Instruction: What field is the article from?

Input:
Title: Having Second Thoughts? Let's hear it
Abstract: Deep learning models loosely mimic bottom-up signal pathways from low-order
sensory areas to high-order cognitive areas. After training, DL models can
outperform humans on some domain-specific tasks, but their decision-making
process has been known to be easily disrupted. Since the human brain consists
of multiple functional areas highly connected to one another and relies on
intricate interplays between bottom-up and top-down (from high-order to
low-order areas) processing, we hypothesize that incorporating top-down signal
processing may make DL models more robust. To address this hypothesis, we
propose a certification process mimicking selective attention and test if it
could make DL models more robust. Our empirical evaluations suggest that this
newly proposed certification can improve DL models' accuracy and help us build
safety measures to alleviate their vulnerabilities with both artificial and
natural adversarial examples.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning
Abstract: Temporal knowledge graphs (TKGs) have been identified as a promising approach
to represent the dynamics of facts along the timeline. The extrapolation of TKG
is to predict unknowable facts happening in the future, holding significant
practical value across diverse fields. Most extrapolation studies in TKGs focus
on modeling global historical fact repeating and cyclic patterns, as well as
local historical adjacent fact evolution patterns, showing promising
performance in predicting future unknown facts. Yet, existing methods still
face two major challenges: (1) They usually neglect the importance of
historical information in KG snapshots related to the queries when encoding the
local and global historical information; (2) They exhibit weak anti-noise
capabilities, which hinders their performance when the inputs are contaminated
with noise.To this end, we propose a novel \blue{Lo}cal-\blue{g}lobal
history-aware \blue{C}ontrastive \blue{L}earning model (\blue{LogCL}) for TKG
reasoning, which adopts contrastive learning to better guide the fusion of
local and global historical information and enhance the ability to resist
interference. Specifically, for the first challenge, LogCL proposes an
entity-aware attention mechanism applied to the local and global historical
facts encoder, which captures the key historical information related to
queries. For the latter issue, LogCL designs four historical query contrast
patterns, effectively improving the robustness of the model. The experimental
results on four benchmark datasets demonstrate that LogCL delivers better and
more robust performance than the state-of-the-art baselines.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Finding Concept Representations in Neural Networks with Self-Organizing Maps
Abstract: In sufficiently complex tasks, it is expected that as a side effect of
learning to solve a problem, a neural network will learn relevant abstractions
of the representation of that problem. This has been confirmed in particular in
machine vision where a number of works showed that correlations could be found
between the activations of specific units (neurons) in a neural network and the
visual concepts (textures, colors, objects) present in the image. Here, we
explore the use of self-organizing maps as a way to both visually and
computationally inspect how activation vectors of whole layers of neural
networks correspond to neural representations of abstract concepts such as
`female person' or `realist painter'. We experiment with multiple measures
applied to those maps to assess the level of representation of a concept in a
network's layer. We show that, among the measures tested, the relative entropy
of the activation map for a concept compared to the map for the whole data is a
suitable candidate and can be used as part of a methodology to identify and
locate the neural representation of a concept, visualize it, and understand its
importance in solving the prediction task at hand.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Enlighten-Your-Voice: When Multimodal Meets Zero-shot Low-light Image Enhancement
Abstract: Low-light image enhancement is a crucial visual task, and many unsupervised
methods tend to overlook the degradation of visible information in low-light
scenes, which adversely affects the fusion of complementary information and
hinders the generation of satisfactory results. To address this, our study
introduces ``Enlighten-Your-Voice'', a multimodal enhancement framework that
innovatively enriches user interaction through voice and textual commands. This
approach does not merely signify a technical leap but also represents a
paradigm shift in user engagement. Our model is equipped with a Dual
Collaborative Attention Module (DCAM) that meticulously caters to distinct
content and color discrepancies, thereby facilitating nuanced enhancements.
Complementarily, we introduce a Semantic Feature Fusion (SFM) plug-and-play
module that synergizes semantic context with low-light enhancement operations,
sharpening the algorithm's efficacy. Crucially, ``Enlighten-Your-Voice''
showcases remarkable generalization in unsupervised zero-shot scenarios. The
source code can be accessed from
https://github.com/zhangbaijin/Enlighten-Your-Voice

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Scalable AI Safety via Doubly-Efficient Debate
Abstract: The emergence of pre-trained AI systems with powerful capabilities across a
diverse and ever-increasing set of complex domains has raised a critical
challenge for AI safety as tasks can become too complicated for humans to judge
directly. Irving et al. [2018] proposed a debate method in this direction with
the goal of pitting the power of such AI models against each other until the
problem of identifying (mis)-alignment is broken down into a manageable
subtask. While the promise of this approach is clear, the original framework
was based on the assumption that the honest strategy is able to simulate
deterministic AI systems for an exponential number of steps, limiting its
applicability. In this paper, we show how to address these challenges by
designing a new set of debate protocols where the honest strategy can always
succeed using a simulation of a polynomial number of steps, whilst being able
to verify the alignment of stochastic AI systems, even when the dishonest
strategy is allowed to use exponentially many simulation steps.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Robot at the Mirror: Learning to Imitate via Associating Self-supervised Models
Abstract: We introduce an approach to building a custom model from ready-made
self-supervised models via their associating instead of training and
fine-tuning. We demonstrate it with an example of a humanoid robot looking at
the mirror and learning to detect the 3D pose of its own body from the image it
perceives. To build our model, we first obtain features from the visual input
and the postures of the robot's body via models prepared before the robot's
operation. Then, we map their corresponding latent spaces by a sample-efficient
robot's self-exploration at the mirror. In this way, the robot builds the
solicited 3D pose detector, which quality is immediately perfect on the
acquired samples instead of obtaining the quality gradually. The mapping, which
employs associating the pairs of feature vectors, is then implemented in the
same way as the key-value mechanism of the famous transformer models. Finally,
deploying our model for imitation to a simulated robot allows us to study, tune
up, and systematically evaluate its hyperparameters without the involvement of
the human counterpart, advancing our previous research.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluation
Abstract: Remote sensing visual question answering (RSVQA) opens new opportunities for
the use of overhead imagery by the general public, by enabling human-machine
interaction with natural language. Building on the recent advances in natural
language processing and computer vision, the goal of RSVQA is to answer a
question formulated in natural language about a remote sensing image. Language
understanding is essential to the success of the task, but has not yet been
thoroughly examined in RSVQA. In particular, the problem of language biases is
often overlooked in the remote sensing community, which can impact model
robustness and lead to wrong conclusions about the performances of the model.
Thus, the present work aims at highlighting the problem of language biases in
RSVQA with a threefold analysis strategy: visual blind models, adversarial
testing and dataset analysis. This analysis focuses both on model and data.
Moreover, we motivate the use of more informative and complementary evaluation
metrics sensitive to the issue. The gravity of language biases in RSVQA is then
exposed for all of these methods with the training of models discarding the
image data and the manipulation of the visual input during inference. Finally,
a detailed analysis of question-answer distribution demonstrates the root of
the problem in the data itself. Thanks to this analytical study, we observed
that biases in remote sensing are more severe than in standard VQA, likely due
to the specifics of existing remote sensing datasets for the task, e.g.
geographical similarities and sparsity, as well as a simpler vocabulary and
question generation strategies. While new, improved and less-biased datasets
appear as a necessity for the development of the promising field of RSVQA, we
demonstrate that more informed, relative evaluation metrics remain much needed
to transparently communicate results of future RSVQA methods.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: LLF-Bench: Benchmark for Interactive Learning from Language Feedback
Abstract: We introduce a new benchmark, LLF-Bench (Learning from Language Feedback
Benchmark; pronounced as "elf-bench"), to evaluate the ability of AI agents to
interactively learn from natural language feedback and instructions. Learning
from language feedback (LLF) is essential for people, largely because the rich
information this feedback provides can help a learner avoid much of trial and
error and thereby speed up the learning process. Large Language Models (LLMs)
have recently enabled AI agents to comprehend natural language -- and hence AI
agents can potentially benefit from language feedback during learning like
humans do. But existing interactive benchmarks do not assess this crucial
capability: they either use numeric reward feedback or require no learning at
all (only planning or information retrieval). LLF-Bench is designed to fill
this omission. LLF-Bench is a diverse collection of sequential decision-making
tasks that includes user recommendation, poem writing, navigation, and robot
control. The objective of an agent is to interactively solve these tasks based
on their natural-language instructions and the feedback received after taking
actions. Crucially, to ensure that the agent actually "learns" from the
feedback, LLF-Bench implements several randomization techniques (such as
paraphrasing and environment randomization) to ensure that the task isn't
familiar to the agent and that the agent is robust to various verbalizations.
In addition, LLF-Bench provides a unified OpenAI Gym interface for all its
tasks and allows the users to easily configure the information the feedback
conveys (among suggestion, explanation, and instantaneous performance) to study
how agents respond to different types of feedback. Together, these features
make LLF-Bench a unique research platform for developing and testing LLF
agents.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency
Abstract: In this paper, we study the problem of continuous 3D shape representations.
