March 2024 arXiv papers — page 99

Keising Honn

Upon re-examining Arnold's established lemma for explaining his famous limit problem, we have determined that while the lemma itself is correct, there is a defect in the original geometric proof. In this paper, we prove the correctness of the lemma using methods of power series, and construct a counterexample to illustrate the defect in Arnold's geometric pr

Adam Chapman

We prove that two cyclically linked $p$-algebras of prime degree become inseparably linked under a prime to $p$ extension if and only if the essential $p$-dimension of the pair is 2. We conclude that the essential $p$-dimension of pairs of cyclically linked $p$-algebras is 3 by constructing an example of a pair that does not become inseparably linked under a

Avnish Singh Jat, Tor-Morten Grønli, George Ghinea

With the increasing importance of remote healthcare monitoring in the healthcare industry, it is essential to evaluate the usefulness and the ease of use the technology brings in remote healthcare. With this research, we want to understand the perspective of healthcare professionals, their competencies in using technology related to remote healthcare monitor

Ruizhe Zhang, Haitao Li, Yueyue Wu, Qingyao Ai

In recent years, the utilization of large language models for natural language dialogue has gained momentum, leading to their widespread adoption across various domains. However, their universal competence in addressing challenges specific to specialized fields such as law remains a subject of scrutiny. The incorporation of legal ethics into the model has be

Mao Hoshino, Kan Kitamura

In this short note, quantum subgroups in finite free products of the Pontryagin duals of free unitary quantum groups are classified. They correspond to pairs of a subgroup $\Gamma$ and a subset $S$ of the free group $\mathbb{F}_n$ such that $S$ is $\Gamma$-invariant, containing $\Gamma$, and connected in the Cayley graph of $\mathbb{F}_n$

Jing Zhang, Liang Zheng, Meng Wang, Dan Guo

This paper develops small vision language models to understand visual art, which, given an art work, aims to identify its emotion category and explain this prediction with natural language. While small models are computationally efficient, their capacity is much limited compared with large models. To break this trade-off, this paper builds a small emotional

Felix Lotter

We scrutinise the notions of cohomologically smooth morphisms and smooth objects for the six functor formalism of \'etale $\mathbb F_p$-sheaves on schemes in characteristic $p$. We show that only cohomologically \'etale morphisms are cohomologically smooth in this setting. This is complemented by a characterisation of cohomologically \'etale morphisms in arb

Ievgen Bondarenko

Let $\mathrm{WP}_G$ denote the word problem in a finitely generated group $G$. We consider the complexity of $\mathrm{WP}_G$ with respect to standard deterministic Turing machines. Let $\mathrm{DTIME}_k(t(n))$ be the complexity class of languages solved in time $O(t(n))$ by a Turing machine with $k$ tapes. We prove that $\mathrm{WP}_G\in\mathrm{DTIME}_1(n\lo

Chul-Moon Yoo

We perform the numerical simulation of primordial black hole formation from a nonspherical profile of the initial curvature perturbation $\zeta$. We consider the background expanding universe filled with the perfect fluid with the linear equation of state $p=w\rho$ ($w=1/3$ or $1/5$), where $p$ and $\rho$ are the pressure and the energy density, respectively

Balint Varga

This paper introduces a novel model-based adaptive shared control to allow for the identification and design challenge for shared-control systems, in which humans and automation share control tasks. The main challenge is the adaptive behavior of the human in such shared control interactions. Consequently, merely identifying human behavior without considering

Fuqiang Niu, Min Yang, Ang Li, Baoquan Zhang

Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discussions concerning the same specific topic, as naturally transpire in authentic social media interactions. This constraint arises primarily due to the scarcity of datasets that authe

Zihan Wang, Fanheng Kong, Shi Feng, Ming Wang

In the realm of time series forecasting (TSF), it is imperative for models to adeptly discern and distill hidden patterns within historical time series data to forecast future states. Transformer-based models exhibit formidable efficacy in TSF, primarily attributed to their advantage in apprehending these patterns. However, the quadratic complexity of the Tr

Włodzimierz Jaskólski

We investigate gated multilayer graphene with stacking order change along the armchair direction. We consider some layers cracked to release shear strain at the stacking domain wall. The energy cones of graphene overlap along the corresponding direction in the k-space, so the topological gapless states from different valleys also overlap. However, these stat

