Instruction: What field is the article from?

Input:
Title: A Case for Competent AI Systems $-$ A Concept Note
Abstract: The efficiency of an AI system is contingent upon its ability to align with
the specified requirements of a given task. How-ever, the inherent complexity
of tasks often introduces the potential for harmful implications or adverse
actions. This note explores the critical concept of capability within AI
systems, representing what the system is expected to deliver. The articulation
of capability involves specifying well-defined out-comes. Yet, the achievement
of this capability may be hindered by deficiencies in implementation and
testing, reflecting a gap in the system's competency (what it can do vs. what
it does successfully).
  A central challenge arises in elucidating the competency of an AI system to
execute tasks effectively. The exploration of system competency in AI remains
in its early stages, occasionally manifesting as confidence intervals denoting
the probability of success. Trust in an AI system hinges on the explicit
modeling and detailed specification of its competency, connected intricately to
the system's capability. This note explores this gap by proposing a framework
for articulating the competency of AI systems.
  Motivated by practical scenarios such as the Glass Door problem, where an
individual inadvertently encounters a glass obstacle due to a failure in their
competency, this research underscores the imperative of delving into competency
dynamics. Bridging the gap between capability and competency at a detailed
level, this note contributes to advancing the discourse on bolstering the
reliability of AI systems in real-world applications.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Entropy Causal Graphs for Multivariate Time Series Anomaly Detection
Abstract: Many multivariate time series anomaly detection frameworks have been proposed
and widely applied. However, most of these frameworks do not consider intrinsic
relationships between variables in multivariate time series data, thus ignoring
the causal relationship among variables and degrading anomaly detection
performance. This work proposes a novel framework called CGAD, an entropy
Causal Graph for multivariate time series Anomaly Detection. CGAD utilizes
transfer entropy to construct graph structures that unveil the underlying
causal relationships among time series data. Weighted graph convolutional
networks combined with causal convolutions are employed to model both the
causal graph structures and the temporal patterns within multivariate time
series data. Furthermore, CGAD applies anomaly scoring, leveraging median
absolute deviation-based normalization to improve the robustness of the anomaly
identification process. Extensive experiments demonstrate that CGAD outperforms
state-of-the-art methods on real-world datasets with a 15% average improvement
based on three different multivariate time series anomaly detection metrics.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification
Abstract: Advancements in clinical treatment and research are limited by supervised
learning techniques that rely on large amounts of annotated data, an expensive
task requiring many hours of clinical specialists' time. In this paper, we
propose using self-supervised and semi-supervised learning. These techniques
perform an auxiliary task that is label-free, scaling up machine-supervision is
easier compared with fully-supervised techniques. This paper proposes S4MI
(Self-Supervision and Semi-Supervision for Medical Imaging), our pipeline to
leverage advances in self and semi-supervision learning. We benchmark them on
three medical imaging datasets to analyze their efficacy for classification and
segmentation. This advancement in self-supervised learning with 10% annotation
performed better than 100% annotation for the classification of most datasets.
The semi-supervised approach yielded favorable outcomes for segmentation,
outperforming the fully-supervised approach by using 50% fewer labels in all
three datasets.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model
Abstract: Recently, large language models (LLMs) have taken the spotlight in natural
language processing. Further, integrating LLMs with vision enables the users to
explore more emergent abilities in multimodality. Visual language models
(VLMs), such as LLaVA, Flamingo, or GPT-4, have demonstrated impressive
performance on various visio-linguistic tasks. Consequently, there are enormous
applications of large models that could be potentially used on social media
platforms. Despite that, there is a lack of related work on detecting or
correcting hateful memes with VLMs. In this work, we study the ability of VLMs
on hateful meme detection and hateful meme correction tasks with zero-shot
prompting. From our empirical experiments, we show the effectiveness of the
pretrained LLaVA model and discuss its strengths and weaknesses in these tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Diversity Enhanced Narrative Question Generation for Storybooks
Abstract: Question generation (QG) from a given context can enhance comprehension,
engagement, assessment, and overall efficacy in learning or conversational
environments. Despite recent advancements in QG, the challenge of enhancing or
measuring the diversity of generated questions often remains unaddressed. In
this paper, we introduce a multi-question generation model (mQG), which is
capable of generating multiple, diverse, and answerable questions by focusing
on context and questions. To validate the answerability of the generated
questions, we employ a SQuAD2.0 fine-tuned question answering model,
classifying the questions as answerable or not. We train and evaluate mQG on
the FairytaleQA dataset, a well-structured QA dataset based on storybooks, with
narrative questions. We further apply a zero-shot adaptation on the TellMeWhy
and SQuAD1.1 datasets. mQG shows promising results across various evaluation
metrics, among strong baselines.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Multi-Granularity Framework for Unsupervised Representation Learning of Time Series
Abstract: Representation learning plays a critical role in the analysis of time series
data and has high practical value across a wide range of applications.
including trend analysis, time series data retrieval and forecasting. In
practice, data confusion is a significant issue as it can considerably impact
the effectiveness and accuracy of data analysis, machine learning models and
decision-making processes. In general, previous studies did not consider the
variability at various levels of granularity, thus resulting in inadequate
information utilization, which further exacerbated the issue of data confusion.
This paper proposes an unsupervised framework to realize multi-granularity
representation learning for time series. Specifically, we employed a
cross-granularity transformer to develop an association between fine- and
coarse-grained representations. In addition, we introduced a retrieval task as
an unsupervised training task to learn the multi-granularity representation of
time series. Moreover, a novel loss function was designed to obtain the
comprehensive multi-granularity representation of the time series via
unsupervised learning. The experimental results revealed that the proposed
framework demonstrates significant advantages over alternative representation
learning models.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Bayes in the age of intelligent machines
Abstract: The success of methods based on artificial neural networks in creating
intelligent machines seems like it might pose a challenge to explanations of
human cognition in terms of Bayesian inference. We argue that this is not the
case, and that in fact these systems offer new opportunities for Bayesian
modeling. Specifically, we argue that Bayesian models of cognition and
artificial neural networks lie at different levels of analysis and are
complementary modeling approaches, together offering a way to understand human
cognition that spans these levels. We also argue that the same perspective can
be applied to intelligent machines, where a Bayesian approach may be uniquely
valuable in understanding the behavior of large, opaque artificial neural
networks that are trained on proprietary data.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Towards General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks
Abstract: The integration of deep learning systems into the medical domain has been
hindered by the resource-intensive process of data annotation and the inability
of these systems to generalize to different data distributions. Foundation
models, which are models pre-trained on large datasets, have emerged as a
solution to reduce reliance on annotated data and enhance model
generalizability and robustness. DINOv2, an open-source foundation model
pre-trained with self-supervised learning on 142 million curated natural
images, excels in extracting general-purpose visual representations, exhibiting
promising capabilities across various vision tasks. Nevertheless, a critical
question remains unanswered regarding DINOv2's adaptability to radiological
imaging, and the clarity on whether its features are sufficiently general to
benefit radiology image analysis is yet to be established. Therefore, this
study comprehensively evaluates DINOv2 for radiology, conducting over 100
experiments across diverse modalities (X-ray, CT, and MRI). Tasks include
disease classification and organ segmentation on both 2D and 3D images,
evaluated under different settings like kNN, few-shot learning, linear-probing,
end-to-end fine-tuning, and parameter-efficient fine-tuning, to measure the
effectiveness and generalizability of the DINOv2 feature embeddings.
Comparative analyses with established medical image analysis models, U-Net and
TransUnet for segmentation, and CNN and ViT models pre-trained via supervised,
weakly supervised, and self-supervised learning for classification, reveal
DINOv2's superior performance in segmentation tasks and competitive results in
disease classification. The findings contribute insights to potential avenues
for optimizing pre-training strategies for medical imaging and enhancing the
broader understanding of DINOv2's role in bridging the gap between natural and
radiological image analysis.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: AiluRus: A Scalable ViT Framework for Dense Prediction
Abstract: Vision transformers (ViTs) have emerged as a prevalent architecture for
vision tasks owing to their impressive performance. However, when it comes to
handling long token sequences, especially in dense prediction tasks that
require high-resolution input, the complexity of ViTs increases significantly.
Notably, dense prediction tasks, such as semantic segmentation or object
detection, emphasize more on the contours or shapes of objects, while the
texture inside objects is less informative. Motivated by this observation, we
propose to apply adaptive resolution for different regions in the image
according to their importance. Specifically, at the intermediate layer of the
ViT, we utilize a spatial-aware density-based clustering algorithm to select
representative tokens from the token sequence. Once the representative tokens
are determined, we proceed to merge other tokens into their closest
representative token. Consequently, semantic similar tokens are merged together
to form low-resolution regions, while semantic irrelevant tokens are preserved
independently as high-resolution regions. This strategy effectively reduces the
number of tokens, allowing subsequent layers to handle a reduced token sequence
and achieve acceleration. We evaluate our proposed method on three different
datasets and observe promising performance. For example, the "Segmenter ViT-L"
model can be accelerated by 48% FPS without fine-tuning, while maintaining the
performance. Additionally, our method can be applied to accelerate fine-tuning
as well. Experimental results demonstrate that we can save 52% training time
while accelerating 2.46 times FPS with only a 0.09% performance drop. The code
is available at https://github.com/caddyless/ailurus/tree/main.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: WebWISE: Web Interface Control and Sequential Exploration with Large Language Models
Abstract: The paper investigates using a Large Language Model (LLM) to automatically
perform web software tasks using click, scroll, and text input operations.
