bluuebunny/crossref_metadata_2025 huggingface dataset:
MedSegDiff-V2: Diffusion-Based Medical Image Segmentation with Transformer
Authors: Wu Junde , Ji Wei , Fu Huazhu , Xu Min , Jin Yueming , Xu Yanwu
Published: March 2024

The Diffusion Probabilistic Model (DPM) has recently gained popularity in the field of computer vision, thanks to its image generation applications, such as Imagen, Latent Diffusion Models, and Stable Diffusion, which have demonstrated impressive capabilities and sparked much discussion within the community. Recent investigations have further unveiled the utility of DPM in the domain of medical image analysis, as underscored by the commendable performance exhibited by the medical image segmentation model across various tasks. Although these models were originally underpinned by a UNet architecture, there exists a potential avenue for enhancing their performance through the integration of vision transformer mechanisms. However, we discovered that simply combining these two models resulted in subpar performance. To effectively integrate these two cutting-edge techniques for the Medical image segmentation, we propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2. We verify its effectiveness on 20 medical image segmentation tasks with different image modalities. Through comprehensive evaluation, our approach demonstrates superiority over prior state-of-the-art (SOTA) methodologies. Code is released at https://github.com/KidsWithTokens/MedSegDiff.
Chain of Generation: Multi-Modal Gesture Synthesis via Cascaded Conditional Control
Authors: Xu Zunnan , Zhang Yachao , Yang Sicheng , Li Ronghui , Li Xiu
Published: March 2024

This study aims to improve the generation of 3D gestures by utilizing multimodal information from human speech. Previous studies have focused on incorporating additional modalities to enhance the quality of generated gestures. However, these methods perform poorly when certain modalities are missing during inference. To address this problem, we suggest using speech-derived multimodal priors to improve gesture generation. We introduce a novel method that separates priors from speech and employs multimodal priors as constraints for generating gestures. Our approach utilizes a chain-like modeling method to generate facial blendshapes, body movements, and hand gestures sequentially. Specifically, we incorporate rhythm cues derived from facial deformation and stylization prior based on speech emotions, into the process of generating gestures. By incorporating multimodal priors, our method improves the quality of generated gestures and eliminate the need for expensive setup preparation during inference. Extensive experiments and user studies confirm that our proposed approach achieves state-of-the-art performance.
Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues
Authors: Yang Xingxing , Chen Jie , Yang Zaifeng
Published: March 2024

Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands. As such, we propose to investigate the chromatic inter-dependencies in their respective hyperspectral embedding space. These embedded features can be fully exploited by querying the inter-channel correlations in a combinatorial manner, with the unique and complementary information efficiently fused into the final prediction. We found such independent modeling and combinatorial excavation mechanisms are extremely beneficial to uncover marginal spectral features, especially in the long wavelength bands. In addition, we have proposed a spatio-spectral attention block and a spectrum-fusion attention module, which greatly facilitates the excavation and fusion of information at both semantically long-range levels and fine-grained pixel levels across all dimensions. Extensive quantitative and qualitative experiments show that our method (dubbed CESST) achieves SOTA performance. Code for this project is at: https://github.com/AlexYangxx/CESST.
DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection
Authors: Ye Yunfan , Xu Kai , Huang Yuhang , Yi Renjiao , Cai Zhiping
Published: March 2024

Limited by the encoder-decoder architecture, learning-based edge detectors usually have difficulty predicting edge maps that satisfy both correctness and crispness. With the recent success of the diffusion probabilistic model (DPM), we found it is especially suitable for accurate and crisp edge detection since the denoising process is directly applied to the original image size. Therefore, we propose the first diffusion model for the task of general edge detection, which we call DiffusionEdge. To avoid expensive computational resources while retaining the final performance, we apply DPM in the latent space and enable the classic cross-entropy loss which is uncertainty-aware in pixel level to directly optimize the parameters in latent space in a distillation manner. We also adopt a decoupled architecture to speed up the denoising process and propose a corresponding adaptive Fourier filter to adjust the latent features of specific frequencies. With all the technical designs, DiffusionEdge can be stably trained with limited resources, predicting crisp and accurate edge maps with much fewer augmentation strategies. Extensive experiments on four edge detection benchmarks demonstrate the superiority of DiffusionEdge both in correctness and crispness. On the NYUDv2 dataset, compared to the second best, we increase the ODS, OIS (without post-processing) and AC by 30.2%, 28.1% and 65.1%, respectively. Code: https://github.com/GuHuangAI/DiffusionEdge.
UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation
Authors: Yi Kefu , Luo Kai , Luo Xiaolei , Huang Jiangui , Wu Hao , Hu Rongdong , Hao Wei
Published: March 2024

