Instruction: What field is the article from?

Prompt:
Title: Linking Surface Facts to Large-Scale Knowledge Graphs
Abstract: Open Information Extraction (OIE) methods extract facts from natural language
text in the form of ("subject"; "relation"; "object") triples. These facts are,
however, merely surface forms, the ambiguity of which impedes their downstream
usage; e.g., the surface phrase "Michael Jordan" may refer to either the former
basketball player or the university professor. Knowledge Graphs (KGs), on the
other hand, contain facts in a canonical (i.e., unambiguous) form, but their
coverage is limited by a static schema (i.e., a fixed set of entities and
predicates). To bridge this gap, we need the best of both worlds: (i) high
coverage of free-text OIEs, and (ii) semantic precision (i.e., monosemy) of
KGs. In order to achieve this goal, we propose a new benchmark with novel
evaluation protocols that can, for example, measure fact linking performance on
a granular triple slot level, while also measuring if a system has the ability
to recognize that a surface form has no match in the existing KG. Our extensive
evaluation of several baselines show that detection of out-of-KG entities and
predicates is more difficult than accurate linking to existing ones, thus
calling for more research efforts on this difficult task. We publicly release
all resources (data, benchmark and code) on
https://github.com/nec-research/fact-linking.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning
Abstract: Despite their tremendous success in many applications, large language models
often fall short of consistent reasoning and planning in various (language,
embodied, and social) scenarios, due to inherent limitations in their
inference, learning, and modeling capabilities. In this position paper, we
present a new perspective of machine reasoning, LAW, that connects the concepts
of Language models, Agent models, and World models, for more robust and
versatile reasoning capabilities. In particular, we propose that world and
agent models are a better abstraction of reasoning, that introduces the crucial
elements of deliberate human-like reasoning, including beliefs about the world
and other agents, anticipation of consequences, goals/rewards, and strategic
planning. Crucially, language models in LAW serve as a backend to implement the
system or its elements and hence provide the computational power and
adaptability. We review the recent studies that have made relevant progress and
discuss future research directions towards operationalizing the LAW framework.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest
Abstract: Large Language Models (LLMs), despite their great power in language
generation, often encounter challenges when dealing with intricate and
knowledge-demanding queries in specific domains. This paper introduces a novel
approach to enhance LLMs by effectively extracting the relevant knowledge from
domain-specific textual sources, and the adaptive training of a chatbot with
domain-specific inquiries. Our two-step approach starts from training a
knowledge miner, namely LLMiner, which autonomously extracts Question-Answer
pairs from relevant documents through a chain-of-thought reasoning process.
Subsequently, we blend the mined QA pairs with a conversational dataset to
fine-tune the LLM as a chatbot, thereby enriching its domain-specific expertise
and conversational capabilities. We also developed a new evaluation benchmark
which comprises four domain-specific text corpora and associated human-crafted
QA pairs for testing. Our model shows remarkable performance improvement over
generally aligned LLM and surpasses domain-adapted models directly fine-tuned
on domain corpus. In particular, LLMiner achieves this with minimal human
intervention, requiring only 600 seed instances, thereby providing a pathway
towards self-improvement of LLMs through model-synthesized training data.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Towards Automated Recipe Genre Classification using Semi-Supervised Learning
Abstract: Sharing cooking recipes is a great way to exchange culinary ideas and provide
instructions for food preparation. However, categorizing raw recipes found
online into appropriate food genres can be challenging due to a lack of
adequate labeled data. In this study, we present a dataset named the
``Assorted, Archetypal, and Annotated Two Million Extended (3A2M+) Cooking
Recipe Dataset" that contains two million culinary recipes labeled in
respective categories with extended named entities extracted from recipe
descriptions. This collection of data includes various features such as title,
NER, directions, and extended NER, as well as nine different labels
representing genres including bakery, drinks, non-veg, vegetables, fast food,
cereals, meals, sides, and fusions. The proposed pipeline named 3A2M+ extends
the size of the Named Entity Recognition (NER) list to address missing named
entities like heat, time or process from the recipe directions using two NER
extraction tools. 3A2M+ dataset provides a comprehensive solution to the
various challenging recipe-related tasks, including classification, named
entity recognition, and recipe generation. Furthermore, we have demonstrated
traditional machine learning, deep learning and pre-trained language models to
classify the recipes into their corresponding genre and achieved an overall
accuracy of 98.6\%. Our investigation indicates that the title feature played a
more significant role in classifying the genre.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: On Tuning Neural ODE for Stability, Consistency and Faster Convergence
Abstract: Neural-ODE parameterize a differential equation using continuous depth neural
network and solve it using numerical ODE-integrator. These models offer a
constant memory cost compared to models with discrete sequence of hidden layers
in which memory cost increases linearly with the number of layers. In addition
to memory efficiency, other benefits of neural-ode include adaptability of
evaluation approach to input, and flexibility to choose numerical precision or
fast training. However, despite having all these benefits, it still has some
limitations. We identify the ODE-integrator (also called ODE-solver) as the
weakest link in the chain as it may have stability, consistency and convergence
(CCS) issues and may suffer from slower convergence or may not converge at all.
We propose a first-order Nesterov's accelerated gradient (NAG) based ODE-solver
which is proven to be tuned vis-a-vis CCS conditions. We empirically
demonstrate the efficacy of our approach by training faster, while achieving
better or comparable performance against neural-ode employing other fixed-step
explicit ODE-solvers as well discrete depth models such as ResNet in three
different tasks including supervised classification, density estimation, and
time-series modelling.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Mitigating Estimation Errors by Twin TD-Regularized Actor and Critic for Deep Reinforcement Learning
Abstract: We address the issue of estimation bias in deep reinforcement learning (DRL)
by introducing solution mechanisms that include a new, twin TD-regularized
actor-critic (TDR) method. It aims at reducing both over and under-estimation
errors. With TDR and by combining good DRL improvements, such as distributional
learning and long N-step surrogate stage reward (LNSS) method, we show that our
new TDR-based actor-critic learning has enabled DRL methods to outperform their
respective baselines in challenging environments in DeepMind Control Suite.
Furthermore, they elevate TD3 and SAC respectively to a level of performance
comparable to that of D4PG (the current SOTA), and they also improve the
performance of D4PG to a new SOTA level measured by mean reward, convergence
speed, learning success rate, and learning variance.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: An Information-Flow Perspective on Algorithmic Fairness
Abstract: This work presents insights gained by investigating the relationship between
algorithmic fairness and the concept of secure information flow. The problem of
enforcing secure information flow is well-studied in the context of information
security: If secret information may "flow" through an algorithm or program in
such a way that it can influence the program's output, then that is considered
insecure information flow as attackers could potentially observe (parts of) the
secret.
  There is a strong correspondence between secure information flow and
algorithmic fairness: if protected attributes such as race, gender, or age are
treated as secret program inputs, then secure information flow means that these
``secret'' attributes cannot influence the result of a program. While most
research in algorithmic fairness evaluation concentrates on studying the impact
of algorithms (often treating the algorithm as a black-box), the concepts
derived from information flow can be used both for the analysis of disparate
treatment as well as disparate impact w.r.t. a structural causal model.
  In this paper, we examine the relationship between quantitative as well as
qualitative information-flow properties and fairness. Moreover, based on this
duality, we derive a new quantitative notion of fairness called fairness
spread, which can be easily analyzed using quantitative information flow and
which strongly relates to counterfactual fairness. We demonstrate that
off-the-shelf tools for information-flow properties can be used in order to
formally analyze a program's algorithmic fairness properties, including the new
notion of fairness spread as well as established notions such as demographic
parity.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Corrupting Convolution-based Unlearnable Datasets with Pixel-based Image Transformations
Abstract: Unlearnable datasets lead to a drastic drop in the generalization performance
of models trained on them by introducing elaborate and imperceptible
perturbations into clean training sets. Many existing defenses, e.g., JPEG
compression and adversarial training, effectively counter UDs based on
norm-constrained additive noise. However, a fire-new type of convolution-based
UDs have been proposed and render existing defenses all ineffective, presenting
a greater challenge to defenders. To address this, we express the
convolution-based unlearnable sample as the result of multiplying a matrix by a
clean sample in a simplified scenario, and formalize the intra-class matrix
inconsistency as $\Theta_{imi}$, inter-class matrix consistency as
$\Theta_{imc}$ to investigate the working mechanism of the convolution-based
UDs. We conjecture that increasing both of these metrics will mitigate the
unlearnability effect. Through validation experiments that commendably support
our hypothesis, we further design a random matrix to boost both $\Theta_{imi}$
and $\Theta_{imc}$, achieving a notable degree of defense effect. Hence, by
building upon and extending these facts, we first propose a brand-new image
COrruption that employs randomly multiplicative transformation via
INterpolation operation to successfully defend against convolution-based UDs.
Our approach leverages global pixel random interpolations, effectively
suppressing the impact of multiplicative noise in convolution-based UDs.
Additionally, we have also designed two new forms of convolution-based UDs, and
find that our defense is the most effective against them.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Trustworthy AI: Deciding What to Decide
Abstract: When engaging in strategic decision-making, we are frequently confronted with
overwhelming information and data. The situation can be further complicated
when certain pieces of evidence contradict each other or become paradoxical.
