October 2023 arXiv papers — page 137

Wentao Jiang, Hao Xiang, Xinyu Cai, Runsheng Xu

Multi-agent cooperative perception is an increasingly popular topic in the field of autonomous driving, where roadside LiDARs play an essential role. However, how to optimize the placement of roadside LiDARs is a crucial but often overlooked problem. This paper proposes an approach to optimize the placement of roadside LiDARs by selecting optimized positions

Xinfa Zhu, Yuanjun Lv, Yi Lei, Tao Li

Language models (LMs) have recently flourished in natural language processing and computer vision, generating high-fidelity texts or images in various tasks. In contrast, the current speech generative models are still struggling regarding speech quality and task generalization. This paper presents Vec-Tok Speech, an extensible framework that resembles multip

Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

Visual crowd counting estimates the density of the crowd using deep learning models such as convolution neural networks (CNNs). The performance of the model heavily relies on the quality of the training data that constitutes crowd images. In harsh weather such as fog, dust, and low light conditions, the inference performance may severely degrade on the noisy

Lexing Ying

Sampling from multimodal distributions is a challenging task in scientific computing. When a distribution has an exact symmetry between the modes, direct jumps among them can accelerate the samplings significantly. However, the distributions from most applications do not have exact symmetries. This paper considers the distributions with approximate symmetrie

Faruk Volkan Mutlu, Edmund Yeh

Caching is crucial for enabling high-throughput networks for data intensive applications. Traditional caching technology relies on DRAM, as it can transfer data at a high rate. However, DRAM capacity is subject to contention by most system components and thus is very limited, implying that DRAM-only caches cannot scale to meet growing demand. Fortunately, pe

Caroline Berger, Hanjun Xian, Krishna Madhavan, Niklas Elmqvist

We propose Textiverse, a big data approach for mining geotagged timestamped textual data on a map, such as for Twitter feeds, crime reports, or restaurant reviews. We use a scalable data management pipeline that extracts keyphrases from online databases in parallel. We speed up this time-consuming step so that it outpaces the content creation rate of popular

Nicholas E. Silionis, Konstantinos N. Anyfantis

Fatigue crack growth is one of the most common types of deterioration in metal structures with significant implications on their reliability. Recent advances in Structural Health Monitoring (SHM) have motivated the use of structural response data to predict future crack growth under uncertainty, in order to enable a transition towards predictive maintenance.

Yuhan Liu, Hanchen Li, Yihua Cheng, Siddhant Ray

As large language models (LLMs) take on complex tasks, their inputs are supplemented with longer contexts that incorporate domain knowledge. Yet using long contexts is challenging, as nothing can be generated until the whole context is processed by the LLM. While the context-processing delay can be reduced by reusing the KV cache of a context across differen

Gergely Stomfai, Łukasz Sienkiewicz, Barbara Rychalska

We present the Multidimensional Hopfield Network (DHN), a natural generalisation of the Hopfield Network. In our theoretical investigations we focus on DHNs with a certain activation function and provide energy functions for them. We conclude that these DHNs are convergent in finite time, and are equivalent to greedy methods that aim to find graph clustering

Juan Dávila, Manuel del Pino, Monica Musso, Shrish Parmeshwar

We consider the problem of finding a solution to the incompressible Euler equations $$ \omega_t + v\cdot \nabla \omega = 0 \quad \hbox{ in } \mathbb{R}^2 \times (0,\infty), \quad v(x,t) = \frac 1{2\pi} \int_{{\mathbb R}^2} \frac {(y-x)^\perp}{|y-x|^2} \omega (y,t)\, dy $$ that is close to a superposition of traveling vortices as $t\to \infty$. We employ a co

Jiaming Cui, Jiming Chen, Liang Li

Robust and accurate pose estimation in unknown environments is an essential part of robotic applications. We focus on LiDAR-based point-to-point ICP combined with effective semantic information. This paper proposes a novel semantic information-assisted ICP method named SAGE-ICP, which leverages semantics in odometry. The semantic information for the whole sc

Liyang Chen, Weihong Bao, Shun Lei, Boshi Tang

Speech-driven 3D facial animation aims at generating facial movements that are synchronized with the driving speech, which has been widely explored recently. Existing works mostly neglect the person-specific talking style in generation, including facial expression and head pose styles. Several works intend to capture the personalities by fine-tuning modules.