Multi-object tracking (MOT) in video sequences remains a challenging task, especially in scenarios with significant camera movements. This is because targets can drift considerably on the image plane, leading to erroneous tracking outcomes. Addressing such challenges typically requires supplementary appearance cues or Camera Motion Compensation (CMC). While these strategies are effective, they also introduce a considerable computational burden, posing challenges for real-time MOT. In response to this, we introduce UCMCTrack, a novel motion model-based tracker robust to camera movements. Unlike conventional CMC that computes compensation parameters frame-by-frame, UCMCTrack consistently applies the same compensation parameters throughout a video sequence. It employs a Kalman filter on the ground plane and introduces the Mapped Mahalanobis Distance (MMD) as an alternative to the traditional Intersection over Union (IoU) distance measure. By leveraging projected probability distributions on the ground plane, our approach efficiently captures motion patterns and adeptly manages uncertainties introduced by homography projections. Remarkably, UCMCTrack, relying solely on motion cues, achieves state-of-the-art performance across a variety of challenging datasets, including MOT17, MOT20, DanceTrack and KITTI. More details and code are available at https://github.com/corfyi/UCMCTrack.
TF-CLIP: Learning Text-Free CLIP for Video-Based Person Re-identification
Authors: Yu Chenyang , Liu Xuehu , Wang Yingquan , Zhang Pingping , Lu Huchuan
Published: March 2024

Large-scale language-image pre-trained models (e.g., CLIP) have shown superior performances on many cross-modal retrieval tasks.
However, the problem of transferring the knowledge learned from such models to video-based person re-identification (ReID) has barely been explored.
In addition, there is a lack of decent text descriptions in current ReID benchmarks.
To address these issues, in this work, we propose a novel one-stage text-free CLIP-based learning framework named TF-CLIP for video-based person ReID.
More specifically, we extract the identity-specific sequence feature as the CLIP-Memory to replace the text feature.
Meanwhile, we design a Sequence-Specific Prompt (SSP) module to update the CLIP-Memory online.
To capture temporal information, we further propose a Temporal Memory Diffusion (TMD) module, which consists of two key components: Temporal Memory Construction (TMC) and Memory Diffusion (MD).
Technically, TMC allows the frame-level memories in a sequence to communicate with each other, and to extract temporal information based on the relations within the sequence.
MD further diffuses the temporal memories to each token in the original features to obtain more robust sequence features.
Extensive experiments demonstrate that our proposed method shows much better results than other state-of-the-art methods on MARS, LS-VID and iLIDS-VID.
MM-Point: Multi-View Information-Enhanced Multi-Modal Self-Supervised 3D Point Cloud Understanding
Authors: Yu Hai-Tao , Song Mofei
Published: March 2024

In perception, multiple sensory information is integrated to map visual information from 2D views onto 3D objects, which is beneficial for understanding in 3D environments. But in terms of a single 2D view rendered from different angles, only limited partial information can be provided. The richness and value of Multi-view 2D information can provide superior self-supervised signals for 3D objects. In this paper, we propose a novel self-supervised point cloud representation learning method, MM-Point, which is driven by intra-modal and inter-modal similarity objectives. The core of MM-Point lies in the Multi-modal interaction and transmission between 3D objects and multiple 2D views at the same time. In order to more effectively simultaneously perform the consistent cross-modal objective of 2D multi-view information based on contrastive learning, we further propose Multi-MLP and Multi-level Augmentation strategies. Through carefully designed transformation strategies, we further learn Multi-level invariance in 2D Multi-views. MM-Point demonstrates state-of-the-art (SOTA) performance in various downstream tasks. For instance, it achieves a peak accuracy of 92.4% on the synthetic dataset ModelNet40, and a top accuracy of 87.8% on the real-world dataset ScanObjectNN, comparable to fully supervised methods. Additionally, we demonstrate its effectiveness in tasks such as few-shot classification, 3D part segmentation and 3D semantic segmentation.
Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting
Authors: Zhang Qi , Gong Yunfei , Chen Daijie , Chan Antoni B. , Huang Hui
Published: March 2024

Recent deep learning-based multi-view people detection (MVD) methods have shown promising results on existing datasets. However, current methods are mainly trained and evaluated on small, single scenes with a limited number of multi-view frames and fixed camera views. As a result, these methods may not be practical for detecting people in larger, more complex scenes with severe occlusions and camera calibration errors. This paper focuses on improving multi-view people detection by developing a supervised view-wise contribution weighting approach that better fuses multi-camera information under large scenes. Besides, a large synthetic dataset is adopted to enhance the model's generalization ability and enable more practical evaluation and comparison. The model's performance on new testing scenes is further improved with a simple domain adaptation technique. Experimental results demonstrate the effectiveness of our approach in achieving promising cross-scene multi-view people detection performance.
FaceRSA: RSA-Aware Facial Identity Cryptography Framework
Authors: Zhang Zhongyi , Wei Tianyi , Zhou Wenbo , Zhao Hanqing , Zhang Weiming , Yu Nenghai
Published: March 2024