The primary challenge is how to determine which information can be trusted when
we adopt Artificial Intelligence (AI) systems for decision-making. This issue
is known as deciding what to decide or Trustworthy AI. However, the AI system
itself is often considered an opaque black box. We propose a new approach to
address this issue by introducing a novel framework of Trustworthy AI (TAI)
encompassing three crucial components of AI: representation space, loss
function, and optimizer. Each component is loosely coupled with four TAI
properties. Altogether, the framework consists of twelve TAI properties. We aim
to use this framework to conduct the TAI experiments by quantitive and
qualitative research methods to satisfy TAI properties for the decision-making
context. The framework allows us to formulate an optimal prediction model
trained by the given dataset for applying the strategic investment decision of
credit default swaps (CDS) in the technology sector. Finally, we provide our
view of the future direction of TAI research

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: PathoDuet: Foundation Models for Pathological Slide Analysis of H&E and IHC Stains
Abstract: Large amounts of digitized histopathological data display a promising future
for developing pathological foundation models via self-supervised learning
methods. Foundation models pretrained with these methods serve as a good basis
for downstream tasks. However, the gap between natural and histopathological
images hinders the direct application of existing methods. In this work, we
present PathoDuet, a series of pretrained models on histopathological images,
and a new self-supervised learning framework in histopathology. The framework
is featured by a newly-introduced pretext token and later task raisers to
explicitly utilize certain relations between images, like multiple
magnifications and multiple stains. Based on this, two pretext tasks,
cross-scale positioning and cross-stain transferring, are designed to pretrain
the model on Hematoxylin and Eosin (H\&E) images and transfer the model to
immunohistochemistry (IHC) images, respectively. To validate the efficacy of
our models, we evaluate the performance over a wide variety of downstream
tasks, including patch-level colorectal cancer subtyping and whole slide image
(WSI)-level classification in H\&E field, together with expression level
prediction of IHC marker and tumor identification in IHC field. The
experimental results show the superiority of our models over most tasks and the
efficacy of proposed pretext tasks. The codes and models are available at
https://github.com/openmedlab/PathoDuet.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Informative Priors Improve the Reliability of Multimodal Clinical Data Classification
Abstract: Machine learning-aided clinical decision support has the potential to
significantly improve patient care. However, existing efforts in this domain
for principled quantification of uncertainty have largely been limited to
applications of ad-hoc solutions that do not consistently improve reliability.
In this work, we consider stochastic neural networks and design a tailor-made
multimodal data-driven (M2D2) prior distribution over network parameters. We
use simple and scalable Gaussian mean-field variational inference to train a
Bayesian neural network using the M2D2 prior. We train and evaluate the
proposed approach using clinical time-series data in MIMIC-IV and corresponding
chest X-ray images in MIMIC-CXR for the classification of acute care
conditions. Our empirical results show that the proposed method produces a more
reliable predictive model compared to deterministic and Bayesian neural network
baselines.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: An Eye on Clinical BERT: Investigating Language Model Generalization for Diabetic Eye Disease Phenotyping
Abstract: Diabetic eye disease is a major cause of blindness worldwide. The ability to
monitor relevant clinical trajectories and detect lapses in care is critical to
managing the disease and preventing blindness. Alas, much of the information
necessary to support these goals is found only in the free text of the
electronic medical record. To fill this information gap, we introduce a system
for extracting evidence from clinical text of 19 clinical concepts related to
diabetic eye disease and inferring relevant attributes for each. In developing
this ophthalmology phenotyping system, we are also afforded a unique
opportunity to evaluate the effectiveness of clinical language models at
adapting to new clinical domains. Across multiple training paradigms, we find
that BERT language models pretrained on out-of-distribution clinical data offer
no significant improvement over BERT language models pretrained on non-clinical
data for our domain. Our study tempers recent claims that language models
pretrained on clinical data are necessary for clinical NLP tasks and highlights
the importance of not treating clinical language data as a single homogeneous
domain.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: JaxMARL: Multi-Agent RL Environments in JAX
Abstract: Benchmarks play an important role in the development of machine learning
algorithms. For example, research in reinforcement learning (RL) has been
heavily influenced by available environments and benchmarks. However, RL
environments are traditionally run on the CPU, limiting their scalability with
typical academic compute. Recent advancements in JAX have enabled the wider use
of hardware acceleration to overcome these computational hurdles, enabling
massively parallel RL training pipelines and environments. This is particularly
useful for multi-agent reinforcement learning (MARL) research. First of all,
multiple agents must be considered at each environment step, adding
computational burden, and secondly, the sample complexity is increased due to
non-stationarity, decentralised partial observability, or other MARL
challenges. In this paper, we present JaxMARL, the first open-source code base
that combines ease-of-use with GPU enabled efficiency, and supports a large
number of commonly used MARL environments as well as popular baseline
algorithms. When considering wall clock time, our experiments show that per-run
our JAX-based training pipeline is up to 12500x faster than existing
approaches. This enables efficient and thorough evaluations, with the potential
to alleviate the evaluation crisis of the field. We also introduce and
benchmark SMAX, a vectorised, simplified version of the popular StarCraft
Multi-Agent Challenge, which removes the need to run the StarCraft II game
engine. This not only enables GPU acceleration, but also provides a more
flexible MARL environment, unlocking the potential for self-play,
meta-learning, and other future applications in MARL. We provide code at
https://github.com/flairox/jaxmarl.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Tackling the Abstraction and Reasoning Corpus (ARC) with Object-centric Models and the MDL Principle
Abstract: The Abstraction and Reasoning Corpus (ARC) is a challenging benchmark,
introduced to foster AI research towards human-level intelligence. It is a
collection of unique tasks about generating colored grids, specified by a few
examples only. In contrast to the transformation-based programs of existing
work, we introduce object-centric models that are in line with the natural
programs produced by humans. Our models can not only perform predictions, but
also provide joint descriptions for input/output pairs. The Minimum Description
Length (MDL) principle is used to efficiently search the large model space. A
diverse range of tasks are solved, and the learned models are similar to the
natural programs. We demonstrate the generality of our approach by applying it
to a different domain.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
Abstract: Temporal graphs are widely used to model dynamic systems with time-varying
interactions. In real-world scenarios, the underlying mechanisms of generating
future interactions in dynamic systems are typically governed by a set of
recurring substructures within the graph, known as temporal motifs. Despite the
success and prevalence of current temporal graph neural networks (TGNN), it
remains uncertain which temporal motifs are recognized as the significant
indications that trigger a certain prediction from the model, which is a
critical challenge for advancing the explainability and trustworthiness of
current TGNNs. To address this challenge, we propose a novel approach, called
Temporal Motifs Explainer (TempME), which uncovers the most pivotal temporal
motifs guiding the prediction of TGNNs. Derived from the information bottleneck
principle, TempME extracts the most interaction-related motifs while minimizing
the amount of contained information to preserve the sparsity and succinctness
of the explanation. Events in the explanations generated by TempME are verified
to be more spatiotemporally correlated than those of existing approaches,
providing more understandable insights. Extensive experiments validate the
superiority of TempME, with up to 8.21% increase in terms of explanation
accuracy across six real-world datasets and up to 22.96% increase in boosting
the prediction Average Precision of current TGNNs.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Discriminator Guidance for Autoregressive Diffusion Models
Abstract: We introduce discriminator guidance in the setting of Autoregressive
Diffusion Models. The use of a discriminator to guide a diffusion process has
previously been used for continuous diffusion models, and in this work we
derive ways of using a discriminator together with a pretrained generative
model in the discrete case. First, we show that using an optimal discriminator
will correct the pretrained model and enable exact sampling from the underlying
data distribution. Second, to account for the realistic scenario of using a
sub-optimal discriminator, we derive a sequential Monte Carlo algorithm which
iteratively takes the predictions from the discrimiator into account during the
generation process. We test these approaches on the task of generating
molecular graphs and show how the discriminator improves the generative
performance over using only the pretrained model.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Causality Analysis for Evaluating the Security of Large Language Models
Abstract: Large Language Models (LLMs) such as GPT and Llama2 are increasingly adopted
in many safety-critical applications. Their security is thus essential. Even
with considerable efforts spent on reinforcement learning from human feedback
(RLHF), recent studies have shown that LLMs are still subject to attacks such
as adversarial perturbation and Trojan attacks. Further research is thus needed
to evaluate their security and/or understand the lack of it. In this work, we
propose a framework for conducting light-weight causality-analysis of LLMs at
the token, layer, and neuron level. We applied our framework to open-source
LLMs such as Llama2 and Vicuna and had multiple interesting discoveries. Based
on a layer-level causality analysis, we show that RLHF has the effect of
overfitting a model to harmful prompts. It implies that such security can be
easily overcome by `unusual' harmful prompts. As evidence, we propose an
adversarial perturbation method that achieves 100\% attack success rate on the
red-teaming tasks of the Trojan Detection Competition 2023. Furthermore, we
show the existence of one mysterious neuron in both Llama2 and Vicuna that has
an unreasonably high causal effect on the output. While we are uncertain on why
such a neuron exists, we show that it is possible to conduct a ``Trojan''
attack targeting that particular neuron to completely cripple the LLM, i.e., we
can generate transferable suffixes to prompts that frequently make the LLM
produce meaningless responses.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Architecture of Data Anomaly Detection-Enhanced Decentralized Expert System for Early-Stage Alzheimer's Disease Prediction
Abstract: Alzheimer's Disease is a global health challenge that requires early and
accurate detection to improve patient outcomes. Magnetic Resonance Imaging
(MRI) holds significant diagnostic potential, but its effective analysis
remains a formidable task. This study introduces a groundbreaking decentralized
expert system that cleverly combines blockchain technology with Artificial
Intelligence (AI) to integrate robust anomaly detection for patient-submitted
data.