Previous approaches, such as reinforcement learning (RL) or imitation learning,
are inefficient to train and task-specific. Our method uses filtered Document
Object Model (DOM) elements as observations and performs tasks step-by-step,
sequentially generating small programs based on the current observations. We
use in-context learning, either benefiting from a single manually provided
example, or an automatically generated example based on a successful zero-shot
trial. We evaluate the proposed method on the MiniWob++ benchmark. With only
one in-context example, our WebWISE method achieves similar or better
performance than other methods that require many demonstrations or trials.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Neural Speech Embeddings for Speech Synthesis Based on Deep Generative Networks
Abstract: Brain-to-speech technology represents a fusion of interdisciplinary
applications encompassing fields of artificial intelligence, brain-computer
interfaces, and speech synthesis. Neural representation learning based
intention decoding and speech synthesis directly connects the neural activity
to the means of human linguistic communication, which may greatly enhance the
naturalness of communication. With the current discoveries on representation
learning and the development of the speech synthesis technologies, direct
translation of brain signals into speech has shown great promise. Especially,
the processed input features and neural speech embeddings which are given to
the neural network play a significant role in the overall performance when
using deep generative models for speech generation from brain signals. In this
paper, we introduce the current brain-to-speech technology with the possibility
of speech synthesis from brain signals, which may ultimately facilitate
innovation in non-verbal communication. Also, we perform comprehensive analysis
on the neural features and neural speech embeddings underlying the
neurophysiological activation while performing speech, which may play a
significant role in the speech synthesis works.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Can Large Language Models Understand Content and Propagation for Misinformation Detection: An Empirical Study
Abstract: Large Language Models (LLMs) have garnered significant attention for their
powerful ability in natural language understanding and reasoning. In this
paper, we present a comprehensive empirical study to explore the performance of
LLMs on misinformation detection tasks. This study stands as the pioneering
investigation into the understanding capabilities of multiple LLMs regarding
both content and propagation across social media platforms. Our empirical
studies on five misinformation detection datasets show that LLMs with diverse
prompts achieve comparable performance in text-based misinformation detection
but exhibit notably constrained capabilities in comprehending propagation
structure compared to existing models in propagation-based misinformation
detection. Besides, we further design four instruction-tuned strategies to
enhance LLMs for both content and propagation-based misinformation detection.
These strategies boost LLMs to actively learn effective features from multiple
instances or hard instances, and eliminate irrelevant propagation structures,
thereby achieving better detection performance. Extensive experiments further
demonstrate LLMs would play a better capacity in content and propagation
structure under these proposed strategies and achieve promising detection
performance. These findings highlight the potential ability of LLMs to detect
misinformation.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning
Abstract: While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention,
its algorithmic robustness against adversarial perturbations remains
unexplored. The attacks and robust representation training methods that are
designed for traditional RL become less effective when applied to GCRL. To
address this challenge, we first propose the Semi-Contrastive Representation
attack, a novel approach inspired by the adversarial contrastive attack. Unlike
existing attacks in RL, it only necessitates information from the policy
function and can be seamlessly implemented during deployment. Then, to mitigate
the vulnerability of existing GCRL algorithms, we introduce Adversarial
Representation Tactics, which combines Semi-Contrastive Adversarial
Augmentation with Sensitivity-Aware Regularizer to improve the adversarial
robustness of the underlying RL agent against various types of perturbations.
Extensive experiments validate the superior performance of our attack and
defence methods across multiple state-of-the-art GCRL algorithms. Our tool
ReRoGCRL is available at https://github.com/TrustAI/ReRoGCRL.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms
Abstract: Fairness in machine learning (ML) is an ever-growing field of research due to
the manifold potential for harm from algorithmic discrimination. To prevent
such harm, a large body of literature develops new approaches to quantify
fairness. Here, we investigate how one can divert the quantification of
fairness by describing a practice we call "fairness hacking" for the purpose of
shrouding unfairness in algorithms. This impacts end-users who rely on learning
algorithms, as well as the broader community interested in fair AI practices.
We introduce two different categories of fairness hacking in reference to the
established concept of p-hacking. The first category, intra-metric fairness
hacking, describes the misuse of a particular metric by adding or removing
sensitive attributes from the analysis. In this context, countermeasures that
have been developed to prevent or reduce p-hacking can be applied to similarly
prevent or reduce fairness hacking. The second category of fairness hacking is
inter-metric fairness hacking. Inter-metric fairness hacking is the search for
a specific fair metric with given attributes. We argue that countermeasures to
prevent or reduce inter-metric fairness hacking are still in their infancy.
Finally, we demonstrate both types of fairness hacking using real datasets. Our
paper intends to serve as a guidance for discussions within the fair ML
community to prevent or reduce the misuse of fairness metrics, and thus reduce
overall harm from ML applications.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Appearance-based gaze estimation enhanced with synthetic images using deep neural networks
Abstract: Human eye gaze estimation is an important cognitive ingredient for successful
human-robot interaction, enabling the robot to read and predict human behavior.
We approach this problem using artificial neural networks and build a modular
system estimating gaze from separately cropped eyes, taking advantage of
existing well-functioning components for face detection (RetinaFace) and head
pose estimation (6DRepNet). Our proposed method does not require any special
hardware or infrared filters but uses a standard notebook-builtin RGB camera,
as often approached with appearance-based methods. Using the MetaHuman tool, we
also generated a large synthetic dataset of more than 57,000 human faces and
made it publicly available. The inclusion of this dataset (with eye gaze and
head pose information) on top of the standard Columbia Gaze dataset into
training the model led to better accuracy with a mean average error below two
degrees in eye pitch and yaw directions, which compares favourably to related
methods. We also verified the feasibility of our model by its preliminary
testing in real-world setting using the builtin 4K camera in NICO semi-humanoid
robot's eye.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Machine Learning Algorithms to Predict Chess960 Result and Develop Opening Themes
Abstract: This work focuses on the analysis of Chess 960, also known as Fischer Random
Chess, a variant of traditional chess where the starting positions of the
pieces are randomized. The study aims to predict the game outcome using machine
learning techniques and develop an opening theme for each starting position.