Xinhui Ruan, Jia-Heng Wang, Dong He, Pengtao Song

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical one by controlling the photon number inside the cavity. The device consists of a transmon

Marvin Schmitt, Yuga Hikida, Stefan T Radev, Filip Sadlo

The simplex projection expands the capabilities of simplex plots (also known as ternary plots) to achieve a lossless visualization of 4D compositional data on a 2D canvas. Previously, this was only possible for 3D compositional data. We demonstrate how our approach can be applied to individual data points, point clouds, and continuous probability density fun

Amit Singh, Seunghan Lee, Hyeonhu Bae, Jahyun Koo

First-principle calculations were employed to analyze the effects induced by vacancies of molybdenum (Mo) and sulfur (S) on the dielectric properties of few-layered MoS2. We explored the combined effects of vacancies and dipole interactions on the dielectric properties of few-layered MoS2. In the presence of dielectric screening, we investigated uniformly di

Bowen Li, Bin Shi

The least absolute shrinkage and selection operator (Lasso) is widely recognized across various fields of mathematics and engineering. Its variant, the generalized Lasso, finds extensive application in the fields of statistics, machine learning, image science, and related areas. Among the optimization techniques used to tackle this issue, saddle-point method

Tianhao Huang, Xuan Pan, Xiangrui Cai, Ying Zhang

Next Point-of-Interests (POIs) recommendation task aims to provide a dynamic ranking of POIs based on users' current check-in trajectories. The recommendation performance of this task is contingent upon a comprehensive understanding of users' personalized behavioral patterns through Location-based Social Networks (LBSNs) data. While prior studies have adeptl

Yuetong Fang, Ziqing Wang, Lingfeng Zhang, Jiahang Cao

Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by emulating the event-driven processing manner of the brain. Incorporating Transformers with SNNs has shown promise for accuracy. However, they struggle to learn high-frequency patterns, such as moving edges and pixel-level brightness changes, because they rel

Yuxuan Song, Jingjing Gong, Yanru Qu, Hao Zhou

Advanced generative model (e.g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry. This work introduces Geometric Bayesian Flow Networks (Geo

Feng Wang, Wangqiang Shen, Yuan Shui, Jun Chen

Single-atom magnetism switching is a key technique towards the ultimate data storage density of computer hard disks and has been conceptually realized by leveraging the spin bistability of a magnetic atom under a scanning tunnelling microscope. However, it has rarely been applied to solid-state transistors, an advancement that would be highly desirable for e

Peilin Zhou, You-Liang Huang, Yueqi Xie, Jingqi Gao

Sequential recommender systems (SRS) are designed to predict users' future behaviors based on their historical interaction data. Recent research has increasingly utilized contrastive learning (CL) to leverage unsupervised signals to alleviate the data sparsity issue in SRS. In general, CL-based SRS first augments the raw sequential interaction data by using

Shun-Cai Zhao, Shuang-Ying Zhang, Qi-Xuan Wu, Jing Jia

Left-handedness with three zero-absorption windows is achieved in a triple-quantum dot (TQD) system. With the typical parameters of a GaAs/AlGaAs heterostructure, the simultaneous negative relative electric permittivity and magnetic permeability are obtained by the adjustable incoherent pumping field and two inter-dot tunnelings. What's more, three zero-abso

Michael Assis

It has been known since 1996 that deciding whether a collection of creases on a piece of paper can be fully folded flat without causing self-intersection or adding new creases is an NP-Hard problem (Bern and Hayes). In their proof, a binary state was implemented as a pleat, with the state corresponding to the pleat layering order; states then interact via pl

Mahdie Ahmadi, Nader Karimi, Shadrokh Samavi

Accurate and early detection of breast cancer is essential for successful treatment. This paper introduces a novel deep-learning approach for improved breast cancer classification in histopathological images, a crucial step in diagnosis. Our method hinges on the Dense Residual Dual-Shuffle Attention Network (DRDA-Net), inspired by ShuffleNet's efficient arch

Tong Wu, Yu-Jie Yuan, Ling-Xiao Zhang, Jie Yang

The emergence of 3D Gaussian Splatting (3DGS) has greatly accelerated the rendering speed of novel view synthesis. Unlike neural implicit representations like Neural Radiance Fields (NeRF) that represent a 3D scene with position and viewpoint-conditioned neural networks, 3D Gaussian Splatting utilizes a set of Gaussian ellipsoids to model the scene so that e

