2023.02.24.txt

1, TITLE: Guidable Local Hamiltonian Problems with Implications to Heuristic Ansätze State Preparation and The Quantum PCP Conjecture
AUTHORS: Jordi Weggemans ; Marten Folkertsma ; Chris Cade
CATEGORY: quant-ph [quant-ph, cs.CC]
HIGHLIGHT: We introduce 'Merlinized' versions of the recently defined Guided Local Hamiltonian problem, which we call 'Guidable Local Hamiltonian' problems.

2, TITLE: Multi-Valued Neural Networks I A Multi-Valued Associative Memory
AUTHORS: Dmitry Maximov ; Vladimir I. Goncharenko ; Yury S. Legovich
CATEGORY: cs.AI [cs.AI, 68Q85]
HIGHLIGHT: In this paper, the lattice is assumed to be Brouwer and determines the implication used, together with other lattice operations, to determine the neural network output.

3, TITLE: Deep Learning Reveals The Common Spectrum Underlying Multiple Brain Disorders in Youth and Elders from Brain Functional Networks
AUTHORS: MIANXIN LIU et. al.
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: However, the key evidence from neuroimaging data for pathological commonness remains unrevealed. To explore this hypothesis, we build a deep learning model, using multi-site functional magnetic resonance imaging data (N=4,410, 6 sites), for classifying 5 different brain disorders from healthy controls, with a set of common features.

4, TITLE: Intermittently Observable Markov Decision Processes
AUTHORS: Gongpu Chen ; Soung-Chang Liew
CATEGORY: cs.AI [cs.AI, cs.SY, eess.SY]
HIGHLIGHT: This paper investigates MDPs with intermittent state information.

5, TITLE: Unsupervised 3D Object Learning Through Neuron Activity Aware Plasticity
AUTHORS: Beomseok Kang ; Biswadeep Chakraborty ; Saibal Mukhopadhyay
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: We present an unsupervised deep learning model for 3D object classification.

6, TITLE: Beyond Bias and Compliance: Towards Individual Agency and Plurality of Ethics in AI
AUTHORS: Thomas Krendl Gilbert ; Megan Welle Brozek ; Andrew Brozek
CATEGORY: cs.AI [cs.AI, cs.CY]
HIGHLIGHT: Two major approaches are focused on bias and compliance, respectively.

7, TITLE: A Scalable Space-efficient In-database Interpretability Framework for Embedding-based Semantic SQL Queries
AUTHORS: Prabhakar Kudva ; Rajesh Bordawekar ; Apoorva Nitsure
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: In this paper, we describe an architecture and implementation of in-database interpretability infrastructure designed to provide simple, transparent, and relatable insights into ranked results of semantic SQL queries supported by AI-DB.

8, TITLE: Information Theory Inspired Pattern Analysis for Time-series Data
AUTHORS: YUSHAN HUANG et. al.
CATEGORY: cs.AI [cs.AI, cs.IT, math.IT]
HIGHLIGHT: Such methods do not generalize well when applied to multivariate, multi-source, state-varying, and noisy time-series data. To address these issues, we propose a highly generalizable method that uses information theory-based features to identify and learn from patterns in multivariate time-series data.

9, TITLE: The Generalizability of Explanations
AUTHORS: Hanxiao Tan
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: This work proposes a novel evaluation methodology from the perspective of generalizability.

10, TITLE: The Joint Weighted Average (JWA) Operator
AUTHORS: Stephen B. Broomell ; Christian Wagner
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: In this paper, we put forward an approach which integrates--rather than partially applies--both approaches, resulting in a novel joint weighted averaging operator.

11, TITLE: Extensions to Generalized Annotated Logic and An Equivalent Neural Architecture
AUTHORS: Paulo Shakarian ; Gerardo I. Simari
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: Neuro symbolic hybrid systems have recently emerged as a straightforward way to extend deep neural networks by incorporating ideas from symbolic reasoning such as computational logic. In this paper, we propose a list desirable criteria for neuro symbolic systems and examine how some of the existing approaches address these criteria.

12, TITLE: Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles
AUTHORS: Biswadeep Chakraborty ; Saibal Mukhopadhyay
CATEGORY: cs.AI [cs.AI, cs.NE]
HIGHLIGHT: This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction performance, enabling spike-efficient (unsupervised) learning.

13, TITLE: Explorative Analysis of Human Disease-symptoms Relations Using The Convolutional Neural Network
AUTHORS: Zolzaya Dashdorj ; Stanislav Grigorev ; Munguntsatsral Dovdondash
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: In this research, we analyze a pre-generated symptom-based human disease dataset and demonstrate the degree of predictability for each disease based on the Convolutional Neural Network and the Support Vector Machine.

14, TITLE: Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering
AUTHORS: Md. Rezaul Karim ; Felix Hermsen ; Sisay Adugna Chala ; Paola de Perthuis ; Avikarsha Mandal
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: In this paper, we employ semi-supervised graph learning techniques on graphs of financial transactions in order to identify nodes involved in potential money laundering.

15, TITLE: A Neural Span-Based Continual Named Entity Recognition Model
AUTHORS: Yunan Zhang ; Qingcai Chen
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: In this paper, we propose SpanKL1, a simple yet effective Span-based model with Knowledge distillation (KD) to preserve memories and multi-Label prediction to prevent conflicts in CL-NER.

16, TITLE: VLSP 2022 -- EVJVQA Challenge: Multilingual Visual Question Answering
AUTHORS: Ngan Luu-Thuy Nguyen ; Nghia Hieu Nguyen ; Duong T. D Vo ; Khanh Quoc Tran ; Kiet Van Nguyen
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results.

17, TITLE: On The Generalization Ability of Retrieval-Enhanced Transformers
AUTHORS: Tobias Norlund ; Ehsan Doostmohammadi ; Richard Johansson ; Marco Kuhlmann
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: It has been suggested that at least some of this performance gain is due to non-trivial generalization based on both model weights and retrieval. In this paper, we try to better understand the relative contributions of these two components.

18, TITLE: MUTANT: A Multi-sentential Code-mixed Hinglish Dataset
AUTHORS: Rahul Gupta ; Vivek Srivastava ; Mayank Singh
CATEGORY: cs.CL [cs.CL, cs.LG]
HIGHLIGHT: In this paper, we propose a novel task of identifying multi-sentential code-mixed text (MCT) from multilingual articles.