The first part of the analysis utilizes machine learning models to predict the
game result based on certain moves in each position. The methodology involves
segregating raw data from .pgn files into usable formats and creating datasets
comprising approximately 500 games for each starting position. Three machine
learning algorithms -- KNN Clustering, Random Forest, and Gradient Boosted
Trees -- have been used to predict the game outcome. To establish an opening
theme, the board is divided into five regions: center, white kingside, white
queenside, black kingside, and black queenside. The data from games played by
top engines in all 960 positions is used to track the movement of pieces in the
opening. By analysing the change in the number of pieces in each region at
specific moves, the report predicts the region towards which the game is
developing. These models provide valuable insights into predicting game
outcomes and understanding the opening theme in Chess 960.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Mask Propagation for Efficient Video Semantic Segmentation
Abstract: Video Semantic Segmentation (VSS) involves assigning a semantic label to each
pixel in a video sequence. Prior work in this field has demonstrated promising
results by extending image semantic segmentation models to exploit temporal
relationships across video frames; however, these approaches often incur
significant computational costs. In this paper, we propose an efficient mask
propagation framework for VSS, called MPVSS. Our approach first employs a
strong query-based image segmentor on sparse key frames to generate accurate
binary masks and class predictions. We then design a flow estimation module
utilizing the learned queries to generate a set of segment-aware flow maps,
each associated with a mask prediction from the key frame. Finally, the
mask-flow pairs are warped to serve as the mask predictions for the non-key
frames. By reusing predictions from key frames, we circumvent the need to
process a large volume of video frames individually with resource-intensive
segmentors, alleviating temporal redundancy and significantly reducing
computational costs. Extensive experiments on VSPW and Cityscapes demonstrate
that our mask propagation framework achieves SOTA accuracy and efficiency
trade-offs. For instance, our best model with Swin-L backbone outperforms the
SOTA MRCFA using MiT-B5 by 4.0% mIoU, requiring only 26% FLOPs on the VSPW
dataset. Moreover, our framework reduces up to 4x FLOPs compared to the
per-frame Mask2Former baseline with only up to 2% mIoU degradation on the
Cityscapes validation set. Code is available at
https://github.com/ziplab/MPVSS.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model
Abstract: Using reinforcement learning with human feedback (RLHF) has shown significant
promise in fine-tuning diffusion models. Previous methods start by training a
reward model that aligns with human preferences, then leverage RL techniques to
fine-tune the underlying models. However, crafting an efficient reward model
demands extensive datasets, optimal architecture, and manual hyperparameter
tuning, making the process both time and cost-intensive. The direct preference
optimization (DPO) method, effective in fine-tuning large language models,
eliminates the necessity for a reward model. However, the extensive GPU memory
requirement of the diffusion model's denoising process hinders the direct
application of the DPO method. To address this issue, we introduce the Direct
Preference for Denoising Diffusion Policy Optimization (D3PO) method to
directly fine-tune diffusion models. The theoretical analysis demonstrates that
although D3PO omits training a reward model, it effectively functions as the
optimal reward model trained using human feedback data to guide the learning
process. This approach requires no training of a reward model, proving to be
more direct, cost-effective, and minimizing computational overhead. In
experiments, our method uses the relative scale of objectives as a proxy for
human preference, delivering comparable results to methods using ground-truth
rewards. Moreover, D3PO demonstrates the ability to reduce image distortion
rates and generate safer images, overcoming challenges lacking robust reward
models. Our code is publicly available in
https://github.com/yk7333/D3PO/tree/main.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: ZEETAD: Adapting Pretrained Vision-Language Model for Zero-Shot End-to-End Temporal Action Detection
Abstract: Temporal action detection (TAD) involves the localization and classification
of action instances within untrimmed videos. While standard TAD follows fully
supervised learning with closed-set setting on large training data, recent
zero-shot TAD methods showcase the promising open-set setting by leveraging
large-scale contrastive visual-language (ViL) pretrained models. However,
existing zero-shot TAD methods have limitations on how to properly construct
the strong relationship between two interdependent tasks of localization and
classification and adapt ViL model to video understanding. In this work, we
present ZEETAD, featuring two modules: dual-localization and zero-shot proposal
classification. The former is a Transformer-based module that detects action
events while selectively collecting crucial semantic embeddings for later
recognition. The latter one, CLIP-based module, generates semantic embeddings
from text and frame inputs for each temporal unit. Additionally, we enhance
discriminative capability on unseen classes by minimally updating the frozen
CLIP encoder with lightweight adapters. Extensive experiments on THUMOS14 and
ActivityNet-1.3 datasets demonstrate our approach's superior performance in
zero-shot TAD and effective knowledge transfer from ViL models to unseen action
categories.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Benchmarking and Analysis of Unsupervised Object Segmentation from Real-world Single Images
Abstract: In this paper, we study the problem of unsupervised object segmentation from
single images. We do not introduce a new algorithm, but systematically
investigate the effectiveness of existing unsupervised models on challenging
real-world images. We first introduce seven complexity factors to
quantitatively measure the distributions of background and foreground object
biases in appearance and geometry for datasets with human annotations. With the
aid of these factors, we empirically find that, not surprisingly, existing
unsupervised models fail to segment generic objects in real-world images,
although they can easily achieve excellent performance on numerous simple
synthetic datasets, due to the vast gap in objectness biases between synthetic
and real images. By conducting extensive experiments on multiple groups of
ablated real-world datasets, we ultimately find that the key factors underlying
the failure of existing unsupervised models on real-world images are the
challenging distributions of background and foreground object biases in
appearance and geometry. Because of this, the inductive biases introduced in
existing unsupervised models can hardly capture the diverse object
distributions. Our research results suggest that future work should exploit
more explicit objectness biases in the network design.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: SugarViT -- Multi-objective Regression of UAV Images with Vision Transformers and Deep Label Distribution Learning Demonstrated on Disease Severity Prediction in Sugar Beet
Abstract: Remote sensing and artificial intelligence are pivotal technologies of
precision agriculture nowadays. The efficient retrieval of large-scale field
imagery combined with machine learning techniques shows success in various
tasks like phenotyping, weeding, cropping, and disease control. This work will
introduce a machine learning framework for automatized large-scale
plant-specific trait annotation for the use case disease severity scoring for
Cercospora Leaf Spot (CLS) in sugar beet. With concepts of Deep Label
Distribution Learning (DLDL), special loss functions, and a tailored model
architecture, we develop an efficient Vision Transformer based model for
disease severity scoring called SugarViT. One novelty in this work is the
combination of remote sensing data with environmental parameters of the
experimental sites for disease severity prediction. Although the model is
evaluated on this special use case, it is held as generic as possible to also
be applicable to various image-based classification and regression tasks. With
our framework, it is even possible to learn models on multi-objective problems
as we show by a pretraining on environmental metadata.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Red AI? Inconsistent Responses from GPT3.5 Models on Political Issues in the US and China
Abstract: The rising popularity of ChatGPT and other AI-powered large language models
(LLMs) has led to increasing studies highlighting their susceptibility to
mistakes and biases. However, most of these studies focus on models trained on
English texts. Taking an innovative approach, this study investigates political
biases in GPT's multilingual models. We posed the same question about
high-profile political issues in the United States and China to GPT in both
English and simplified Chinese, and our analysis of the bilingual responses
revealed that GPT's bilingual models' political "knowledge" (content) and the
political "attitude" (sentiment) are significantly more inconsistent on
political issues in China. The simplified Chinese GPT models not only tended to
provide pro-China information but also presented the least negative sentiment
towards China's problems, whereas the English GPT was significantly more
negative towards China. This disparity may stem from Chinese state censorship
and US-China geopolitical tensions, which influence the training corpora of GPT
bilingual models. Moreover, both Chinese and English models tended to be less
critical towards the issues of "their own" represented by the language used,
than the issues of "the other." This suggests that GPT multilingual models
could potentially develop a "political identity" and an associated sentiment
bias based on their training language. We discussed the implications of our
findings for information transmission and communication in an increasingly
divided world.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Beyond Still Images: Robust Multi-Stream Spatiotemporal Networks
Abstract: A defining characteristic of natural vision is its ability to withstand a
variety of input alterations, resulting in the creation of an invariant
representation of the surroundings. While convolutional neural networks exhibit
resilience to certain forms of spatial input variation, modifications in the
spatial and temporal aspects can significantly affect the representations of
video content in deep neural networks. Inspired by the resilience of natural
vision to input variations, we employ a simple multi-stream model to explore
its potential to address spatiotemporal changes by including temporal features.
Our primary goal is to introduce a video-trained model and evaluate its
robustness to diverse image and video inputs, with a particular focus on
exploring the role of temporal features in invariant recognition. Results show
that including videos and the temporal stream during training mitigates the
decline in accuracy and mAP in image and video understanding tasks by 1.36% and
3.14%, respectively.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Replay-enhanced Continual Reinforcement Learning
Abstract: Replaying past experiences has proven to be a highly effective approach for
averting catastrophic forgetting in supervised continual learning. However,
some crucial factors are still largely ignored, making it vulnerable to serious
failure, when used as a solution to forgetting in continual reinforcement
learning, even in the context of perfect memory where all data of previous
tasks are accessible in the current task. On the one hand, since most
reinforcement learning algorithms are not invariant to the reward scale, the
previously well-learned tasks (with high rewards) may appear to be more salient
to the current learning process than the current task (with small initial
rewards). This causes the agent to concentrate on those salient tasks at the
expense of generality on the current task. On the other hand, offline learning
on replayed tasks while learning a new task may induce a distributional shift
between the dataset and the learned policy on old tasks, resulting in
forgetting. In this paper, we introduce RECALL, a replay-enhanced method that
greatly improves the plasticity of existing replay-based methods on new tasks
while effectively avoiding the recurrence of catastrophic forgetting in
continual reinforcement learning. RECALL leverages adaptive normalization on
approximate targets and policy distillation on old tasks to enhance generality
and stability, respectively. Extensive experiments on the Continual World
benchmark show that RECALL performs significantly better than purely perfect
memory replay, and achieves comparable or better overall performance against
state-of-the-art continual learning methods.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: SAME: Sample Reconstruction Against Model Extraction Attacks
Abstract: While deep learning models have shown significant performance across various
domains, their deployment needs extensive resources and advanced computing
infrastructure. As a solution, Machine Learning as a Service (MLaaS) has
emerged, lowering the barriers for users to release or productize their deep
learning models. However, previous studies have highlighted potential privacy
and security concerns associated with MLaaS, and one primary threat is model
extraction attacks. To address this, there are many defense solutions but they
suffer from unrealistic assumptions and generalization issues, making them less
practical for reliable protection. Driven by these limitations, we introduce a
novel defense mechanism, SAME, based on the concept of sample reconstruction.