The majority of existing successful methods are coordinate-based implicit
neural representations. However, they are inefficient to render novel views or
recover explicit surface points. A few works start to formulate 3D shapes as
ray-based neural functions, but the learned structures are inferior due to the
lack of multi-view geometry consistency. To tackle these challenges, we propose
a new framework called RayDF. It consists of three major components: 1) the
simple ray-surface distance field, 2) the novel dual-ray visibility classifier,
and 3) a multi-view consistency optimization module to drive the learned
ray-surface distances to be multi-view geometry consistent. We extensively
evaluate our method on three public datasets, demonstrating remarkable
performance in 3D surface point reconstruction on both synthetic and
challenging real-world 3D scenes, clearly surpassing existing coordinate-based
and ray-based baselines. Most notably, our method achieves a 1000x faster speed
than coordinate-based methods to render an 800x800 depth image, showing the
superiority of our method for 3D shape representation. Our code and data are
available at https://github.com/vLAR-group/RayDF

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems
Abstract: Federated Learning (FL) represents a growing machine learning (ML) paradigm
designed for training models across numerous nodes that retain local datasets,
all without directly exchanging the underlying private data with the parameter
server (PS). Its increasing popularity is attributed to notable advantages in
terms of training deep neural network (DNN) models under privacy aspects and
efficient utilization of communication resources. Unfortunately, DNNs suffer
from high computational and communication costs, as well as memory consumption
in intricate tasks. These factors restrict the applicability of FL algorithms
in communication-constrained systems with limited hardware resources.
  In this paper, we develop a novel algorithm that overcomes these limitations
by synergistically combining a pruning-based method with the FL process,
resulting in low-dimensional representations of the model with minimal
communication cost, dubbed Masked Pruning over FL (MPFL). The algorithm
operates by initially distributing weights to the nodes through the PS.
Subsequently, each node locally trains its model and computes pruning masks.
These low-dimensional masks are then transmitted back to the PS, which
generates a consensus pruning mask, broadcasted back to the nodes. This
iterative process enhances the robustness and stability of the masked pruning
model. The generated mask is used to train the FL model, achieving significant
bandwidth savings. We present an extensive experimental study demonstrating the
superior performance of MPFL compared to existing methods. Additionally, we
have developed an open-source software package for the benefit of researchers
and developers in related fields.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Visual-information-driven model for crowd simulation using temporal convolutional network
Abstract: Crowd simulations play a pivotal role in building design, influencing both
user experience and public safety. While traditional knowledge-driven models
have their merits, data-driven crowd simulation models promise to bring a new
dimension of realism to these simulations. However, most of the existing
data-driven models are designed for specific geometries, leading to poor
adaptability and applicability. A promising strategy for enhancing the
adaptability and realism of data-driven crowd simulation models is to
incorporate visual information, including the scenario geometry and pedestrian
locomotion. Consequently, this paper proposes a novel visual-information-driven
(VID) crowd simulation model. The VID model predicts the pedestrian velocity at
the next time step based on the prior social-visual information and motion data
of an individual. A radar-geometry-locomotion method is established to extract
the visual information of pedestrians. Moreover, a temporal convolutional
network (TCN)-based deep learning model, named social-visual TCN, is developed
for velocity prediction. The VID model is tested on three public pedestrian
motion datasets with distinct geometries, i.e., corridor, corner, and
T-junction. Both qualitative and quantitative metrics are employed to evaluate
the VID model, and the results highlight the improved adaptability of the model
across all three geometric scenarios. Overall, the proposed method demonstrates
effectiveness in enhancing the adaptability of data-driven crowd models.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: A Novel Metric for Measuring Data Quality in Classification Applications (extended version)
Abstract: Data quality is a key element for building and optimizing good learning
models. Despite many attempts to characterize data quality, there is still a
need for rigorous formalization and an efficient measure of the quality from
available observations. Indeed, without a clear understanding of the training
and testing processes, it is hard to evaluate the intrinsic performance of a
model. Besides, tools allowing to measure data quality specific to machine
learning are still lacking. In this paper, we introduce and explain a novel
metric to measure data quality. This metric is based on the correlated
evolution between the classification performance and the deterioration of data.
The proposed method has the major advantage of being model-independent.
Furthermore, we provide an interpretation of each criterion and examples of
assessment levels. We confirm the utility of the proposed metric with intensive
numerical experiments and detail some illustrative cases with controlled and
interpretable qualities.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: DyRA: Dynamic Resolution Adjustment for Scale-robust Object Detection
Abstract: In object detection, achieving constant accuracy is challenging due to the
variability of object sizes. One possible solution to this problem is to
optimize the input resolution, known as a multi-resolution strategy. Previous
approaches for optimizing resolution are often based on pre-defined resolutions
or a dynamic neural network, but there is a lack of study for run-time
resolution optimization for existing architecture. In this paper, we propose an
adaptive resolution scaling network called DyRA, which comprises convolutions
and transformer encoder blocks, for existing detectors. Our DyRA returns a
scale factor from an input image, which enables instance-specific scaling. This
network is jointly trained with detectors with specially designed loss
functions, namely ParetoScaleLoss and BalanceLoss. The ParetoScaleLoss produces
an adaptive scale factor from the image, while the BalanceLoss optimizes the
scale factor according to localization power for the dataset. The loss function
is designed to minimize accuracy drop about the contrasting objective of small
and large objects. Our experiments on COCO, RetinaNet, Faster-RCNN, FCOS, and
Mask-RCNN achieved 1.3%, 1.1%, 1.3%, and 0.8% accuracy improvement than a
multi-resolution baseline with solely resolution adjustment. The code is
available at https://github.com/DaEunFullGrace/DyRA.git.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: WCLD: Curated Large Dataset of Criminal Cases from Wisconsin Circuit Courts
Abstract: Machine learning based decision-support tools in criminal justice systems are
subjects of intense discussions and academic research. There are important open
questions about the utility and fairness of such tools. Academic researchers
often rely on a few small datasets that are not sufficient to empirically study
various real-world aspects of these questions. In this paper, we contribute
WCLD, a curated large dataset of 1.5 million criminal cases from circuit courts
in the U.S. state of Wisconsin. We used reliable public data from 1970 to 2020
to curate attributes like prior criminal counts and recidivism outcomes. The
dataset contains large number of samples from five racial groups, in addition
to information like sex and age (at judgment and first offense). Other
attributes in this dataset include neighborhood characteristics obtained from
census data, detailed types of offense, charge severity, case decisions,
sentence lengths, year of filing etc. We also provide pseudo-identifiers for
judge, county and zipcode. The dataset will not only enable researchers to more
rigorously study algorithmic fairness in the context of criminal justice, but
also relate algorithmic challenges with various systemic issues. We also
discuss in detail the process of constructing the dataset and provide a
datasheet. The WCLD dataset is available at
\url{https://clezdata.github.io/wcld/}.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools
Abstract: Generating unit tests is a crucial task in software development, demanding
substantial time and effort from programmers. The advent of Large Language
Models (LLMs) introduces a novel avenue for unit test script generation. This
research aims to experimentally investigate the effectiveness of LLMs,
specifically exemplified by ChatGPT, for generating unit test scripts for
Python programs, and how the generated test cases compare with those generated
by an existing unit test generator (Pynguin). For experiments, we consider
three types of code units: 1) Procedural scripts, 2) Function-based modular
code, and 3) Class-based code. The generated test cases are evaluated based on
criteria such as coverage, correctness, and readability. Our results show that
ChatGPT's performance is comparable with Pynguin in terms of coverage. At the
same time, ChatGPT's ability to generate tests is superior to Pynguin, as the
latter is not able to generate test cases for Category 1. We also find that
about 39% and 28% of assertions generated by ChatGPT for Category 2 and 3,
respectively, were incorrect. Our results also show that there is minimal
overlap in missed statements between ChatGPT and Pynguin, thus, suggesting that
a combination of both tools may enhance unit test generation performance.