With the flourishing of the Internet, sharing one's photos or automated processing of faces using computer vision technology has become an everyday occurrence. While enjoying the convenience, the concern for identity privacy is also emerging. Therefore, some efforts introduced the concept of ``password'' from traditional cryptography such as RSA into the face anonymization and deanonymization task to protect the facial identity without compromising the usability of the face image. However, these methods either suffer from the poor visual quality of the synthesis results or do not possess the full cryptographic properties, resulting in compromised security. In this paper, we present the first facial identity cryptography framework with full properties analogous to RSA. Our framework leverages the powerful generative capabilities of StyleGAN to achieve megapixel-level facial identity anonymization and deanonymization. Thanks to the great semantic decoupling of StyleGAN's latent space, the identity encryption and decryption process are performed in latent space by a well-designed password mapper in the manner of editing latent code. Meanwhile, the password-related information is imperceptibly hidden in the edited latent code owing to the redundant nature of the latent space. To make our cryptographic framework possesses all the properties analogous to RSA, we propose three types of loss functions: single anonymization loss, sequential anonymization loss, and associated anonymization loss. Extensive experiments and ablation analyses demonstrate the superiority of our method in terms of the quality of synthesis results, identity-irrelevant attributes preservation, deanonymization accuracy, and completeness of properties analogous to RSA.
ODTrack: Online Dense Temporal Token Learning for Visual Tracking
Authors: Zheng Yaozong , Zhong Bineng , Liang Qihua , Mo Zhiyi , Zhang Shengping , Li Xianxian
Published: March 2024

Online contextual reasoning and association across consecutive video frames are critical to perceive instances in visual tracking. However, most current top-performing trackers persistently lean on sparse temporal relationships between reference and search frames via an offline mode. Consequently, they can only interact independently within each image-pair and establish limited temporal correlations. To alleviate the above problem, we propose a simple, flexible and effective video-level tracking pipeline, named ODTrack, which densely associates the contextual relationships of video frames in an online token propagation manner. ODTrack receives video frames of arbitrary length to capture the spatio-temporal trajectory relationships of an instance, and compresses the discrimination features (localization information) of a target into a token sequence to achieve frame-to-frame association. This new solution brings the following benefits: 1) the purified token sequences can serve as prompts for the inference in the next video frame, whereby past information is leveraged to guide future inference; 2) the complex online update strategies are effectively avoided by the iterative propagation of token sequences, and thus we can achieve more efficient model representation and computation. ODTrack achieves a new SOTA performance on seven benchmarks, while running at real-time speed. Code and models are available at https://github.com/GXNU-ZhongLab/ODTrack.
SasWOT: Real-Time Semantic Segmentation Architecture Search WithOut Training
Authors: Zhu Chendi , Li Lujun , Wu Yuli , Sun Zhengxing
Published: March 2024

In this paper, we present SasWOT, the first training-free Semantic segmentation Architecture Search (SAS) framework via an auto-discovery proxy. Semantic segmentation is widely used in many real-time applications. For fast inference and memory efficiency, Previous SAS seeks the optimal segmenter by differentiable or RL Search. However, the significant computational costs of these training-based SAS limit their practical usage. To improve the search efficiency, we explore the training-free route but empirically observe that the existing zero-cost proxies designed on the classification task are sub-optimal on the segmentation benchmark. To address this challenge, we develop a customized proxy search framework for SAS tasks to augment its predictive capabilities. Specifically, we design the proxy search space based on the some observations: (1) different inputs of segmenter statistics can be well combined; (2) some basic operators can effectively improve the correlation. Thus, we build computational graphs with multiple statistics as inputs and different advanced basis arithmetic as the primary operations to represent candidate proxies. Then, we employ an evolutionary algorithm to crossover and mutate the superior candidates in the population based on correlation evaluation. Finally, based on the searched proxy, we perform the segmenter search without candidate training. In this way, SasWOT not only enables automated proxy optimization for SAS tasks but also achieves significant search acceleration before the retrain stage. Extensive experiments on Cityscapes and CamVid datasets demonstrate that SasWOT achieves superior trade-off between accuracy and speed over several state-of-the-art techniques. More remarkably, on Cityscapes dataset, SasWOT achieves the performance of 71.3% mIoU with the speed of 162 FPS.
Neural Time-Reversed Generalized Riccati Equation
Authors: Betti Alessandro , Casoni Michele , Gori Marco , Marullo Simone , Melacci Stefano , Tiezzi Matteo
Published: March 2024

Optimal control deals with optimization problems in which variables steer a dynamical system, and its outcome contributes to the objective function. Two classical approaches to solving these problems are Dynamic Programming and the Pontryagin Maximum Principle. In both approaches, Hamiltonian equations offer an interpretation of optimality through auxiliary variables known as costates. However, Hamiltonian equations are rarely used due to their reliance on forward-backward algorithms across the entire temporal domain. This paper introduces a novel neural-based approach to optimal control. Neural networks are employed not only for implementing state dynamics but also for estimating costate variables. The parameters of the latter network are determined at each time step using a newly introduced local policy referred to as the time-reversed generalized Riccati equation. This policy is inspired by a result discussed in the Linear Quadratic (LQ) problem, which we conjecture stabilizes state dynamics. We support this conjecture by discussing experimental results from a range of optimal control case studies.
Composing Biases by Using CP to Decompose Minimal Functional Dependencies for Acquiring Complex Formulae
Authors: Gindullin Ramiz , Beldiceanu Nicolas , Cheukam-Ngouonou Jovial , Douence Rémi , Quimper Claude-Guy
Published: March 2024