This strategy imposes minimal prerequisites on the defender's capabilities,
eliminating the need for auxiliary Out-of-Distribution (OOD) datasets, user
query history, white-box model access, and additional intervention during model
training. It is compatible with existing active defense methods. Our extensive
experiments corroborate the superior efficacy of SAME over state-of-the-art
solutions. Our code is available at https://github.com/xythink/SAME.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Towards model-free RL algorithms that scale well with unstructured data
Abstract: Conventional reinforcement learning (RL) algorithms exhibit broad generality
in their theoretical formulation and high performance on several challenging
domains when combined with powerful function approximation. However, developing
RL algorithms that perform well across problems with unstructured observations
at scale remains challenging because most function approximation methods rely
on externally provisioned knowledge about the structure of the input for good
performance (e.g. convolutional networks, graph neural networks, tile-coding).
A common practice in RL is to evaluate algorithms on a single problem, or on
problems with limited variation in the observation scale. RL practitioners lack
a systematic way to study how well a single RL algorithm performs when
instantiated across a range of problem scales, and they lack function
approximation techniques that scale well with unstructured observations.
  We address these limitations by providing environments and algorithms to
study scaling for unstructured observation vectors and flat action spaces. We
introduce a family of combinatorial RL problems with an exponentially large
state space and high-dimensional dynamics but where linear computation is
sufficient to learn a (nonlinear) value function estimate for performant
control. We provide an algorithm that constructs reward-relevant general value
function (GVF) questions to find and exploit predictive structure directly from
the experience stream. In an empirical evaluation of the approach on synthetic
problems, we observe a sample complexity that scales linearly with the
observation size. The proposed algorithm reliably outperforms a conventional
deep RL algorithm on these scaling problems, and they exhibit several desirable
auxiliary properties. These results suggest new algorithmic mechanisms by which
algorithms can learn at scale from unstructured data.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Aligned: A Platform-based Process for Alignment
Abstract: We are introducing Aligned, a platform for global governance and alignment of
frontier models, and eventually superintelligence. While previous efforts at
the major AI labs have attempted to gather inputs for alignment, these are
often conducted behind closed doors. We aim to set the foundation for a more
trustworthy, public-facing approach to safety: a constitutional committee
framework. Initial tests with 680 participants result in a 30-guideline
constitution with 93% overall support. We show the platform naturally scales,
instilling confidence and enjoyment from the community. We invite other AI labs
and teams to plug and play into the Aligned ecosystem.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: SODA: Bottleneck Diffusion Models for Representation Learning
Abstract: We introduce SODA, a self-supervised diffusion model, designed for
representation learning. The model incorporates an image encoder, which
distills a source view into a compact representation, that, in turn, guides the
generation of related novel views. We show that by imposing a tight bottleneck
between the encoder and a denoising decoder, and leveraging novel view
synthesis as a self-supervised objective, we can turn diffusion models into
strong representation learners, capable of capturing visual semantics in an
unsupervised manner. To the best of our knowledge, SODA is the first diffusion
model to succeed at ImageNet linear-probe classification, and, at the same
time, it accomplishes reconstruction, editing and synthesis tasks across a wide
range of datasets. Further investigation reveals the disentangled nature of its
emergent latent space, that serves as an effective interface to control and
manipulate the model's produced images. All in all, we aim to shed light on the
exciting and promising potential of diffusion models, not only for image
generation, but also for learning rich and robust representations.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers
Abstract: The launch of ChatGPT has garnered global attention, marking a significant
milestone in the field of Generative Artificial Intelligence. While Generative
AI has been in effect for the past decade, the introduction of ChatGPT has
ignited a new wave of research and innovation in the AI domain. This surge in
interest has led to the development and release of numerous cutting-edge tools,
such as Bard, Stable Diffusion, DALL-E, Make-A-Video, Runway ML, and Jukebox,
among others. These tools exhibit remarkable capabilities, encompassing tasks
ranging from text generation and music composition, image creation, video
production, code generation, and even scientific work. They are built upon
various state-of-the-art models, including Stable Diffusion, transformer models
like GPT-3 (recent GPT-4), variational autoencoders, and generative adversarial
networks. This advancement in Generative AI presents a wealth of exciting
opportunities and, simultaneously, unprecedented challenges. Throughout this
paper, we have explored these state-of-the-art models, the diverse array of
tasks they can accomplish, the challenges they pose, and the promising future
of Generative Artificial Intelligence.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science
Abstract: Large language models record impressive performance on many natural language
processing tasks. However, their knowledge capacity is limited to the
pretraining corpus. Retrieval augmentation offers an effective solution by
retrieving context from external knowledge sources to complement the language
model. However, existing retrieval augmentation techniques ignore the
structural relationships between these documents. Furthermore, retrieval models
are not explored much in scientific tasks, especially in regard to the
faithfulness of retrieved documents. In this paper, we propose a novel
structure-aware retrieval augmented language model that accommodates document
structure during retrieval augmentation. We create a heterogeneous document
graph capturing multiple types of relationships (e.g., citation, co-authorship,
etc.) that connect documents from more than 15 scientific disciplines (e.g.,
Physics, Medicine, Chemistry, etc.). We train a graph neural network on the
curated document graph to act as a structural encoder for the corresponding
passages retrieved during the model pretraining. Particularly, along with text
embeddings of the retrieved passages, we obtain structural embeddings of the
documents (passages) and fuse them together before feeding them to the language
model. We evaluate our model extensively on various scientific benchmarks that
include science question-answering and scientific document classification
tasks. Experimental results demonstrate that structure-aware retrieval improves
retrieving more coherent, faithful and contextually relevant passages, while
showing a comparable performance in the overall accuracy.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: PCoQA: Persian Conversational Question Answering Dataset
Abstract: Humans seek information regarding a specific topic through performing a
conversation containing a series of questions and answers. In the pursuit of
conversational question answering research, we introduce the PCoQA, the first
\textbf{P}ersian \textbf{Co}nversational \textbf{Q}uestion \textbf{A}nswering
dataset, a resource comprising information-seeking dialogs encompassing a total
of 9,026 contextually-driven questions. Each dialog involves a questioner, a
responder, and a document from the Wikipedia; The questioner asks several
inter-connected questions from the text and the responder provides a span of
the document as the answer for each question. PCoQA is designed to present
novel challenges compared to previous question answering datasets including
having more open-ended non-factual answers, longer answers, and fewer lexical
overlaps. This paper not only presents the comprehensive PCoQA dataset but also
reports the performance of various benchmark models. Our models include
baseline models and pre-trained models, which are leveraged to boost the
performance of the model. The dataset and benchmarks are available at our
Github page.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Proxy-based Item Representation for Attribute and Context-aware Recommendation
Abstract: Neural network approaches in recommender systems have shown remarkable
success by representing a large set of items as a learnable vector embedding
table. However, infrequent items may suffer from inadequate training
opportunities, making it difficult to learn meaningful representations. We
examine that in attribute and context-aware settings, the poorly learned
embeddings of infrequent items impair the recommendation accuracy. To address
such an issue, we propose a proxy-based item representation that allows each
item to be expressed as a weighted sum of learnable proxy embeddings. Here, the
proxy weight is determined by the attributes and context of each item and may
incorporate bias terms in case of frequent items to further reflect
collaborative signals. The proxy-based method calculates the item
representations compositionally, ensuring each representation resides inside a
well-trained simplex and, thus, acquires guaranteed quality. Additionally, that
the proxy embeddings are shared across all items allows the infrequent items to
borrow training signals of frequent items in a unified model structure and
end-to-end manner. Our proposed method is a plug-and-play model that can
replace the item encoding layer of any neural network-based recommendation
model, while consistently improving the recommendation performance with much
smaller parameter usage. Experiments conducted on real-world recommendation
benchmark datasets demonstrate that our proposed model outperforms
state-of-the-art models in terms of recommendation accuracy by up to 17% while
using only 10% of the parameters.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews
Abstract: Obtaining stakeholders' diverse experiences and opinions about current policy
in a timely manner is crucial for policymakers to identify strengths and gaps
in resource allocation, thereby supporting effective policy design and
implementation. However, manually coding even moderately sized interview texts
or open-ended survey responses from stakeholders can often be labor-intensive
and time-consuming. This study explores the integration of Large Language
Models (LLMs)--like GPT-4--with human expertise to enhance text analysis of
stakeholder interviews regarding K-12 education policy within one U.S. state.
Employing a mixed-methods approach, human experts developed a codebook and
coding processes as informed by domain knowledge and unsupervised topic
modeling results. They then designed prompts to guide GPT-4 analysis and
iteratively evaluate different prompts' performances. This combined
human-computer method enabled nuanced thematic and sentiment analysis. Results
reveal that while GPT-4 thematic coding aligned with human coding by 77.89% at
specific themes, expanding to broader themes increased congruence to 96.02%,
surpassing traditional Natural Language Processing (NLP) methods by over 25%.