Finally, prompt engineering improved ChatGPT's performance, achieving an
average 28% coverage improvement in Category 2 and 15% improvement in Category
3 after about 4 iterations.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Cultural Adaptation of Recipes
Abstract: Building upon the considerable advances in Large Language Models (LLMs), we
are now equipped to address more sophisticated tasks demanding a nuanced
understanding of cross-cultural contexts. A key example is recipe adaptation,
which goes beyond simple translation to include a grasp of ingredients,
culinary techniques, and dietary preferences specific to a given culture. We
introduce a new task involving the translation and cultural adaptation of
recipes between Chinese and English-speaking cuisines. To support this
investigation, we present CulturalRecipes, a unique dataset comprised of
automatically paired recipes written in Mandarin Chinese and English. This
dataset is further enriched with a human-written and curated test set. In this
intricate task of cross-cultural recipe adaptation, we evaluate the performance
of various methods, including GPT-4 and other LLMs, traditional machine
translation, and information retrieval techniques. Our comprehensive analysis
includes both automatic and human evaluation metrics. While GPT-4 exhibits
impressive abilities in adapting Chinese recipes into English, it still lags
behind human expertise when translating English recipes into Chinese. This
underscores the multifaceted nature of cultural adaptations. We anticipate that
these insights will significantly contribute to future research on
culturally-aware language models and their practical application in culturally
diverse contexts.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Reasoning over Description Logic-based Contexts with Transformers
Abstract: One way that the current state of the art measures the reasoning ability of
transformer-based models is by evaluating accuracy in downstream tasks like
logical question answering or proof generation over synthetic contexts
expressed in natural language. However, most of the contexts used are in
practice very simple; in most cases, they are generated from short first-order
logic sentences with only a few logical operators and quantifiers. In this
work, we seek to answer the question how well a transformer-based model will
perform reasoning over expressive contexts. For this purpose, we construct a
synthetic natural language question-answering dataset, generated by description
logic knowledge bases. For the generation of the knowledge bases, we use the
expressive language $\mathcal{ALCQ}$. The resulting dataset contains 384K
examples, and increases in two dimensions: i) reasoning depth, and ii) length
of sentences. We show that the performance of our DeBERTa-based model,
DELTA$_M$, is marginally affected when the reasoning depth is increased and it
is not affected at all when the length of the sentences is increasing. We also
evaluate the generalization ability of the model on reasoning depths unseen at
training, both increasing and decreasing, revealing interesting insights into
the model's adaptive generalization abilities.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: CIDR: A Cooperative Integrated Dynamic Refining Method for Minimal Feature Removal Problem
Abstract: The minimal feature removal problem in the post-hoc explanation area aims to
identify the minimal feature set (MFS). Prior studies using the greedy
algorithm to calculate the minimal feature set lack the exploration of feature
interactions under a monotonic assumption which cannot be satisfied in general
scenarios. In order to address the above limitations, we propose a Cooperative
Integrated Dynamic Refining method (CIDR) to efficiently discover minimal
feature sets. Specifically, we design Cooperative Integrated Gradients (CIG) to
detect interactions between features. By incorporating CIG and characteristics
of the minimal feature set, we transform the minimal feature removal problem
into a knapsack problem. Additionally, we devise an auxiliary Minimal Feature
Refinement algorithm to determine the minimal feature set from numerous
candidate sets. To the best of our knowledge, our work is the first to address
the minimal feature removal problem in the field of natural language
processing. Extensive experiments demonstrate that CIDR is capable of tracing
representative minimal feature sets with improved interpretability across
various models and datasets.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Explore Spurious Correlations at the Concept Level in Language Models for Text Classification
Abstract: Language models (LMs) have gained great achievement in various NLP tasks for
both fine-tuning and in-context learning (ICL) methods. Despite its outstanding
performance, evidence shows that spurious correlations caused by imbalanced
label distributions in training data (or exemplars in ICL) lead to robustness
issues. However, previous studies mostly focus on word- and phrase-level
features and fail to tackle it from the concept level, partly due to the lack
of concept labels and subtle and diverse expressions of concepts in text. In
this paper, we first use the LLM to label the concept for each text and then
measure the concept bias of models for fine-tuning or ICL on the test data.
Second, we propose a data rebalancing method to mitigate the spurious
correlations by adding the LLM-generated counterfactual data to make a balanced
label distribution for each concept. We verify the effectiveness of our
mitigation method and show its superiority over the token removal method.
Overall, our results show that there exist label distribution biases in
concepts across multiple text classification datasets, and LMs will utilize
these shortcuts to make predictions in both fine-tuning and ICL methods.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Efficient Symbolic Policy Learning with Differentiable Symbolic Expression
Abstract: Deep reinforcement learning (DRL) has led to a wide range of advances in
sequential decision-making tasks. However, the complexity of neural network
policies makes it difficult to understand and deploy with limited computational
resources. Currently, employing compact symbolic expressions as symbolic
policies is a promising strategy to obtain simple and interpretable policies.
Previous symbolic policy methods usually involve complex training processes and
pre-trained neural network policies, which are inefficient and limit the
application of symbolic policies. In this paper, we propose an efficient
gradient-based learning method named Efficient Symbolic Policy Learning (ESPL)
that learns the symbolic policy from scratch in an end-to-end way. We introduce
a symbolic network as the search space and employ a path selector to find the
compact symbolic policy. By doing so we represent the policy with a
differentiable symbolic expression and train it in an off-policy manner which
further improves the efficiency. In addition, in contrast with previous
symbolic policies which only work in single-task RL because of complexity, we
expand ESPL on meta-RL to generate symbolic policies for unseen tasks.
Experimentally, we show that our approach generates symbolic policies with
higher performance and greatly improves data efficiency for single-task RL. In
meta-RL, we demonstrate that compared with neural network policies the proposed
symbolic policy achieves higher performance and efficiency and shows the
potential to be interpretable.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition
Abstract: With the continuous development of deep learning (DL), the task of multimodal
dialogue emotion recognition (MDER) has recently received extensive research
attention, which is also an essential branch of DL. The MDER aims to identify
the emotional information contained in different modalities, e.g., text, video,
and audio, in different dialogue scenes. However, existing research has focused
on modeling contextual semantic information and dialogue relations between
speakers while ignoring the impact of event relations on emotion. To tackle the
above issues, we propose a novel Dialogue and Event Relation-Aware Graph
Convolutional Neural Network for Multimodal Emotion Recognition (DER-GCN)
method. It models dialogue relations between speakers and captures latent event
relations information. Specifically, we construct a weighted multi-relationship
graph to simultaneously capture the dependencies between speakers and event
relations in a dialogue. Moreover, we also introduce a Self-Supervised Masked
Graph Autoencoder (SMGAE) to improve the fusion representation ability of
features and structures. Next, we design a new Multiple Information Transformer
(MIT) to capture the correlation between different relations, which can provide
a better fuse of the multivariate information between relations. Finally, we
propose a loss optimization strategy based on contrastive learning to enhance
the representation learning ability of minority class features. We conduct
extensive experiments on the IEMOCAP and MELD benchmark datasets, which verify
the effectiveness of the DER-GCN model. The results demonstrate that our model
significantly improves both the average accuracy and the f1 value of emotion
recognition.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review
Abstract: The COVID-19 pandemic has forced many people to limit their social
activities, which has resulted in a rise in mental illnesses, particularly
depression. To diagnose these illnesses with accuracy and speed, and prevent
severe outcomes such as suicide, the use of machine learning has become
increasingly important. Additionally, to provide precise and understandable
diagnoses for better treatment, AI scientists and researchers must develop
interpretable AI-based solutions. This article provides an overview of relevant
articles in the field of machine learning and interpretable AI, which helps to
understand the advantages and disadvantages of using AI in psychiatry disorder
detection applications.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Efficient Quantization Strategies for Latent Diffusion Models
Abstract: Latent Diffusion Models (LDMs) capture the dynamic evolution of latent
variables over time, blending patterns and multimodality in a generative
system. Despite the proficiency of LDM in various applications, such as
text-to-image generation, facilitated by robust text encoders and a variational
autoencoder, the critical need to deploy large generative models on edge
devices compels a search for more compact yet effective alternatives. Post
Training Quantization (PTQ), a method to compress the operational size of deep
learning models, encounters challenges when applied to LDM due to temporal and
structural complexities. This study proposes a quantization strategy that
efficiently quantize LDMs, leveraging Signal-to-Quantization-Noise Ratio (SQNR)
as a pivotal metric for evaluation. By treating the quantization discrepancy as
relative noise and identifying sensitive part(s) of a model, we propose an
efficient quantization approach encompassing both global and local strategies.
The global quantization process mitigates relative quantization noise by
initiating higher-precision quantization on sensitive blocks, while local
treatments address specific challenges in quantization-sensitive and
time-sensitive modules. The outcomes of our experiments reveal that the
implementation of both global and local treatments yields a highly efficient
and effective Post Training Quantization (PTQ) of LDMs.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Steering Responsible AI: A Case for Algorithmic Pluralism
Abstract: In this paper, I examine questions surrounding AI neutrality through the
prism of existing literature and scholarship about mediation and media
pluralism. Such traditions, I argue, provide a valuable theoretical framework
for how we should approach the (likely) impending era of AI mediation. In
particular, I suggest examining further the notion of algorithmic pluralism.
Contrasting this notion to the dominant idea of algorithmic transparency, I
seek to describe what algorithmic pluralism may be, and present both its
opportunities and challenges. Implemented thoughtfully and responsibly, I
argue, Algorithmic or AI pluralism has the potential to sustain the diversity,
multiplicity, and inclusiveness that are so vital to democracy.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction
Abstract: Predictive Process Monitoring (PPM) aims at leveraging historic process
execution data to predict how ongoing executions will continue up to their
completion. In recent years, PPM techniques for the prediction of the next
activities have matured significantly, mainly thanks to the use of Neural
Networks (NNs) as a predictor. While their performance is difficult to beat in
the general case, there are specific situations where background process
knowledge can be helpful. Such knowledge can be leveraged for improving the
quality of predictions for exceptional process executions or when the process
changes due to a concept drift. In this paper, we present a Symbolic[Neuro]
system that leverages background knowledge expressed in terms of a procedural
process model to offset the under-sampling in the training data. More
specifically, we make predictions using NNs with attention mechanism, an
emerging technology in the NN field. The system has been tested on several
real-life logs showing an improvement in the performance of the prediction
task.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Security and Privacy Challenges in Deep Learning Models
Abstract: These days, deep learning models have achieved great success in multiple
fields, from autonomous driving to medical diagnosis. These models have
expanded the abilities of artificial intelligence by offering great solutions
to complex problems that were very difficult to solve earlier. In spite of
their unseen success in various, it has been identified, through research
conducted, that deep learning models can be subjected to various attacks that
compromise model security and data privacy of the Deep Neural Network models.