Xinliang An, Taoran He, Dawei Shen

Utilizing recent mathematical advances in proving stability of Minkowski spacetime with minimal decay rates and nonlinear stability of Kerr black holes with small angular momentum, we investigate the detailed asymptotic behaviors of gravitational waves generated in these spacetimes. Here we report and propose a new angular momentum memory effect along future

Niklas Friedrich, Jingcheng Li, Iago Pozo, Diego Peña

Unimolecular current rectifiers are fundamental building blocks in organic electronics. Rectifying behavior has been identified in numerous organic systems due to electron-hole asymmetries of orbital levels interfaced by a metal electrode. As a consequence, the rectifying ratio (RR) determining the diode efficiency remains fixed for a chosen molecule-metal i

Yonggan Fu, Huaizhi Qu, Zhifan Ye, Chaojian Li

Recent breakthroughs in Neural Radiance Fields (NeRFs) have sparked significant demand for their integration into real-world 3D applications. However, the varied functionalities required by different 3D applications often necessitate diverse NeRF models with various pipelines, leading to tedious NeRF training for each target task and cumbersome trial-and-err

Mohamed Taher Alrefaie, Nour Eldin Morsy, Nada Samir

This paper presents a comprehensive examination of the impact of tokenization strategies and vocabulary sizes on the performance of Arabic language models in downstream natural language processing tasks. Our investigation focused on the effectiveness of four tokenizers across various tasks, including News Classification, Hate Speech Detection, Sentiment Anal

Baiyan Zhang, Qin Chen, Jie Zhou, Jian Jin

Document-level Event Causality Identification (DECI) aims to identify causal relations between two events in documents. Recent research tends to use pre-trained language models to generate the event causal relations. Whereas, these methods are prone to the errors of sequential generation due to multiple events in a document. Moreover, the potential structure

Honglin Mu, Yang Xu, Yunlong Feng, Xiaofeng Han

With the rise of Large Language Models (LLMs), AI assistants' ability to utilize tools, especially through API calls, has advanced notably. This progress has necessitated more accurate evaluation methods. Many existing studies adopt static evaluation, where they assess AI assistants' API call based on pre-defined dialogue histories. However, such evaluation

Jiangshan Wang, Yifan Pu, Yizeng Han, Jiayi Guo

Oriented object detection, an emerging task in recent years, aims to identify and locate objects across varied orientations. This requires the detector to accurately capture the orientation information, which varies significantly within and across images. Despite the existing substantial efforts, simultaneously ensuring model effectiveness and parameter effi

Hongyang Wang, Hao Wu, Ning Chen, Xianfeng Qiao

By codoping sulfur and copper into lead apatite, the crystal grains are directionally stacked and the room-temperature resistivity is reduced from insulating to $2\times10^{-5}~\Omega\cdot$m. The resistance-temperature curve exhibits a nearly linear relationship at low temperature suggesting the presence of strange-metal phase, and a second-order phase trans

Lisang Zhou, Ziqian Luo, Xueting Pan

Machine learning-based reliability analysis methods have shown great advancements for their computational efficiency and accuracy. Recently, many efficient learning strategies have been proposed to enhance the computational performance. However, few of them explores the theoretical optimal learning strategy. In this article, we propose several theorems that

Feifan Song, Bowen Yu, Hao Lang, Haiyang Yu

Alignment with human preference prevents large language models (LLMs) from generating misleading or toxic content while requiring high-cost human feedback. Assuming resources of human annotation are limited, there are two different ways of allocating considered: more diverse PROMPTS or more diverse RESPONSES to be labeled. Nonetheless, a straightforward comp

Taha Aksu, Nancy F. Chen

Current metrics for evaluating Dialogue State Tracking (DST) systems exhibit three primary limitations. They: i) erroneously presume a uniform distribution of slots throughout the dialog, ii) neglect to assign partial scores for individual turns, iii) frequently overestimate or underestimate performance by repeatedly counting the models' successful or failed

Hanze Ding, Zhangkai Wu, Jiyan Zhang, Ming Ping

Few-shot segmentation models excel in metal defect detection due to their rapid generalization ability to new classes and pixel-level segmentation, rendering them ideal for addressing data scarcity issues and achieving refined object delineation in industrial applications. Existing works neglect the \textit{Intra-Class Differences}, inherent in metal surface