Nimrah Mustafa, Aleksandar Bojchevski, Rebekka Burkholz

While the expressive power and computational capabilities of graph neural networks (GNNs) have been theoretically studied, their optimization and learning dynamics, in general, remain largely unexplored. Our study undertakes the Graph Attention Network (GAT), a popular GNN architecture in which a node's neighborhood aggregation is weighted by parameterized a

Liyuan Wang, Jingyi Xie, Xingxing Zhang, Mingyi Huang

Prompt-based continual learning is an emerging direction in leveraging pre-trained knowledge for downstream continual learning, and has almost reached the performance pinnacle under supervised pre-training. However, our empirical research reveals that the current strategies fall short of their full potential under the more realistic self-supervised pre-train

Luan Hemi, Li Xuan, Liu Liang-Feng, Li Min

Alzheimer's disease (AD) has emerged as a progressively pervasive neurodegenerative disorder worldwide. Bile acids, synthesized in the liver and modified by the gut microbiota, play pivotal roles in diverse physiological processes, and their dysregulation in individuals with AD has been well-documented. However, the protein targets associated with microbiota

Zijian Lin, Cuixiang Wang, Daqiang Chen, Sheng Meng

Zirconium pentatelluride (ZrTe$_{5}$), a system with a Dirac linear band across the Fermi level and anomalous transport features, has attracted considerable research interest for it is predicted to be located at the boundary between strong and weak topological insulators separated by a topological semimetal phase. However, the experimental verification of th

Omar Tout

In this note we generalize the definition of partial permutations of Ivanov and Kerov and we build a universal algebra which projects onto the m-centraliser algebra defined by Creedon. We use it to present a new proof for the polynomiality property of the structure coefficients of the m-centraliser algebra and to obtain upper bounds for the polynomial degree

Renato Huzak, Kristian Uldall Kristiansen

The goal of this paper is to study the number of sliding limit cycles of a regularized piecewise linear $VI_3$ two-fold using the notion of slow divergence integral. We focus on limit cycles produced by canard cycles located in the half-plane with an invisible fold point. We prove that the integral has at most $1$ zero counting multiplicity (when it is not i

Bowen Gao, Yinjun Jia, Yuanle Mo, Yuyan Ni

Pocket representations play a vital role in various biomedical applications, such as druggability estimation, ligand affinity prediction, and de novo drug design. While existing geometric features and pretrained representations have demonstrated promising results, they usually treat pockets independent of ligands, neglecting the fundamental interactions betw

Ioannis Soranidis

In this paper, we investigate the thermodynamic properties of a regular black hole model which exhibits the most significant subleading corrections to the Schwarzchild asymptotic behavior, in the context of general relativity, using the Euclidean path integral approach. We review the derivation of the Lagrangian for the matter fields which act as a source fo

Tapas Das, Pavan P D, Sagnik Sen, S Taruni

This article deals with homomorphisms of oriented graphs with respect to push equivalence. Here homomorphisms refer to arc preserving vertex mappings, and push equivalence refers to the equivalence class of orientations of a graph $G$ those can be obtained from one another by reversing arcs of an edge cut. We study and prove some fundamental properties of pu

Shubham kumar, Nihar Kumar Mahato, Md Abu T Ansary, Debdas Ghosh

In this paper, we propose a quasi Newton method to solve the robust counterpart of an uncertain multiobjective optimization problem under an arbitrary finite uncertainty set. Here the robust counterpart of an uncertain multiobjective optimization problem is the minimum of objective-wise worst case, which is a nonsmooth deterministic multiobjective optimizati