Deep learning models can be subjected to various attacks at different stages of
their lifecycle. During the testing phase, attackers can exploit
vulnerabilities through different kinds of attacks such as Model Extraction
Attacks, Model Inversion attacks, and Adversarial attacks. Model Extraction
Attacks are aimed at reverse-engineering a trained deep learning model, with
the primary objective of revealing its architecture and parameters. Model
inversion attacks aim to compromise the privacy of the data used in the Deep
learning model. These attacks are done to compromise the confidentiality of the
model by going through the sensitive training data from the model's
predictions. By analyzing the model's responses, attackers aim to reconstruct
sensitive information. In this way, the model's data privacy is compromised.
Adversarial attacks, mainly employed on computer vision models, are made to
corrupt models into confidently making incorrect predictions through malicious
testing data. These attacks subtly alter the input data, making it look normal
but misleading deep learning models to make incorrect decisions. Such attacks
can happen during both the model's evaluation and training phases. Data
Poisoning Attacks add harmful data to the training set, disrupting the learning
process and reducing the reliability of the deep learning mode.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks
Abstract: We explore the abstract reasoning abilities of text-only and multimodal
versions of GPT-4, using the ConceptARC benchmark [10], which is designed to
evaluate robust understanding and reasoning with core-knowledge concepts. We
extend the work of Moskvichev et al. [10] by evaluating GPT-4 on more detailed,
one-shot prompting (rather than simple, zero-shot prompts) with text versions
of ConceptARC tasks, and by evaluating GPT-4V, the multimodal version of GPT-4,
on zero- and one-shot prompts using image versions of the simplest tasks. Our
experimental results support the conclusion that neither version of GPT-4 has
developed robust abstraction abilities at humanlike levels.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized method
Abstract: Analogical reasoning derives information from known relations and generalizes
this information to similar yet unfamiliar situations. One of the first
generalized ways in which deep learning models were able to solve verbal
analogies was through vector arithmetic of word embeddings, essentially
relating words that were mapped to a vector space (e.g., king - man + woman =
__?). In comparison, most attempts to solve visual analogies are still
predominantly task-specific and less generalizable. This project focuses on
visual analogical reasoning and applies the initial generalized mechanism used
to solve verbal analogies to the visual realm. Taking the Abstraction and
Reasoning Corpus (ARC) as an example to investigate visual analogy solving, we
use a variational autoencoder (VAE) to transform ARC items into low-dimensional
latent vectors, analogous to the word embeddings used in the verbal approaches.
Through simple vector arithmetic, underlying rules of ARC items are discovered
and used to solve them. Results indicate that the approach works well on simple
items with fewer dimensions (i.e., few colors used, uniform shapes), similar
input-to-output examples, and high reconstruction accuracy on the VAE.
Predictions on more complex items showed stronger deviations from expected
outputs, although, predictions still often approximated parts of the item's
rule set. Error patterns indicated that the model works as intended. On the
official ARC paradigm, the model achieved a score of 2% (cf. current world
record is 21%) and on ConceptARC it scored 8.8%. Although the methodology
proposed involves basic dimensionality reduction techniques and standard vector
arithmetic, this approach demonstrates promising outcomes on ARC and can easily
be generalized to other abstract visual reasoning tasks.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning
Abstract: Automatic differentiation (AD) is a critical step in physics-informed machine
learning, required for computing the high-order derivatives of network output
w.r.t. coordinates of collocation points. In this paper, we present a novel and
lightweight algorithm to conduct AD for physics-informed operator learning,
which we call the trick of Zero Coordinate Shift (ZCS). Instead of making all
sampled coordinates as leaf variables, ZCS introduces only one scalar-valued
leaf variable for each spatial or temporal dimension, simplifying the wanted
derivatives from "many-roots-many-leaves" to "one-root-many-leaves" whereby
reverse-mode AD becomes directly utilisable. It has led to an outstanding
performance leap by avoiding the duplication of the computational graph along
the dimension of functions (physical parameters). ZCS is easy to implement with
current deep learning libraries; our own implementation is achieved by
extending the DeepXDE package. We carry out a comprehensive benchmark analysis
and several case studies, training physics-informed DeepONets to solve partial
differential equations (PDEs) without data. The results show that ZCS has
persistently reduced GPU memory consumption and wall time for training by an
order of magnitude, and such reduction factor scales with the number of
functions. As a low-level optimisation technique, ZCS imposes no restrictions
on data, physics (PDE) or network architecture and does not compromise training
results from any aspect.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset
Abstract: The CoNLL-03 corpus is arguably the most well-known and utilized benchmark
dataset for named entity recognition (NER). However, prior works found
significant numbers of annotation errors, incompleteness, and inconsistencies
in the data. This poses challenges to objectively comparing NER approaches and
analyzing their errors, as current state-of-the-art models achieve F1-scores
that are comparable to or even exceed the estimated noise level in CoNLL-03. To
address this issue, we present a comprehensive relabeling effort assisted by
automatic consistency checking that corrects 7.0% of all labels in the English
CoNLL-03. Our effort adds a layer of entity linking annotation both for better
explainability of NER labels and as additional safeguard of annotation quality.
Our experimental evaluation finds not only that state-of-the-art approaches
reach significantly higher F1-scores (97.1%) on our data, but crucially that
the share of correct predictions falsely counted as errors due to annotation
noise drops from 47% to 6%. This indicates that our resource is well suited to
analyze the remaining errors made by state-of-the-art models, and that the
theoretical upper bound even on high resource, coarse-grained NER is not yet
reached. To facilitate such analysis, we make CleanCoNLL publicly available to
the research community.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document
Abstract: The facts and time in the document are intricately intertwined, making
temporal reasoning over documents challenging. Previous work models time
implicitly, making it difficult to handle such complex relationships. To
address this issue, we propose MTGER, a novel Multi-view Temporal Graph
Enhanced Temporal Reasoning framework for temporal reasoning over time-involved
documents. Concretely, MTGER explicitly models the temporal relationships among
facts by multi-view temporal graphs. On the one hand, the heterogeneous
temporal graphs explicitly model the temporal and discourse relationships among
facts; on the other hand, the multi-view mechanism captures both time-focused
and fact-focused information, allowing the two views to complement each other
through adaptive fusion. To further improve the implicit reasoning capability
of the model, we design a self-supervised time-comparing objective. Extensive
experimental results demonstrate the effectiveness of our method on the TimeQA
and SituatedQA datasets. Furthermore, MTGER gives more consistent answers under
question perturbations.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Work State-Centric AI Agents: Design, Implementation, and Management of Cognitive Work Threads
Abstract: AI agents excel in executing predefined tasks, but the dynamic management of
work state information during task execution remains an underexplored area. We
propose a work state-centric AI agent model employing "work notes" to record
and reflect the state throughout task execution. This paper details the model's
architecture, featuring worker threads for task oversight, planner modules for
task decomposition and planning, and executor modules for performing subtasks
using a ReAct-inspired thought-action loop. We provide an exhaustive work state
record incorporating plans and outcomes, constituting a comprehensive work
journal. Our results show that this model not only improves task execution
efficiency but also lays a solid foundation for subsequent task analysis and
auditing.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation
Abstract: Text-to-image diffusion models achieved a remarkable leap in capabilities
over the last few years, enabling high-quality and diverse synthesis of images
from a textual prompt. However, even the most advanced models often struggle to
precisely follow all of the directions in their prompts. The vast majority of
these models are trained on datasets consisting of (image, caption) pairs where
the images often come from the web, and the captions are their HTML alternate
text. A notable example is the LAION dataset, used by Stable Diffusion and
other models. In this work we observe that these captions are often of low
quality, and argue that this significantly affects the model's capability to
understand nuanced semantics in the textual prompts. We show that by relabeling
the corpus with a specialized automatic captioning model and training a
text-to-image model on the recaptioned dataset, the model benefits
substantially across the board. First, in overall image quality: e.g. FID 14.84
vs. the baseline of 17.87, and 64.3% improvement in faithful image generation
according to human evaluation. Second, in semantic alignment, e.g. semantic
object accuracy 84.34 vs. 78.90, counting alignment errors 1.32 vs. 1.44 and
positional alignment 62.42 vs. 57.60. We analyze various ways to relabel the
corpus and provide evidence that this technique, which we call RECAP, both
reduces the train-inference discrepancy and provides the model with more
information per example, increasing sample efficiency and allowing the model to
better understand the relations between captions and images.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Efficient Rotation Invariance in Deep Neural Networks through Artificial Mental Rotation
Abstract: Humans and animals recognize objects irrespective of the beholder's point of
view, which may drastically change their appearances. Artificial pattern
recognizers also strive to achieve this, e.g., through translational invariance
in convolutional neural networks (CNNs). However, both CNNs and vision
transformers (ViTs) perform very poorly on rotated inputs. Here we present
artificial mental rotation (AMR), a novel deep learning paradigm for dealing
with in-plane rotations inspired by the neuro-psychological concept of mental
rotation. Our simple AMR implementation works with all common CNN and ViT
architectures. We test it on ImageNet, Stanford Cars, and Oxford Pet. With a
top-1 error (averaged across datasets and architectures) of $0.743$, AMR
outperforms the current state of the art (rotational data augmentation, average
top-1 error of $0.626$) by $19\%$. We also easily transfer a trained AMR module
to a downstream task to improve the performance of a pre-trained semantic
segmentation model on rotated CoCo from $32.7$ to $55.2$ IoU.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: PointOBB: Learning Oriented Object Detection via Single Point Supervision
Abstract: Single point-supervised object detection is gaining attention due to its
cost-effectiveness. However, existing approaches focus on generating horizontal
bounding boxes (HBBs) while ignoring oriented bounding boxes (OBBs) commonly
used for objects in aerial images. This paper proposes PointOBB, the first
single Point-based OBB generation method, for oriented object detection.