Wei-Shi Zheng, Junkai Yan, Yi-Xing Peng

Person Re-identification (ReID) has been extensively developed for a decade in order to learn the association of images of the same person across non-overlapping camera views. To overcome significant variations between images across camera views, mountains of variants of ReID models were developed for solving a number of challenges, such as resolution change

Sunghwan Hong, Seokju Cho, Seungryong Kim, Stephen Lin

This paper introduces a Transformer-based integrative feature and cost aggregation network designed for dense matching tasks. In the context of dense matching, many works benefit from one of two forms of aggregation: feature aggregation, which pertains to the alignment of similar features, or cost aggregation, a procedure aimed at instilling coherence in the

Yu-Hao Shen, Yiqun Liu, Min Luo

With spin-layer locking, the manipulation of spin degree of freedom via perpendicular electric field can be realized in a typical antiferromagnetically coupled bilayer. In analogy to the electric control of the anomalous layer Hall effect of electron within such bilayer system, we propose here its magnon counterpart i.e., thermal Hall effect controlled by a

Yao Lu

We report a simulation and quantification of the impact of the Starlink constellation on LSST in terms of the trail surface brightness using a BRDF-based satellite photometric model. A total of 11,908 satellites from the Gen1 and Gen2A constellations are used to focus on the interference to the initial phase of LSST operation. The all-sky simulation shows th

Yingjie Pei, Xinwei Yue, Chongwen Huang, Zhiping Lu

Reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC) have been envisioned as two promising technologies due to their high transmission reliability as well as energy-efficiency. This paper investigates the secrecy performance of RIS assisted AmBC networks. New closed-form and asymptotic expressions of secrecy outage probabilit

Jiazhen Liu, Yuhan Fu, Ruobing Xie, Runquan Xie

Multimodal Large Language Models (MLLMs) hallucinate, resulting in an emerging topic of visual hallucination evaluation (VHE). This paper contributes a ChatGPT-Prompted visual hallucination evaluation Dataset (PhD) for objective VHE at a large scale. The essence of VHE is to ask an MLLM questions about specific images to assess its susceptibility to hallucin

Hongxia Wang, Yeming Xu, Ziyuan Guo, Huanshui Zhang

This paper proposes several novel optimization algorithms for minimizing a nonlinear objective function. The algorithms are enlightened by the optimal state trajectory of an optimal control problem closely related to the minimized objective function. They are superlinear convergent when appropriate parameters are selected as required. Unlike Newton's method,

Jingcheng Jiang, Haiyin Piao, Yu Fu, Yihang Hao

Reviewing the previous work of diversity Rein-forcement Learning,diversity is often obtained via an augmented loss function,which requires a balance between reward and diversity.Generally,diversity optimization algorithms use Multi-armed Bandits algorithms to select the coefficient in the pre-defined space. However, the dynamic distribution of reward signals

Yiyang Chen, Lunhao Duan, Shanshan Zhao, Changxing Ding

Rotation invariance is an important requirement for point shape analysis. To achieve this, current state-of-the-art methods attempt to construct the local rotation-invariant representation through learning or defining the local reference frame (LRF). Although efficient, these LRF-based methods suffer from perturbation of local geometric relations, resulting

Kiyoshi Kato

The use of the dimensional regularization in the on-mass-shell renormalization scheme sometimes fails to locally cancel the ultraviolet divergence for a class of diagrams in the two-loop order. The mechanism is discussed based on an example with explicit computation.

Yongtao Ge, Wenjia Wang, Yongfan Chen, Fanzhou Wang

Despite remarkable progress having been made on the problem of 3D human pose and shape estimation (HPS), current state-of-the-art methods rely heavily on either confined indoor mocap datasets or datasets generated by a rendering engine using computer graphics (CG). Both categories of datasets exhibit inadequacies in furnishing adequate human identities and a

Sheetal Patil, Sauvik Banerjee, Siddharth Tallur

Most reported research for monitoring health of pipelines using ultrasonic guided waves (GW) typically utilize bulky piezoelectric transducer rings and laboratory-grade ultrasonic non-destructive testing (NDT) equipment. Consequently, the translation of these approaches from laboratory settings to field-deployable systems for real-time structural health moni

Xuehua Li, Yingjie Pei, Xinwei Yue, Yuanwei Liu

As a revolutionary technology, reconfigurable intelligent surface (RIS) has been deemed as an indispensable part of the 6th generation communications due to its inherent ability to regulate the wireless channels. However, passive RIS (PRIS) still suffers from some pressing issues, one of which is that the fading of the entire reflection link is proportional