Andrew M. Bean, Karolina Korgul, Felix Krones, Robert McCraith

Large language models (LLMs) have made rapid improvement on medical benchmarks, but their unreliability remains a persistent challenge for safe real-world uses. To design for the use LLMs as a category, rather than for specific models, requires developing an understanding of shared strengths and weaknesses which appear across models. To address this challeng

Jake Roth, Ying Cui

The \emph{top-$k$-sum} operator computes the sum of the largest $k$ components of a given vector. The Euclidean projection onto the top-$k$-sum sublevel set serves as a crucial subroutine in iterative methods to solve composite superquantile optimization problems. In this paper, we introduce a solver that implements two finite-termination algorithms to compu

José Rodríguez-Ortega, Rohaifa Khaldi, Domingo Alcaraz-Segura, Siham Tabik

Remotely sensed data are dominated by mixed Land Use and Land Cover (LULC) types. Spectral unmixing (SU) is a key technique that disentangles mixed pixels into constituent LULC types and their abundance fractions. While existing studies on Deep Learning (DL) for SU typically focus on single time-step hyperspectral (HS) or multispectral (MS) data, our work pi

Shiyuan Yang, Xiaodong Chen, Jing Liao

Recently, text-to-image denoising diffusion probabilistic models (DDPMs) have demonstrated impressive image generation capabilities and have also been successfully applied to image inpainting. However, in practice, users often require more control over the inpainting process beyond textual guidance, especially when they want to composite objects with customi

Abhishek Jaiswal, Gautam Chauhan, Nisheeth Srivastava

Good posture and form are essential for safe and productive exercising. Even in gym settings, trainers may not be readily available for feedback. Rehabilitation therapies and fitness workouts can thus benefit from recommender systems that provide real-time evaluation. In this paper, we present an algorithmic pipeline that can diagnose problems in exercise te

Xiyao Wang, Ruijie Zheng, Yanchao Sun, Ruonan Jia

Dyna-style model-based reinforcement learning contains two phases: model rollouts to generate sample for policy learning and real environment exploration using current policy for dynamics model learning. However, due to the complex real-world environment, it is inevitable to learn an imperfect dynamics model with model prediction error, which can further mis

Shlomit Shachor, Natalia Razinkov, Abigail Goldsteen

Artificial intelligence systems are prevalent in everyday life, with use cases in retail, manufacturing, health, and many other fields. With the rise in AI adoption, associated risks have been identified, including privacy risks to the people whose data was used to train models. Assessing the privacy risks of machine learning models is crucial to enabling kn

Yuxin Chen, Chen Tang, Ran Tian, Chenran Li

Generalization poses a significant challenge in Multi-agent Reinforcement Learning (MARL). The extent to which an agent is influenced by unseen co-players depends on the agent's policy and the specific scenario. A quantitative examination of this relationship sheds light on effectively training agents for diverse scenarios. In this study, we present the Leve

Alessio Burrello, Matteo Risso, Beatrice Alessandra Motetti, Enrico Macii

The rapid proliferation of computing domains relying on Internet of Things (IoT) devices has created a pressing need for efficient and accurate deep-learning (DL) models that can run on low-power devices. However, traditional DL models tend to be too complex and computationally intensive for typical IoT end-nodes. To address this challenge, Neural Architectu

Jaehyeong Jo, Sung Ju Hwang

Learning the distribution of data on Riemannian manifolds is crucial for modeling data from non-Euclidean space, which is required by many applications in diverse scientific fields. Yet, existing generative models on manifolds suffer from expensive divergence computation or rely on approximations of heat kernel. These limitations restrict their applicability

Joshua P. Turner

We compute the Borel-Moore homology of unramified affine Springer fibers for $\mathrm{GL}_n$ under the assumption that they are equivariantly formal and relate them to certain ideals discussed by Haiman. For $n=3$, we give an explicit description of these ideals, compute their Hilbert series, generators and relations, and compare them to generalized $(q,t)$

Rong Chen, Kaiyang Lan, Xinheng Lin, Yidong Zhou

The Borodin-Kostochka Conjecture states that for a graph $G$, if $\Delta(G)\geq 9$, then $\chi(G)\leq\max\{\Delta(G)-1,\omega(G)\}$. In this paper, we prove the Borodin-Kostochka Conjecture holding for odd-hole-free graphs.