PointOBB operates through the collaborative utilization of three distinctive
views: an original view, a resized view, and a rotated/flipped (rot/flp) view.
Upon the original view, we leverage the resized and rot/flp views to build a
scale augmentation module and an angle acquisition module, respectively. In the
former module, a Scale-Sensitive Consistency (SSC) loss is designed to enhance
the deep network's ability to perceive the object scale. For accurate object
angle predictions, the latter module incorporates self-supervised learning to
predict angles, which is associated with a scale-guided Dense-to-Sparse (DS)
matching strategy for aggregating dense angles corresponding to sparse objects.
The resized and rot/flp views are switched using a progressive multi-view
switching strategy during training to achieve coupled optimization of scale and
angle. Experimental results on the DIOR-R and DOTA-v1.0 datasets demonstrate
that PointOBB achieves promising performance, and significantly outperforms
potential point-supervised baselines.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning
Abstract: Multi-modal conversation emotion recognition (MCER) aims to recognize and
track the speaker's emotional state using text, speech, and visual information
in the conversation scene. Analyzing and studying MCER issues is significant to
affective computing, intelligent recommendations, and human-computer
interaction fields. Unlike the traditional single-utterance multi-modal emotion
recognition or single-modal conversation emotion recognition, MCER is a more
challenging problem that needs to deal with more complex emotional interaction
relationships. The critical issue is learning consistency and complementary
semantics for multi-modal feature fusion based on emotional interaction
relationships. To solve this problem, people have conducted extensive research
on MCER based on deep learning technology, but there is still a lack of
systematic review of the modeling methods. Therefore, a timely and
comprehensive overview of MCER's recent advances in deep learning is of great
significance to academia and industry. In this survey, we provide a
comprehensive overview of MCER modeling methods and roughly divide MCER methods
into four categories, i.e., context-free modeling, sequential context modeling,
speaker-differentiated modeling, and speaker-relationship modeling. In
addition, we further discuss MCER's publicly available popular datasets,
multi-modal feature extraction methods, application areas, existing challenges,
and future development directions. We hope that our review can help MCER
researchers understand the current research status in emotion recognition,
provide some inspiration, and develop more efficient models.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: ADaPT: As-Needed Decomposition and Planning with Language Models
Abstract: Large Language Models (LLMs) are increasingly being used for interactive
decision-making tasks requiring planning and adapting to the environment.
Recent works employ LLMs-as-agents in broadly two ways: iteratively determining
the next action (iterative executors) or generating plans and executing
sub-tasks using LLMs (plan-and-execute). However, these methods struggle with
task complexity, as the inability to execute any sub-task may lead to task
failure. To address these shortcomings, we introduce As-Needed Decomposition
and Planning for complex Tasks (ADaPT), an approach that explicitly plans and
decomposes complex sub-tasks as-needed, i.e., when the LLM is unable to execute
them. ADaPT recursively decomposes sub-tasks to adapt to both task complexity
and LLM capability. Our results demonstrate that ADaPT substantially
outperforms established strong baselines, achieving success rates up to 28.3%
higher in ALFWorld, 27% in WebShop, and 33% in TextCraft -- a novel
compositional dataset that we introduce. Through extensive analysis, we
illustrate the importance of multilevel decomposition and establish that ADaPT
dynamically adjusts to the capabilities of the executor LLM as well as to task
complexity.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Environmental-Impact Based Multi-Agent Reinforcement Learning
Abstract: To promote cooperation and strengthen the individual impact on the collective
outcome in social dilemmas, we propose the Environmental-impact Multi-Agent
Reinforcement Learning (EMuReL) method where each agent estimates the
"environmental impact" of every other agent, that is, the difference in the
current environment state compared to the hypothetical environment in the
absence of that other agent. Inspired by the Inequity Aversion model, the agent
then compares its own reward with those of its fellows multiplied by their
environmental impacts. If its reward exceeds the scaled reward of one of its
fellows, the agent takes "social responsibility" toward that fellow by reducing
its own reward. Therefore, the less influential an agent is in reaching the
current state, the more social responsibility is taken by other agents.
Experiments in the Cleanup (resp. Harvest) test environment demonstrate that
agents trained based on EMuReL learn to cooperate more effectively and obtain
$54\%$ ($39\%$) and $20\%$ ($44\%$) more total rewards while preserving the
same cooperation levels compared to when they are trained based on the two
state-of-the-art reward reshaping methods inequity aversion and social
influence.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Hypothesis Network Planned Exploration for Rapid Meta-Reinforcement Learning Adaptation
Abstract: Meta Reinforcement Learning (Meta RL) trains agents that adapt to
fast-changing environments and tasks. Current strategies often lose adaption
efficiency due to the passive nature of model exploration, causing delayed
understanding of new transition dynamics. This results in particularly
fast-evolving tasks being impossible to solve. We propose a novel approach,
Hypothesis Network Planned Exploration (HyPE), that integrates an active and
planned exploration process via the hypothesis network to optimize adaptation
speed. HyPE uses a generative hypothesis network to form potential models of
state transition dynamics, then eliminates incorrect models through
strategically devised experiments. Evaluated on a symbolic version of the
Alchemy game, HyPE outpaces baseline methods in adaptation speed and model
accuracy, validating its potential in enhancing reinforcement learning
adaptation in rapidly evolving settings.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics
Abstract: Combining gradient-based trajectory optimization with differentiable physics
simulation is an efficient technique for solving soft-body manipulation
problems. Using a well-crafted optimization objective, the solver can quickly
converge onto a valid trajectory. However, writing the appropriate objective
functions requires expert knowledge, making it difficult to collect a large set
of naturalistic problems from non-expert users. We introduce DiffVL, a method
that enables non-expert users to communicate soft-body manipulation tasks -- a
combination of vision and natural language, given in multiple stages -- that
can be readily leveraged by a differential physics solver. We have developed
GUI tools that enable non-expert users to specify 100 tasks inspired by
real-life soft-body manipulations from online videos, which we'll make public.
We leverage large language models to translate task descriptions into
machine-interpretable optimization objectives. The optimization objectives can
help differentiable physics solvers to solve these long-horizon multistage
tasks that are challenging for previous baselines.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Maximum Entropy Model Correction in Reinforcement Learning
Abstract: We propose and theoretically analyze an approach for planning with an
approximate model in reinforcement learning that can reduce the adverse impact
of model error. If the model is accurate enough, it accelerates the convergence
to the true value function too. One of its key components is the MaxEnt Model
Correction (MoCo) procedure that corrects the model's next-state distributions
based on a Maximum Entropy density estimation formulation. Based on MoCo, we
introduce the Model Correcting Value Iteration (MoCoVI) algorithm, and its
sampled-based variant MoCoDyna. We show that MoCoVI and MoCoDyna's convergence
can be much faster than the conventional model-free algorithms. Unlike
traditional model-based algorithms, MoCoVI and MoCoDyna effectively utilize an
approximate model and still converge to the correct value function.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Can LLMs Configure Software Tools
Abstract: In software engineering, the meticulous configuration of software tools is
crucial in ensuring optimal performance within intricate systems. However, the
complexity inherent in selecting optimal configurations is exacerbated by the
high-dimensional search spaces presented in modern applications. Conventional
trial-and-error or intuition-driven methods are both inefficient and
error-prone, impeding scalability and reproducibility. In this study, we embark
on an exploration of leveraging Large-Language Models (LLMs) to streamline the
software configuration process. We identify that the task of hyperparameter
configuration for machine learning components within intelligent applications
is particularly challenging due to the extensive search space and
performance-critical nature. Existing methods, including Bayesian optimization,
have limitations regarding initial setup, computational cost, and convergence
efficiency. Our work presents a novel approach that employs LLMs, such as
Chat-GPT, to identify starting conditions and narrow down the search space,
improving configuration efficiency. We conducted a series of experiments to
investigate the variability of LLM-generated responses, uncovering intriguing
findings such as potential response caching and consistent behavior based on
domain-specific keywords. Furthermore, our results from hyperparameter
optimization experiments reveal the potential of LLMs in expediting
initialization processes and optimizing configurations. While our initial
insights are promising, they also indicate the need for further in-depth
investigations and experiments in this domain.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: On Exploring the Reasoning Capability of Large Language Models with Knowledge Graphs
Abstract: This paper examines the capacity of LLMs to reason with knowledge graphs
using their internal knowledge graph, i.e., the knowledge graph they learned
during pre-training. Two research questions are formulated to investigate the
accuracy of LLMs in recalling information from pre-training knowledge graphs
and their ability to infer knowledge graph relations from context. To address
these questions, we employ LLMs to perform four distinct knowledge graph
reasoning tasks. Furthermore, we identify two types of hallucinations that may
occur during knowledge reasoning with LLMs: content and ontology hallucination.