  Traditional diagnostic methods often lead to delayed and imprecise
predictions, especially in the early stages of the disease. Centralized data
repositories struggle to manage the immense volumes of MRI data, and persistent
privacy concerns hinder collaborative efforts. Our innovative solution
harnesses decentralization to protect data integrity and patient privacy,
facilitated by blockchain technology. It not only emphasizes AI-driven MRI
analysis but also incorporates a sophisticated data anomaly detection
architecture. These mechanisms scrutinize patient-contributed data for various
issues, including data quality problems and atypical findings within MRI
images.
  Conducting an exhaustive check of MRI image correctness and quality directly
on the blockchain is impractical due to computational complexity and cost
constraints. Typically, such checks are performed off-chain, and the blockchain
securely records the results. This comprehensive approach empowers our
decentralized app to provide more precise early-stage Alzheimer's Disease
predictions. By merging the strengths of blockchain, AI, and anomaly detection,
our system represents a pioneering step towards revolutionizing disease
diagnostics.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Defense semantics of argumentation: revisit
Abstract: In this paper we introduce a novel semantics, called defense semantics, for
Dung's abstract argumentation frameworks in terms of a notion of (partial)
defence, which is a triple encoding that one argument is (partially) defended
by another argument via attacking the attacker of the first argument. In terms
of defense semantics, we show that defenses related to self-attacked arguments
and arguments in 3-cycles are unsatifiable under any situation and therefore
can be removed without affecting the defense semantics of an AF. Then, we
introduce a new notion of defense equivalence of AFs, and compare defense
equivalence with standard equivalence and strong equivalence, respectively.
Finally, by exploiting defense semantics, we define two kinds of reasons for
accepting arguments, i.e., direct reasons and root reasons, and a notion of
root equivalence of AFs that can be used in argumentation summarization.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers
Abstract: We propose a framework that leverages foundation models as teachers, guiding
a reinforcement learning agent to acquire semantically meaningful behavior
without human feedback. In our framework, the agent receives task instructions
grounded in a training environment from large language models. Then, a
vision-language model guides the agent in learning the multi-task
language-conditioned policy by providing reward feedback. We demonstrate that
our method can learn semantically meaningful skills in a challenging open-ended
MineDojo environment while prior unsupervised skill discovery methods struggle.
Additionally, we discuss observed challenges of using off-the-shelf foundation
models as teachers and our efforts to address them.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Method to Improve the Performance of Reinforcement Learning Based on the Y Operator for a Class of Stochastic Differential Equation-Based Child-Mother Systems
Abstract: This paper introduces a novel operator, termed the Y operator, to elevate
control performance in Actor-Critic(AC) based reinforcement learning for
systems governed by stochastic differential equations(SDEs). The Y operator
ingeniously integrates the stochasticity of a class of child-mother system into
the Critic network's loss function, yielding substantial advancements in the
control performance of RL algorithms.Additionally, the Y operator elegantly
reformulates the challenge of solving partial differential equations for the
state-value function into a parallel problem for the drift and diffusion
functions within the system's SDEs.A rigorous mathematical proof confirms the
operator's validity.This transformation enables the Y Operator-based
Reinforcement Learning(YORL) framework to efficiently tackle optimal control
problems in both model-based and data-driven systems.The superiority of YORL is
demonstrated through linear and nonlinear numerical examples showing its
enhanced performance over existing methods post convergence.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Gradient Informed Proximal Policy Optimization
Abstract: We introduce a novel policy learning method that integrates analytical
gradients from differentiable environments with the Proximal Policy
Optimization (PPO) algorithm. To incorporate analytical gradients into the PPO
framework, we introduce the concept of an {\alpha}-policy that stands as a
locally superior policy. By adaptively modifying the {\alpha} value, we can
effectively manage the influence of analytical policy gradients during
learning. To this end, we suggest metrics for assessing the variance and bias
of analytical gradients, reducing dependence on these gradients when high
variance or bias is detected. Our proposed approach outperforms baseline
algorithms in various scenarios, such as function optimization, physics
simulations, and traffic control environments. Our code can be found online:
https://github.com/SonSang/gippo.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Comparison of metaheuristics for the firebreak placement problem: a simulation-based optimization approach
Abstract: The problem of firebreak placement is crucial for fire prevention, and its
effectiveness at landscape scale will depend on their ability to impede the
progress of future wildfires. To provide an adequate response, it is therefore
necessary to consider the stochastic nature of fires, which are highly
unpredictable from ignition to extinction. Thus, the placement of firebreaks
can be considered a stochastic optimization problem where: (1) the objective
function is to minimize the expected cells burnt of the landscape; (2) the
decision variables being the location of firebreaks; and (3) the random
variable being the spatial propagation/behavior of fires. In this paper, we
propose a solution approach for the problem from the perspective of
simulation-based optimization (SbO), where the objective function is not
available (a black-box function), but can be computed (and/or approximated) by
wildfire simulations. For this purpose, Genetic Algorithm and GRASP are
implemented. The final implementation yielded favorable results for the Genetic
Algorithm, demonstrating strong performance in scenarios with medium to high
operational capacity, as well as medium levels of stochasticity

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: What Planning Problems Can A Relational Neural Network Solve?
Abstract: Goal-conditioned policies are generally understood to be "feed-forward"
circuits, in the form of neural networks that map from the current state and
the goal specification to the next action to take. However, under what
circumstances such a policy can be learned and how efficient the policy will be
are not well understood. In this paper, we present a circuit complexity
analysis for relational neural networks (such as graph neural networks and
transformers) representing policies for planning problems, by drawing
connections with serialized goal regression search (S-GRS). We show that there
are three general classes of planning problems, in terms of the growth of
circuit width and depth as a function of the number of objects and planning
horizon, providing constructive proofs. We also illustrate the utility of this
analysis for designing neural networks for policy learning.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: New Boolean satisfiability problem heuristic strategy: Minimal Positive Negative Product Strategy
Abstract: This study presents a novel heuristic algorithm called the "Minimal Positive
Negative Product Strategy" to guide the CDCL algorithm in solving the Boolean
satisfiability problem. It provides a mathematical explanation for the
superiority of this algorithm over widely used heuristics such as the Dynamic
Largest Individual Sum (DLIS) and the Variable State Independent Decaying Sum
(VSIDS). Experimental results further confirm the effectiveness of this
heuristic strategy in problem-solving.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Mixed Distillation Helps Smaller Language Model Better Reasoning
Abstract: Despite the remarkable performance of large language models (LLMs) in recent
NLP tasks, their deployment poses substantial challenges due to high
computational and memory demands. Recent research has concentrated on improving
open-source smaller models through knowledge distillation from LLMs to reduce
computational resource costs with promising outcomes. Nevertheless, they
frequently fall short of attaining LLM-level performance, particularly in tasks
demanding advanced reasoning. In this work, we introduce the \textbf{Mixed
Distillation} framework, which capitalizes on the strengths of
Program-of-Thought (PoT) and Chain-of-Thought (CoT) capabilities within LLMs
and distills these capabilities to smaller models. Regarding these two
capabilities, the PoT is dedicated to enhancing the performance of reasoning
results generated by smaller models, while CoT simultaneously optimizes the
results. Our Mixed Distillation framework offers a promising approach to
enhance the capabilities of smaller models, bridging the gap with LLMs, and
demonstrating better performance across various tasks. Specifically, on the
SVAMP dataset, employing a 7 billion parameter Llama2 and CodeLlama in a mixed
distillation framework not only boosts distillation capabilities beyond
single-path distillation methods but also outperforms the LLM (GPT-3.5-turbo)
in terms of reasoning accuracy. Through sampling in multiple-path reasoning,
the models achieve impressive accuracy performances of 85% and 85.5%,
respectively, signifying advancements over previous distillation methods.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation
Abstract: Label aggregation such as majority voting is commonly used to resolve
annotator disagreement in dataset creation. However, this may disregard
minority values and opinions. Recent studies indicate that learning from
individual annotations outperforms learning from aggregated labels, though they
require a considerable amount of annotation. Active learning, as an annotation
cost-saving strategy, has not been fully explored in the context of learning
from disagreement. We show that in the active learning setting, a multi-head
model performs significantly better than a single-head model in terms of
uncertainty estimation. By designing and evaluating acquisition functions with
annotator-specific heads on two datasets, we show that group-level entropy
works generally well on both datasets. Importantly, it achieves performance in
terms of both prediction and uncertainty estimation comparable to full-scale
training from disagreement, while saving up to 70% of the annotation budget.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Decomposing Hard SAT Instances with Metaheuristic Optimization
Abstract: In the article, within the framework of the Boolean Satisfiability problem
(SAT), the problem of estimating the hardness of specific Boolean formulas
w.r.t. a specific complete SAT solving algorithm is considered. Based on the
well-known Strong Backdoor Set (SBS) concept, we introduce the notion of
decomposition hardness (d-hardness). If $B$ is an arbitrary subset of the set
of variables occurring in a SAT formula $C$, and $A$ is an arbitrary complete
SAT solver , then the d-hardness expresses an estimate of the hardness of $C$
w.r.t. $A$ and $B$. We show that the d-hardness of $C$ w.r.t. a particular $B$
can be expressed in terms of the expected value of a special random variable
associated with $A$, $B$, and $C$. For its computational evaluation, algorithms
based on the Monte Carlo method can be used. The problem of finding $B$ with
the minimum value of d-hardness is formulated as an optimization problem for a
pseudo-Boolean function whose values are calculated as a result of a
probabilistic experiment. To minimize this function, we use evolutionary
algorithms. In the experimental part, we demonstrate the applicability of the
concept of d-hardness and the methods of its estimation to solving hard
unsatisfiable SAT instances.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Dates Fruit Disease Recognition using Machine Learning
Abstract: Many countries such as Saudi Arabia, Morocco and Tunisia are among the top
exporters and consumers of palm date fruits. Date fruit production plays a
major role in the economies of the date fruit exporting countries. Date fruits
are susceptible to disease just like any fruit and early detection and
intervention can end up saving the produce. However, with the vast farming
lands, it is nearly impossible for farmers to observe date trees on a frequent
basis for early disease detection. In addition, even with human observation the
process is prone to human error and increases the date fruit cost. With the
recent advances in computer vision, machine learning, drone technology, and
other technologies; an integrated solution can be proposed for the automatic
detection of date fruit disease. In this paper, a hybrid features based method
with the standard classifiers is proposed based on the extraction of L*a*b
color features, statistical features, and Discrete Wavelet Transform (DWT)
texture features for the early detection and classification of date fruit
disease. A dataset was developed for this work consisting of 871 images divided
into the following classes; Healthy date, Initial stage of disease,
Malnourished date, and Parasite infected. The extracted features were input to
common classifiers such as the Random Forest (RF), Multilayer Perceptron (MLP),
Na\"ive Bayes (NB), and Fuzzy Decision Trees (FDT). The highest average
accuracy was achieved when combining the L*a*b, Statistical, and DWT Features.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Language Model Agents Suffer from Compositional Generalization in Web Automation
Abstract: Language model agents (LMA) recently emerged as a promising paradigm on
muti-step decision making tasks, often outperforming humans and other
reinforcement learning agents. Despite the promise, their performance on
real-world applications that often involve combinations of tasks is still
underexplored. In this work, we introduce a new benchmark, called CompWoB -- 50
new compositional web automation tasks reflecting more realistic assumptions.