Ritesh Kumar, Ojaswee Bhalla, Madhu Vanthi, Shehlat Maknoon Wani

In this paper, we discuss the development of an annotation schema to build datasets for evaluating the offline harm potential of social media texts. We define "harm potential" as the potential for an online public post to cause real-world physical harm (i.e., violence). Understanding that real-world violence is often spurred by a web of triggers, often combi

Souradeep Chakraborty, Dimitris Samaras

Our paper introduces a novel two-stage self-supervised approach for detecting co-occurring salient objects (CoSOD) in image groups without requiring segmentation annotations. Unlike existing unsupervised methods that rely solely on patch-level information (e.g. clustering patch descriptors) or on computation heavy off-the-shelf components for CoSOD, our ligh

Kaiqi Zhao, Ming Zhao

Quantization-aware training (QAT) and Knowledge Distillation (KD) are combined to achieve competitive performance in creating low-bit deep learning models. However, existing works applying KD to QAT require tedious hyper-parameter tuning to balance the weights of different loss terms, assume the availability of labeled training data, and require complex, com

Ruibin Li, Ruihuang Li, Song Guo, Lei Zhang

Text-driven diffusion models have significantly advanced the image editing performance by using text prompts as inputs. One crucial step in text-driven image editing is to invert the original image into a latent noise code conditioned on the source prompt. While previous methods have achieved promising results by refactoring the image synthesizing process, t

Suyong Park, Duc Giap Nguyen, Jinrak Park, Dohee Kim

This study presents a method for deep neural network nonlinear model predictive control (DNN-MPC) to reduce computational complexity, and we show its practical utility through its application in optimizing the energy management of hybrid electric vehicles (HEVs). For optimal power management of HEVs, we first design the online NMPC to collect the data set, a

Yuzhao Heng, Chunyuan Deng, Yitong Li, Yue Yu

Although Large Language Models (LLMs) exhibit remarkable adaptability across domains, these models often fall short in structured knowledge extraction tasks such as named entity recognition (NER). This paper explores an innovative, cost-efficient strategy to harness LLMs with modest NER capabilities for producing superior NER datasets. Our approach diverges

Xuefei Li, Mingzhe Chen, Ye Hu, Zhilong Zhang

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication

Zuyuan He, Zongyong Deng, Qiaoyun He, Qijun Zhao

Face morphing attacks circumvent face recognition systems (FRSs) by creating a morphed image that contains multiple identities. However, existing face morphing attack methods either sacrifice image quality or compromise the identity preservation capability. Consequently, these attacks fail to bypass FRSs verification well while still managing to deceive huma

Suryam Arnav Kalra, Arindam Biswas, Pabitra Mitra, Biswajit Basu

Expansion property of a graph refers to its strong connectivity as well as sparseness. It has been reported that deep neural networks can be pruned to a high degree of sparsity while maintaining their performance. Such pruning is essential for performing real time sequence learning tasks using recurrent neural networks in resource constrained platforms. We p

Xiaoyao Zhong, Jiabao Jin, Peng Cheng, Wangze Ni

Ridesharing services, such as Uber or Didi, have attracted considerable attention in recent years due to their positive impact on environmental protection and the economy. Existing studies require quick responses to orders, which lack the flexibility to accommodate longer wait times for better grouping opportunities. In this paper, we address a NP-hard rides

Xu Lu, Yewei Chen, Francesco Mazza, Siyi He

Thermometric techniques with high accuracy, fast response speed and ease of implementation are desirable for the study of dynamic combustion environments, transient reacting flows, and non-equilibrium plasmas. Herein, single-shot single-beam coherent Raman scattering (SS-CRS) thermometry is developed, for the first time to our knowledge, by using air lasing

Yingjie Pei, Xinwei Yue, Wenqiang Yi, Yuanwei Liu

Reconfigurable intelligent surface (RIS) has been regarded as a promising technology since it has ability to create the favorable channel conditions. This paper investigates the secure communications of RIS assisted non-orthogonal multiple access (NOMA) networks, where both external and internal eavesdropping scenarios are taken into consideration. More spec