Our experimental results demonstrate that LLMs can successfully tackle both
simple and complex knowledge graph reasoning tasks from their own memory, as
well as infer from input context.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Saturn Platform: Foundation Model Operations and Generative AI for Financial Services
Abstract: Saturn is an innovative platform that assists Foundation Model (FM) building
and its integration with IT operations (Ops). It is custom-made to meet the
requirements of data scientists, enabling them to effectively create and
implement FMs while enhancing collaboration within their technical domain. By
offering a wide range of tools and features, Saturn streamlines and automates
different stages of FM development, making it an invaluable asset for data
science teams. This white paper introduces prospective applications of
generative AI models derived from FMs in the financial sector.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: An Experiment in Retrofitting Competency Questions for Existing Ontologies
Abstract: Competency Questions (CQs) are a form of ontology functional requirements
expressed as natural language questions. Inspecting CQs together with the
axioms in an ontology provides critical insights into the intended scope and
applicability of the ontology. CQs also underpin a number of tasks in the
development of ontologies e.g. ontology reuse, ontology testing, requirement
specification, and the definition of patterns that implement such requirements.
Although CQs are integral to the majority of ontology engineering
methodologies, the practice of publishing CQs alongside the ontological
artefacts is not widely observed by the community. In this context, we present
an experiment in retrofitting CQs from existing ontologies. We propose
RETROFIT-CQs, a method to extract candidate CQs directly from ontologies using
Generative AI. In the paper we present the pipeline that facilitates the
extraction of CQs by leveraging Large Language Models (LLMs) and we discuss its
application to a number of existing ontologies.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Values
Abstract: The rapid advancement of Large Language Models (LLMs) has attracted much
attention to value alignment for their responsible development. However, how to
define values in this context remains a largely unexplored question. Existing
work mainly follows the Helpful, Honest, Harmless principle and specifies
values as risk criteria formulated in the AI community, e.g., fairness and
privacy protection, suffering from poor clarity, adaptability and transparency.
Inspired by basic values in humanity and social science across cultures, this
work proposes a novel basic value alignment paradigm and introduces a value
space spanned by basic value dimensions. All LLMs' behaviors can be mapped into
the space by identifying the underlying values, possessing the potential to
address the three challenges. To foster future research, we apply the
representative Schwartz's Theory of Basic Values as an initialized example and
construct FULCRA, a dataset consisting of 5k (LLM output, value vector) pairs.
Our extensive analysis of FULCRA reveals the underlying relation between basic
values and LLMs' behaviors, demonstrating that our approach not only covers
existing mainstream risks but also anticipates possibly unidentified ones.
Additionally, we present an initial implementation of the basic value
evaluation and alignment, paving the way for future research in this line.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
Abstract: Effective user representations are pivotal in personalized advertising.
However, stringent constraints on training throughput, serving latency, and
memory, often limit the complexity and input feature set of online ads ranking
models. This challenge is magnified in extensive systems like Meta's, which
encompass hundreds of models with diverse specifications, rendering the
tailoring of user representation learning for each model impractical. To
address these challenges, we present Scaling User Modeling (SUM), a framework
widely deployed in Meta's ads ranking system, designed to facilitate efficient
and scalable sharing of online user representation across hundreds of ads
models. SUM leverages a few designated upstream user models to synthesize user
embeddings from massive amounts of user features with advanced modeling
techniques. These embeddings then serve as inputs to downstream online ads
ranking models, promoting efficient representation sharing. To adapt to the
dynamic nature of user features and ensure embedding freshness, we designed SUM
Online Asynchronous Platform (SOAP), a latency free online serving system
complemented with model freshness and embedding stabilization, which enables
frequent user model updates and online inference of user embeddings upon each
user request. We share our hands-on deployment experiences for the SUM
framework and validate its superiority through comprehensive experiments. To
date, SUM has been launched to hundreds of ads ranking models in Meta,
processing hundreds of billions of user requests daily, yielding significant
online metric gains and infrastructure cost savings.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Interactive Joint Planning for Autonomous Vehicles
Abstract: In highly interactive driving scenarios, the actions of one agent greatly
influences those of its neighbors. Planning safe motions for autonomous
vehicles in such interactive environments, therefore, requires reasoning about
the impact of the ego's intended motion plan on nearby agents' behavior.
Deep-learning-based models have recently achieved great success in trajectory
prediction and many models in the literature allow for ego-conditioned
prediction. However, leveraging ego-conditioned prediction remains challenging
in downstream planning due to the complex nature of neural networks, limiting
the planner structure to simple ones, e.g., sampling-based planner. Despite
their ability to generate fine-grained high-quality motion plans, it is
difficult for gradient-based planning algorithms, such as model predictive
control (MPC), to leverage ego-conditioned prediction due to their iterative
nature and need for gradient. We present Interactive Joint Planning (IJP) that
bridges MPC with learned prediction models in a computationally scalable manner
to provide us the best of both the worlds. In particular, IJP jointly optimizes
over the behavior of the ego and the surrounding agents and leverages
deep-learned prediction models as prediction priors that the join trajectory
optimization tries to stay close to. Furthermore, by leveraging homotopy
classes, our joint optimizer searches over diverse motion plans to avoid
getting stuck at local minima. Closed-loop simulation result shows that IJP
significantly outperforms the baselines that are either without joint
optimization or running sampling-based planning.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: AI Alignment in the Design of Interactive AI: Specification Alignment, Process Alignment, and Evaluation Support
Abstract: AI alignment considers the overall problem of ensuring an AI produces desired
outcomes, without undesirable side effects. While often considered from the
perspectives of safety and human values, AI alignment can also be considered in
the context of designing and evaluating interfaces for interactive AI systems.
This paper maps concepts from AI alignment onto a basic, three step interaction
cycle, yielding a corresponding set of alignment objectives: 1) specification
alignment: ensuring the user can efficiently and reliably communicate
objectives to the AI, 2) process alignment: providing the ability to verify and
optionally control the AI's execution process, and 3) evaluation support:
ensuring the user can verify and understand the AI's output. We also introduce
the concepts of a surrogate process, defined as a simplified, separately
derived, but controllable representation of the AI's actual process; and the
notion of a Process Gulf, which highlights how differences between human and AI
processes can lead to challenges in AI control. To illustrate the value of this
framework, we describe commercial and research systems along each of the three
alignment dimensions, and show how interfaces that provide interactive
alignment mechanisms can lead to qualitatively different and improved user
experiences.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications
Abstract: Adversarial testing of large language models (LLMs) is crucial for their safe
and responsible deployment. We introduce a novel approach for automated
generation of adversarial evaluation datasets to test the safety of LLM
generations on new downstream applications. We call it AI-assisted Red-Teaming
(AART) - an automated alternative to current manual red-teaming efforts. AART
offers a data generation and augmentation pipeline of reusable and customizable
recipes that reduce human effort significantly and enable integration of
adversarial testing earlier in new product development. AART generates
evaluation datasets with high diversity of content characteristics critical for
effective adversarial testing (e.g. sensitive and harmful concepts, specific to
a wide range of cultural and geographic regions and application scenarios). The
data generation is steered by AI-assisted recipes to define, scope and
prioritize diversity within the application context. This feeds into a
structured LLM-generation process that scales up evaluation priorities.
Compared to some state-of-the-art tools, AART shows promising results in terms
of concept coverage and data quality.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning
Abstract: Nowadays, denoising diffusion probabilistic models have been adapted for many
image segmentation tasks. However, existing end-to-end models have already
demonstrated remarkable capabilities. Rather than using denoising diffusion
probabilistic models alone, integrating the abilities of both denoising
diffusion probabilistic models and existing end-to-end models can better
improve the performance of image segmentation. Based on this, we implicitly
introduce residual term into the diffusion process and propose
ResEnsemble-DDPM, which seamlessly integrates the diffusion model and the
end-to-end model through ensemble learning. The output distributions of these
two models are strictly symmetric with respect to the ground truth
distribution, allowing us to integrate the two models by reducing the residual
term. Experimental results demonstrate that our ResEnsemble-DDPM can further
improve the capabilities of existing models. Furthermore, its ensemble learning
strategy can be generalized to other downstream tasks in image generation and
get strong competitiveness.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities
Abstract: Contemporary artificial intelligence systems exhibit rapidly growing
abilities accompanied by the growth of required resources, expansive datasets
and corresponding investments into computing infrastructure. Although earlier
successes predominantly focus on constrained settings, recent strides in
fundamental research and applications aspire to create increasingly general
systems. This evolving landscape presents a dual panorama of opportunities and
challenges in refining the generalisation and transfer of knowledge - the
extraction from existing sources and adaptation as a comprehensive foundation
for tackling new problems. Within the domain of reinforcement learning (RL),
the representation of knowledge manifests through various modalities, including
dynamics and reward models, value functions, policies, and the original data.