Damian van de Heisteeg, Cumrun Vafa, Max Wiesner, David H. Wu

In a quantum theory of gravity, the species scale $\Lambda_s$ can be defined as the scale at which corrections to the Einstein action become important or alternatively as codifying the "number of light degrees of freedom", due to the fact that $\Lambda_s^{-1}$ is the smallest size black hole described by the EFT involving only the Einstein term. In this pape

Jingxiang Qu, Ryan Wen Liu, Chenjie Zhao, Yu Guo

The accurate and efficient vessel draft reading (VDR) is an important component of intelligent maritime surveillance, which could be exploited to assist in judging whether the vessel is normally loaded or overloaded. The computer vision technique with an excellent price-to-performance ratio has become a popular medium to estimate vessel draft depth. However,

S. Amano, Y. Aritomo, M. Ohta

It is challenging to distinguish between fusion-fission and quasifission experimentally. To determine the characteristics of quasifission processes associated with dominant phenomena in heavy-ion collisions is important for estimating precisely the fusion cross section, which is relevant to the synthesis of new elements. We classified fusion-fission and quas

Mahapara Khurshid, Mayank Vatsa, Richa Singh

Skin cancer is one of the deadliest diseases and has a high mortality rate if left untreated. The diagnosis generally starts with visual screening and is followed by a biopsy or histopathological examination. Early detection can aid in lowering mortality rates. Visual screening can be limited by the experience of the doctor. Due to the long tail distribution

Deeparnab Chakrabarty, Luc Cote, Ankita Sarkar

We present approximation algorithms for the Fault-tolerant $k$-Supplier with Outliers ($\mathsf{F}k\mathsf{SO}$) problem. This is a common generalization of two known problems -- $k$-Supplier with Outliers, and Fault-tolerant $k$-Supplier -- each of which generalize the well-known $k$-Supplier problem. In the $k$-Supplier problem the goal is to serve $n$ cli

Zeyang Li, Chuxiong Hu, Shengbo Eben Li, Jia Cheng

Safety is a primary concern when applying reinforcement learning to real-world control tasks, especially in the presence of external disturbances. However, existing safe reinforcement learning algorithms rarely account for external disturbances, limiting their applicability and robustness in practice. To address this challenge, this paper proposes a robust s

Rong Wang, Wei Mao, Hongdong Li

This paper addresses the task of 3D pose estimation for a hand interacting with an object from a single image observation. When modeling hand-object interaction, previous works mainly exploit proximity cues, while overlooking the dynamical nature that the hand must stably grasp the object to counteract gravity and thus preventing the object from slipping or

S. -X. Yi, C. -W. Wang, X. -Y. Shao, R. Moradi

The second brightest GRB in history, GRB230307A, provides an ideal laboratory to study the mechanism of GRB prompt emission thanks to its extraordinarily high photon statistics and its single episode activity. Here we demonstrate that the rapidly variable components of its prompt emission compose an overall broad single pulse-like profile. Although these ind

Ryan Po, Wang Yifan, Vladislav Golyanik, Kfir Aberman

The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes. In these domains, diffusion models are the generative AI architecture of choice. Within the last year alone, the literatu

Subrata Kumar Panda, Siddharth Dhanpal, Simon J. Murphy, Shravan Hanasoge

Asteroseismology is a powerful tool to probe the structure of stars. Space-borne instruments like CoRoT, Kepler and TESS have observed the oscillations of numerous stars, among which {\delta} Scutis are particularly interesting owing to their fast rotation rates and complex pulsation mechanisms. In this work, we inferred model-dependent masses, metallicities

Xiaozhi Liu, Yong Xia

We propose a unified dynamic tracking algorithmic framework (PLAY-CS) to reconstruct signal sequences with their intrinsic structured dynamic sparsity. By capitalizing on specific statistical assumptions concerning the dynamic filter of the signal sequences, the proposed framework exhibits versatility by encompassing various existing dynamic compressive sens