19, TITLE: KHAN: Knowledge-Aware Hierarchical Attention Networks for Political Stance Prediction
AUTHORS: YUNYONG KO et. al.
CATEGORY: cs.CL [cs.CL, cs.AI, cs.LG]
HIGHLIGHT: Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance. Based on this observation, we propose a novel knowledge-aware approach to political stance prediction (KHAN), employing (1) hierarchical attention networks (HAN) to learn the relationships among words and sentences in three different levels and (2) knowledge encoding (KE) to incorporate external knowledge for real-world entities into the process of political stance prediction.

20, TITLE: FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering
AUTHORS: Qichen Ye ; Bowen Cao ; Nuo Chen ; Weiyuan Xu ; Yuexian Zou
CATEGORY: cs.CL [cs.CL, cs.LG]
HIGHLIGHT: Despite the promising result of recent KAQA systems which tend to integrate linguistic knowledge from pre-trained language models (PLM) and factual knowledge from knowledge graphs (KG) to answer complex questions, a bottleneck exists in effectively fusing the representations from PLMs and KGs because of (i) the semantic and distributional gaps between them, and (ii) the difficulties in joint reasoning over the provided knowledge from both modalities. To address the above two problems, we propose a Fine-grained Two-stage training framework (FiTs) to boost the KAQA system performance: The first stage aims at aligning representations from the PLM and the KG, thus bridging the modality gaps between them, named knowledge adaptive post-training.

21, TITLE: Sentence Simplification Via Large Language Models
AUTHORS: Yutao Feng ; Jipeng Qiang ; Yun Li ; Yunhao Yuan ; Yi Zhu
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: However, it is not yet known whether LLMs can be served as a high-quality sentence simplification system. In this work, we empirically analyze the zero-/few-shot learning ability of LLMs by evaluating them on a number of benchmark test sets.

22, TITLE: Empathetic Response Generation Via Emotion Cause Transition Graph
AUTHORS: YUSHAN QIAN et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we propose an emotion cause transition graph to explicitly model the natural transition of emotion causes between two adjacent turns in empathetic dialogue.

23, TITLE: Teacher Intervention: Improving Convergence of Quantization Aware Training for Ultra-Low Precision Transformers
AUTHORS: Minsoo Kim ; Kyuhong Shim ; Seongmin Park ; Wonyong Sung ; Jungwook Choi
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: This work proposes a proactive knowledge distillation method called Teacher Intervention (TI) for fast converging QAT of ultra-low precision pre-trained Transformers.

24, TITLE: Federated Nearest Neighbor Machine Translation
AUTHORS: YICHAO DU et. al.
CATEGORY: cs.CL [cs.CL, cs.AI, cs.LG]
HIGHLIGHT: In this paper, we propose a novel federated nearest neighbor (FedNN) machine translation framework that, instead of multi-round model-based interactions, leverages one-round memorization-based interaction to share knowledge across different clients to build low-overhead privacy-preserving systems.

25, TITLE: Prosodic Features Improve Sentence Segmentation and Parsing
AUTHORS: Elizabeth Nielsen ; Sharon Goldwater ; Mark Steedman
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: We suggest that the best parses instead come from modelling sentence boundaries jointly with other constituent boundaries.

26, TITLE: Active Prompting with Chain-of-Thought for Large Language Models
AUTHORS: Shizhe Diao ; Pengcheng Wang ; Yong Lin ; Tong Zhang
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: This paper proposes a new method, Active-Prompt, to adapt LLMs to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning).

27, TITLE: What Makes A Language Easy to Deep-learn?
AUTHORS: Lukas Galke ; Yoav Ram ; Limor Raviv
CATEGORY: cs.CL [cs.CL, I.2.7]
HIGHLIGHT: What Makes A Language Easy to Deep-learn?

28, TITLE: HL Dataset: Grounding High-Level Linguistic Concepts in Vision
AUTHORS: Michele Cafagna ; Kees van Deemter ; Albert Gatt
CATEGORY: cs.CL [cs.CL, cs.CV]
HIGHLIGHT: We present the High-Level Dataset; a dataset extending 14997 images of the COCO dataset with 134973 human-annotated (high-level) abstract captions collected along three axes: scenes, actions and rationales. We describe and release such dataset and we show how it can be used to assess models' multimodal grounding of abstract concepts and enrich models' visio-lingusitic representations.

29, TITLE: Simple and Scalable Nearest Neighbor Machine Translation
AUTHORS: YUHAN DAI et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we propose a simple and scalable nearest neighbor machine translation framework to drastically promote the decoding and storage efficiency of $k$NN-based models while maintaining the translation performance.

30, TITLE: ProsAudit, A Prosodic Benchmark for Self-supervised Speech Models
AUTHORS: MAUREEN DE SEYSSEL et. al.
CATEGORY: cs.CL [cs.CL, cs.SD, eess.AS]
HIGHLIGHT: We present ProsAudit, a benchmark in English to assess structural prosodic knowledge in self-supervised learning (SSL) speech models.

31, TITLE: SPINDLE: Spinning Raw Text Into Lambda Terms with Graph Attention
AUTHORS: Konstantinos Kogkalidis ; Michael Moortgat ; Richard Moot
CATEGORY: cs.CL [cs.CL, cs.AI, cs.LG, cs.LO]
HIGHLIGHT: This paper describes SPINDLE - an open source Python module implementing an efficient and accurate parser for written Dutch that transforms raw text input to programs for meaning composition, expressed as {\lambda} terms.

32, TITLE: Evaluating Automatic Speech Recognition in An Incremental Setting
AUTHORS: Ryan Whetten ; Mir Tahsin Imtiaz ; Casey Kennington
CATEGORY: cs.CL [cs.CL, cs.SD, eess.AS]
HIGHLIGHT: In this paper, we systematically evaluate six speech recognizers using metrics including word error rate, latency, and the number of updates to already recognized words on English test data, as well as propose and compare two methods for streaming audio into recognizers for incremental recognition.