This taxonomy systematically targets these modalities and frames its discussion
based on their inherent properties and alignment with different objectives and
mechanisms for transfer. Where possible, we aim to provide coarse guidance
delineating approaches which address requirements such as limiting environment
interactions, maximising computational efficiency, and enhancing generalisation
across varying axes of change. Finally, we analyse reasons contributing to the
prevalence or scarcity of specific forms of transfer, the inherent potential
behind pushing these frontiers, and underscore the significance of
transitioning from designed to learned transfer.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Learning Spatially-Continuous Fiber Orientation Functions
Abstract: Our understanding of the human connectome is fundamentally limited by the
resolution of diffusion MR images. Reconstructing a connectome's constituent
neural pathways with tractography requires following a continuous field of
fiber directions. Typically, this field is found with simple trilinear
interpolation in low-resolution, noisy diffusion MRIs. However, trilinear
interpolation struggles following fine-scale changes in low-quality data.
Recent deep learning methods in super-resolving diffusion MRIs have focused on
upsampling to a fixed spatial grid, but this does not satisfy tractography's
need for a continuous field. In this work, we propose FENRI, a novel method
that learns spatially-continuous fiber orientation density functions from
low-resolution diffusion-weighted images. To quantify FENRI's capabilities in
tractography, we also introduce an expanded simulated dataset built for
evaluating deep-learning tractography models. We demonstrate that FENRI
accurately predicts high-resolution fiber orientations from realistic
low-quality data, and that FENRI-based tractography offers improved streamline
reconstruction over the current use of trilinear interpolation.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Video Face Re-Aging: Toward Temporally Consistent Face Re-Aging
Abstract: Video face re-aging deals with altering the apparent age of a person to the
target age in videos. This problem is challenging due to the lack of paired
video datasets maintaining temporal consistency in identity and age. Most
re-aging methods process each image individually without considering the
temporal consistency of videos. While some existing works address the issue of
temporal coherence through video facial attribute manipulation in latent space,
they often fail to deliver satisfactory performance in age transformation. To
tackle the issues, we propose (1) a novel synthetic video dataset that features
subjects across a diverse range of age groups; (2) a baseline architecture
designed to validate the effectiveness of our proposed dataset, and (3) the
development of three novel metrics tailored explicitly for evaluating the
temporal consistency of video re-aging techniques. Our comprehensive
experiments on public datasets, such as VFHQ and CelebV-HQ, show that our
method outperforms the existing approaches in terms of both age transformation
and temporal consistency.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: The sample complexity of multi-distribution learning
Abstract: Multi-distribution learning generalizes the classic PAC learning to handle
data coming from multiple distributions. Given a set of $k$ data distributions
and a hypothesis class of VC dimension $d$, the goal is to learn a hypothesis
that minimizes the maximum population loss over $k$ distributions, up to
$\epsilon$ additive error. In this paper, we settle the sample complexity of
multi-distribution learning by giving an algorithm of sample complexity
$\widetilde{O}((d+k)\epsilon^{-2}) \cdot (k/\epsilon)^{o(1)}$. This matches the
lower bound up to sub-polynomial factor and resolves the COLT 2023 open problem
of Awasthi, Haghtalab and Zhao [AHZ23].

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: 3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V
Abstract: In this work, we present a new visual prompting method called 3DAxiesPrompts
(3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks.
Our investigation reveals that while GPT-4V exhibits proficiency in discerning
the position and interrelations of 2D entities through current visual prompting
techniques, its abilities in handling 3D spatial tasks have yet to be explored.
In our approach, we create a 3D coordinate system tailored to 3D imagery,
complete with annotated scale information. By presenting images infused with
the 3DAP visual prompt as inputs, we empower GPT-4V to ascertain the spatial
positioning information of the given 3D target image with a high degree of
precision. Through experiments, We identified three tasks that could be stably
completed using the 3DAP method, namely, 2D to 3D Point Reconstruction, 2D to
3D point matching, and 3D Object Detection. We perform experiments on our
proposed dataset 3DAP-Data, the results from these experiments validate the
efficacy of 3DAP-enhanced GPT-4V inputs, marking a significant stride in 3D
spatial task execution.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications
Abstract: The advent of Large Language Models (LLMs) heralds a pivotal shift in online
user interactions with information. Traditional Information Retrieval (IR)
systems primarily relied on query-document matching, whereas LLMs excel in
comprehending and generating human-like text, thereby enriching the IR
experience significantly. While LLMs are often associated with chatbot
functionalities, this paper extends the discussion to their explicit
application in information retrieval. We explore methodologies to optimize the
retrieval process, select optimal models, and effectively scale and orchestrate
LLMs, aiming for cost-efficiency and enhanced result accuracy. A notable
challenge, model hallucination-where the model yields inaccurate or
misinterpreted data-is addressed alongside other model-specific hurdles. Our
discourse extends to crucial considerations including user privacy, data
optimization, and the necessity for system clarity and interpretability.
Through a comprehensive examination, we unveil not only innovative strategies
for integrating Language Models (LLMs) with Information Retrieval (IR) systems,
but also the consequential considerations that underline the need for a
balanced approach aligned with user-centric principles.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: A Machine Learning-Based Framework for Clustering Residential Electricity Load Profiles to Enhance Demand Response Programs
Abstract: Load shapes derived from smart meter data are frequently employed to analyze
daily energy consumption patterns, particularly in the context of applications
like Demand Response (DR). Nevertheless, one of the most important challenges
to this endeavor lies in identifying the most suitable consumer clusters with
similar consumption behaviors. In this paper, we present a novel machine
learning based framework in order to achieve optimal load profiling through a
real case study, utilizing data from almost 5000 households in London. Four
widely used clustering algorithms are applied specifically K-means, K-medoids,
Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An
empirical analysis as well as multiple evaluation metrics are leveraged to
assess those algorithms. Following that, we redefine the problem as a
probabilistic classification one, with the classifier emulating the behavior of
a clustering algorithm,leveraging Explainable AI (xAI) to enhance the
interpretability of our solution. According to the clustering algorithm
analysis the optimal number of clusters for this case is seven. Despite that,
our methodology shows that two of the clusters, almost 10\% of the dataset,
exhibit significant internal dissimilarity and thus it splits them even further
to create nine clusters in total. The scalability and versatility of our
solution makes it an ideal choice for power utility companies aiming to segment
their users for creating more targeted Demand Response programs.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Can LLMs Grade Short-answer Reading Comprehension Questions : Foundational Literacy Assessment in LMICs
Abstract: This paper presents emerging evidence of using generative large language
models (i.e., GPT-4) to reliably evaluate short-answer reading comprehension
questions. Specifically, we explore how various configurations of generative
(LLMs) are able to evaluate student responses from a new dataset, drawn from a
battery of reading assessments conducted with over 150 students in Ghana. As
this dataset is novel and hence not used in training runs of GPT, it offers an
opportunity to test for domain shift and evaluate the generalizability of
generative LLMs, which are predominantly designed and trained on data from
high-income North American countries. We found that GPT-4, with minimal prompt
engineering performed extremely well on evaluating the novel dataset (Quadratic
Weighted Kappa 0.923, F1 0.88), substantially outperforming transfer-learning
based approaches, and even exceeding expert human raters (Quadratic Weighted
Kappa 0.915, F1 0.87). To the best of our knowledge, our work is the first to
empirically evaluate the performance of generative LLMs on short-answer reading
comprehension questions, using real student data, and suggests that generative
LLMs have the potential to reliably evaluate foundational literacy. Currently
the assessment of formative literacy and numeracy is infrequent in many low and
middle-income countries (LMICs) due to the cost and operational complexities of
conducting them at scale. Automating the grading process for reading assessment
could enable wider usage, and in turn improve decision-making regarding
curricula, school management, and teaching practice at the classroom level.
Importantly, in contrast transfer learning based approaches, generative LLMs
generalize well and the technical barriers to their use are low, making them
more feasible to implement and scale in lower resource educational contexts.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Exploring and Improving the Spatial Reasoning Abilities of Large Language Models
Abstract: Large Language Models (LLMs) represent formidable tools for sequence
modeling, boasting an innate capacity for general pattern recognition.
Nevertheless, their broader spatial reasoning capabilities, especially applied
to numerical trajectory data, remain insufficiently explored. In this paper, we
investigate the out-of-the-box performance of ChatGPT-3.5, ChatGPT-4 and Llama
2 7B models when confronted with 3D robotic trajectory data from the CALVIN
baseline and associated tasks, including 2D directional and shape labeling.