Yunpeng Jin, Ling Zhu, Stefano Zibetti, Luca Costantin

We employ a recently developed population-orbit superposition technique to simultaneously fit the stellar kinematic and age maps of 82 CALIFA spiral galaxies and obtain the ages of stars in different dynamical structures. We first evaluated the capabilities of this method on CALIFA-like mock data created from the Auriga simulations. The recovered mean ages o

Xuehan Wang, Xu Shi, Jintao Wang, Jian Song

In orthogonal time frequency space (OTFS) systems, the impact of frequency-dependent Doppler which is referred to as the Doppler squint effect (DSE) is accumulated through longer duration, whose negligence has prevented OTFS systems from exploiting the performance superiority. In this paper, practical OFDM system using cyclic prefix time guard interval (CP-O

T. Nishiwaki, T. Makino, Z. Sun, D. Oka

We have observed a new optical transition in the photoreflectance spectra of indirect-gap BiOCl thin films, which were grown on SrTiO$_3$ substrates. The position of this transition is close in energy to its bulk critical point energy. Moreover, these are significantly lower than a higher-lying direct-type critical point from an energetic point of view. The

Ricardo Gallego Torrome, Shabir Barzanjeh

Quantum sensing, built upon fundamental quantum phenomena like entanglement and squeezing, is revolutionizing precision and sensitivity across diverse domains, including quantum metrology and imaging. Its impact is now stretching into radar and LiDAR applications, giving rise to the concept of quantum radar. Unlike traditional radar systems relying on classi

Ziyi Chen, Fankai Xie, Meng Wan, Yang Yuan

The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence (GAI), including auto

Ángel Chávez, Stephan Ramon Garcia, Jackson Hurley

We improve and expand in two directions the theory of norms on complex matrices induced by random vectors. We first provide a simple proof of the classification of weakly unitarily invariant norms on the Hermitian matrices. We use this to extend the main theorem in [7] from exponent $d\geq 2$ to $d \geq 1$. Our proofs are much simpler than the originals: the

Gavin N. Nop, Jonathan D. H. Smith, Daniel Stick, Durga Paudyal

Junctions are fundamental elements that support qubit locomotion in two-dimensional ion trap arrays and enhance connectivity in emerging trapped-ion quantum computers. In surface ion traps they have typically been implemented by shaping radio frequency (RF) electrodes in a single plane to minimize the disturbance to the pseudopotential. However, this method

Hee-Youl Kwak, Dae-Young Yun, Yongjune Kim, Sang-Hyo Kim

Low-density parity-check (LDPC) codes have been successfully commercialized in communication systems due to their strong error correction capabilities and simple decoding process. However, the error-floor phenomenon of LDPC codes, in which the error rate stops decreasing rapidly at a certain level, presents challenges for achieving extremely low error rates

Andrew Steinmetz

Magnetism is a rich subject touching all aspects of physics. My goal with this dissertation is to explore spin and magnetic moments in \emph{relativistic} mechanics from both a quantum and classical perspective. We emphasize the special case of gyromagnetic ratio $g\!=\!2$ and its relationship to the algebraic spin structure of wave equations. In relativisti

Hongjie Dong, Yan Guo, Zhimeng Ouyang, Timur Yastrzhembskiy

We prove the local-in-time well-posedness of the relativistic Vlasov-Maxwell-Landau system in a bounded domain $\Omega$ with the specular reflection condition. Our result covers the case when $\Omega$ is a non-convex domain, e.g., solid torus. To the best of our knowledge, this is the first local well-posedness result for a nonlinear kinetic model with a sel

Zhihao Wang, Juan Cao, Tuan Guan, Zhonggui Chen

This paper introduces a novel class of fair and interpolatory curves called $p\kappa$-curves. These curves are comprised of smoothly stitched B\'ezier curve segments, where the curvature distribution of each segment is made to closely resemble a parabola, resulting in an aesthetically pleasing shape. Moreover, each segment passes through an interpolated poin