33, TITLE: Generative Sentiment Transfer Via Adaptive Masking
AUTHORS: YINGZE XIE et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Generative Sentiment Transfer Via Adaptive Masking

34, TITLE: Exploring Social Media for Early Detection of Depression in COVID-19 Patients
AUTHORS: JIAGENG WU et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis.

35, TITLE: Conversational Agents and Children: Let Children Learn
AUTHORS: Casey Kennington ; Jerry Alan Fails ; Katherine Landau Wright ; Maria Soledad Pera
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Using online information discovery as a case study, in this position paper we discuss the need to design, develop, and deploy (conversational) agents that can -- non-intrusively -- guide children in their quest for online resources rather than simply finding resources for them.

36, TITLE: Natural Language Processing in The Legal Domain
AUTHORS: Daniel Martin Katz ; Dirk Hartung ; Lauritz Gerlach ; Abhik Jana ; Michael J. Bommarito II
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: In this paper, we summarize the current state of the field of NLP & Law with a specific focus on recent technical and substantive developments.

37, TITLE: Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs
AUTHORS: YEQIN ZHANG et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: This paper proposes Re3G, which aims to optimize both coarse-grained knowledge retrieval and fine-grained knowledge extraction in a unified framework.

38, TITLE: Deep Learning Model for Mongolian Citizens Feedback Analysis Using Word Vector Embeddings
AUTHORS: Zolzaya Dashdorj ; Tsetsentsengel Munkhbayar ; Stanislav Grigorev
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: We use feedback data in Cyrillic collected from 2012-2018.

39, TITLE: Improved Training of Mixture-of-Experts Language GANs
AUTHORS: Yekun Chai ; Qiyue Yin ; Junge Zhang
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Empirical study on synthetic and real benchmarks shows the superior performance in quantitative evaluation and demonstrates the effectiveness of our approach to adversarial text generation.

40, TITLE: Cutting Barnette Graphs Perfectly Is Hard
AUTHORS: Édouard Bonnet ; Dibyayan Chakraborty ; Julien Duron
CATEGORY: cs.CC [cs.CC, cs.DM, math.CO, 68Q25, F.2.2]
HIGHLIGHT: We settle both questions at once by showing that Perfect Matching Cut is NP-complete in 3-connected cubic bipartite planar graphs or Barnette graphs.

41, TITLE: Single-Peaked Jump Schelling Games
AUTHORS: Tobias Friedrich ; Pascal Lenzner ; Louise Molitor ; Lars Seifert
CATEGORY: cs.GT [cs.GT, cs.AI, cs.MA, math.DS]
HIGHLIGHT: We contribute to the recent endeavor of investigating residential segregation models with realistic agent behavior by studying Jump Schelling Games with agents having a single-peaked utility function.

42, TITLE: Learning to Manipulate A Commitment Optimizer
AUTHORS: Yurong Chen ; Xiaotie Deng ; Jiarui Gan ; Yuhao Li
CATEGORY: cs.GT [cs.GT, cs.AI, cs.DS, cs.LG, econ.TH]
HIGHLIGHT: That is, the follower knows the full information about both players' payoffs whereas the leader only knows their own payoffs. In this paper, we study the manipulation problem with this information advantage relaxed.

43, TITLE: DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models
AUTHORS: Jamie Wynn ; Daniyar Turmukhambetov
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: However, the scene geometry and color fields are severely under-constrained, which can lead to artifacts, especially when trained with few input views. To alleviate this problem we learn a prior over scene geometry and color, using a denoising diffusion model (DDM).

44, TITLE: Designing An Encoder for Fast Personalization of Text-to-Image Models
AUTHORS: RINON GAL et. al.
CATEGORY: cs.CV [cs.CV, cs.GR, cs.LG]
HIGHLIGHT: However, current personalization approaches struggle with lengthy training times, high storage requirements or loss of identity. To overcome these limitations, we propose an encoder-based domain-tuning approach.

45, TITLE: A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness
AUTHORS: Huaqi Tao ; Bingxi Liu ; Jinqiang Cui ; Hong Zhang
CATEGORY: cs.CV [cs.CV, eess.IV]
HIGHLIGHT: However, the long and sharp topological features and complex background of cracks make the task of crack segmentation extremely challenging. In this paper, we propose a novel convolutional-transformer network based on encoder-decoder architecture to solve this challenge.

46, TITLE: Open-World Object Detection Via Discriminative Class Prototype Learning
AUTHORS: Jinan Yu ; Liyan Ma ; Zhenglin Li ; Yan Peng ; Shaorong Xie
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We propose a novel and efficient OWOD solution from a prototype perspective, which we call OCPL: Open-world object detection via discriminative Class Prototype Learning, which consists of a Proposal Embedding Aggregator (PEA), an Embedding Space Compressor (ESC) and a Cosine Similarity-based Classifier (CSC).

47, TITLE: Unsupervised Domain Adaptation Via Distilled Discriminative Clustering
AUTHORS: Hui Tang ; Yaowei Wang ; Kui Jia
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To distill discriminative source information for target clustering, we propose to jointly train the network using parallel, supervised learning objectives over labeled source data.

48, TITLE: A Metric to Compare The Anatomy Variation Between Image Time Series
AUTHORS: Alphin J Thottupattu ; Jayanthi Sivaswamy
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: A method to untangle and quantify the path and inter-subject anatomy(shape) difference between the TS is presented in this paper.

49, TITLE: Crossing Points Detection in Plain Weave for Old Paintings with Deep Learning
AUTHORS: A. Delgado ; L. Alba-Carcelén ; J. J. Murillo-Fuentes
CATEGORY: cs.CV [cs.CV, cs.NE, 68T07, I.4.6]
HIGHLIGHT: In the forensic studies of painting masterpieces, the analysis of the support is of major importance.

50, TITLE: Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
AUTHORS: YANG CHEN et. al.
CATEGORY: cs.CV [cs.CV, cs.AI, cs.CL]
HIGHLIGHT: With recent progress on web-scale visual and language pre-training, do these models also understand how to answer visual information seeking questions? To answer this question, we present InfoSeek, a Visual Question Answering dataset that focuses on asking information-seeking questions, where the information can not be answered by common sense knowledge.