Jungbin Yim, Jeonghun Park, Namyoon Lee

Integrated satellite-terrestrial networks (ISTNs) can significantly expand network coverage while diminishing reliance on terrestrial infrastructure. Despite the enticing potential of ISTNs, there is no comprehensive mathematical performance analysis framework for these emerging networks. In this paper, we introduce a tractable approach to analyze the downli

Sahand Khoshdel, Qi Luo, Fatemeh Afghah

Motivated by agility, 3D mobility, and low-risk operation compared to human-operated management systems of autonomous unmanned aerial vehicles (UAVs), this work studies UAV-based active wildfire monitoring where a UAV detects fire incidents in remote areas and tracks the fire frontline. A UAV path planning solution is proposed considering realistic wildfire

Hyowon Lee, Jungyeon Kim, Geon Choi, Ian P. Roberts

Nonlinear self-interference cancellation (SIC) is essential for full-duplex communication systems, which can offer twice the spectral efficiency of traditional half-duplex systems. The challenge of nonlinear SIC is similar to the classic problem of system identification in adaptive filter theory, whose crux lies in identifying the optimal nonlinear basis fun

Zixian Yang, Lei Ying

We consider a dynamic system with multiple types of customers and servers. Each type of waiting customer or server joins a separate queue, forming a bipartite graph with customer-side queues and server-side queues. The platform can match the servers and customers if their types are compatible. The matched pairs then leave the system. The platform will charge

Michael Saxon, Yiran Luo, Sharon Levy, Chitta Baral

Benchmarks of the multilingual capabilities of text-to-image (T2I) models compare generated images prompted in a test language to an expected image distribution over a concept set. One such benchmark, "Conceptual Coverage Across Languages" (CoCo-CroLa), assesses the tangible noun inventory of T2I models by prompting them to generate pictures from a concept l

Liang Zou, Genwei Yan, Ruoyu Wang, Jun Du

This paper focuses on few-shot Sound Event Detection (SED), which aims to automatically recognize and classify sound events with limited samples. However, prevailing methods methods in few-shot SED predominantly rely on segment-level predictions, which often providing detailed, fine-grained predictions, particularly for events of brief duration. Although fra

Jinzhu Yan, Haotian Xu, Zhuotao Liu, Qi Li

The emerging programmable networks sparked significant research on Intelligent Network Data Plane (INDP), which achieves learning-based traffic analysis at line-speed. Prior art in INDP focus on deploying tree/forest models on the data plane. We observe a fundamental limitation in tree-based INDP approaches: although it is possible to represent even larger t

Erik García Neefjes, David Nigro, Raphaël C. Assier, William J. Parnell

In this paper, we theoretically analyse wave propagation in two canonical problems of interest: fluid-filled thermo-visco-elastic slits and fluid-loaded thermo-visco-elastic plates. We show that these two configurations can be studied via the same pair of dispersion equations with the aid of the framework developed in [https://doi.org/10.1098/rspa.2022.0193]

Marco Gaboardi, Michael Hay, Salil Vadhan

Many programming frameworks have been introduced to support the development of differentially private software applications. In this chapter, we survey some of the conceptual ideas underlying these frameworks in a way that we hope will be helpful for both practitioners and researchers. For practitioners, the survey can provide a starting point for understand

Qiankun Li, Haobing Liu, Ruobing Jiang, Tingting Wang

Clustering holds profound significance in data mining. In recent years, graph convolutional network (GCN) has emerged as a powerful tool for deep clustering, integrating both graph structural information and node attributes. However, most existing methods ignore the higher-order structural information of the graph. Evidently, nodes within the same cluster ca

Josue N. Rivera, Dengfeng Sun

Aerial cargo transport is anticipated to play a pivotal role in the distribution of goods within urban environments. The shift is propelled by the surge in e-commerce, the imperative to deliver essential supplies to isolated areas, and the growing demand for expedited and more accessible deliveries. Our research introduces a quantifiable standard for definin

Zixian Ma, Weikai Huang, Jieyu Zhang, Tanmay Gupta

Real-world multi-modal problems are rarely solved by a single machine learning model, and often require multi-step computational plans that involve stitching several models. Tool-augmented LLMs hold tremendous promise for automating the generation of such computational plans. However, the lack of standardized benchmarks for evaluating LLMs as planners for mu

Xiaoxuan Wu, Zhengyi Cao, Tianxiang Zhao, Yun Wu

Graphene is promising for nanoscale, efficient, ultra-fast photo- and opto-electronic devices because of its remarkable electrical and optical properties, such as fast electron relaxation and heat dissipation. Here, we realize high-performance graphene integrated photonics platform enabled by gold-assisted transfer. Thanks to our optimized transfer technique