Given a table with a minimal set of input columns that functionally determines an output column, we introduce a method that tries to gradually decompose the corresponding minimal functional dependency (mfd) to acquire a formula expressing the output column in terms of the input columns. A first key element of the method is to create sub-problems that are easier to solve than the original formula acquisition problem, either because it learns formulae with fewer inputs parameters, or as it focuses on formulae of a particular class, such as Boolean formulae; as a result, the acquired formulae can mix different learning biases such as polynomials, conditionals or Boolean expressions. A second key feature of the method is that it can be applied recursively to find formulae that combine polynomial, conditional or Boolean sub-terms in a nested manner. The method was tested on data for eight families of combinatorial objects; new conjectures were found that were previously unattainable. The method often creates conjectures that combine several formulae into one with a limited number of automatically found Boolean terms.
Unveiling Implicit Deceptive Patterns in Multi-Modal Fake News via Neuro-Symbolic Reasoning
Authors: Dong Yiqi , He Dongxiao , Wang Xiaobao , Jin Youzhu , Ge Meng , Yang Carl , Jin Di
Published: March 2024

In the current Internet landscape, the rampant spread of fake news, particularly in the form of multi-modal content, poses a great social threat. While automatic multi-modal fake news detection methods have shown promising results, the lack of explainability remains a significant challenge. Existing approaches provide superficial explainability by displaying learned important components or views from well-trained networks, but they often fail to uncover the implicit deceptive patterns that reveal how fake news is fabricated. To address this limitation, we begin by predefining three typical deceptive patterns, namely image manipulation, cross-modal inconsistency, and image repurposing, which shed light on the mechanisms underlying fake news fabrication. Then, we propose a novel Neuro-Symbolic Latent Model called NSLM, that not only derives accurate judgments on the veracity of news but also uncovers the implicit deceptive patterns as explanations. Specifically, the existence of each deceptive pattern is expressed as a two-valued learnable latent variable, which is acquired through amortized variational inference and weak supervision based on symbolic logic rules. Additionally, we devise pseudo-siamese networks to capture distinct deceptive patterns effectively. Experimental results on two real-world datasets demonstrate that our NSLM achieves the best performance in fake news detection while providing insightful explanations of deceptive patterns.
Your Career Path Matters in Person-Job Fit
Authors: Gong Zhuocheng , Song Yang , Zhang Tao , Wen Ji-Rong , Zhao Dongyan , Yan Rui
Published: March 2024

We are again confronted with one of the most vexing aspects of the advancement of technology: automation and AI technology cause the devaluation of human labor, resulting in unemployment. With this background, automatic person-job fit systems are promising solutions to promote the employment rate. The purpose of person-job fit is to calculate a matching score between the job seeker's resume and the job posting, determining whether the job seeker is suitable for the position. In this paper, we propose a new approach to person-job fit that characterizes the hidden preference derived from the job seeker's career path. We categorize and utilize three types of preferences in the career path: consistency, likeness, and continuity. We prove that understanding the career path enables us to provide more appropriate career suggestions to job seekers. To demonstrate the practical value of our proposed model, we conduct extensive experiments on real-world data extracted from an online recruitment platform and then present detailed cases to show how the career path matter in person-job fit.
Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis
Authors: Kong Dexu , Zhang Anping , Li Yang
Published: March 2024

Dynamic community detection methods often lack effective mechanisms to ensure temporal consistency, hindering the analysis of network evolution. In this paper, we propose a novel deep graph clustering framework with temporal consistency regularization on inter-community structures, inspired by the concept of minimal network topological changes within short intervals. Specifically, to address the representation collapse problem, we first introduce MFC, a matrix factorization-based deep graph clustering algorithm that preserves node embedding. Based on static clustering results, we construct probabilistic community networks and compute their persistence homology, a robust topological measure, to assess structural similarity between them. Moreover, a novel neural network regularization TopoReg is introduced to ensure the preservation of topological similarity between inter-community structures over time intervals. Our approach enhances temporal consistency and clustering accuracy on real-world datasets with both fixed and varying numbers of communities. It is also a pioneer application of TDA in temporally persistent community detection, offering an insightful contribution to field of network analysis. Code and data are available at the public git repository: https://github.com/kundtx/MFC-TopoReg.
UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models
Authors: Li Xiaoxi , Zhou Yujia , Dou Zhicheng
Published: March 2024

Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in natural language processing. Existing methods for GDR and GAR rely on separate retrieval and reader modules, which hinder simultaneous optimization. To overcome this, we present UniGen, a Unified Generative framework for retrieval and question answering that integrates both tasks into a single generative model leveraging the capabilities of large language models. UniGen employs a shared encoder and two distinct decoders for generative retrieval and question answering. To facilitate the learning of both tasks, we introduce connectors, generated by large language models, to bridge the gaps between query inputs and generation targets, as well as between document identifiers and answers. Furthermore, we propose an iterative enhancement strategy that leverages generated answers and retrieved documents to iteratively improve both tasks. Through extensive experiments on the MS MARCO and NQ datasets, we demonstrate the effectiveness of UniGen, showcasing its superior performance in both retrieval and question answering tasks.
Graph Contrastive Invariant Learning from the Causal Perspective
Authors: Mo Yanhu , Wang Xiao , Fan Shaohua , Shi Chuan
Published: March 2024

Graph contrastive learning (GCL), learning the node representation by contrasting two augmented graphs in a self-supervised way, has attracted considerable attention. GCL is usually believed to learn the invariant representation. However, does this understanding always hold in practice? In this paper, we first study GCL from the perspective of causality. By analyzing GCL with the structural causal model (SCM), we discover that traditional GCL may not well learn the invariant representations due to the non-causal information contained in the graph. How can we fix it and encourage the current GCL to learn better invariant representations? The SCM offers two requirements and motives us to propose a novel GCL method. Particularly, we introduce the spectral graph augmentation to simulate the intervention upon non-causal factors. Then we design the invariance objective and independence objective to better capture the causal factors. Specifically, (i) the invariance objective encourages the encoder to capture the invariant information contained in causal variables, and (ii) the independence objective aims to reduce the influence of confounders on the causal variables. Experimental results demonstrate the effectiveness of our approach on node classification tasks.
Preference Aware Dual Contrastive Learning for Item Cold-Start Recommendation
Authors: Wang Wenbo , Liu Bingquan , Shan Lili , Sun Chengjie , Chen Ben , Guan Jian
Published: March 2024

Existing cold-start recommendation methods often adopt item-level alignment strategies to align the content feature and the collaborative feature of warm items for model training, however, cold items in the test stage have no historical interactions with users to obtain the collaborative feature. These existing models ignore the aforementioned condition of cold items in the training stage, resulting in the performance limitation. In this paper, we propose a preference aware dual contrastive learning based recommendation model (PAD-CLRec), where the user preference is explored to take into account the condition of cold items for feature alignment. Here, the user preference is obtained by aggregating a group of collaborative feature of the warm items in the user's purchase records. Then, a group-level alignment between the user preference and the item's content feature can be realized via a proposed preference aware contrastive function for enhancing cold-item recommendation. In addition, a joint objective function is introduced to achieve a better trade-off between the recommendation performance of warm items and cold items from both item-level and group-level perspectives, yielding better overall recommendation performance. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method, and the results show the superiority of our method, as compared with the state-of-the-arts.
Approval-Based Committee Voting in Practice: A Case Study of (over-)Representation in the Polkadot Blockchain
Authors: Boehmer Niclas , Brill Markus , Cevallos Alfonso , Gehrlein Jonas , Sánchez-Fernández Luis , Schmidt-Kraepelin Ulrike
Published: March 2024

We provide the first large-scale data collection of real-world approval-based committee elections. These elections have been conducted on the Polkadot blockchain as part of their Nominated Proof-of-Stake mechanism and contain around one thousand candidates and tens of thousands of (weighted) voters each. We conduct an in-depth study of application-relevant questions, including a quantitative and qualitative analysis of the outcomes returned by different voting rules. Besides considering proportionality measures that are standard in the multiwinner voting literature, we pay particular attention to less-studied measures of overrepresentation, as these are closely related to the security of the Polkadot network. We also analyze how different design decisions such as the committee size affect the examined measures.
Implications of Distance over Redistricting Maps: Central and Outlier Maps
Authors: Esmaeili Seyed A. , Chakrabarti Darshan , Grape Hayley , Brubach Brian
Published: March 2024

In representative democracy, a redistricting map is chosen to partition an electorate into districts which each elects a representative. A valid redistricting map must satisfy a collection of constraints such as being compact, contiguous, and of almost-equal population. However, these constraints are loose enough to enable an enormous ensemble of valid redistricting maps. This enables a partisan legislature to gerrymander by choosing a map which unfairly favors it. In this paper, we introduce an interpretable and tractable distance measure over redistricting maps which does not use election results and study its implications over the ensemble of redistricting maps. Specifically, we define a central map which may be considered "most typical" and give a rigorous justification for it by showing that it mirrors the Kemeny ranking in a scenario where we have a committee voting over a collection of redistricting maps to be drawn. We include runnning time and sample complexity analysis for our algorithms, including some negative results which hold using any algorithm. We further study outlier detection based on this distance measure and show that our framework can detect some gerrymandered maps. More precisely, we show some maps that are widely considered to be gerrymandered that lie very far away from our central maps in comparison to a large ensemble of valid redistricting maps. Since our distance measure does not rely on election results, this gives a significant advantage in gerrymandering detection which is lacking in all previous methods.
Eliciting Honest Information from Authors Using Sequential Review
Authors: Zhang Yichi , Schoenebeck Grant , Su Weijie
Published: March 2024