Additionally, GPT-4 is more closely matched to expert sentiment analysis than
lexicon-based methods. Findings from quantitative measures and qualitative
reviews underscore the complementary roles of human domain expertise and
automated analysis as LLMs offer new perspectives and coding consistency. The
human-computer interactive approach enhances efficiency, validity, and
interpretability of educational policy research.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: OneLLM: One Framework to Align All Modalities with Language
Abstract: Multimodal large language models (MLLMs) have gained significant attention
due to their strong multimodal understanding capability. However, existing
works rely heavily on modality-specific encoders, which usually differ in
architecture and are limited to common modalities. In this paper, we present
OneLLM, an MLLM that aligns eight modalities to language using a unified
framework. We achieve this through a unified multimodal encoder and a
progressive multimodal alignment pipeline. In detail, we first train an image
projection module to connect a vision encoder with LLM. Then, we build a
universal projection module (UPM) by mixing multiple image projection modules
and dynamic routing. Finally, we progressively align more modalities to LLM
with the UPM. To fully leverage the potential of OneLLM in following
instructions, we also curated a comprehensive multimodal instruction dataset,
including 2M items from image, audio, video, point cloud, depth/normal map, IMU
and fMRI brain activity. OneLLM is evaluated on 25 diverse benchmarks,
encompassing tasks such as multimodal captioning, question answering and
reasoning, where it delivers excellent performance. Code, data, model and
online demo are available at https://github.com/csuhan/OneLLM

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Topology-aware Debiased Self-supervised Graph Learning for Recommendation
Abstract: In recommendation, graph-based Collaborative Filtering (CF) methods mitigate
the data sparsity by introducing Graph Contrastive Learning (GCL). However, the
random negative sampling strategy in these GCL-based CF models neglects the
semantic structure of users (items), which not only introduces false negatives
(negatives that are similar to anchor user (item)) but also ignores the
potential positive samples. To tackle the above issues, we propose
Topology-aware Debiased Self-supervised Graph Learning (TDSGL) for
recommendation, which constructs contrastive pairs according to the semantic
similarity between users (items). Specifically, since the original user-item
interaction data commendably reflects the purchasing intent of users and
certain characteristics of items, we calculate the semantic similarity between
users (items) on interaction data. Then, given a user (item), we construct its
negative pairs by selecting users (items) which embed different semantic
structures to ensure the semantic difference between the given user (item) and
its negatives. Moreover, for a user (item), we design a feature extraction
module that converts other semantically similar users (items) into an auxiliary
positive sample to acquire a more informative representation. Experimental
results show that the proposed model outperforms the state-of-the-art models
significantly on three public datasets. Our model implementation codes are
available at https://github.com/malajikuai/TDSGL.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records
Abstract: This study explored the usability of prompt generation on named entity
recognition (NER) tasks and the performance in different settings of the
prompt. The prompt generation by GPT-J models was utilized to directly test the
gold standard as well as to generate the seed and further fed to the RoBERTa
model with the spaCy package. In the direct test, a lower ratio of negative
examples with higher numbers of examples in prompt achieved the best results
with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the
F1 score, in all settings after training with the RoBERTa model. The study
highlighted the importance of seed quality rather than quantity in feeding NER
models. This research reports on an efficient and accurate way to mine clinical
notes for periodontal diagnoses, allowing researchers to easily and quickly
build a NER model with the prompt generation approach.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: PowerFlowNet: Leveraging Message Passing GNNs for Improved Power Flow Approximation
Abstract: Accurate and efficient power flow (PF) analysis is crucial in modern
electrical networks' efficient operation and planning. Therefore, there is a
need for scalable algorithms capable of handling large-scale power networks
that can provide accurate and fast solutions. Graph Neural Networks (GNNs) have
emerged as a promising approach for enhancing the speed of PF approximations by
leveraging their ability to capture distinctive features from the underlying
power network graph. In this study, we introduce PowerFlowNet, a novel GNN
architecture for PF approximation that showcases similar performance with the
traditional Newton-Raphson method but achieves it 4 times faster in the simple
IEEE 14-bus system and 145 times faster in the realistic case of the French
high voltage network (6470rte). Meanwhile, it significantly outperforms other
traditional approximation methods, such as the DC relaxation method, in terms
of performance and execution time; therefore, making PowerFlowNet a highly
promising solution for real-world PF analysis. Furthermore, we verify the
efficacy of our approach by conducting an in-depth experimental evaluation,
thoroughly examining the performance, scalability, interpretability, and
architectural dependability of PowerFlowNet. The evaluation provides insights
into the behavior and potential applications of GNNs in power system analysis.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Slot-Mixup with Subsampling: A Simple Regularization for WSI Classification
Abstract: Whole slide image (WSI) classification requires repetitive zoom-in and out
for pathologists, as only small portions of the slide may be relevant to
detecting cancer. Due to the lack of patch-level labels, multiple instance
learning (MIL) is a common practice for training a WSI classifier. One of the
challenges in MIL for WSIs is the weak supervision coming only from the
slide-level labels, often resulting in severe overfitting. In response,
researchers have considered adopting patch-level augmentation or applying mixup
augmentation, but their applicability remains unverified. Our approach augments
the training dataset by sampling a subset of patches in the WSI without
significantly altering the underlying semantics of the original slides.
Additionally, we introduce an efficient model (Slot-MIL) that organizes patches
into a fixed number of slots, the abstract representation of patches, using an
attention mechanism. We empirically demonstrate that the subsampling
augmentation helps to make more informative slots by restricting the
over-concentration of attention and to improve interpretability. Finally, we
illustrate that combining our attention-based aggregation model with
subsampling and mixup, which has shown limited compatibility in existing MIL
methods, can enhance both generalization and calibration. Our proposed methods
achieve the state-of-the-art performance across various benchmark datasets
including class imbalance and distribution shifts.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Cooperation Does Matter: Exploring Multi-Order Bilateral Relations for Audio-Visual Segmentation
Abstract: Recently, an audio-visual segmentation (AVS) task has been introduced, aiming
to group pixels with sounding objects within a given video. This task
necessitates a first-ever audio-driven pixel-level understanding of the scene,
posing significant challenges. In this paper, we propose an innovative
audio-visual transformer framework, termed COMBO, an acronym for COoperation of
Multi-order Bilateral relatiOns. For the first time, our framework explores
three types of bilateral entanglements within AVS: pixel entanglement, modality
entanglement, and temporal entanglement. Regarding pixel entanglement, we
employ a Siam-Encoder Module (SEM) that leverages prior knowledge to generate
more precise visual features from the foundational model. For modality
entanglement, we design a Bilateral-Fusion Module (BFM), enabling COMBO to
align corresponding visual and auditory signals bi-directionally. As for
temporal entanglement, we introduce an innovative adaptive inter-frame
consistency loss according to the inherent rules of temporal. Comprehensive
experiments and ablation studies on AVSBench-object (84.7 mIoU on S4, 59.2 mIou
on MS3) and AVSBench-semantic (42.1 mIoU on AVSS) datasets demonstrate that
COMBO surpasses previous state-of-the-art methods. Code and more results will
be publicly available at https://combo-avs.github.io/.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: IASCAR: Incremental Answer Set Counting by Anytime Refinement
Abstract: Answer set programming (ASP) is a popular declarative programming paradigm
with various applications. Programs can easily have many answer sets that
cannot be enumerated in practice, but counting still allows quantifying
solution spaces. If one counts under assumptions on literals, one obtains a
tool to comprehend parts of the solution space, so-called answer set
navigation. However, navigating through parts of the solution space requires
counting many times, which is expensive in theory. Knowledge compilation
compiles instances into representations on which counting works in polynomial
time. However, these techniques exist only for CNF formulas, and compiling ASP
programs into CNF formulas can introduce an exponential overhead. This paper
introduces a technique to iteratively count answer sets under assumptions on
knowledge compilations of CNFs that encode supported models. Our anytime
technique uses the inclusion-exclusion principle to improve bounds by over- and
undercounting systematically. In a preliminary empirical analysis, we
demonstrate promising results. After compiling the input (offline phase), our
approach quickly (re)counts.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: FreePIH: Training-Free Painterly Image Harmonization with Diffusion Model
Abstract: This paper provides an efficient training-free painterly image harmonization
(PIH) method, dubbed FreePIH, that leverages only a pre-trained diffusion model
to achieve state-of-the-art harmonization results. Unlike existing methods that
require either training auxiliary networks or fine-tuning a large pre-trained
backbone, or both, to harmonize a foreground object with a painterly-style
background image, our FreePIH tames the denoising process as a plug-in module
for foreground image style transfer. Specifically, we find that the very last
few steps of the denoising (i.e., generation) process strongly correspond to
the stylistic information of images, and based on this, we propose to augment
the latent features of both the foreground and background images with Gaussians
for a direct denoising-based harmonization. To guarantee the fidelity of the
harmonized image, we make use of multi-scale features to enforce the
consistency of the content and stability of the foreground objects in the
latent space, and meanwhile, aligning both fore-/back-grounds with the same
style. Moreover, to accommodate the generation with more structural and
textural details, we further integrate text prompts to attend to the latent
features, hence improving the generation quality. Quantitative and qualitative
evaluations on COCO and LAION 5B datasets demonstrate that our method can
surpass representative baselines by large margins.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Prompting Large Language Models for Topic Modeling
Abstract: Topic modeling is a widely used technique for revealing underlying thematic
structures within textual data. However, existing models have certain
limitations, particularly when dealing with short text datasets that lack
co-occurring words. Moreover, these models often neglect sentence-level
semantics, focusing primarily on token-level semantics. In this paper, we
propose PromptTopic, a novel topic modeling approach that harnesses the
advanced language understanding of large language models (LLMs) to address
these challenges. It involves extracting topics at the sentence level from
individual documents, then aggregating and condensing these topics into a
predefined quantity, ultimately providing coherent topics for texts of varying
lengths. This approach eliminates the need for manual parameter tuning and
improves the quality of extracted topics. We benchmark PromptTopic against the
state-of-the-art baselines on three vastly diverse datasets, establishing its
proficiency in discovering meaningful topics. Furthermore, qualitative analysis
showcases PromptTopic's ability to uncover relevant topics in multiple
datasets.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Open Set Dandelion Network for IoT Intrusion Detection
Abstract: As IoT devices become widely, it is crucial to protect them from malicious
intrusions. However, the data scarcity of IoT limits the applicability of
traditional intrusion detection methods, which are highly data-dependent. To
address this, in this paper we propose the Open-Set Dandelion Network (OSDN)
based on unsupervised heterogeneous domain adaptation in an open-set manner.