51, TITLE: Controlled and Conditional Text to Image Generation with Diffusion Prior
AUTHORS: PRANAV AGGARWAL et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Denoising Diffusion models have shown remarkable performance in generating diverse, high quality images from text.

52, TITLE: Evaluating The Efficacy of Skincare Product: A Realistic Short-Term Facial Pore Simulation
AUTHORS: LING LI et. al.
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: In this paper, we propose the first simulation model to reveal facial pore changes after using skincare products.

53, TITLE: Pixel Difference Convolutional Network for RGB-D Semantic Segmentation
AUTHORS: JUN YANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Considering the fixed grid kernel structure, CNNs are limited to lack the ability to capture detailed, fine-grained information and thus cannot achieve accurate pixel-level semantic segmentation. To solve this problem, we propose a Pixel Difference Convolutional Network (PDCNet) to capture detailed intrinsic patterns by aggregating both intensity and gradient information in the local range for Depth data and global range for RGB data, respectively.

54, TITLE: Region-Aware Diffusion for Zero-shot Text-driven Image Editing
AUTHORS: Nisha Huang ; Fan Tang ; Weiming Dong ; Tong-Yee Lee ; Changsheng Xu
CATEGORY: cs.CV [cs.CV, cs.GR, cs.MM]
HIGHLIGHT: In this study, we focus on the regional editing of images with the guidance of given text prompts.

55, TITLE: Transformers in Single Object Tracking: An Experimental Survey
AUTHORS: Janani Thangavel ; Thanikasalam Kokul ; Amirthalingam Ramanan ; Subha Fernando
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this survey, we aim to analyze the literature and performances of Transformer tracking approaches.

56, TITLE: Efficient Context Integration Through Factorized Pyramidal Learning for Ultra-Lightweight Semantic Segmentation
AUTHORS: Nadeem Atif ; Saquib Mazhar ; Debajit Sarma ; M. K. Bhuyan ; Shaik Rafi Ahamed
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Since, accuracy and model size of CNNs are intrinsically contentious in nature, the challenge is to achieve a decent trade-off between accuracy and model size. To address this, we propose a novel Factorized Pyramidal Learning (FPL) module to aggregate rich contextual information in an efficient manner.

57, TITLE: Real-Time Damage Detection in Fiber Lifting Ropes Using Convolutional Neural Networks
AUTHORS: Tuomas Jalonen ; Mohammad Al-Sa'd ; Roope Mellanen ; Serkan Kiranyaz ; Moncef Gabbouj
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: This task is time-consuming, prone to human error, halts operation, and may result in the premature disposal of ropes. Therefore, we propose using deep learning and computer vision methods to automate the process of detecting damaged ropes.

58, TITLE: ACE: Zero-Shot Image to Image Translation Via Pretrained Auto-Contrastive-Encoder
AUTHORS: Sihan Xu ; Zelong Jiang ; Ruisi Liu ; Kaikai Yang ; Zhijie Huang
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: However, learning such mapping between domains is challenging because data from different domains can be highly unbalanced in terms of both quality and quantity. To address this problem, we propose a new approach to extract image features by learning the similarities and differences of samples within the same data distribution via a novel contrastive learning framework, which we call Auto-Contrastive-Encoder (ACE).

59, TITLE: A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification
AUTHORS: Lin Zhan ; Jiayuan Fan ; Peng Ye ; Jianjian Cao
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Meanwhile, plenty of previous works ignore asymmetric spectral-spatial dimensions in HSI. To address the above issues, we propose a multi-stage search architecture in order to overcome asymmetric spectral-spatial dimensions and capture significant features.

60, TITLE: Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks
AUTHORS: Raluca Jalaboi ; Ole Winther ; Alfiia Galimzianova
CATEGORY: cs.CV [cs.CV, cs.LG, 92B20, 92C50, 68T45]
HIGHLIGHT: A quantitative evaluation of the Grad-CAM explainability has been recently made possible by the release of DermXDB, a skin disease diagnosis explainability dataset which enables explainability benchmarking of ConvNet architectures. In this paper, we perform a literature review to identify the most common ConvNet architectures used for this task, and compare their Grad-CAM explanations with the explanation maps provided by DermXDB.

61, TITLE: A Novel Efficient Multi-view Traffic-related Object Detection Framework
AUTHORS: KUN YANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Accordingly, we propose a novel traffic-related framework named CEVAS to achieve efficient object detection using multi-view video data.

62, TITLE: EfficientFace: An Efficient Deep Network with Feature Enhancement for Accurate Face Detection
AUTHORS: GUANGTAO WANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To achieve efficient face detection without sacrificing accuracy, we design an efficient deep face detector termed EfficientFace in this study, which contains three modules for feature enhancement.

63, TITLE: Deep OC-SORT: Multi-Pedestrian Tracking By Adaptive Re-Identification
AUTHORS: Gerard Maggiolino ; Adnan Ahmad ; Jinkun Cao ; Kris Kitani
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a novel way to leverage objects' appearances to adaptively integrate appearance matching into existing high-performance motion-based methods.

64, TITLE: DisCO: Portrait Distortion Correction with Perspective-Aware 3D GANs
AUTHORS: ZHIXIANG WANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We propose a simple yet effective method for correcting perspective distortions in a single close-up face.

65, TITLE: Boosting Adversarial Transferability Using Dynamic Cues
AUTHORS: Muzammal Naseer ; Ahmad Mahmood ; Salman Khan ; Fahad Khan
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In this work, we induce dynamic cues within the image models without sacrificing their original performance on images.

66, TITLE: VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion
AUTHORS: YIMING LI et. al.
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG, cs.RO]
HIGHLIGHT: This appealing ability is vital for recognition and understanding. To enable such capability in AI systems, we propose VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics from only 2D images.

67, TITLE: MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes
AUTHORS: CHRISTIAN REISER et. al.
CATEGORY: cs.CV [cs.CV, cs.GR]
HIGHLIGHT: We present a Memory-Efficient Radiance Field (MERF) representation that achieves real-time rendering of large-scale scenes in a browser.

68, TITLE: Learning Visual Representations Via Language-Guided Sampling
AUTHORS: Mohamed El Banani ; Karan Desai ; Justin Johnson
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This happens because language abstracts away visual variation to represent and communicate concepts. Building on this intuition, we propose an alternative approach to visual learning: using language similarity to sample semantically similar image pairs for contrastive learning.