We show that while existing prompted LMAs (gpt-3.5-turbo or gpt-4) achieve
94.0% average success rate on base tasks, their performance degrades to 24.9%
success rate on compositional tasks. On the other hand, transferred LMAs
(finetuned only on base tasks) show less generalization gap, dropping from
85.4% to 54.8%. By balancing data distribution across tasks, we train a new
model, HTML-T5++, that surpasses human-level performance (95.2%) on MiniWoB,
and achieves the best zero-shot performance on CompWoB (61.5%). While these
highlight the promise of small-scale finetuned and transferred models for
compositional generalization, their performance further degrades under
different instruction compositions changing combinational order. In contrast to
the recent remarkable success of LMA, our benchmark and detailed analysis
emphasize the necessity of building LMAs that are robust and generalizable to
task compositionality for real-world deployment.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: The Linear Representation Hypothesis and the Geometry of Large Language Models
Abstract: Informally, the 'linear representation hypothesis' is the idea that
high-level concepts are represented linearly as directions in some
representation space. In this paper, we address two closely related questions:
What does "linear representation" actually mean? And, how do we make sense of
geometric notions (e.g., cosine similarity or projection) in the representation
space? To answer these, we use the language of counterfactuals to give two
formalizations of "linear representation", one in the output (word)
representation space, and one in the input (sentence) space. We then prove
these connect to linear probing and model steering, respectively. To make sense
of geometric notions, we use the formalization to identify a particular
(non-Euclidean) inner product that respects language structure in a sense we
make precise. Using this causal inner product, we show how to unify all notions
of linear representation. In particular, this allows the construction of probes
and steering vectors using counterfactual pairs. Experiments with LLaMA-2
demonstrate the existence of linear representations of concepts, the connection
to interpretation and control, and the fundamental role of the choice of inner
product.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: UWB Based Static Gesture Classification
Abstract: Our paper presents a robust framework for UWB-based static gesture
recognition, leveraging proprietary UWB radar sensor technology. Extensive data
collection efforts were undertaken to compile datasets containing five commonly
used gestures. Our approach involves a comprehensive data pre-processing
pipeline that encompasses outlier handling, aspect ratio-preserving resizing,
and false-color image transformation. Both CNN and MobileNet models were
trained on the processed images. Remarkably, our best-performing model achieved
an accuracy of 96.78%. Additionally, we developed a user-friendly GUI framework
to assess the model's system resource usage and processing times, which
revealed low memory utilization and real-time task completion in under one
second. This research marks a significant step towards enhancing static gesture
recognition using UWB technology, promising practical applications in various
domains.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Bridging the Gap: Addressing Discrepancies in Diffusion Model Training for Classifier-Free Guidance
Abstract: Diffusion models have emerged as a pivotal advancement in generative models,
setting new standards to the quality of the generated instances. In the current
paper we aim to underscore a discrepancy between conventional training methods
and the desired conditional sampling behavior of these models. While the
prevalent classifier-free guidance technique works well, it's not without
flaws. At higher values for the guidance scale parameter $w$, we often get out
of distribution samples and mode collapse, whereas at lower values for $w$ we
may not get the desired specificity. To address these challenges, we introduce
an updated loss function that better aligns training objectives with sampling
behaviors. Experimental validation with FID scores on CIFAR-10 elucidates our
method's ability to produce higher quality samples with fewer sampling
timesteps, and be more robust to the choice of guidance scale $w$. We also
experiment with fine-tuning Stable Diffusion on the proposed loss, to provide
early evidence that large diffusion models may also benefit from this refined
loss function.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Labeling Neural Representations with Inverse Recognition
Abstract: Deep Neural Networks (DNNs) demonstrated remarkable capabilities in learning
complex hierarchical data representations, but the nature of these
representations remains largely unknown. Existing global explainability
methods, such as Network Dissection, face limitations such as reliance on
segmentation masks, lack of statistical significance testing, and high
computational demands. We propose Inverse Recognition (INVERT), a scalable
approach for connecting learned representations with human-understandable
concepts by leveraging their capacity to discriminate between these concepts.
In contrast to prior work, INVERT is capable of handling diverse types of
neurons, exhibits less computational complexity, and does not rely on the
availability of segmentation masks. Moreover, INVERT provides an interpretable
metric assessing the alignment between the representation and its corresponding
explanation and delivering a measure of statistical significance, emphasizing
its utility and credibility. We demonstrate the applicability of INVERT in
various scenarios, including the identification of representations affected by
spurious correlations, and the interpretation of the hierarchical structure of
decision-making within the models.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Chatbots Are Not Reliable Text Annotators
Abstract: Recent research highlights the significant potential of ChatGPT for text
annotation in social science research. However, ChatGPT is a closed-source
product which has major drawbacks with regards to transparency,
reproducibility, cost, and data protection. Recent advances in open-source (OS)
large language models (LLMs) offer alternatives which remedy these challenges.
This means that it is important to evaluate the performance of OS LLMs relative
to ChatGPT and standard approaches to supervised machine learning
classification. We conduct a systematic comparative evaluation of the
performance of a range of OS LLM models alongside ChatGPT, using both zero- and
few-shot learning as well as generic and custom prompts, with results compared
to more traditional supervised classification models. Using a new dataset of
Tweets from US news media, and focusing on simple binary text annotation tasks
for standard social science concepts, we find significant variation in the
performance of ChatGPT and OS models across the tasks, and that supervised
classifiers consistently outperform both. Given the unreliable performance of
ChatGPT and the significant challenges it poses to Open Science we advise
against using ChatGPT for substantive text annotation tasks in social science
research.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Diffused Task-Agnostic Milestone Planner
Abstract: Addressing decision-making problems using sequence modeling to predict future
trajectories shows promising results in recent years. In this paper, we take a
step further to leverage the sequence predictive method in wider areas such as
long-term planning, vision-based control, and multi-task decision-making. To
this end, we propose a method to utilize a diffusion-based generative sequence
model to plan a series of milestones in a latent space and to have an agent to
follow the milestones to accomplish a given task. The proposed method can learn
control-relevant, low-dimensional latent representations of milestones, which
makes it possible to efficiently perform long-term planning and vision-based
control. Furthermore, our approach exploits generation flexibility of the
diffusion model, which makes it possible to plan diverse trajectories for
multi-task decision-making. We demonstrate the proposed method across offline
reinforcement learning (RL) benchmarks and an visual manipulation environment.
The results show that our approach outperforms offline RL methods in solving
long-horizon, sparse-reward tasks and multi-task problems, while also achieving
the state-of-the-art performance on the most challenging vision-based
manipulation benchmark.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Smart Home Goal Feature Model -- A guide to support Smart Homes for Ageing in Place
Abstract: Smart technologies are significant in supporting ageing in place for elderly.