Xiaohao Xu, Yunkang Cao, Huaxin Zhang, Nong Sang

Anomaly detection is vital in various industrial scenarios, including the identification of unusual patterns in production lines and the detection of manufacturing defects for quality control. Existing techniques tend to be specialized in individual scenarios and lack generalization capacities. In this study, our objective is to develop a generic anomaly det

Javad Rafiei Asl, Prajwal Panzade, Eduardo Blanco, Daniel Takabi

Pre-trained language models (PLMs) have consistently demonstrated outstanding performance across a diverse spectrum of natural language processing tasks. Nevertheless, despite their success with unseen data, current PLM-based representations often exhibit poor robustness in adversarial settings. In this paper, we introduce RobustSentEmbed, a self-supervised

Ronglan Huang, Fei ji, Zeng Hu, Dehuan Wan

Non-orthogonal multiple access (NOMA) has been widely nominated as an emerging spectral efficiency (SE) multiple access technique for the next generation of wireless communication network. To meet the growing demands in massive connectivity and huge data in transmission, a novel index modulation aided NOMA with the rotation of signal constellation of low pow

Namit Pai, Indradev Samajdar, Anirban Patra

Rapid solidification in Additively Manufactured (AM) metallic materials results in the development of significant microscale internal stresses, which are attributed to the printing induced dislocation substructures. The resulting backstress due to the Geometrically Necessary Dislocations (GNDs) is responsible for the observed Tension-Compression (TC) asymmet

Xin Zhou, DongGyun Han, David Lo

Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can be classified into two categories: 1) simple models using traditional machine learning classifiers with hand-crafted features, and 2) complex models using deep learning techniques t

Jialu Sui, Xianping Ma, Xiaokang Zhang, Man-On Pun

Remote sensing image super-resolution (SR) is a crucial task to restore high-resolution (HR) images from low-resolution (LR) observations. Recently, the Denoising Diffusion Probabilistic Model (DDPM) has shown promising performance in image reconstructions by overcoming problems inherent in generative models, such as over-smoothing and mode collapse. However

Kangyang Xie, Binbin Yang, Hao Chen, Meng Wang

Beyond the superiority of the text-to-image diffusion model in generating high-quality images, recent studies have attempted to uncover its potential for adapting the learned semantic knowledge to visual perception tasks. In this work, instead of translating a generative diffusion model into a visual perception model, we explore to retain the generative abil

Anton Glushchenko, Konstantin Lastochkin

In adaptive control theory, the dynamic regressor extension and mixing (DREM) procedure has become widespread as it allows one to describe major of adaptive control problems in unified terms of the parameter estimation problem of a regression equation with a scalar regressor. However, when the system/parameterization is affected by perturbations, the estimat

Lance Ying, Kunal Jha, Shivam Aarya, Joshua B. Tenenbaum

Verbal communication plays a crucial role in human cooperation, particularly when the partners only have incomplete information about the task, environment, and each other's mental state. In this paper, we propose a novel cooperative communication framework, Goal-Oriented Mental Alignment (GOMA). GOMA formulates verbal communication as a planning problem tha

Mohamed Mahmoud Chems-Eddin, Moha Ben Taleb El Hamam, Moulay Ahmed Hajjami

Let $p\equiv 1\pmod{8}$ and $q\equiv7\pmod 8$ be two prime numbers. The purpose of this paper is to compute the unit group of the fields $\KK=\QQ(\sqrt 2, \sqrt{p}, \sqrt{q} )$ and give their $2$-class numbers.

Jinxiang Liu, Yikun Liu, Fei Zhang, Chen Ju

Audio-visual segmentation (AVS) aims to segment the sounding objects in video frames. Although great progress has been witnessed, we experimentally reveal that current methods reach marginal performance gain within the use of the unlabeled frames, leading to the underutilization issue. To fully explore the potential of the unlabeled frames for AVS, we explic

Haoxi Zhang, Xinxu Zhang, Yuanxin Lin, Maiqi Wang

Automatic karyotype analysis is often defined as a visual perception task focused solely on chromosomal object-level modeling. This definition has led most existing methods to overlook componential and holistic information, significantly constraining model performance. Moreover, the lack of interpretability in current technologies hinders clinical adoption.