69, TITLE: ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection
AUTHORS: Md Awsafur Rahman ; Bishmoy Paul ; Najibul Haque Sarker ; Zaber Ibn Abdul Hakim ; Shaikh Anowarul Fattah
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: To assess the generalizability and robustness of synthetic image detectors in the face of real-world impairments, this paper presents a large-scale dataset named ArtiFact, comprising diverse generators, object categories, and real-world challenges.

70, TITLE: Patch Network for Medical Image Segmentation
AUTHORS: Weihu Song ; Heng Yu ; Jianhua Wu
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we present a Patch Network (PNet) that incorporates the Swin Transformer notion into a convolutional neural network, allowing it to gather richer contextual information while achieving the balance of speed and accuracy.

71, TITLE: Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement
AUTHORS: CHONGYI LI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Unlike existing methods that address the problem in the spatial domain, we propose a new solution, UHDFour, that embeds Fourier transform into a cascaded network.

72, TITLE: Set Features for Fine-grained Anomaly Detection
AUTHORS: Niv Cohen. Issar Tzachor ; Yedid Hoshen
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: However, such approaches do not extend to scenarios where the anomalies are expressed by an unusual combination of normal elements. In this paper, we overcome this limitation by proposing set features that model each sample by the distribution its elements.

73, TITLE: Side Adapter Network for Open-Vocabulary Semantic Segmentation
AUTHORS: Mengde Xu ; Zheng Zhang ; Fangyun Wei ; Han Hu ; Xiang Bai
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: This paper presents a new framework for open-vocabulary semantic segmentation with the pre-trained vision-language model, named Side Adapter Network (SAN).

74, TITLE: Learning Neural Volumetric Representations of Dynamic Humans in Minutes
AUTHORS: Chen Geng ; Sida Peng ; Zhen Xu ; Hujun Bao ; Xiaowei Zhou
CATEGORY: cs.CV [cs.CV, cs.AI, cs.GR]
HIGHLIGHT: In this paper, we propose a novel method for learning neural volumetric videos of dynamic humans from sparse view videos in minutes with competitive visual quality.

75, TITLE: Open Challenges for Monocular Single-shot 6D Object Pose Estimation
AUTHORS: Stefan Thalhammer ; Peter Hönig ; Jean-Baptiste Weibel ; Markus Vincze
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Following that, we identify promising research directions in order to help researchers to formulate relevant research ideas and effectively advance the state of the art.

76, TITLE: RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement
AUTHORS: JINGXIA JIANG et. al.
CATEGORY: cs.CV [cs.CV, eess.IV]
HIGHLIGHT: Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles. To address this issue, we propose a Real-time Spatial and Frequency Domains Modulation Network (RSFDM-Net) for the efficient enhancement of colors and details in underwater images.

77, TITLE: Attention Mechanism for Contrastive Learning in GAN-based Image-to-Image Translation
AUTHORS: Hanzhen Zhang ; Liguo Zhou ; Ruining Wang ; Alois Knoll
CATEGORY: cs.CV [cs.CV, cs.LG, eess.IV]
HIGHLIGHT: Using real road testing to optimize autonomous driving algorithms is time-consuming and capital-intensive. To solve this problem, we propose a GAN-based model that is capable of generating high-quality images across different domains.

78, TITLE: Object-Centric Video Prediction Via Decoupling of Object Dynamics and Interactions
AUTHORS: Angel Villar-Corrales ; Ismail Wahdan ; Sven Behnke
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We propose a novel framework for the task of object-centric video prediction, i.e., extracting the compositional structure of a video sequence, as well as modeling objects dynamics and interactions from visual observations in order to predict the future object states, from which we can then generate subsequent video frames.

79, TITLE: StudyFormer : Attention-Based and Dynamic Multi View Classifier for X-ray Images
AUTHORS: Lucas Wannenmacher ; Michael Fitzke ; Diane Wilson ; Andre Dourson
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: In this work, we propose a novel approach for combining information from multiple views to improve the performance of X-ray image classification.

80, TITLE: Teaching CLIP to Count to Ten
AUTHORS: RONI PAISS et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We introduce a simple yet effective method to improve the quantitative understanding of VLMs, while maintaining their overall performance on common benchmarks.

81, TITLE: A Survey of Secure Computation Using Trusted Execution Environments
AUTHORS: XIAOGUO LI et. al.
CATEGORY: cs.CR [cs.CR, cs.AI, cs.DB]
HIGHLIGHT: This article provides a systematic review and comparison of TEE-based secure computation protocols.

82, TITLE: More Than You've Asked For: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models
AUTHORS: KAI GRESHAKE et. al.
CATEGORY: cs.CR [cs.CR, cs.AI, cs.CL, cs.CY]
HIGHLIGHT: In this work, we show that augmenting LLMs with retrieval and API calling capabilities (so-called Application-Integrated LLMs) induces a whole new set of attack vectors.

83, TITLE: Adaptive Approximate Implicitization of Planar Parametric Curves Via Weak Gradient Constraints
AUTHORS: Minghao Guo ; Yan Gao ; Zheng Pan
CATEGORY: cs.GR [cs.GR, cs.CG, cs.CV]
HIGHLIGHT: Adaptive Approximate Implicitization of Planar Parametric Curves Via Weak Gradient Constraints

84, TITLE: Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data
AUTHORS: Alexander Flick ; Sebastian Jäger ; Ivana Trajanovska ; Felix Biessmann
CATEGORY: cs.IR [cs.IR, cs.AI, cs.LG]
HIGHLIGHT: Our models can reliably predict product attributes across online shops, languages, or both.

85, TITLE: Concept Learning for Interpretable Multi-Agent Reinforcement Learning
AUTHORS: Renos Zabounidis ; Joseph Campbell ; Simon Stepputtis ; Dana Hughes ; Katia Sycara
CATEGORY: cs.LG [cs.LG, cs.AI, cs.RO]
HIGHLIGHT: Multi-agent robotic systems are increasingly operating in real-world environments in close proximity to humans, yet are largely controlled by policy models with inscrutable deep neural network representations. We introduce a method for incorporating interpretable concepts from a domain expert into models trained through multi-agent reinforcement learning, by requiring the model to first predict such concepts then utilize them for decision making.