Leveraging Artificial Intelligence (AI) and Machine Learning (ML), it provides
peace of mind, enabling the elderly to continue living independently. Elderly
use smart technologies for entertainment and social interactions, this can be
extended to provide safety and monitor health and environmental conditions,
detect emergencies and notify informal and formal caregivers when care is
needed. This paper provides an overview of the smart home technologies
commercially available to support ageing in place, the advantages and
challenges of smart home technologies, and their usability from elderlys
perspective. Synthesizing prior knowledge, we created a structured Smart Home
Goal Feature Model (SHGFM) to resolve heuristic approaches used by the Subject
Matter Experts (SMEs) at aged care facilities and healthcare researchers in
adapting smart homes. The SHGFM provides SMEs the ability to (i) establish
goals and (ii) identify features to set up strategies to design, develop and
deploy smart homes for the elderly based on personalised needs. Our model
provides guidance to healthcare researchers and aged care industries to set up
smart homes based on the needs of elderly, by defining a set of goals at
different levels mapped to a different set of features.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Abstract: We present Ego-Exo4D, a diverse, large-scale multimodal multiview video
dataset and benchmark challenge. Ego-Exo4D centers around
simultaneously-captured egocentric and exocentric video of skilled human
activities (e.g., sports, music, dance, bike repair). More than 800
participants from 13 cities worldwide performed these activities in 131
different natural scene contexts, yielding long-form captures from 1 to 42
minutes each and 1,422 hours of video combined. The multimodal nature of the
dataset is unprecedented: the video is accompanied by multichannel audio, eye
gaze, 3D point clouds, camera poses, IMU, and multiple paired language
descriptions -- including a novel "expert commentary" done by coaches and
teachers and tailored to the skilled-activity domain. To push the frontier of
first-person video understanding of skilled human activity, we also present a
suite of benchmark tasks and their annotations, including fine-grained activity
understanding, proficiency estimation, cross-view translation, and 3D hand/body
pose. All resources will be open sourced to fuel new research in the community.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Past as a Guide: Leveraging Retrospective Learning for Python Code Completion
Abstract: This work presents Past as a Guide (PaG), a simple approach for Large
Language Models (LLMs) to improve the coding capabilities by integrating the
past history with interactive and iterative code refinements. To be specific,
inspired by human cognitive processes, the proposed method enables LLMs to
utilize previous programming and debugging experiences to enhance the Python
code completion tasks. The framework facilitates LLMs to iteratively refine the
Python code based on previous execution and debugging results and optimize
learning and reasoning capabilities. The proposed methodology achieved a 92\%
pass@1 on HumanEval, demonstrating the potential to advance the field by
leveraging retrospection from past experiences and interactive and iterative
refinement processes without external correctness indicators.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: ICRA Roboethics Challenge 2023: Intelligent Disobedience in an Elderly Care Home
Abstract: With the projected surge in the elderly population, service robots offer a
promising avenue to enhance their well-being in elderly care homes. Such robots
will encounter complex scenarios which will require them to perform decisions
with ethical consequences. In this report, we propose to leverage the
Intelligent Disobedience framework in order to give the robot the ability to
perform a deliberation process over decisions with potential ethical
implications. We list the issues that this framework can assist with, define it
formally in the context of the specific elderly care home scenario, and
delineate the requirements for implementing an intelligently disobeying robot.
We conclude this report with some critical analysis and suggestions for future
work.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Unleashing the potential of GNNs via Bi-directional Knowledge Transfer
Abstract: Based on the message-passing paradigm, there has been an amount of research
proposing diverse and impressive feature propagation mechanisms to improve the
performance of GNNs. However, less focus has been put on feature
transformation, another major operation of the message-passing framework. In
this paper, we first empirically investigate the performance of the feature
transformation operation in several typical GNNs. Unexpectedly, we notice that
GNNs do not completely free up the power of the inherent feature transformation
operation. By this observation, we propose the Bi-directional Knowledge
Transfer (BiKT), a plug-and-play approach to unleash the potential of the
feature transformation operations without modifying the original architecture.
Taking the feature transformation operation as a derived representation
learning model that shares parameters with the original GNN, the direct
prediction by this model provides a topological-agnostic knowledge feedback
that can further instruct the learning of GNN and the feature transformations
therein. On this basis, BiKT not only allows us to acquire knowledge from both
the GNN and its derived model but promotes each other by injecting the
knowledge into the other. In addition, a theoretical analysis is further
provided to demonstrate that BiKT improves the generalization bound of the GNNs
from the perspective of domain adaption. An extensive group of experiments on
up to 7 datasets with 5 typical GNNs demonstrates that BiKT brings up to 0.5% -
4% performance gain over the original GNN, which means a boosted GNN is
obtained. Meanwhile, the derived model also shows a powerful performance to
compete with or even surpass the original GNN, enabling us to flexibly apply it
independently to some other specific downstream tasks.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: @ve: A Chatbot for Latin
Abstract: Dead, extinct, and endangered languages have been preserved primarily through
audio conservation and the collection and digitization of scripts and have been
promoted through targeted language acquisition efforts. Another possibility
would be to build conversational agents that can master these languages. This
would provide an artificial, active conversational partner which has knowledge
of the vocabulary and grammar, and one learns with it in a different way. The
chatbot @ve, with which one can communicate in Latin, was developed in
2022/2023 based on GPT-3.0. It was additionally equipped with a manually
created knowledge base. After conceptual groundwork, this paper presents the
preparation and implementation of the project. In addition, it summarizes the
test that a Latin expert conducted with the chatbot. A critical discussion
elaborates advantages and disadvantages. @ve could be a new tool for teaching
Latin in a memorable and entertaining way through dialogue. However, the
present implementation is still too prone to glitches for stand-alone use -
i.e., without the accompaniment of a teacher. The use of GPT-4 could be a
solution as well as the extension of the knowledge base. In conclusion, it can
be argued that conversational agents are an innovative approach to promoting
and preserving languages.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Fake Alignment: Are LLMs Really Aligned Well?
Abstract: The growing awareness of safety concerns in large language models (LLMs) has
sparked considerable interest in the evaluation of safety within current
research endeavors. This study investigates an interesting issue pertaining to
the evaluation of LLMs, namely the substantial discrepancy in performance
between multiple-choice questions and open-ended questions. Inspired by
research on jailbreak attack patterns, we argue this is caused by mismatched
generalization. That is, the LLM does not have a comprehensive understanding of
the complex concept of safety. Instead, it only remembers what to answer for
open-ended safety questions, which makes it unable to solve other forms of
safety tests. We refer to this phenomenon as fake alignment and construct a
comparative benchmark to empirically verify its existence in LLMs. Such fake
alignment renders previous evaluation protocols unreliable. To address this, we
introduce the Fake alIgNment Evaluation (FINE) framework and two novel
metrics--Consistency Score (CS) and Consistent Safety Score (CSS), which
jointly assess two complementary forms of evaluation to quantify fake alignment
and obtain corrected performance estimates. Applying FINE to 14 widely-used
LLMs reveals several models with purported safety are poorly aligned in
practice. Our work highlights potential limitations in prevailing alignment
methodologies.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: AutoML for Large Capacity Modeling of Meta's Ranking Systems
Abstract: Web-scale ranking systems at Meta serving billions of users is complex.
Improving ranking models is essential but engineering heavy. Automated Machine
Learning (AutoML) can release engineers from labor intensive work of tuning
ranking models; however, it is unknown if AutoML is efficient enough to meet
tight production timeline in real-world and, at the same time, bring additional
improvements to the strong baselines. Moreover, to achieve higher ranking
performance, there is an ever-increasing demand to scale up ranking models to
even larger capacity, which imposes more challenges on the efficiency. The
large scale of models and tight production schedule requires AutoML to
outperform human baselines by only using a small number of model evaluation
trials (around 100). We presents a sampling-based AutoML method, focusing on
neural architecture search and hyperparameter optimization, addressing these
challenges in Meta-scale production when building large capacity models. Our
approach efficiently handles large-scale data demands. It leverages a
lightweight predictor-based searcher and reinforcement learning to explore vast
search spaces, significantly reducing the number of model evaluations. Through
experiments in large capacity modeling for CTR and CVR applications, we show
that our method achieves outstanding Return on Investment (ROI) versus human
tuned baselines, with up to 0.09% Normalized Entropy (NE) loss reduction or
$25\%$ Query per Second (QPS) increase by only sampling one hundred models on
average from a curated search space. The proposed AutoML method has already
made real-world impact where a discovered Instagram CTR model with up to -0.36%
NE gain (over existing production baseline) was selected for large-scale online
A/B test and show statistically significant gain. These production results
proved AutoML efficacy and accelerated its adoption in ranking systems at Meta.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: tagE: Enabling an Embodied Agent to Understand Human Instructions
Abstract: Natural language serves as the primary mode of communication when an
intelligent agent with a physical presence engages with human beings. While a
plethora of research focuses on natural language understanding (NLU),
encompassing endeavors such as sentiment analysis, intent prediction, question
answering, and summarization, the scope of NLU directed at situations
necessitating tangible actions by an embodied agent remains limited. The
inherent ambiguity and incompleteness inherent in natural language present
challenges for intelligent agents striving to decipher human intention. To
tackle this predicament head-on, we introduce a novel system known as task and
argument grounding for Embodied agents (tagE). At its core, our system employs
an inventive neural network model designed to extract a series of tasks from
complex task instructions expressed in natural language. Our proposed model
adopts an encoder-decoder framework enriched with nested decoding to
effectively extract tasks and their corresponding arguments from these
intricate instructions. These extracted tasks are then mapped (or grounded) to
the robot's established collection of skills, while the arguments find
grounding in objects present within the environment. To facilitate the training
and evaluation of our system, we have curated a dataset featuring complex
instructions. The results of our experiments underscore the prowess of our
approach, as it outperforms robust baseline models.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: RSG: Fast Learning Adaptive Skills for Quadruped Robots by Skill Graph
Abstract: Developing robotic intelligent systems that can adapt quickly to unseen wild
situations is one of the critical challenges in pursuing autonomous robotics.