In the setting of conference peer review, the conference aims to accept high-quality papers and reject low-quality papers based on noisy review scores. A recent work proposes the isotonic mechanism, which can elicit the ranking of paper qualities from an author with multiple submissions to help improve the conference's decisions. However, the isotonic mechanism relies on the assumption that the author's utility is both an increasing and a convex function with respect to the review score, which is often violated in realistic settings (e.g.~when authors aim to maximize the number of accepted papers). In this paper, we propose a sequential review mechanism that can truthfully elicit the ranking information from authors while only assuming the agent's utility is increasing with respect to the true quality of her accepted papers. The key idea is to review the papers of an author in a sequence based on the provided ranking and conditioning the review of the next paper on the review scores of the previous papers. Advantages of the sequential review mechanism include: 1) eliciting truthful ranking information in a more realistic setting than prior work; 2) reducing the reviewing workload and increasing the average quality of papers being reviewed; 3) incentivizing authors to write fewer papers of higher quality.
Working Memory Capacity of ChatGPT: An Empirical Study
Authors: Gong Dongyu , Wan Xingchen , Wang Dingmin
Published: March 2024

Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In this paper, we systematically assess the working memory capacity of ChatGPT, a large language model developed by OpenAI, by examining its performance in verbal and spatial n-back tasks under various conditions. Our experiments reveal that ChatGPT has a working memory capacity limit strikingly similar to that of humans. Furthermore, we investigate the impact of different instruction strategies on ChatGPT's performance and observe that the fundamental patterns of a capacity limit persist. From our empirical findings, we propose that n-back tasks may serve as tools for benchmarking the working memory capacity of large language models and hold potential for informing future efforts aimed at enhancing AI working memory.
DanceMVP: Self-Supervised Learning for Multi-Task Primitive-Based Dance Performance Assessment via Transformer Text Prompting
Authors: Zhong Yun , Demiris Yiannis
Published: March 2024

Dance is generally considered to be complex for most people as it requires coordination of numerous body motions and accurate responses to the musical content and rhythm. Studies on automatic dance performance assessment could help people improve their sensorimotor skills and promote research in many fields, including human motion analysis and motion generation. Recent papers on dance performance assessment usually evaluate simple dance motions with a single task - estimating final performance scores. In this paper, we propose DanceMVP: multi-task dance performance assessment via text prompting that solves three related tasks - (i) dance vocabulary recognition, (ii) dance performance scoring and (iii) dance rhythm evaluation. In the pre-training phase, we contrastively learn the primitive-based features of complex dance motion and music using the InfoNCE loss. For the downstream task, we propose a transformer-based text prompter to perform multi-task evaluations for the three proposed assessment tasks. Also, we build a multimodal dance-music dataset named ImperialDance. The novelty of our ImperialDance is that it contains dance motions for diverse expertise levels and a significant amount of repeating dance sequences for the same choreography to keep track of the dance performance progression. Qualitative results show that our pre-trained feature representation could cluster dance pieces for different dance genres, choreographies, expertise levels and primitives, which generalizes well on both ours and other dance-music datasets. The downstream experiments demonstrate the robustness and improvement of our method over several ablations and baselines across all three tasks, as well as monitoring the users' dance level progression.
LINGO-Space: Language-Conditioned Incremental Grounding for Space
Authors: Kim Dohyun , Oh Nayoung , Hwang Deokmin , Park Daehyung
Published: March 2024

We aim to solve the problem of spatially localizing composite instructions referring to space: space grounding. Compared to current instance grounding, space grounding is challenging due to the ill-posedness of identifying locations referred to by discrete expressions and the compositional ambiguity of referring expressions. Therefore, we propose a novel probabilistic space-grounding methodology (LINGO-Space) that accurately identifies a probabilistic distribution of space being referred to and incrementally updates it, given subsequent referring expressions leveraging configurable polar distributions. Our evaluations show that the estimation using polar distributions enables a robot to ground locations successfully through 20 table-top manipulation benchmark tests. We also show that updating the distribution helps the grounding method accurately narrow the referring space. We finally demonstrate the robustness of the space grounding with simulated manipulation and real quadruped robot navigation tasks. Code and videos are available at https://lingo-space.github.io.
Reinforcement Learning and Data-Generation for Syntax-Guided Synthesis
Authors: Parsert Julian , Polgreen Elizabeth
Published: March 2024