86, TITLE: Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective
AUTHORS: Zhengbao He ; Tao Li ; Sizhe Chen ; Xiaolin Huang
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this paper, we for the first time decouple the FGSM examples into data-information and self-information, which reveals an interesting phenomenon called "self-fitting".

87, TITLE: Learning Manifold Dimensions with Conditional Variational Autoencoders
AUTHORS: Yijia Zheng ; Tong He ; Yixuan Qiu ; David Wipf
CATEGORY: cs.LG [cs.LG, cs.AI, stat.ML]
HIGHLIGHT: Moreover, it remains unclear how such considerations would change when various types of conditioning variables are introduced, or when the data support is extended to a union of manifolds (e.g., as is likely the case for MNIST digits and related). In this work, we address these points by first proving that VAE global minima are indeed capable of recovering the correct manifold dimension.

88, TITLE: K-SHAP: Policy Clustering Algorithm for Anonymous State-Action Pairs
AUTHORS: Andrea Coletta ; Svitlana Vyetrenko ; Tucker Balch
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this paper, we propose a Policy Clustering algorithm, called K-SHAP, that learns to group anonymous state-action pairs according to the agent policies.

89, TITLE: Diverse Policy Optimization for Structured Action Space
AUTHORS: Wenhao Li ; Baoxiang Wang ; Shanchao Yang ; Hongyuan Zha
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this paper, we aim to seek diverse policies in an under-explored setting, namely RL tasks with structured action spaces with the two properties of composability and local dependencies.

90, TITLE: Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model Market
AUTHORS: Naibo Wang ; Wenjie Feng ; Fusheng Liu ; Moming Duan ; See-Kiong Ng
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this paper, we present a novel Data-Free Diversity-Based method called DeDES to address the ensemble selection problem for models generated by one-shot federated learning in practical applications such as model markets.

91, TITLE: Power Time Series Forecasting By Pretrained LM
AUTHORS: Tian Zhou ; PeiSong Niu ; Xue Wang ; Liang Sun ; Rong Jin
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this study, we examine the effectiveness of using a transformer model that has been pre-trained on natural language or image data and then fine-tuned for time series forecasting with minimal modifications, specifically, without altering the self-attention and feedforward layers of the residual blocks.

92, TITLE: A Dynamic-Neighbor Particle Swarm Optimizer for Accurate Latent Factor Analysis
AUTHORS: Jia Chen ; Yixian Chun ; Yuanyi Liu ; Renyu Zhang ; Yang Hu
CATEGORY: cs.LG [cs.LG, cs.NE]
HIGHLIGHT: However, the particles within the swarm follow the static evolution paths and only share the global best information, which limits the particles' searching area to cause sub-optimum issue. To address this issue, this paper proposes a Dynamic-neighbor-cooperated Hierarchical PSO-enhanced LFA model with two-fold main ideas.

93, TITLE: Bayesian Structure Scores for Probabilistic Circuits
AUTHORS: Yang Yang ; Gennaro Gala ; Robert Peharz
CATEGORY: cs.LG [cs.LG, cs.AI, stat.ML]
HIGHLIGHT: In this paper, we develop Bayesian structure scores for deterministic PCs, i.e., the structure likelihood with parameters marginalized out, which are well known as rigorous objectives for structure learning in probabilistic graphical models.

94, TITLE: What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers
AUTHORS: Ido Galil ; Mohammed Dabbah ; Ran El-Yaniv
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: We present a novel and comprehensive study of selective prediction and the uncertainty estimation performance of 523 existing pretrained deep ImageNet classifiers that are available in popular repositories.

95, TITLE: An Adam-enhanced Particle Swarm Optimizer for Latent Factor Analysis
AUTHORS: Jia Chen ; Renyu Zhang ; Yuanyi Liu
CATEGORY: cs.LG [cs.LG, cs.NE]
HIGHLIGHT: However, the hyper-parameters of the PSO-LFA model have to tune manually, which is inconvenient for widely adoption and limits the learning rate as a fixed value. To address this issue, we propose an Adam-enhanced Hierarchical PSO-LFA model, which refines the latent factors with a sequential Adam-adjusting hyper-parameters PSO algorithm.

96, TITLE: Revisiting The Gumbel-Softmax in MADDPG
AUTHORS: Callum Rhys Tilbury ; Filippos Christianos ; Stefano V. Albrecht
CATEGORY: cs.LG [cs.LG, cs.AI, cs.MA, stat.ML]
HIGHLIGHT: MADDPG is an algorithm in multi-agent reinforcement learning (MARL) that extends the popular single-agent method, DDPG, to multi-agent scenarios.

97, TITLE: Uncertainty Guided Ensemble Self-Training for Semi-Supervised Global Field Reconstruction
AUTHORS: Yunyang Zhang ; Zhiqiang Gong ; Xiaoyu Zhao ; Wen Yao
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: To solve the problem, this paper proposes Uncertainty Guided Ensemble Self-Training (UGE-ST), using plentiful unlabeled data to improve reconstruction performance.

98, TITLE: Local and Global Explainability Metrics for Machine Learning Predictions
AUTHORS: Cristian Munoz ; Kleyton da Costa ; Bernardo Modenesi ; Adriano Koshiyama
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this paper, we present novel quantitative metrics frameworks for interpreting the predictions of classifier and regressor models.

99, TITLE: Out-of-Domain Robustness Via Targeted Augmentations
AUTHORS: Irena Gao ; Shiori Sagawa ; Pang Wei Koh ; Tatsunori Hashimoto ; Percy Liang
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: In this work, we study principles for designing data augmentations for out-of-domain (OOD) generalization.

100, TITLE: A Deep Neural Network Based Approach to Building Budget-Constrained Models for Big Data Analysis
AUTHORS: Rui Ming ; Haiping Xu ; Shannon E. Gibbs ; Donghui Yan ; Ming Shao
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this paper, we introduce an approach to eliminating less important features for big data analysis using Deep Neural Networks (DNNs).

