effectively engaged
students, and building foundational modules proved beneficial for introducing
more complex topics. Furthermore, using Google Colab notebooks for coding
assignments proved challenging to most students. Students' activity on the
platform and their answers to quizzes showed proficient engagement and a grasp
of the material.
  Our results strongly highlight the need for compelling and accessible AI
education methods for the next generation and the potential for informal
learning to fill the gap of providing early AI education to teenagers.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Reinforcement Learning for Wildfire Mitigation in Simulated Disaster Environments
Abstract: Climate change has resulted in a year over year increase in adverse weather
and weather conditions which contribute to increasingly severe fire seasons.
Without effective mitigation, these fires pose a threat to life, property,
ecology, cultural heritage, and critical infrastructure. To better prepare for
and react to the increasing threat of wildfires, more accurate fire modelers
and mitigation responses are necessary. In this paper, we introduce SimFire, a
versatile wildland fire projection simulator designed to generate realistic
wildfire scenarios, and SimHarness, a modular agent-based machine learning
wrapper capable of automatically generating land management strategies within
SimFire to reduce the overall damage to the area. Together, this publicly
available system allows researchers and practitioners the ability to emulate
and assess the effectiveness of firefighter interventions and formulate
strategic plans that prioritize value preservation and resource allocation
optimization. The repositories are available for download at
https://github.com/mitrefireline.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: RoboGPT: an intelligent agent of making embodied long-term decisions for daily instruction tasks
Abstract: Robotic agents must master common sense and long-term sequential decisions to
solve daily tasks through natural language instruction. The developments in
Large Language Models (LLMs) in natural language processing have inspired
efforts to use LLMs in complex robot planning. Despite LLMs' great
generalization and comprehension of instruction tasks, LLMs-generated task
plans sometimes lack feasibility and correctness. To address the problem, we
propose a RoboGPT agent\footnote{our code and dataset will be released soon}
for making embodied long-term decisions for daily tasks, with two modules: 1)
LLMs-based planning with re-plan to break the task into multiple sub-goals; 2)
RoboSkill individually designed for sub-goals to learn better navigation and
manipulation skills. The LLMs-based planning is enhanced with a new robotic
dataset and re-plan, called RoboGPT. The new robotic dataset of 67k daily
instruction tasks is gathered for fine-tuning the Llama model and obtaining
RoboGPT. RoboGPT planner with strong generalization can plan hundreds of daily
instruction tasks. Additionally, a low-computational Re-Plan module is designed
to allow plans to flexibly adapt to the environment, thereby addressing the
nomenclature diversity challenge. The proposed RoboGPT agent outperforms SOTA
methods on the ALFRED daily tasks. Moreover, RoboGPT planner exceeds SOTA
LLM-based planners like ChatGPT in task-planning rationality for hundreds of
unseen daily tasks, and even other domain tasks, while keeping the large
model's original broad application and generality.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: A Generative Neural Network Approach for 3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle
Abstract: One of the most promising developments in computer vision in recent years is
the use of generative neural networks for functionality condition-based 3D
design reconstruction and generation. Here, neural networks learn dependencies
between functionalities and a geometry in a very effective way. For a neural
network the functionalities are translated in conditions to a certain geometry.
But the more conditions the design generation needs to reflect, the more
difficult it is to learn clear dependencies. This leads to a multi criteria
design problem due various conditions, which are not considered in the neural
network structure so far.
  In this paper, we address this multi-criteria challenge for a 3D design use
case related to an unmanned aerial vehicle (UAV) motor mount. We generate
10,000 abstract 3D designs and subject them all to simulations for three
physical disciplines: mechanics, thermodynamics, and aerodynamics. Then, we
train a Conditional Variational Autoencoder (CVAE) using the geometry and
corresponding multicriteria functional constraints as input. We use our trained
CVAE as well as the Marching cubes algorithm to generate meshes for simulation
based evaluation. The results are then evaluated with the generated UAV
designs. Subsequently, we demonstrate the ability to generate optimized designs
under self-defined functionality conditions using the trained neural network.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: GAIA: a benchmark for General AI Assistants
Abstract: We introduce GAIA, a benchmark for General AI Assistants that, if solved,
would represent a milestone in AI research. GAIA proposes real-world questions
that require a set of fundamental abilities such as reasoning, multi-modality
handling, web browsing, and generally tool-use proficiency. GAIA questions are
conceptually simple for humans yet challenging for most advanced AIs: we show
that human respondents obtain 92\% vs. 15\% for GPT-4 equipped with plugins.
This notable performance disparity contrasts with the recent trend of LLMs
outperforming humans on tasks requiring professional skills in e.g. law or
chemistry. GAIA's philosophy departs from the current trend in AI benchmarks
suggesting to target tasks that are ever more difficult for humans. We posit
that the advent of Artificial General Intelligence (AGI) hinges on a system's
capability to exhibit similar robustness as the average human does on such
questions. Using GAIA's methodology, we devise 466 questions and their answer.
We release our questions while retaining answers to 300 of them to power a
leader-board available at https://huggingface.co/gaia-benchmark.

Output: Computational Linguistics