Alireza Hariri, Hadi Zayyani, Mehdi Korki

This paper presents a novel sparse signal detection scheme designed for a correlated Markovian Bernoulli-Gaussian sparse signal model, which can equivalently be viewed as a block sparse signal model. Despite the inherent complexity of the model, our approach yields a closed-form detection criterion. Theoretical analyses of the proposed detector are provided,

Daniel Kawai, Bruno Leonardo Macedo Ferreira

We address the conjectures left by the recent article by Ferreira et al. titled ``Commuting maps and identities with inverses on alternative division rings.'' We also present an example showing the necessity of the conditions of the results that answer the conjectures.

Xufeng Guo, Yuanbin Chen, Ying Wang, Zhaocheng Wang

In this paper, we investigate the sparse channel estimation in holographic multiple-input multiple-output (HMIMO) systems. The conventional angular-domain representation fails to capture the continuous angular power spectrum characterized by the spatially-stationary electromagnetic random field, thus leading to the ambiguous detection of the significant angu

Daniel Kawai, Bruno Leonardo Macedo Ferreira

In this paper, we tackle unresolved inquiries by Ferreira et al. \cite{bruno} in their recent publication, ``Functional Identity on Division Algebras". We delve into the intricate behavior of additive functions on matrix algebras over division rings through rigorous analysis and theorem-proving. Our findings offer valuable insights into the nature of these f

Yuan Zhou, Richang Hong, Yanrong Guo, Lin Liu

In this paper, we propose to tackle Few-Shot Class-Incremental Learning (FSCIL) from a new perspective, i.e., relation disentanglement, which means enhancing FSCIL via disentangling spurious relation between categories. The challenge of disentangling spurious correlations lies in the poor controllability of FSCIL. On one hand, an FSCIL model is required to b

Mohammad Heydari, Mohsen Khazeni, Mohammad Ali Soltanshahi

Recently, there has been a growing interest in the use of deep learning techniques for tasks in natural language processing (NLP), with sentiment analysis being one of the most challenging areas, particularly in the Persian language. The vast amounts of content generated by Persian users on thousands of websites, blogs, and social networks such as Telegram,

Zhenyi Yuan, Guido Cavraro, Ahmed S. Zamzam, Jorge Cortés

In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers. We propose an unsupervised learning framework to train functions that can closely approximate optimal power flow (OPF) solutions. The primary aim is to establish specific conditions under which thes

Clayton Blosser, Adrian Bauer, Jessica E. Ruyle, K. C. Kerby-Patel

The gain, received power bandwidth, transient characteristics, and signal fidelity of two time-varying electrically small antennas based on parametric amplifier design are studied using practical QAM signals. Results show that interference from the difference harmonic present in the response of degenerate-mode parametric amplification decreases its signal th

Shi-Ji Cao, Jing-Juan Qi, Xin-Heng Guo, Zhen-Yang Wang

In this work, we investigate possible bound states of the $\Lambda_c\bar{\Lambda}_c$ system in the Bethe-Salpeter formalism in the ladder and instantaneous approximations. By numerically solving the Bethe-Salpeter equation, we confirm the existence of $\Lambda_c\bar{\Lambda}_c$ bound states with quantum numbers $J^{PC}=0^{-+}$ and $J^{PC}=1^{--}$. We further

Petr Kosenko

We provide a complex-analytic approach to the classification of stationary probability measures on $S^1$ with respect to the action of $PSU(1,1)$ on the unit circle via M\"{o}bius transformations by studying their Cauchy transforms from the perspective of generalized analytic continuation. We improve upon results of Bourgain and present a complete characteri

Mehdi Korki, Fatemehsadat Hosseiniamin, Hadi Zayyani, Mehdi Bekrani

In this brief, we present an enhanced privacy-preserving distributed estimation algorithm, referred to as the ``Double-Private Algorithm," which combines the principles of both differential privacy (DP) and cryptography. The proposed algorithm enhances privacy by introducing DP noise into the intermediate estimations of neighboring nodes. Additionally, we em

Liu Yang, Qing-Dong Jiang

The realization of Haldane's topological graphene model in practical materials has presented significant challenges. Here, we propose achieving this model by embedding graphene in chiral cavities, using the asymptotically decoupled framework detailed in Ref. [Phys. Rev. Lett. 126, 153603 (2021)]. Additionally, we introduce an equilibrium strategy for achievi