Program synthesis is the task of automatically generating code based on a specification. In Syntax-Guided Synthesis (SyGuS) this specification is a combination of a syntactic template and a logical formula, and the result is guaranteed to satisfy both. We present a reinforcement-learning guided algorithm for SyGuS which uses Monte-Carlo Tree Search (MCTS) to search the space of candidate solutions. Our algorithm learns policy and value functions which, combined with the upper confidence bound for trees, allow it to balance exploration and exploitation. A common challenge in applying machine learning approaches to syntax-guided synthesis is the scarcity of training data. To address this, we present a method for automatically generating training data for SyGuS based on anti-unification of existing first-order satisfiability problems, which we use to train our MCTS policy. We implement and evaluate this setup and demonstrate that learned policy and value improve the synthesis performance over a baseline by over 26 percentage points in the training and testing sets. Our tool outperforms state-of-the-art tool cvc5 on the training set and performs comparably in terms of the total number of problems solved on the testing set (solving 23% of the benchmarks on which cvc5 fails). We make our data set publicly available, to enable further application of machine learning methods to the SyGuS problem.
Adaptive Reactive Synthesis for LTL and LTLf Modulo Theories
Authors: Rodríguez Andoni , Sánchez César
Published: March 2024

Reactive synthesis is the process of generate correct con- trollers from temporal logic specifications. Typically, synthesis is restricted to Boolean specifications in LTL. Recently, a Boolean abstraction technique allows to translate LTLT specifications that contain literals in theories into equi-realizable LTL specifications, but no full synthesis procedure exists yet. In synthesis modulo theories, the system receives valuations of environment variables (from a first-order theory T ) and outputs valuations of system variables from T . In this paper, we address how to syntheize a full controller using a combination of the static Boolean controller obtained from the Booleanized LTL specification together with on-the-fly queries to a solver that produces models of satisfiable existential T formulae. This is the first synthesis method for LTL modulo theories. Additionally, our method can produce adaptive responses which increases explainability and can improve runtime properties like performance. Our approach is applicable to both LTL modulo theories and LTLf modulo theories.
BeliefFlow: A Framework for Logic-Based Belief Diffusion via Iterated Belief Change
Authors: Schwind Nicolas , Inoue Katsumi , Konieczny Sébastien , Marquis Pierre
Published: March 2024

This paper presents BeliefFlow, a novel framework for representing how logical beliefs spread among interacting agents within a network. In a Belief Flow Network (BFN), agents communicate asynchronously. The agents' beliefs are represented using epistemic states, which encompass their current beliefs and conditional beliefs guiding future changes. When communication occurs between two connected agents, the receiving agent changes its epistemic state using an improvement operator, a well-known type of rational iterated belief change operator that generalizes belief revision operators. We show that BFNs satisfy appealing properties, leading to two significant outcomes. First, in any BFN with strong network connectivity, the beliefs of all agents converge towards a global consensus. Second, within any BFN, we show that it is possible to compute an optimal strategy for influencing the global beliefs. This strategy, which involves controlling the beliefs of a least number of agents through bribery, can be identified from the topology of the network and can be computed in polynomial time.
Mechanised deep tunnel excavation in saturated clayey soils: a pre-design hydro-mechanically coupled method for the assessment of both spoil and face volume loss
Authors: Flessati Luca , Di Prisco Claudio
Published: March 2024

Mechanised tunnel excavation in soils causes over-excavations, potentially leading to large amounts of spoil and settlements at ground level. An accurate estimation of over-excavations is crucial in the pre-design phase for assessing costs, determining the appropriate excavation method and choosing the muck management strategy. Currently, the estimation is based on experience and data from similar projects, but this becomes difficult when project conditions are heterogeneous. As an alternative, finite-element analyses are time-consuming and not suitable for early design stages; therefore, simplified tools are needed. In this paper, the authors present a simplified approach putting in relation face extrusion with estimated spoil mass and face volume loss. This approach, conceived for deep tunnels, is the extension to the case of mechanised tunnelling of a hydro-mechanical coupled meta-model derived from finite-element numerical analyses for tunnels in clayey soils excavated by using conventional techniques (i.e. without any use of tunnel-boring machines). The model has been validated against field data relative to a case study. The approach can be used in the early design process to identify tunnel-boring machine characteristics and provide preliminary cost estimates. In addition, during the construction phase, the method can be employed to interpret monitoring data and pre-design mitigation measures for unforeseen soil profile variations.
Fframwaith ar gyfer ymgysylltiad teuluoedd: Mynd y tu hwnt i Fframwaith Epstein
Authors: Goodall Janet
Published: March 2024

Mae’r papur hwn yn cynnig fframwaith newydd i ysgolion ei ddefnyddio wrth werthuso a chefnogi eu gwaith yn ymwneud ag ymgysylltiad rhieni. Mae’r papur yn dechrau drwy wneud archwiliad byr o’r cysyniad o ymgysylltiad rhieni ac yn symud ymlaen i archwilio fframwaith Epstein, sef y fframwaith a ddefnyddir amlaf. Cynigiwyd y fframwaith hwn am y tro cyntaf yn y ganrif ddiwethaf, ac mae’r papur yn nodi’r datblygiadau yn ein dealltwriaeth o ymgysylltiad rhieni ers hynny. Daw’r papur i ben drwy gynnig fframwaith newydd i ddisodli’r un a ddefnyddir amlaf ar hyn o bryd. Mae atodiad yn rhoi fersiwn ymarferydd o’r fframwaith i alluogi cynllunio a gwerthuso.
Safety and Efficacy of Using MaiLi® Precise by Mean of Intradermal Injections for Correcting Perioral Fine Lines
Authors: P Borregón , J Caicedo , R Glacet , E Santaella , F Vercesi
Published: March 2024