The OSDN model performs intrusion knowledge transfer from the knowledge-rich
source network intrusion domain to facilitate more accurate intrusion detection
for the data-scarce target IoT intrusion domain. Under the open-set setting, it
can also detect newly-emerged target domain intrusions that are not observed in
the source domain. To achieve this, the OSDN model forms the source domain into
a dandelion-like feature space in which each intrusion category is compactly
grouped and different intrusion categories are separated, i.e., simultaneously
emphasising inter-category separability and intra-category compactness. The
dandelion-based target membership mechanism then forms the target dandelion.
Then, the dandelion angular separation mechanism achieves better inter-category
separability, and the dandelion embedding alignment mechanism further aligns
both dandelions in a finer manner. To promote intra-category compactness, the
discriminating sampled dandelion mechanism is used. Assisted by the intrusion
classifier trained using both known and generated unknown intrusion knowledge,
a semantic dandelion correction mechanism emphasises easily-confused categories
and guides better inter-category separability. Holistically, these mechanisms
form the OSDN model that effectively performs intrusion knowledge transfer to
benefit IoT intrusion detection. Comprehensive experiments on several intrusion
datasets verify the effectiveness of the OSDN model, outperforming three
state-of-the-art baseline methods by 16.9%.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance
Abstract: Multi-Robot System (MRS) has garnered widespread research interest and
fostered tremendous interesting applications, especially in cooperative control
fields. Yet little light has been shed on the compound ability of formation,
monitoring and defence in decentralized large-scale MRS for pursuit avoidance,
which puts stringent requirements on the capability of coordination and
adaptability. In this paper, we put forward a decentralized Imitation learning
based Alternative Multi-Agent Proximal Policy Optimization (IA-MAPPO) algorithm
to provide a flexible and communication-economic solution to execute the
pursuit avoidance task in well-formed swarm. In particular, a
policy-distillation based MAPPO executor is firstly devised to capably
accomplish and swiftly switch between multiple formations in a centralized
manner. Furthermore, we utilize imitation learning to decentralize the
formation controller, so as to reduce the communication overheads and enhance
the scalability. Afterwards, alternative training is leveraged to compensate
the performance loss incurred by decentralization. The simulation results
validate the effectiveness of IA-MAPPO and extensive ablation experiments
further show the performance comparable to a centralized solution with
significant decrease in communication overheads.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences
Abstract: Occlusions between consecutive frames have long posed a significant challenge
in optical flow estimation. The inherent ambiguity introduced by occlusions
directly violates the brightness constancy constraint and considerably hinders
pixel-to-pixel matching. To address this issue, multi-frame optical flow
methods leverage adjacent frames to mitigate the local ambiguity. Nevertheless,
prior multi-frame methods predominantly adopt recursive flow estimation,
resulting in a considerable computational overlap. In contrast, we propose a
streamlined in-batch framework that eliminates the need for extensive redundant
recursive computations while concurrently developing effective spatio-temporal
modeling approaches under in-batch estimation constraints. Specifically, we
present a Streamlined In-batch Multi-frame (SIM) pipeline tailored to video
input, attaining a similar level of time efficiency to two-frame networks.
Furthermore, we introduce an efficient Integrative Spatio-temporal Coherence
(ISC) modeling method for effective spatio-temporal modeling during the
encoding phase, which introduces no additional parameter overhead.
Additionally, we devise a Global Temporal Regressor (GTR) that effectively
explores temporal relations during decoding. Benefiting from the efficient SIM
pipeline and effective modules, StreamFlow not only excels in terms of
performance on the challenging KITTI and Sintel datasets, with particular
improvement in occluded areas but also attains a remarkable $63.82\%$
enhancement in speed compared with previous multi-frame methods. The code will
be available soon at https://github.com/littlespray/StreamFlow.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: TSST: A Benchmark and Evaluation Models for Text Speech-Style Transfer
Abstract: Text style is highly abstract, as it encompasses various aspects of a
speaker's characteristics, habits, logical thinking, and the content they
express. However, previous text-style transfer tasks have primarily focused on
data-driven approaches, lacking in-depth analysis and research from the
perspectives of linguistics and cognitive science. In this paper, we introduce
a novel task called Text Speech-Style Transfer (TSST). The main objective is to
further explore topics related to human cognition, such as personality and
emotion, based on the capabilities of existing LLMs. Considering the objective
of our task and the distinctive characteristics of oral speech in real-life
scenarios, we trained multi-dimension (i.e. filler words, vividness,
interactivity, emotionality) evaluation models for the TSST and validated their
correlation with human assessments. We thoroughly analyze the performance of
several large language models (LLMs) and identify areas where further
improvement is needed. Moreover, driven by our evaluation models, we have
released a new corpus that improves the capabilities of LLMs in generating text
with speech-style characteristics. In summary, we present the TSST task, a new
benchmark for style transfer and emphasizing human-oriented evaluation,
exploring and advancing the performance of current LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Human-Machine Cooperative Multimodal Learning Method for Cross-subject Olfactory Preference Recognition
Abstract: Odor sensory evaluation has a broad application in food, clothing, cosmetics,
and other fields. Traditional artificial sensory evaluation has poor
repeatability, and the machine olfaction represented by the electronic nose
(E-nose) is difficult to reflect human feelings. Olfactory electroencephalogram
(EEG) contains odor and individual features associated with human olfactory
preference, which has unique advantages in odor sensory evaluation. However,
the difficulty of cross-subject olfactory EEG recognition greatly limits its
application. It is worth noting that E-nose and olfactory EEG are more
advantageous in representing odor information and individual emotions,
respectively. In this paper, an E-nose and olfactory EEG multimodal learning
method is proposed for cross-subject olfactory preference recognition. Firstly,
the olfactory EEG and E-nose multimodal data acquisition and preprocessing
paradigms are established. Secondly, a complementary multimodal data mining
strategy is proposed to effectively mine the common features of multimodal data
representing odor information and the individual features in olfactory EEG
representing individual emotional information. Finally, the cross-subject
olfactory preference recognition is achieved in 24 subjects by fusing the
extracted common and individual features, and the recognition effect is
superior to the state-of-the-art recognition methods. Furthermore, the
advantages of the proposed method in cross-subject olfactory preference
recognition indicate its potential for practical odor evaluation applications.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
Abstract: Multi-label classification poses challenges due to imbalanced and noisy
labels in training data. We propose a unified data augmentation method, named
BalanceMix, to address these challenges. Our approach includes two samplers for
imbalanced labels, generating minority-augmented instances with high diversity.