101, TITLE: FAIlureNotes: Supporting Designers in Understanding The Limits of AI Models for Computer Vision Tasks
AUTHORS: Steven Moore ; Q. Vera Liao ; Hariharan Subramonyam
CATEGORY: cs.LG [cs.LG, cs.CV, cs.HC]
HIGHLIGHT: In this work, we introduced a failure-driven design approach to AI, a workflow that encourages designers to explore model behavior and failure patterns early in the design process.

102, TITLE: Change Is Hard: A Closer Look at Subpopulation Shift
AUTHORS: Yuzhe Yang ; Haoran Zhang ; Dina Katabi ; Marzyeh Ghassemi
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CV]
HIGHLIGHT: In this work, we provide a fine-grained analysis of subpopulation shift.

103, TITLE: LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
AUTHORS: ZHICHEN LAI et. al.
CATEGORY: cs.LG [cs.LG, cs.AI, cs.DB]
HIGHLIGHT: To achieve this goal, we characterize popular CTS forecasting models and yield two observations that indicate directions for lightweight CTS forecasting. On this basis, we propose the LightCTS framework that adopts plain stacking of temporal and spatial operators instead of alternate stacking that is much more computationally expensive.

104, TITLE: A Framework for Benchmarking Class-out-of-distribution Detection and Its Application to ImageNet
AUTHORS: Ido Galil ; Mohammed Dabbah ; Ran El-Yaniv
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: In this paper we present a novel framework to benchmark the ability of image classifiers to detect class-out-of-distribution instances (i.e., instances whose true labels do not appear in the training distribution) at various levels of detection difficulty.

105, TITLE: Quantifying & Modeling Feature Interactions: An Information Decomposition Framework
AUTHORS: PAUL PU LIANG et. al.
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CL, cs.CV, cs.IT, math.IT]
HIGHLIGHT: Subsequently, how can we capture these interactions using suitable data-driven methods? To answer this question, we propose an information-theoretic approach to quantify the degree of redundancy, uniqueness, and synergy across input features, which we term the PID statistics of a multimodal distribution.

106, TITLE: A Comparative Assessment of Deep Learning Models for Day-ahead Load Forecasting: Investigating Key Accuracy Drivers
AUTHORS: SOTIRIS PELEKIS et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: To that end, different forecasting methods have been proposed in the literature for day-ahead load forecasting, including a variety of deep learning models that are currently considered to achieve state-of-the-art performance.

107, TITLE: A Comprehensive Survey on Source-free Domain Adaptation
AUTHORS: Zhiqi Yu ; Jingjing Li ; Zhekai Du ; Lei Zhu ; Heng Tao Shen
CATEGORY: cs.LG [cs.LG, cs.CV, cs.MM]
HIGHLIGHT: Instead of presenting each approach independently, we modularize several components of each method to more clearly illustrate their relationships and mechanics in light of the composite properties of each method.

108, TITLE: Does Deep Learning Learn to Abstract? A Systematic Probing Framework
AUTHORS: SHENGNAN AN et. al.
CATEGORY: cs.LG [cs.LG, cs.CL]
HIGHLIGHT: In this paper, we introduce a systematic probing framework to explore the abstraction capability of deep learning models from a transferability perspective.

109, TITLE: Aligning Text-to-Image Models Using Human Feedback
AUTHORS: KIMIN LEE et. al.
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CV]
HIGHLIGHT: However, current text-to-image models often generate images that are inadequately aligned with text prompts. We propose a fine-tuning method for aligning such models using human feedback, comprising three stages.

110, TITLE: Combining Search Strategies to Improve Performance in The Calibration of Economic ABMs
AUTHORS: Aldo Glielmo ; Marco Favorito ; Debmallya Chanda ; Domenico Delli Gatti
CATEGORY: cs.LG [cs.LG, cs.AI, cs.MA, econ.GN, q-fin.EC]
HIGHLIGHT: We find that methods based on random-forest surrogates are particularly efficient, and that combining search methods generally increases performance since the biases of any single method are mitigated. Moving from these observations, we propose a reinforcement learning (RL) scheme to automatically select and combine search methods on-the-fly during a calibration run.

111, TITLE: PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks
AUTHORS: XINLING YU et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: \textit{Objective:} In this paper, we introduce Physics-Informed Fourier Networks (PIFONs) for Electrical Properties (EP) Tomography (EPT).

112, TITLE: Domain Generalisation Via Domain Adaptation: An Adversarial Fourier Amplitude Approach
AUTHORS: Minyoung Kim ; Da Li ; Timothy Hospedales
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: We tackle the domain generalisation (DG) problem by posing it as a domain adaptation (DA) task where we adversarially synthesise the worst-case target domain and adapt a model to that worst-case domain, thereby improving the model's robustness.

113, TITLE: Active Learning for Structural Reliability Analysis with Multiple Limit State Functions Through Variance-enhanced PC-Kriging Surrogate Models
AUTHORS: J. Moran A. ; P. G. Morato ; P. Rigo
CATEGORY: cs.LG [cs.LG, cs.AI, stat.AP, stat.CO]
HIGHLIGHT: In this work, we investigate the capability of active learning approaches for efficiently selecting training samples under a limited computational budget while still preserving the accuracy associated with multiple surrogated limit states.

114, TITLE: Do We Really Need Complicated Model Architectures For Temporal Networks?
AUTHORS: WEILIN CONG et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this paper, we propose GraphMixer, a conceptually and technically simple architecture that consists of three components: (1) a link-encoder that is only based on multi-layer perceptrons (MLP) to summarize the information from temporal links, (2) a node-encoder that is only based on neighbor mean-pooling to summarize node information, and (3) an MLP-based link classifier that performs link prediction based on the outputs of the encoders.

115, TITLE: Particle Swarm Optimization in 3D Medical Image Registration: A Systematic Review
AUTHORS: Lucia Ballerini
CATEGORY: cs.NE [cs.NE]
HIGHLIGHT: Swarm Intelligence techniques are very effective and efficient optimization methods. This systematic review focuses on 3D medical image registration using Particle Swarm Optimization

116, TITLE: Language Model Crossover: Variation Through Few-Shot Prompting
AUTHORS: ELLIOT MEYERSON et. al.
CATEGORY: cs.NE [cs.NE]
HIGHLIGHT: This paper pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover.