Although some impressive progress has been made in walking stability and skill
learning in the field of legged robots, their ability to fast adaptation is
still inferior to that of animals in nature. Animals are born with massive
skills needed to survive, and can quickly acquire new ones, by composing
fundamental skills with limited experience. Inspired by this, we propose a
novel framework, named Robot Skill Graph (RSG) for organizing massive
fundamental skills of robots and dexterously reusing them for fast adaptation.
Bearing a structure similar to the Knowledge Graph (KG), RSG is composed of
massive dynamic behavioral skills instead of static knowledge in KG and enables
discovering implicit relations that exist in be-tween of learning context and
acquired skills of robots, serving as a starting point for understanding subtle
patterns existing in robots' skill learning. Extensive experimental results
demonstrate that RSG can provide rational skill inference upon new tasks and
environments and enable quadruped robots to adapt to new scenarios and learn
new skills rapidly.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination
Abstract: Meta reinforcement learning (Meta RL) has been amply explored to quickly
learn an unseen task by transferring previously learned knowledge from similar
tasks. However, most state-of-the-art algorithms require the meta-training
tasks to have a dense coverage on the task distribution and a great amount of
data for each of them. In this paper, we propose MetaDreamer, a context-based
Meta RL algorithm that requires less real training tasks and data by doing
meta-imagination and MDP-imagination. We perform meta-imagination by
interpolating on the learned latent context space with disentangled properties,
as well as MDP-imagination through the generative world model where physical
knowledge is added to plain VAE networks. Our experiments with various
benchmarks show that MetaDreamer outperforms existing approaches in data
efficiency and interpolated generalization.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Pre-training with Random Orthogonal Projection Image Modeling
Abstract: Masked Image Modeling (MIM) is a powerful self-supervised strategy for visual
pre-training without the use of labels. MIM applies random crops to input
images, processes them with an encoder, and then recovers the masked inputs
with a decoder, which encourages the network to capture and learn structural
information about objects and scenes. The intermediate feature representations
obtained from MIM are suitable for fine-tuning on downstream tasks. In this
paper, we propose an Image Modeling framework based on random orthogonal
projection instead of binary masking as in MIM. Our proposed Random Orthogonal
Projection Image Modeling (ROPIM) reduces spatially-wise token information
under guaranteed bound on the noise variance and can be considered as masking
entire spatial image area under locally varying masking degrees. Since ROPIM
uses a random subspace for the projection that realizes the masking step, the
readily available complement of the subspace can be used during unmasking to
promote recovery of removed information. In this paper, we show that using
random orthogonal projection leads to superior performance compared to
crop-based masking. We demonstrate state-of-the-art results on several popular
benchmarks.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Understanding and Improving In-Context Learning on Vision-language Models
Abstract: Recently, in-context learning (ICL) on large language models (LLMs) has
received great attention, and this technique can also be applied to
vision-language models (VLMs) built upon LLMs. These VLMs can respond to
queries by conditioning responses on a series of multimodal demonstrations,
which comprise images, queries, and answers. Though ICL has been extensively
studied on LLMs, its research on VLMs remains limited. The inclusion of
additional visual information in the demonstrations motivates the following
research questions: which of the two modalities in the demonstration is more
significant? How can we select effective multimodal demonstrations to enhance
ICL performance? This study investigates the significance of both visual and
language information. Our findings indicate that ICL in VLMs is predominantly
driven by the textual information in the demonstrations whereas the visual
information in the demonstrations barely affects the ICL performance.
Subsequently, we provide an understanding of the findings by analyzing the
model information flow and comparing model inner states given different ICL
settings. Motivated by our analysis, we propose a simple yet effective
approach, termed Mixed Modality In-Context Example Selection (MMICES), which
considers both visual and language modalities when selecting demonstrations and
shows better ICL performance. Extensive experiments are conducted to support
our findings, understanding, and improvement of the ICL performance of VLMs.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Towards Calibrated Robust Fine-Tuning of Vision-Language Models
Abstract: While fine-tuning unlocks the potential of a pre-trained model for a specific
task, it compromises the model's ability to generalize to out-of-distribution
(OOD) datasets. To mitigate this, robust fine-tuning aims to ensure performance
on OOD datasets as well as on an in-distribution (ID) dataset for which the
model is being tuned. However, another criterion for reliable machine learning
(ML), confidence calibration, has been overlooked despite its increasing demand
for real-world high-stakes ML applications (e.g., autonomous driving and
medical diagnosis). For the first time, we raise concerns about the calibration
of fine-tuned vision-language models (VLMs) under distribution shift by showing
that naive fine-tuning and even state-of-the-art robust fine-tuning methods
hurt the calibration of pre-trained VLMs, especially on OOD datasets. To
address this issue, we provide a simple approach, called calibrated robust
fine-tuning (CaRot), that incentivizes calibration and robustness on both ID
and OOD datasets. Empirical results on ImageNet-1K distribution shift
evaluation verify the effectiveness of our method.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Anticipating User Needs: Insights from Design Fiction on Conversational Agents for Computational Thinking
Abstract: Computational thinking, and by extension, computer programming, is
notoriously challenging to learn. Conversational agents and generative
artificial intelligence (genAI) have the potential to facilitate this learning
process by offering personalized guidance, interactive learning experiences,
and code generation. However, current genAI-based chatbots focus on
professional developers and may not adequately consider educational needs.
Involving educators in conceiving educational tools is critical for ensuring
usefulness and usability. We enlisted \numParticipants{} instructors to engage
in design fiction sessions in which we elicited abilities such a conversational
agent supported by genAI should display. Participants envisioned a
conversational agent that guides students stepwise through exercises, tuning
its method of guidance with an awareness of the educational background, skills
and deficits, and learning preferences. The insights obtained in this paper can
guide future implementations of tutoring conversational agents oriented toward
teaching computational thinking and computer programming.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Post-Training Quantization for Re-parameterization via Coarse & Fine Weight Splitting
Abstract: Although neural networks have made remarkable advancements in various
applications, they require substantial computational and memory resources.
Network quantization is a powerful technique to compress neural networks,
allowing for more efficient and scalable AI deployments. Recently,
Re-parameterization has emerged as a promising technique to enhance model
performance while simultaneously alleviating the computational burden in
various computer vision tasks. However, the accuracy drops significantly when
applying quantization on the re-parameterized networks. We identify that the
primary challenge arises from the large variation in weight distribution across
the original branches. To address this issue, we propose a coarse & fine weight
splitting (CFWS) method to reduce quantization error of weight, and develop an
improved KL metric to determine optimal quantization scales for activation. To
the best of our knowledge, our approach is the first work that enables
post-training quantization applicable on re-parameterized networks. For
example, the quantized RepVGG-A1 model exhibits a mere 0.3% accuracy loss. The
code is in https://github.com/NeonHo/Coarse-Fine-Weight-Split.git

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Global Transformer Architecture for Indoor Room Temperature Forecasting
Abstract: A thorough regulation of building energy systems translates in relevant
energy savings and in a better comfort for the occupants. Algorithms to predict
the thermal state of a building on a certain time horizon with a good
confidence are essential for the implementation of effective control systems.
This work presents a global Transformer architecture for indoor temperature
forecasting in multi-room buildings, aiming at optimizing energy consumption
and reducing greenhouse gas emissions associated with HVAC systems. Recent
advancements in deep learning have enabled the development of more
sophisticated forecasting models compared to traditional feedback control
systems. The proposed global Transformer architecture can be trained on the
entire dataset encompassing all rooms, eliminating the need for multiple
room-specific models, significantly improving predictive performance, and
simplifying deployment and maintenance. Notably, this study is the first to
apply a Transformer architecture for indoor temperature forecasting in
multi-room buildings. The proposed approach provides a novel solution to
enhance the accuracy and efficiency of temperature forecasting, serving as a
valuable tool to optimize energy consumption and decrease greenhouse gas
emissions in the building sector.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Vital Sign Forecasting for Sepsis Patients in ICUs
Abstract: Sepsis and septic shock are a critical medical condition affecting millions
globally, with a substantial mortality rate. This paper uses state-of-the-art
deep learning (DL) architectures to introduce a multi-step forecasting system
to predict vital signs indicative of septic shock progression in Intensive Care
Units (ICUs). Our approach utilizes a short window of historical vital sign
data to forecast future physiological conditions. We introduce a DL-based vital
sign forecasting system that predicts up to 3 hours of future vital signs from
6 hours of past data. We further adopt the DILATE loss function to capture
better the shape and temporal dynamics of vital signs, which are critical for
clinical decision-making. We compare three DL models, N-BEATS, N-HiTS, and
Temporal Fusion Transformer (TFT), using the publicly available eICU
Collaborative Research Database (eICU-CRD), highlighting their forecasting
capabilities in a critical care setting. We evaluate the performance of our
models using mean squared error (MSE) and dynamic time warping (DTW) metrics.