It also refines multi-labels at the label-wise granularity, categorizing noisy
labels as clean, re-labeled, or ambiguous for robust optimization. Extensive
experiments on three benchmark datasets demonstrate that BalanceMix outperforms
existing state-of-the-art methods. We release the code at
https://github.com/DISL-Lab/BalanceMix.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Target-agnostic Source-free Domain Adaptation for Regression Tasks
Abstract: Unsupervised domain adaptation (UDA) seeks to bridge the domain gap between
the target and source using unlabeled target data. Source-free UDA removes the
requirement for labeled source data at the target to preserve data privacy and
storage. However, work on source-free UDA assumes knowledge of domain gap
distribution, and hence is limited to either target-aware or classification
task. To overcome it, we propose TASFAR, a novel target-agnostic source-free
domain adaptation approach for regression tasks. Using prediction confidence,
TASFAR estimates a label density map as the target label distribution, which is
then used to calibrate the source model on the target domain. We have conducted
extensive experiments on four regression tasks with various domain gaps,
namely, pedestrian dead reckoning for different users, image-based people
counting in different scenes, housing-price prediction at different districts,
and taxi-trip duration prediction from different departure points. TASFAR is
shown to substantially outperform the state-of-the-art source-free UDA
approaches by averagely reducing 22% errors for the four tasks and achieve
notably comparable accuracy as source-based UDA without using source data.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Evaluating Large Language Model Creativity from a Literary Perspective
Abstract: This paper assesses the potential for large language models (LLMs) to serve
as assistive tools in the creative writing process, by means of a single,
in-depth case study. In the course of the study, we develop interactive and
multi-voice prompting strategies that interleave background descriptions (scene
setting, plot elements), instructions that guide composition, samples of text
in the target style, and critical discussion of the given samples. We
qualitatively evaluate the results from a literary critical perspective, as
well as from the standpoint of computational creativity (a sub-field of
artificial intelligence). Our findings lend support to the view that the
sophistication of the results that can be achieved with an LLM mirrors the
sophistication of the prompting.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Towards Explainability in Monocular Depth Estimation
Abstract: The estimation of depth in two-dimensional images has long been a challenging
and extensively studied subject in computer vision. Recently, significant
progress has been made with the emergence of Deep Learning-based approaches,
which have proven highly successful. This paper focuses on the explainability
in monocular depth estimation methods, in terms of how humans perceive depth.
This preliminary study emphasizes on one of the most significant visual cues,
the relative size, which is prominent in almost all viewed images. We designed
a specific experiment to mimic the experiments in humans and have tested
state-of-the-art methods to indirectly assess the explainability in the context
defined. In addition, we observed that measuring the accuracy required further
attention and a particular approach is proposed to this end. The results show
that a mean accuracy of around 77% across methods is achieved, with some of the
methods performing markedly better, thus, indirectly revealing their
corresponding potential to uncover monocular depth cues, like relative size.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Deep Multimodal Fusion for Surgical Feedback Classification
Abstract: Quantification of real-time informal feedback delivered by an experienced
surgeon to a trainee during surgery is important for skill improvements in
surgical training. Such feedback in the live operating room is inherently
multimodal, consisting of verbal conversations (e.g., questions and answers) as
well as non-verbal elements (e.g., through visual cues like pointing to
anatomic elements). In this work, we leverage a clinically-validated
five-category classification of surgical feedback: "Anatomic", "Technical",
"Procedural", "Praise" and "Visual Aid". We then develop a multi-label machine
learning model to classify these five categories of surgical feedback from
inputs of text, audio, and video modalities. The ultimate goal of our work is
to help automate the annotation of real-time contextual surgical feedback at
scale. Our automated classification of surgical feedback achieves AUCs ranging
from 71.5 to 77.6 with the fusion improving performance by 3.1%. We also show
that high-quality manual transcriptions of feedback audio from experts improve
AUCs to between 76.5 and 96.2, which demonstrates a clear path toward future
improvements. Empirically, we find that the Staged training strategy, with
first pre-training each modality separately and then training them jointly, is
more effective than training different modalities altogether. We also present
intuitive findings on the importance of modalities for different feedback
categories. This work offers an important first look at the feasibility of
automated classification of real-world live surgical feedback based on text,
audio, and video modalities.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Mitigating Perspective Distortion-induced Shape Ambiguity in Image Crops
Abstract: Objects undergo varying amounts of perspective distortion as they move across
a camera's field of view. Models for predicting 3D from a single image often
work with crops around the object of interest and ignore the location of the
object in the camera's field of view. We note that ignoring this location
information further exaggerates the inherent ambiguity in making 3D inferences
from 2D images and can prevent models from even fitting to the training data.
To mitigate this ambiguity, we propose Intrinsics-Aware Positional Encoding
(KPE), which incorporates information about the location of crops in the image
and camera intrinsics. Experiments on three popular 3D-from-a-single-image
benchmarks: depth prediction on NYU, 3D object detection on KITTI & nuScenes,
and predicting 3D shapes of articulated objects on ARCTIC, show the benefits of
KPE.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
Abstract: With the prosperity of contrastive learning for visual representation
learning (VCL), it is also adapted to the graph domain and yields promising
performance. However, through a systematic study of various graph contrastive
learning (GCL) methods, we observe that some common phenomena among existing
GCL methods that are quite different from the original VCL methods, including
1) positive samples are not a must for GCL; 2) negative samples are not
necessary for graph classification, neither for node classification when
adopting specific normalization modules; 3) data augmentations have much less
influence on GCL, as simple domain-agnostic augmentations (e.g., Gaussian
noise) can also attain fairly good performance. By uncovering how the implicit
inductive bias of GNNs works in contrastive learning, we theoretically provide
insights into the above intriguing properties of GCL. Rather than directly
porting existing VCL methods to GCL, we advocate for more attention toward the
unique architecture of graph learning and consider its implicit influence when
designing GCL methods. Code is available at https:
//github.com/PKU-ML/ArchitectureMattersGCL.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Advancing Surgical VQA with Scene Graph Knowledge
Abstract: Modern operating room is becoming increasingly complex, requiring innovative
intra-operative support systems. While the focus of surgical data science has
largely been on video analysis, integrating surgical computer vision with
language capabilities is emerging as a necessity. Our work aims to advance
Visual Question Answering (VQA) in the surgical context with scene graph
knowledge, addressing two main challenges in the current surgical VQA systems:
removing question-condition bias in the surgical VQA dataset and incorporating
scene-aware reasoning in the surgical VQA model design. First, we propose a
Surgical Scene Graph-based dataset, SSG-QA, generated by employing segmentation
and detection models on publicly available datasets. We build surgical scene
graphs using spatial and action information of instruments and anatomies. These
graphs are fed into a question engine, generating diverse QA pairs. Our SSG-QA
dataset provides a more complex, diverse, geometrically grounded, unbiased, and
surgical action-oriented dataset compared to existing surgical VQA datasets. We
then propose SSG-QA-Net, a novel surgical VQA model incorporating a lightweight
Scene-embedded Interaction Module (SIM), which integrates geometric scene
knowledge in the VQA model design by employing cross-attention between the
textual and the scene features. Our comprehensive analysis of the SSG-QA
dataset shows that SSG-QA-Net outperforms existing methods across different
question types and complexities. We highlight that the primary limitation in
the current surgical VQA systems is the lack of scene knowledge to answer
complex queries. We present a novel surgical VQA dataset and model and show
that results can be significantly improved by incorporating geometric scene
features in the VQA model design. The source code and the dataset will be made
publicly available at: https://github.com/CAMMA-public/SSG-QA

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Navigating Privacy and Copyright Challenges Across the Data Lifecycle of Generative AI
Abstract: The advent of Generative AI has marked a significant milestone in artificial
intelligence, demonstrating remarkable capabilities in generating realistic
images, texts, and data patterns. However, these advancements come with
heightened concerns over data privacy and copyright infringement, primarily due
to the reliance on vast datasets for model training. Traditional approaches
like differential privacy, machine unlearning, and data poisoning only offer
fragmented solutions to these complex issues. Our paper delves into the
multifaceted challenges of privacy and copyright protection within the data
lifecycle. We advocate for integrated approaches that combines technical
innovation with ethical foresight, holistically addressing these concerns by
investigating and devising solutions that are informed by the lifecycle
perspective. This work aims to catalyze a broader discussion and inspire
concerted efforts towards data privacy and copyright integrity in Generative
AI.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Intelligent methods for business rule processing: State-of-the-art
Abstract: In this article, we provide an overview of the latest intelligent techniques
used for processing business rules. We have conducted a comprehensive survey of
the relevant literature on robot process automation, with a specific focus on
machine learning and other intelligent approaches. Additionally, we have
examined the top vendors in the market and their leading solutions to tackle
this issue.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills
Abstract: Mutual information-based reinforcement learning (RL) has been proposed as a
promising framework for retrieving complex skills autonomously without a
task-oriented reward function through mutual information (MI) maximization or
variational empowerment. However, learning complex skills is still challenging,
due to the fact that the order of training skills can largely affect sample
efficiency. Inspired by this, we recast variational empowerment as curriculum
learning in goal-conditioned RL with an intrinsic reward function, which we
name Variational Curriculum RL (VCRL). From this perspective, we propose a
novel approach to unsupervised skill discovery based on information theory,
called Value Uncertainty Variational Curriculum (VUVC). We prove that, under