Introduction: Over the last twenty years, dermal fillers have become the backbone for rejuvenating the perioral area, which is one of the most difficult areas to treat. Due to the development of highly sophisticated fillers with radically different rheological properties and innovating techniques, it has become possible to correct specific regions with more natural results than before. This four-center pilot study sought to investigate the efficacy and safety of MaiLi® Precise in correcting fine-line wrinkles of the perioral area. Material and Methods: MaiLi® Precise is a recently introduced Hyaluronic Acid (HA) filler, harnessing the OxiFree™ technology, for suppleness, flexibility, and high longevity. These properties were deemed particularly suitable for correcting fine-line perioral wrinkles. All injections were performed using intradermal injections, with the fluid HA injected in a very superficial manner, resulting in the formation of very small papules at the wrinkle, disappearing within 1-2 hours. Follow-up occurred at one (T1) and three months (T2) post-injection. Treatment effectiveness was evaluated using the Global Aesthetic Improvement Scale (GAIS), as was the satisfaction of the subjects and investigators. Adverse Events (AEs) were also reported. Results: Overall, 20 patients aged 56 years on average, from four centers, were injected MaiLi® Precise into the perioral area to correct perioral lines using intradermal injections. No specific safety concerns were detected, given that AEs remained mild, were almost all resolved by T1, and were completely absent by T2. Patient and physician satisfaction were both >90% during follow-up. All physicians attributed improved, much improved, or very much improved GAIS scores at T1 and T2, indicating a marked improvement in appearance after injection. Conclusion: Based on the current study results, MaiLi® Precise was shown to be safe and provided satisfactory results from both the patient and the physician’s points of view during a 3-month follow-up.
Kalça Kırığı Hastasına Bakım Veren Aile Üyelerine Uygulanan Danışmanlığın Bakım Yükü, Stres Düzeyi ve Yaşam Kalitesine Etkisi
Authors: Çelik Buket , Bilik Özlem
Published: March 2024

Amaç: Bu çalışmanın amacı kalça kırığı olan aile üyesi bakım verenlere uygulanan danışmanlığın bakım verenlerin bakım yükü, stress düzeyi ve yaşam kalitesine etkisini incelemektir.

Yöntemler: Bu çalışma yarı deneysel araştırma dizaynındadır. Kontrol grubundaki bakım veren aile üyeleri rutin bakım almıştır. Girişim grubuna yüz yüze ve telefonla bireyselleştirilmiş danışmanlık yapılmıştır. Veriler; hasta tanıtım formu, bakım veren aile üyesi tanıtım formu, Zarit Bakım Yükü, Bakım Veren Stres İndeksi ve Yaşam Kalitesi ölçeği kullanılarak girişim öncesi, taburculukta, ameliyat sonrası birinci ayda ve üçüncü ayda toplanmıştır.

Bulgular: Girişim grubunun; bakım yükü, stress ve yaşam kalitesi mental sağlık, mental rol, enerji/canlılık, sosyal fonksiyon ve genel sağlık alt boyutları puan ortalamalarının birinci ve üçüncü ayda kontrol grubundan daha yüksek olduğu bulunmuştur.

Sonuç: Bu çalışma hemşire tarafından yapılan danışmanlığın bakım veren aile üyelerinin bakım yükünü ve stress düzeyini azalttığını ve yaşam kalitesini iyileştirdiğini göstermektedir.
Specialization in Nursing from the Perspective of Academician, Clinician and Student Nurses: A Qualitative Research
Authors: Saatçi Gamze , Ünsal Ayla , Büyüksoy Gizem Deniz
Published: March 2024

Objective: The health care needs in the community make the specialization in nursing more crucial. The aim of this study is to examine the views of academicians, clinicians and student nurses towards specialization in nursing.

Methods: This qualitative type of study was conducted in the case study design. The study was carried out between October and November, 2021, with 21 participants selected by the maximum diversity sampling technique. The semi-structured interview technique was used and the expressions were recorded. The theme and subtheme were determined by the content analysis. The Standards for Reporting Qualitative Research (COREQ) was used to report this research.

Results: Three main themes and six sub-themes were determined. The main themes were quality of care, obstacles and constructive ideas. The sub-themes were clinical knowledge and skills, scientific attitude, education system, institutional factors, change in policies and dignity.

Conclusion: The participants have faith in the fact that specialization in nursing would increase the quality of health care and nursing. They believed that the health care system and working conditions prevent specialization. They emphasize the need to develop policies for the betterment of specialization.