Our findings show that while TFT excels in capturing overall trends, N-HiTS is
superior in retaining short-term fluctuations within a predefined range. This
paper demonstrates the potential of deep learning in transforming the
monitoring systems in ICUs, potentially leading to significant improvements in
patient care and outcomes by accurately forecasting vital signs to assist
healthcare providers in detecting early signs of physiological instability and
anticipating septic shock.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Dataset Distillation in Large Data Era
Abstract: Dataset distillation aims to generate a smaller but representative subset
from a large dataset, which allows a model to be trained efficiently, meanwhile
evaluating on the original testing data distribution to achieve decent
performance. Many prior works have aimed to align with diverse aspects of the
original datasets, such as matching the training weight trajectories, gradient,
feature/BatchNorm distributions, etc. In this work, we show how to distill
various large-scale datasets such as full ImageNet-1K/21K under a conventional
input resolution of 224$\times$224 to achieve the best accuracy over all
previous approaches, including SRe$^2$L, TESLA and MTT. To achieve this, we
introduce a simple yet effective ${\bf C}$urriculum ${\bf D}$ata ${\bf
A}$ugmentation ($\texttt{CDA}$) during data synthesis that obtains the accuracy
on large-scale ImageNet-1K and 21K with 63.2% under IPC (Images Per Class) 50
and 36.1% under IPC 20, respectively. Finally, we show that, by integrating all
our enhancements together, the proposed model beats the current
state-of-the-art by more than 4% Top-1 accuracy on ImageNet-1K/21K and for the
first time, reduces the gap to its full-data training counterpart to less than
absolute 15%. Moreover, this work represents the inaugural success in dataset
distillation on larger-scale ImageNet-21K under the standard 224$\times$224
resolution. Our code and distilled ImageNet-21K dataset of 20 IPC, 2K recovery
budget are available at https://github.com/VILA-Lab/SRe2L/tree/main/CDA.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Low-Rank MDPs with Continuous Action Spaces
Abstract: Low-Rank Markov Decision Processes (MDPs) have recently emerged as a
promising framework within the domain of reinforcement learning (RL), as they
allow for provably approximately correct (PAC) learning guarantees while also
incorporating ML algorithms for representation learning. However, current
methods for low-rank MDPs are limited in that they only consider finite action
spaces, and give vacuous bounds as $|\mathcal{A}| \to \infty$, which greatly
limits their applicability. In this work, we study the problem of extending
such methods to settings with continuous actions, and explore multiple concrete
approaches for performing this extension. As a case study, we consider the
seminal FLAMBE algorithm (Agarwal et al., 2020), which is a reward-agnostic
method for PAC RL with low-rank MDPs. We show that, without any modifications
to the algorithm, we obtain similar PAC bound when actions are allowed to be
continuous. Specifically, when the model for transition functions satisfies a
Holder smoothness condition w.r.t. actions, and either the policy class has a
uniformly bounded minimum density or the reward function is also Holder smooth,
we obtain a polynomial PAC bound that depends on the order of smoothness.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: CreoleVal: Multilingual Multitask Benchmarks for Creoles
Abstract: Creoles represent an under-explored and marginalized group of languages, with
few available resources for NLP research. While the genealogical ties between
Creoles and other highly-resourced languages imply a significant potential for
transfer learning, this potential is hampered due to this lack of annotated
data. In this work we present CreoleVal, a collection of benchmark datasets
spanning 8 different NLP tasks, covering up to 28 Creole languages; it is an
aggregate of brand new development datasets for machine comprehension, relation
classification, and machine translation for Creoles, in addition to a practical
gateway to a handful of preexisting benchmarks. For each benchmark, we conduct
baseline experiments in a zero-shot setting in order to further ascertain the
capabilities and limitations of transfer learning for Creoles. Ultimately, the
goal of CreoleVal is to empower research on Creoles in NLP and computational
linguistics. We hope this resource will contribute to technological inclusion
for Creole language users around the globe.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms
Abstract: Many applications in Reinforcement Learning (RL) usually have noise or
stochasticity present in the environment. Beyond their impact on learning,
these uncertainties lead the exact same policy to perform differently, i.e.
yield different return, from one roll-out to another. Common evaluation
procedures in RL summarise the consequent return distributions using solely the
expected return, which does not account for the spread of the distribution. Our
work defines this spread as the policy reproducibility: the ability of a policy
to obtain similar performance when rolled out many times, a crucial property in
some real-world applications. We highlight that existing procedures that only
use the expected return are limited on two fronts: first an infinite number of
return distributions with a wide range of performance-reproducibility
trade-offs can have the same expected return, limiting its effectiveness when
used for comparing policies; second, the expected return metric does not leave
any room for practitioners to choose the best trade-off value for considered
applications. In this work, we address these limitations by recommending the
use of Lower Confidence Bound, a metric taken from Bayesian optimisation that
provides the user with a preference parameter to choose a desired
performance-reproducibility trade-off. We also formalise and quantify policy
reproducibility, and demonstrate the benefit of our metrics using extensive
experiments of popular RL algorithms on common uncertain RL tasks.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Studying Artist Sentiments around AI-generated Artwork
Abstract: Art created using generated Artificial Intelligence has taken the world by
storm and generated excitement for many digital creators and technologists.
However, the reception and reaction from artists have been mixed. Concerns
about plagiarizing their artworks and styles for datasets and uncertainty
around the future of digital art sparked movements in artist communities
shunning the use of AI for generating art and protecting artists' rights.
Collaborating with these tools for novel creative use cases also sparked hope
from some creators. Artists are an integral stakeholder in the rapidly evolving
digital creativity industry and understanding their concerns and hopes inform
responsible development and use of creativity support tools. In this work, we
study artists' sentiments about AI-generated art. We interviewed 7 artists and
analyzed public posts from artists on social media platforms Reddit, Twitter
and Artstation. We report artists' main concerns and hopes around AI-generated
artwork, informing a way forward for inclusive development of these tools.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: CLIP-Motion: Learning Reward Functions for Robotic Actions Using Consecutive Observations
Abstract: This paper presents a novel method for learning reward functions for robotic
motions by harnessing the power of a CLIP-based model. Traditional reward
function design often hinges on manual feature engineering, which can struggle
to generalize across an array of tasks. Our approach circumvents this challenge
by capitalizing on CLIP's capability to process both state features and image
inputs effectively. Given a pair of consecutive observations, our model excels
in identifying the motion executed between them. We showcase results spanning
various robotic activities, such as directing a gripper to a designated target
and adjusting the position of a cube. Through experimental evaluations, we
underline the proficiency of our method in precisely deducing motion and its
promise to enhance reinforcement learning training in the realm of robotics.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery
Abstract: Deep learning for Earth imagery plays an increasingly important role in
geoscience applications such as agriculture, ecology, and natural disaster
management. Still, progress is often hindered by the limited training labels.
Given Earth imagery with limited training labels, a base deep neural network
model, and a spatial knowledge base with label constraints, our problem is to
infer the full labels while training the neural network. The problem is
challenging due to the sparse and noisy input labels, spatial uncertainty
within the label inference process, and high computational costs associated
with a large number of sample locations. Existing works on neuro-symbolic
models focus on integrating symbolic logic into neural networks (e.g., loss
function, model architecture, and training label augmentation), but these
methods do not fully address the challenges of spatial data (e.g., spatial
uncertainty, the trade-off between spatial granularity and computational
costs). To bridge this gap, we propose a novel Spatial Knowledge-Infused
Hierarchical Learning (SKI-HL) framework that iteratively infers sample labels
within a multi-resolution hierarchy. Our framework consists of a module to
selectively infer labels in different resolutions based on spatial uncertainty
and a module to train neural network parameters with uncertainty-aware
multi-instance learning. Extensive experiments on real-world flood mapping
datasets show that the proposed model outperforms several baseline methods. The
code is available at \url{https://github.com/ZelinXu2000/SKI-HL}.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: MemoryCompanion: A Smart Healthcare Solution to Empower Efficient Alzheimer's Care Via Unleashing Generative AI
Abstract: With the rise of Large Language Models (LLMs), notably characterized by GPT
frameworks, there emerges a catalyst for novel healthcare applications. Earlier
iterations of chatbot caregivers, though existent, have yet to achieve a
dimension of human-like authenticity. This paper unveils `MemoryCompanion' a
pioneering digital health solution explicitly tailored for Alzheimer's disease
(AD) patients and their caregivers. Drawing upon the nuances of GPT technology
and prompt engineering, MemoryCompanion manifests a personalized caregiving
paradigm, fostering interactions via voice-cloning and talking-face mechanisms
that resonate with the familiarity of known companions. Using advanced
prompt-engineering, the system intricately adapts to each patient's distinct
profile, curating its content and communication style accordingly. This
approach strives to counteract prevalent issues of social isolation and
loneliness frequently observed in AD demographics. Our methodology, grounded in
its innovative design, addresses both the caregiving and technological
challenges intrinsic to this domain.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Efficient Data Learning for Open Information Extraction with Pre-trained Language Models
Abstract: Open Information Extraction (OpenIE) is a fundamental yet challenging task in
Natural Language Processing, which involves extracting all triples (subject,
predicate, object) from a given sentence. While labeling-based methods have
their merits, generation-based techniques offer unique advantages, such as the
ability to generate tokens not present in the original sentence. However, these
generation-based methods often require a significant amount of training data to
learn the task form of OpenIE and substantial training time to overcome slow
model convergence due to the order penalty. In this paper, we introduce a novel
framework, OK-IE, that ingeniously transforms the task form of OpenIE into the
pre-training task form of the T5 model, thereby reducing the need for extensive
training data. Furthermore, we introduce an innovative concept of Anchor to
control the sequence of model outputs, effectively eliminating the impact of
order penalty on model convergence and significantly reducing training time.