regularity conditions, VUVC accelerates the increase of entropy in the visited
states compared to the uniform curriculum. We validate the effectiveness of our
approach on complex navigation and robotic manipulation tasks in terms of
sample efficiency and state coverage speed. We also demonstrate that the skills
discovered by our method successfully complete a real-world robot navigation
task in a zero-shot setup and that incorporating these skills with a global
planner further increases the performance.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Making Translators Privacy-aware on the User's Side
Abstract: We propose PRISM to enable users of machine translation systems to preserve
the privacy of data on their own initiative. There is a growing demand to apply
machine translation systems to data that require privacy protection. While
several machine translation engines claim to prioritize privacy, the extent and
specifics of such protection are largely ambiguous. First, there is often a
lack of clarity on how and to what degree the data is protected. Even if
service providers believe they have sufficient safeguards in place,
sophisticated adversaries might still extract sensitive information. Second,
vulnerabilities may exist outside of these protective measures, such as within
communication channels, potentially leading to data leakage. As a result, users
are hesitant to utilize machine translation engines for data demanding high
levels of privacy protection, thereby missing out on their benefits. PRISM
resolves this problem. Instead of relying on the translation service to keep
data safe, PRISM provides the means to protect data on the user's side. This
approach ensures that even machine translation engines with inadequate privacy
measures can be used securely. For platforms already equipped with privacy
safeguards, PRISM acts as an additional protection layer, reinforcing their
security furthermore. PRISM adds these privacy features without significantly
compromising translation accuracy. Our experiments demonstrate the
effectiveness of PRISM using real-world translators, T5 and ChatGPT
(GPT-3.5-turbo), and the datasets with two languages. PRISM effectively
balances privacy protection with translation accuracy.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: ExpNote: Black-box Large Language Models are Better Task Solvers with Experience Notebook
Abstract: Black-box Large Language Models (LLMs) have shown great power in solving
various tasks and are considered general problem solvers. However, LLMs still
fail in many specific tasks although understand the task instruction. In this
paper, we focus on the problem of boosting the ability of black-box LLMs to
solve downstream tasks. We propose ExpNote, an automated framework to help LLMs
better adapt to unfamiliar tasks through reflecting and noting experiences from
training data and retrieving them from external memory during testing. We
evaluate ExpNote on multiple tasks and the experimental results demonstrate
that the proposed method significantly improves the performance of black-box
LLMs. The data and code are available at
https://github.com/forangel2014/ExpNote

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Abstraction via exemplars? A representational case study on lexical category inference in BERT
Abstract: Exemplar based accounts are often considered to be in direct opposition to
pure linguistic abstraction in explaining language learners' ability to
generalize to novel expressions. However, the recent success of neural network
language models on linguistically sensitive tasks suggests that perhaps
abstractions can arise via the encoding of exemplars. We provide empirical
evidence for this claim by adapting an existing experiment that studies how an
LM (BERT) generalizes the usage of novel tokens that belong to lexical
categories such as Noun/Verb/Adjective/Adverb from exposure to only a single
instance of their usage. We analyze the representational behavior of the novel
tokens in these experiments, and find that BERT's capacity to generalize to
unseen expressions involving the use of these novel tokens constitutes the
movement of novel token representations towards regions of known category
exemplars in two-dimensional space. Our results suggest that learners' encoding
of exemplars can indeed give rise to abstraction like behavior.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Search Still Matters: Information Retrieval in the Era of Generative AI
Abstract: Objective: Information retrieval (IR, also known as search) systems are
ubiquitous in modern times. How does the emergence of generative artificial
intelligence (AI), based on large language models (LLMs), fit into the IR
process? Process: This perspective explores the use of generative AI in the
context of the motivations, considerations, and outcomes of the IR process with
a focus on the academic use of such systems. Conclusions: There are many
information needs, from simple to complex, that motivate use of IR. Users of
such systems, particularly academics, have concerns for authoritativeness,
timeliness, and contextualization of search. While LLMs may provide
functionality that aids the IR process, the continued need for search systems,
and research into their improvement, remains essential.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Towards more Practical Threat Models in Artificial Intelligence Security
Abstract: Recent works have identified a gap between research and practice in
artificial intelligence security: threats studied in academia do not always
reflect the practical use and security risks of AI. For example, while models
are often studied in isolation, they form part of larger ML pipelines in
practice. Recent works also brought forward that adversarial manipulations
introduced by academic attacks are impractical. We take a first step towards
describing the full extent of this disparity. To this end, we revisit the
threat models of the six most studied attacks in AI security research and match
them to AI usage in practice via a survey with \textbf{271} industrial
practitioners. On the one hand, we find that all existing threat models are
indeed applicable. On the other hand, there are significant mismatches:
research is often too generous with the attacker, assuming access to
information not frequently available in real-world settings. Our paper is thus
a call for action to study more practical threat models in artificial
intelligence security.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: An Explainable Machine Learning Framework for the Accurate Diagnosis of Ovarian Cancer
Abstract: Ovarian cancer (OC) is one of the most prevalent types of cancer in women.
Early and accurate diagnosis is crucial for the survival of the patients.
However, the majority of women are diagnosed in advanced stages due to the lack
of effective biomarkers and accurate screening tools. While previous studies
sought a common biomarker, our study suggests different biomarkers for the
premenopausal and postmenopausal populations. This can provide a new
perspective in the search for novel predictors for the effective diagnosis of
OC. Lack of explainability is one major limitation of current AI systems. The
stochastic nature of the ML algorithms raises concerns about the reliability of
the system as it is difficult to interpret the reasons behind the decisions. To
increase the trustworthiness and accountability of the diagnostic system as
well as to provide transparency and explanations behind the predictions,
explainable AI has been incorporated into the ML framework. SHAP is employed to
quantify the contributions of the selected biomarkers and determine the most
discriminative features. A hybrid decision support system has been established
that can eliminate the bottlenecks caused by the black-box nature of the ML
algorithms providing a safe and trustworthy AI tool. The diagnostic accuracy
obtained from the proposed system outperforms the existing methods as well as
the state-of-the-art ROMA algorithm by a substantial margin which signifies its
potential to be an effective tool in the differential diagnosis of OC.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System
Abstract: Misinformation has emerged as a major societal threat in recent years in
general; specifically in the context of the COVID-19 pandemic, it has wrecked
havoc, for instance, by fuelling vaccine hesitancy. Cost-effective, scalable
solutions for combating misinformation are the need of the hour. This work
explored how existing information obtained from social media and augmented with
more curated fact checked data repositories can be harnessed to facilitate
automated rebuttal of misinformation at scale. While the ideas herein can be
generalized and reapplied in the broader context of misinformation mitigation
using a multitude of information sources and catering to the spectrum of social
media platforms, this work serves as a proof of concept, and as such, it is
confined in its scope to only rebuttal of tweets, and in the specific context
of misinformation regarding COVID-19. It leverages two publicly available
datasets, viz. FaCov (fact-checked articles) and misleading (social media
Twitter) data on COVID-19 Vaccination.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: The primacy bias in Model-based RL
Abstract: The primacy bias in deep reinforcement learning (DRL), which refers to the
agent's tendency to overfit early data and lose the ability to learn from new
data, can significantly decrease the performance of DRL algorithms. Previous
studies have shown that employing simple techniques, such as resetting the
agent's parameters, can substantially alleviate the primacy bias. However, we
observe that resetting the agent's parameters harms its performance in the
context of model-based reinforcement learning (MBRL). In fact, on further
investigation, we find that the primacy bias in MBRL differs from that in
model-free RL. In this work, we focus on investigating the primacy bias in MBRL
and propose world model resetting, which works in MBRL. We apply our method to
two different MBRL algorithms, MBPO and DreamerV2. We validate the
effectiveness of our method on multiple continuous control tasks on MuJoCo and
DeepMind Control Suite, as well as discrete control tasks on Atari 100k
benchmark. The results show that world model resetting can significantly
alleviate the primacy bias in model-based setting and improve algorithm's
performance. We also give a guide on how to perform world model resetting
effectively.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping
Abstract: Inverse reinforcement learning (IRL) is computationally challenging, with
common approaches requiring the solution of multiple reinforcement learning
(RL) sub-problems. This work motivates the use of potential-based reward
shaping to reduce the computational burden of each RL sub-problem. This work
serves as a proof-of-concept and we hope will inspire future developments
towards computationally efficient IRL.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Generating Illustrated Instructions
Abstract: We introduce the new task of generating Illustrated Instructions, i.e.,
visual instructions customized to a user's needs. We identify desiderata unique
to this task, and formalize it through a suite of automatic and human
evaluation metrics, designed to measure the validity, consistency, and efficacy
of the generations. We combine the power of large language models (LLMs)
together with strong text-to-image generation diffusion models to propose a
simple approach called StackedDiffusion, which generates such illustrated
instructions given text as input. The resulting model strongly outperforms
baseline approaches and state-of-the-art multimodal LLMs; and in 30% of cases,
users even prefer it to human-generated articles. Most notably, it enables
various new and exciting applications far beyond what static articles on the
web can provide, such as personalized instructions complete with intermediate
steps and pictures in response to a user's individual situation.

Output: Computer Vision


Instruction: What field is the article from?

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

Output: Computational Linguistics