Guergana Petrova, Przemyslaw Wojtaszczyk

We give estimates from below for the error of approximation of a compact subset from a Banach space by the outputs of feed-forward neural networks with width W, depth l and Lipschitz activation functions. We show that, modulo logarithmic factors, rates better that entropy numbers' rates are possibly attainable only for neural networks for which the depth l g

Hongwei Ren, Yue Zhou, Yulong Huang, Haotian Fu

Event cameras are bio-inspired sensors that respond to local changes in light intensity and feature low latency, high energy efficiency, and high dynamic range. Meanwhile, Spiking Neural Networks (SNNs) have gained significant attention due to their remarkable efficiency and fault tolerance. By synergistically harnessing the energy efficiency inherent in eve

Jiamin Li, Qiang Su, Yitao Yang, Yimin Jiang

Large language models, such as OpenAI's ChatGPT, have demonstrated exceptional language understanding capabilities in various NLP tasks. Sparsely activated mixture-of-experts (MoE) has emerged as a promising solution for scaling models while maintaining a constant number of computational operations. Existing MoE model adopts a fixed gating network where each

Yaohua Rong, Sihai Dave Zhao, Xia Zheng, Yi Li

Wide heterogeneity exists in cancer patients' survival, ranging from a few months to several decades. To accurately predict clinical outcomes, it is vital to build an accurate predictive model that relates patients' molecular profiles with patients' survival. With complex relationships between survival and high-dimensional molecular predictors, it is challen

Jie Zhang, Yongshan Zhang, Yicong Zhou

Identifying the land cover category for each pixel in a hyperspectral image (HSI) relies on spectral and spatial information. An HSI cuboid with a specific patch size is utilized to extract spatial-spectral feature representation for the central pixel. In this article, we investigate that scene-specific but not essential correlations may be recorded in an HS

Benjamin M. Alessio, Ankur Gupta

Advection of entities induced by gradients in attractant concentration fields is observed via diffusiophoresis in colloids and via chemotaxis in microorganisms. Mathematically, both diffusiophoresis and chemotaxis follow similar mathematical descriptions and display a variety of interesting behaviors that are not observed through other transport mechanisms.

Yeong-Joon Ju, Ji-Hoon Park, Seong-Whan Lee

Despite deep learning (DL) has achieved remarkable progress in various domains, the DL models are still prone to making mistakes. This issue necessitates effective debugging tools for DL practitioners to interpret the decision-making process within the networks. However, existing debugging methods often demand extra data or adjustments to the decision proces

Xinrun Chen, Chengliang Wang, Haojian Ning, Shiying Li

Segmenting specific targets or biomarkers is necessary to analyze optical coherence tomography angiography (OCTA) images. Previous methods typically segment all the targets in an OCTA sample, such as retinal vessels (RVs). Although these methods perform well in accuracy and precision, OCTA analyses often focusing local information within the images which has

Fengqi Liu, Zaonan Tan, Weilai Xiang, Chenhao Lu

The path tracing method generates incoherent rays by randomly sampling directions. This randomness makes it unsuitable for modern processor architectures that rely on coherence to achieve optimal performance. Many efforts have been made to address this issue by reordering rays based on their origin, end, or direction to enhance coherence. However, a drawback

Anirudh Pradhan, Safiqul Islam, M. Zeyauddin, Ayan Banerjee

In this present article, we explore the physical properties and characteristics of static, spherically symmetric wormholes in the background of Rastall-Rainbow gravity. The Rastall-Rainbow gravity theory has recently been proposed as a combination of two theories, namely, the Rastall theory and the Rainbow description. We implemented noncommutativity by adop

Zhiqing Wei, Wangjun Jiang, Zhiyong Feng, Huici Wu

Driven by the intelligent applications of sixth-generation (6G) mobile communication systems such as smart city and autonomous driving, which connect the physical and cyber space, the integrated sensing and communication (ISAC) brings a revolutionary change to the base stations (BSs) of 6G by integrating radar sensing and communication in the same hardware a