Experimental results indicate that, compared to previous SOTA methods, OK-IE
requires only 1/100 of the training data (900 instances) and 1/120 of the
training time (3 minutes) to achieve comparable results.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Efficient Causal Discovery for Robotics Applications
Abstract: Using robots for automating tasks in environments shared with humans, such as
warehouses, shopping centres, or hospitals, requires these robots to comprehend
the fundamental physical interactions among nearby agents and objects.
Specifically, creating models to represent cause-and-effect relationships among
these elements can aid in predicting unforeseen human behaviours and anticipate
the outcome of particular robot actions. To be suitable for robots, causal
analysis must be both fast and accurate, meeting real-time demands and the
limited computational resources typical in most robotics applications. In this
paper, we present a practical demonstration of our approach for fast and
accurate causal analysis, known as Filtered PCMCI (F-PCMCI), along with a
real-world robotics application. The provided application illustrates how our
F-PCMCI can accurately and promptly reconstruct the causal model of a
human-robot interaction scenario, which can then be leveraged to enhance the
quality of the interaction.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: A Comprehensive Study of Vision Transformers in Image Classification Tasks
Abstract: Image Classification is a fundamental task in the field of computer vision
that frequently serves as a benchmark for gauging advancements in Computer
Vision. Over the past few years, significant progress has been made in image
classification due to the emergence of deep learning. However, challenges still
exist, such as modeling fine-grained visual information, high computation
costs, the parallelism of the model, and inconsistent evaluation protocols
across datasets. In this paper, we conduct a comprehensive survey of existing
papers on Vision Transformers for image classification. We first introduce the
popular image classification datasets that influenced the design of models.
Then, we present Vision Transformers models in chronological order, starting
with early attempts at adapting attention mechanism to vision tasks followed by
the adoption of vision transformers, as they have demonstrated success in
capturing intricate patterns and long-range dependencies within images.
Finally, we discuss open problems and shed light on opportunities for image
classification to facilitate new research ideas.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Interaction is all You Need? A Study of Robots Ability to Understand and Execute
Abstract: This paper aims to address a critical challenge in robotics, which is
enabling them to operate seamlessly in human environments through natural
language interactions. Our primary focus is to equip robots with the ability to
understand and execute complex instructions in coherent dialogs to facilitate
intricate task-solving scenarios. To explore this, we build upon the Execution
from Dialog History (EDH) task from the Teach benchmark. We employ a
multi-transformer model with BART LM. We observe that our best configuration
outperforms the baseline with a success rate score of 8.85 and a
goal-conditioned success rate score of 14.02. In addition, we suggest an
alternative methodology for completing this task. Moreover, we introduce a new
task by expanding the EDH task and making predictions about game plans instead
of individual actions. We have evaluated multiple BART models and an LLaMA2
LLM, which has achieved a ROGUE-L score of 46.77 for this task.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Characterizing Large Language Models as Rationalizers of Knowledge-intensive Tasks
Abstract: Large language models (LLMs) are proficient at generating fluent text with
minimal task-specific supervision. Yet, their ability to provide well-grounded
rationalizations for knowledge-intensive tasks remains under-explored. Such
tasks, like commonsense multiple-choice questions, require rationales based on
world knowledge to support predictions and refute alternate options. We
consider the task of generating knowledge-guided rationalization in natural
language by using expert-written examples in a few-shot manner. Surprisingly,
crowd-workers preferred knowledge-grounded rationales over crowdsourced
rationalizations, citing their factuality, sufficiency, and comprehensive
refutations. Although LLMs-generated rationales were preferable, further
improvements in conciseness and novelty are required. In another study, we show
how rationalization of incorrect model predictions erodes humans' trust in
LLM-generated rationales. Motivated by these observations, we create a
two-stage pipeline to review task predictions and eliminate potential incorrect
decisions before rationalization, enabling trustworthy rationale generation.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Noise in Relation Classification Dataset TACRED: Characterization and Reduction
Abstract: The overarching objective of this paper is two-fold. First, to explore
model-based approaches to characterize the primary cause of the noise. in the
RE dataset TACRED Second, to identify the potentially noisy instances. Towards
the first objective, we analyze predictions and performance of state-of-the-art
(SOTA) models to identify the root cause of noise in the dataset. Our analysis
of TACRED shows that the majority of the noise in the dataset originates from
the instances labeled as no-relation which are negative examples. For the
second objective, we explore two nearest-neighbor-based strategies to
automatically identify potentially noisy examples for elimination and
reannotation. Our first strategy, referred to as Intrinsic Strategy (IS), is
based on the assumption that positive examples are clean. Thus, we have used
false-negative predictions to identify noisy negative examples. Whereas, our
second approach, referred to as Extrinsic Strategy, is based on using a clean
subset of the dataset to identify potentially noisy negative examples. Finally,
we retrained the SOTA models on the eliminated and reannotated dataset. Our
empirical results based on two SOTA models trained on TACRED-E following the IS
show an average 4% F1-score improvement, whereas reannotation (TACRED-R) does
not improve the original results. However, following ES, SOTA models show the
average F1-score improvement of 3.8% and 4.4% when trained on respective
eliminated (TACRED-EN) and reannotated (TACRED-RN) datasets respectively. We
further extended the ES for cleaning positive examples as well, which resulted
in an average performance improvement of 5.8% and 5.6% for the eliminated
(TACRED-ENP) and reannotated (TACRED-RNP) datasets respectively.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Complex Organ Mask Guided Radiology Report Generation
Abstract: The goal of automatic report generation is to generate a clinically accurate
and coherent phrase from a single given X-ray image, which could alleviate the
workload of traditional radiology reporting. However, in a real-world scenario,
radiologists frequently face the challenge of producing extensive reports
derived from numerous medical images, thereby medical report generation from
multi-image perspective is needed. In this paper, we propose the Complex Organ
Mask Guided (termed as COMG) report generation model, which incorporates masks
from multiple organs (e.g., bones, lungs, heart, and mediastinum), to provide
more detailed information and guide the model's attention to these crucial body
regions. Specifically, we leverage prior knowledge of the disease corresponding
to each organ in the fusion process to enhance the disease identification phase
during the report generation process. Additionally, cosine similarity loss is
introduced as target function to ensure the convergence of cross-modal
consistency and facilitate model optimization.Experimental results on two
public datasets show that COMG achieves a 11.4% and 9.7% improvement in terms
of BLEU@4 scores over the SOTA model KiUT on IU-Xray and MIMIC, respectively.
The code is publicly available at https://github.com/GaryGuTC/COMG_model.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion
Abstract: Model fusion research aims to aggregate the knowledge of multiple models to
enhance performance by combining their weights. In this work, we study the
inverse, investigating whether and how can model fusion interfere and reduce
unwanted knowledge. We delve into the effects of model fusion on the evolution
of learned shortcuts, social biases, and memorization capabilities in
fine-tuned language models. Through several experiments covering text
classification and generation tasks, our analysis highlights that shared
knowledge among models is usually enhanced during model fusion, while unshared
knowledge is usually lost or forgotten. Based on this observation, we
demonstrate the potential of model fusion as a debiasing tool and showcase its
efficacy in addressing privacy concerns associated with language models.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Enhancing Vehicle Entrance and Parking Management: Deep Learning Solutions for Efficiency and Security
Abstract: The auto-management of vehicle entrance and parking in any organization is a
complex challenge encompassing record-keeping, efficiency, and security
concerns. Manual methods for tracking vehicles and finding parking spaces are
slow and a waste of time. To solve the problem of auto management of vehicle
entrance and parking, we have utilized state-of-the-art deep learning models
and automated the process of vehicle entrance and parking into any
organization. To ensure security, our system integrated vehicle detection,
license number plate verification, and face detection and recognition models to
ensure that the person and vehicle are registered with the organization. We
have trained multiple deep-learning models for vehicle detection, license
number plate detection, face detection, and recognition, however, the YOLOv8n
model outperformed all the other models. Furthermore, License plate recognition
is facilitated by Google's Tesseract-OCR Engine. By integrating these
technologies, the system offers efficient vehicle detection, precise
identification, streamlined record keeping, and optimized parking slot
allocation in buildings, thereby enhancing convenience, accuracy, and security.
Future research opportunities lie in fine-tuning system performance for a wide
range of real-world applications.

Output: Computer Vision