Additionally, we introduce a novel prefix-based prompting mechanism, which
yields a 33% improvement on the 3D trajectory data and an increase of up to 10%
on SpartQA tasks over zero-shot prompting (with gains for other prompting types
as well). The experimentation with 3D trajectory data offers an intriguing
glimpse into the manner in which LLMs engage with numerical and spatial
information, thus laying a solid foundation for the identification of target
areas for future enhancements.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgments
Abstract: The social and implicit nature of human communication ramifies readers'
understandings of written sentences. Single gold-standard interpretations
rarely exist, challenging conventional assumptions in natural language
processing. This work introduces the interpretation modeling (IM) task which
involves modeling several interpretations of a sentence's underlying semantics
to unearth layers of implicit meaning. To obtain these, IM is guided by
multiple annotations of social relation and common ground - in this work
approximated by reader attitudes towards the author and their understanding of
moral judgments subtly embedded in the sentence. We propose a number of
modeling strategies that rely on one-to-one and one-to-many generation methods
that take inspiration from the philosophical study of interpretation. A
first-of-its-kind IM dataset is curated to support experiments and analyses.
The modeling results, coupled with scrutiny of the dataset, underline the
challenges of IM as conflicting and complex interpretations are socially
plausible. This interplay of diverse readings is affirmed by automated and
human evaluations on the generated interpretations. Finally, toxicity analyses
in the generated interpretations demonstrate the importance of IM for refining
filters of content and assisting content moderators in safeguarding the safety
in online discourse.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model
Abstract: Feedforward generalizable models for implicit shape reconstruction from
unoriented point cloud present multiple advantages, including high performance
and inference speed. However, they still suffer from generalization issues,
ranging from underfitting the input point cloud, to misrepresenting samples
outside of the training data distribution, or with toplogies unseen at
training. We propose here an efficient mechanism to remedy some of these
limitations at test time. We combine the inter-shape data prior of the network
with an intra-shape regularization prior of a Nystr\"om Kernel Ridge
Regression, that we further adapt by fitting its hyperprameters to the current
shape. The resulting shape function defined in a shape specific Reproducing
Kernel Hilbert Space benefits from desirable stability and efficiency
properties and grants a shape adaptive expressiveness-robustness trade-off. We
demonstrate the improvement obtained through our method with respect to
baselines and the state-of-the-art using synthetic and real data.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Generate and Pray: Using SALLMS to Evaluate the Security of LLM Generated Code
Abstract: With the growing popularity of Large Language Models (e.g. GitHub Copilot,
ChatGPT, etc.) in software engineers' daily practices, it is important to
ensure that the code generated by these tools is not only functionally correct
but also free of vulnerabilities. Although LLMs can help developers to be more
productive, prior empirical studies have shown that LLMs can generate insecure
code. There are two contributing factors to the insecure code generation.
First, existing datasets used to evaluate Large Language Models (LLMs) do not
adequately represent genuine software engineering tasks sensitive to security.
Instead, they are often based on competitive programming challenges or
classroom-type coding tasks. In real-world applications, the code produced is
integrated into larger codebases, introducing potential security risks. There's
a clear absence of benchmarks that focus on evaluating the security of the
generated code. Second, existing evaluation metrics primarily focus on the
functional correctness of the generated code while ignoring security
considerations. Metrics such as pass@k gauge the probability of obtaining the
correct code in the top k suggestions. Other popular metrics like BLEU,
CodeBLEU, ROUGE, and METEOR similarly emphasize functional accuracy, neglecting
security implications. In light of these research gaps, in this paper, we
described SALLM, a framework to benchmark LLMs' abilities to generate secure
code systematically. This framework has three major components: a novel dataset
of security-centric Python prompts, an evaluation environment to test the
generated code, and novel metrics to evaluate the models' performance from the
perspective of secure code generation.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses
Abstract: Large language model (LLM) powered chatbots are primarily text-based today,
and impose a large interactional cognitive load, especially for exploratory or
sensemaking tasks such as planning a trip or learning about a new city. Because
the interaction is textual, users have little scaffolding in the way of
structure, informational "scent", or ability to specify high-level preferences
or goals. We introduce ExploreLLM that allows users to structure thoughts, help
explore different options, navigate through the choices and recommendations,
and to more easily steer models to generate more personalized responses. We
conduct a user study and show that users find it helpful to use ExploreLLM for
exploratory or planning tasks, because it provides a useful schema-like
structure to the task, and guides users in planning. The study also suggests
that users can more easily personalize responses with high-level preferences
with ExploreLLM. Together, ExploreLLM points to a future where users interact
with LLMs beyond the form of chatbots, and instead designed to support complex
user tasks with a tighter integration between natural language and graphical
user interfaces.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Consistent Video-to-Video Transfer Using Synthetic Dataset
Abstract: We introduce a novel and efficient approach for text-based video-to-video
editing that eliminates the need for resource-intensive per-video-per-model
finetuning. At the core of our approach is a synthetic paired video dataset
tailored for video-to-video transfer tasks. Inspired by Instruct Pix2Pix's
image transfer via editing instruction, we adapt this paradigm to the video
domain. Extending the Prompt-to-Prompt to videos, we efficiently generate
paired samples, each with an input video and its edited counterpart. Alongside
this, we introduce the Long Video Sampling Correction during sampling, ensuring
consistent long videos across batches. Our method surpasses current methods
like Tune-A-Video, heralding substantial progress in text-based video-to-video
editing and suggesting exciting avenues for further exploration and deployment.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Deep Emotions Across Languages: A Novel Approach for Sentiment Propagation in Multilingual WordNets
Abstract: Sentiment analysis involves using WordNets enriched with emotional metadata,
which are valuable resources. However, manual annotation is time-consuming and
expensive, resulting in only a few WordNet Lexical Units being annotated. This
paper introduces two new techniques for automatically propagating sentiment
annotations from a partially annotated WordNet to its entirety and to a WordNet
in a different language: Multilingual Structured Synset Embeddings (MSSE) and
Cross-Lingual Deep Neural Sentiment Propagation (CLDNS). We evaluated the
proposed MSSE+CLDNS method extensively using Princeton WordNet and Polish
WordNet, which have many inter-lingual relations. Our results show that the
MSSE+CLDNS method outperforms existing propagation methods, indicating its
effectiveness in enriching WordNets with emotional metadata across multiple
languages. This work provides a solid foundation for large-scale, multilingual
sentiment analysis and is valuable for academic research and practical
applications.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: ATHENA: Mathematical Reasoning with Thought Expansion
Abstract: Solving math word problems depends on how to articulate the problems, the
lens through which models view human linguistic expressions. Real-world
settings count on such a method even more due to the diverse practices of the
same mathematical operations. Earlier works constrain available thinking
processes by limited prediction strategies without considering their
significance in acquiring mathematical knowledge. We introduce Attention-based
THought Expansion Network Architecture (ATHENA) to tackle the challenges of
real-world practices by mimicking human thought expansion mechanisms in the
form of neural network propagation. A thought expansion recurrently generates
the candidates carrying the thoughts of possible math expressions driven from
the previous step and yields reasonable thoughts by selecting the valid
pathways to the goal. Our experiments show that ATHENA achieves a new
state-of-the-art stage toward the ideal model that is compelling in variant
questions even when the informativeness in training examples is restricted.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Tracking Skiers from the Top to the Bottom
Abstract: Skiing is a popular winter sport discipline with a long history of
competitive events. In this domain, computer vision has the potential to
enhance the understanding of athletes' performance, but its application lags
behind other sports due to limited studies and datasets. This paper makes a
step forward in filling such gaps. A thorough investigation is performed on the
task of skier tracking in a video capturing his/her complete performance.
Obtaining continuous and accurate skier localization is preemptive for further
higher-level performance analyses. To enable the study, the largest and most
annotated dataset for computer vision in skiing, SkiTB, is introduced. Several
visual object tracking algorithms, including both established methodologies and
a newly introduced skier-optimized baseline algorithm, are tested using the
dataset. The results provide valuable insights into the applicability of
different tracking methods for vision-based skiing analysis. SkiTB, code, and
results are available at https://machinelearning.uniud.it/datasets/skitb.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Finetuning Offline World Models in the Real World
Abstract: Reinforcement Learning (RL) is notoriously data-inefficient, which makes
training on a real robot difficult. While model-based RL algorithms (world
models) improve data-efficiency to some extent, they still require hours or
days of interaction to learn skills. Recently, offline RL has been proposed as
a framework for training RL policies on pre-existing datasets without any
online interaction. However, constraining an algorithm to a fixed dataset
induces a state-action distribution shift between training and inference, and
limits its applicability to new tasks. In this work, we seek to get the best of
both worlds: we consider the problem of pretraining a world model with offline
data collected on a real robot, and then finetuning the model on online data
collected by planning with the learned model. To mitigate extrapolation errors
during online interaction, we propose to regularize the planner at test-time by
balancing estimated returns and (epistemic) model uncertainty. We evaluate our
method on a variety of visuo-motor control tasks in simulation and on a real
robot, and find that our method enables few-shot finetuning to seen and unseen
tasks even when offline data is limited. Videos, code, and data are available
at https://yunhaifeng.com/FOWM .

Output: Machine Learning