Tongtong Zhang, Yuanxiang Li

Novel view synthesis of satellite images holds a wide range of practical applications. While recent advances in the Neural Radiance Field have predominantly targeted pin-hole cameras, and models for satellite cameras often demand sufficient input views. This paper presents rpcPRF, a Multiplane Images (MPI) based Planar neural Radiance Field for Rational Poly

Junhao Yang, Yuandan Wang, Yu Lin, Wenjing Zhang

We study a quasi-one-dimensional non-reciprocal Hermitian hourglass photonic lattice that can accomplish multiple functions. Under the effect of non-reciprocal coupling, this lattice can produce an energy isolation effect, two kinds of flat bands, and energy band inversion. The excitation and propagation of a single energy band and multiple energy bands can

Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Alvin Cheung

Speculative decoding is a pivotal technique to accelerate the inference of large language models (LLMs) by employing a smaller draft model to predict the target model's outputs. However, its efficacy can be limited due to the low predictive accuracy of the draft model, particularly when faced with diverse text inputs and a significant capability gap between

Ruiwen Ding, James Hall, Neil Tenenholtz, Kristen Severson

In certain types of cancerous tissue, mitotic count has been shown to be associated with tumor proliferation, poor prognosis, and therapeutic resistance. Due to the high inter-rater variability of mitotic counting by pathologists, convolutional neural networks (CNNs) have been employed to reduce the subjectivity of mitosis detection in hematoxylin and eosin

Matthew Scalamandre

An analog of the Tits building is defined and studied for commutative rings. We prove a Solomon-Tits theorem when $R$ either satisfies a stable range condition, or is the ring of $S$-integers of a global field. We then define an analog of the Steinberg module of $R$, and study it both as a $\mathbb{Z}$-module and as a representation. We find the rank of Stei

Jungtaek Kim, Jeongbeen Yoon, Minsu Cho

Sorting is a fundamental operation of all computer systems, having been a long-standing significant research topic. Beyond the problem formulation of traditional sorting algorithms, we consider sorting problems for more abstract yet expressive inputs, e.g., multi-digit images and image fragments, through a neural sorting network. To learn a mapping from a hi

Wilmer Contreras Sepúlveda, Ángel David Torres-Palencia, José Javier Sánchez Mondragón, Braulio Misael Villegas-Martínez

Quantum computing is a rapidly emerging and promising field that has the potential to revolutionize numerous research domains, including drug design, network technologies and sustainable energy. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms

Saurabh Pargal, Junlin Yuan, Stephane Moreau

This study uses high-fidelity simulations (DNS or LES) and experimental datasets to analyse the effect of non-equilibrium streamwise mean pressure gradients (adverse or favourable), including attached and separated flows, on the statistics of boundary layer wall-pressure fluctuations. The datasets collected span a wide range of Reynolds numbers ($Re_\theta$

Zheshun Wu, Zenglin Xu, Dun Zeng, Qifan Wang

Federated Learning (FL) has surged in prominence due to its capability of collaborative model training without direct data sharing. However, the vast disparity in local data distributions among clients, often termed the Non-Independent Identically Distributed (Non-IID) challenge, poses a significant hurdle to FL's generalization efficacy. The scenario become

Tatsuya Ide, Eiki Murata, Daisuke Kawahara, Takato Yamazaki

Despite the remarkable progress in natural language understanding with pretrained Transformers, neural language models often do not handle commonsense knowledge well. Toward commonsense-aware models, there have been attempts to obtain knowledge, ranging from automatic acquisition to crowdsourcing. However, it is difficult to obtain a high-quality knowledge b

Pablo D. Carrasco, Elias Rego, Jana Rodriguez-Hertz

In this work we consider foliations of compact manifolds whose holonomy pseudo-group is expansive, and analyze their number of compact leaves. Our main result is that in the codimension-one case this number is at most finite, and we give examples of such foliations having one compact leaf.