117, TITLE: On The Simulation of Perception Errors in Autonomous Vehicles
AUTHORS: Andrea Piazzoni ; Jim Cherian ; Justin Dauwels ; Lap-Pui Chau
CATEGORY: cs.RO [cs.RO, cs.AI, C.4; I.2; I.6]
HIGHLIGHT: In this article, we define Perception Error Models (PEM), a virtual simulation component that can enable the analysis of the impact of perception errors on AV safety, without the need to model the sensors themselves.

118, TITLE: Improving Safety in Physical Human-robot Collaboration Via Deep Metric Learning
AUTHORS: Maryam Rezayati ; Grammatiki Zanni ; Ying Zaoshi ; Davide Scaramuzza ; Hans Wernher van de Venn
CATEGORY: cs.RO [cs.RO, cs.AI]
HIGHLIGHT: This work uses the Deep Metric Learning (DML) approach to distinguish between non-contact robot movement, intentional contact aimed at physical human-robot interaction, and collision situations.

119, TITLE: Category-level Shape Estimation for Densely Cluttered Objects
AUTHORS: Zhenyu Wu ; Ziwei Wang ; Jiwen Lu ; Haibin Yan
CATEGORY: cs.RO [cs.RO, cs.CV]
HIGHLIGHT: In this paper, we propose a category-level shape estimation method for densely cluttered objects.

120, TITLE: Lang2LTL: Translating Natural Language Commands to Temporal Robot Task Specification
AUTHORS: JASON XINYU LIU et. al.
CATEGORY: cs.RO [cs.RO, cs.AI, cs.CL, cs.FL]
HIGHLIGHT: However, existing approaches cannot accurately and robustly translate English sentences to their equivalent LTL formulas in unseen environments. To address this problem, we propose Lang2LTL, a novel modular system that leverages pretrained large language models to first extract referring expressions from a natural language command, then ground the expressions to real-world landmarks and objects, and finally translate the command into an LTL task specification for the robot.

121, TITLE: MVTrans: Multi-View Perception of Transparent Objects
AUTHORS: YI RU WANG et. al.
CATEGORY: cs.RO [cs.RO, cs.AI, cs.CV]
HIGHLIGHT: In this paper, we forgo the unreliable depth map from RGB-D sensors and extend the stereo based method.

122, TITLE: Trustworthy Reinforcement Learning for Quadrotor UAV Tracking Control Systems
AUTHORS: Yanran Wang ; David Boyle
CATEGORY: cs.RO [cs.RO, cs.AI, cs.SY, eess.SY]
HIGHLIGHT: We propose a novel, interpretable trajectory tracker integrating a Distributional Reinforcement Learning disturbance estimator for unknown aerodynamic effects with a Stochastic Model Predictive Controller (SMPC).

123, TITLE: Kinematics and Dynamics Modeling of 7 Degrees of Freedom Human Lower Limb Using Dual Quaternions Algebra
AUTHORS: Zineb Benhmidouch ; Saad Moufid ; Aissam Ait Omar
CATEGORY: cs.RO [cs.RO, cs.AI]
HIGHLIGHT: Denavit and Hartenberg based methods as Cardan, Fick and Euler angles describe the position and orientation of an end-effector in Three Dimensional (3D) space.

124, TITLE: MCWDST: A Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media
AUTHORS: Ciprian-Octavian Truic? ; Elena-Simona Apostol ; Radu-C?t?lin Nicolescu ; Panagiotis Karras
CATEGORY: cs.SI [cs.SI, cs.AI, cs.CL, cs.NE]
HIGHLIGHT: A need to detect untruthful information, also known as fake news, arises. In this paper, we present an end-to-end solution that accurately detects fake news and immunizes network nodes that spread them in real-time.

125, TITLE: Do Machine Learning Models Produce TypeScript Types That Type Check?
AUTHORS: Ming-Ho Yee ; Arjun Guha
CATEGORY: cs.SE [cs.SE, cs.PL]
HIGHLIGHT: We present TypeWeaver, a TypeScript type migration tool that can be used with an arbitrary type prediction model.

126, TITLE: Unsupervised Noise Adaptation Using Data Simulation
AUTHORS: Chen Chen ; Yuchen Hu ; Heqing Zou ; Linhui Sun ; Eng Siong Chng
CATEGORY: cs.SD [cs.SD, cs.AI, eess.AS]
HIGHLIGHT: In this work, we focus on the unsupervised noise adaptation problem in speech enhancement, where the ground truth of target domain data is completely unavailable.

127, TITLE: Metric-oriented Speech Enhancement Using Diffusion Probabilistic Model
AUTHORS: Chen Chen ; Yuchen Hu ; Weiwei Weng ; Eng Siong Chng
CATEGORY: cs.SD [cs.SD, cs.CL, eess.AS]
HIGHLIGHT: This mismatch between the training objective and evaluation metric likely results in sub-optimal performance. To alleviate it, we propose a metric-oriented speech enhancement method (MOSE), which leverages the recent advances in the diffusion probabilistic model and integrates a metric-oriented training strategy into its reverse process.

128, TITLE: Bridging Synthetic and Real Images: A Transferable and Multiple Consistency Aided Fundus Image Enhancement Framework
AUTHORS: Erjian Guo ; Huazhu Fu ; Luping Zhou ; Dong Xu
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: In this work, we propose an end-to-end optimized teacher-student framework to simultaneously conduct image enhancement and domain adaptation.

129, TITLE: Unified Chest X-ray and Radiology Report Generation Model with Multi-view Chest X-rays
AUTHORS: HYUNGYUNG LEE et. al.
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: As part of this effort, we propose UniXGen, a unified chest X-ray and report generation model, with the following contributions. First, we design a unified model for bidirectional chest X-ray and report generation by adopting a vector quantization method to discretize chest X-rays into discrete visual tokens and formulating both tasks as sequence generation tasks.

130, TITLE: PLU-Net: Extraction of Multi-scale Feature Fusion
AUTHORS: Weihu Song
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: To address the issue, we develop atrous spatial pyramid pooling (ASPP) and combine it with the Squeeze-and-Excitation block (SE block), as well as present the PS module, which employs a broader and multi-scale receptive field at the network's bottom to obtain more detailed semantic information.