Taotao He, Mohit Tawarmalani

In this paper, we develop new discrete relaxations for nonlinear expressions in factorable programming. We utilize specialized convexification results as well as composite relaxations to develop mixed-integer programming (MIP) relaxations. Our relaxations rely on ideal formulations of convex hulls of outer-functions over a combinatorial structure that captur

Akane Kawaharada

When using a cellular automaton (CA) as a fractal generator, consider orbits from the single site seed, an initial configuration that gives only a single cell a positive value. In the case of a two-state CA, since the possible states of each cell are $0$ or $1$, the "seed" in the single site seed is uniquely determined to be the state $1$. However, for a CA

Qiyuan Ou, Siwei Wang, Pei Zhang, Sihang Zhou

Multi-view clustering has attracted growing attention owing to its capabilities of aggregating information from various sources and its promising horizons in public affairs. Up till now, many advanced approaches have been proposed in recent literature. However, there are several ongoing difficulties to be tackled. One common dilemma occurs while attempting t

Lianghaojie Zhou, Youquan Xian, Yipeng Yang, Jianyong Jiang

In the field of energy Internet, blockchain-based distributed energy trading mode is a promising way to replace the traditional centralized trading mode. However, the current power blockchain platform based on public chain has problems such as low consensus efficiency and waste of computing resources. The energy trading platform based on the consortium chain

Zhihong Liu, Jialin Zhang, Hongwei Yu

We explore correlations harvesting by two static detectors locally interacting with vacuum massless scalar fields in the presence of an infinite perfectly reflecting boundary. We study the phenomena of mutual information harvesting and entanglement harvesting for two detector-boundary alignments, i.e., parallel-to-boundary and vertical-to-boundary alignments

Na Wang, Qian Xu, Jun Ma, Zhiyong Liu

This study presents a general outline of the Qitai radio telescope (QTT) project. Qitai, the site of the telescope, is a county of Xinjiang Uygur Autonomous Region of China, located in the east Tianshan Mountains at an elevation of about 1800 m. The QTT is a fully steerable, Gregorian type telescope with a standard parabolic main reflector of 110 m diameter.

Hajime Fukuda, Takeo Moroi, Atsuya Niki, Shang-Fu Wei

Weakly interacting massive particles (WIMPs) with electroweak charges, such as the wino and the Higgsino, stand out as natural candidates for dark matter in the universe. In this paper, we study the search for WIMPs at future multi-TeV $\mu^+\mu^+$ colliders. We investigate both the direct production search of WIMPs through the mono-muon channel and the indi

Joseph Konan, Shikhar Agnihotri, Ojas Bhargave, Shuo Han

Within the ambit of VoIP (Voice over Internet Protocol) telecommunications, the complexities introduced by acoustic transformations merit rigorous analysis. This research, rooted in the exploration of proprietary sender-side denoising effects, meticulously evaluates platforms such as Google Meets and Zoom. The study draws upon the Deep Noise Suppression (DNS

Josh Gardner, Simon Durand, Daniel Stoller, Rachel M. Bittner

Music has a unique and complex structure which is challenging for both expert humans and existing AI systems to understand, and presents unique challenges relative to other forms of audio. We present LLark, an instruction-tuned multimodal model for \emph{music} understanding. We detail our process for dataset creation, which involves augmenting the annotatio

Junhui Cao, Alexey Kavokin

The experimental results hinting at the room temperature and ambient pressure superconductivity and magnetic levitation in LK-99 attracted an unprecedented interest. While attempts of other teams to reproduce the reported observations on similar samples failed so far, it seems worthwhile to try building a theoretical model that would explain the ensemble of

Lanjun Wang, Xinran Qiao, Yanwei Xie, Weizhi Nie

Social platforms such as Twitter are under siege from a multitude of fraudulent users. In response, social bot detection tasks have been developed to identify such fake users. Due to the structure of social networks, the majority of methods are based on the graph neural network(GNN), which is susceptible to attacks. In this study, we propose a node injection

Mayu Tominaga, Masahiro Tsujimoto, Ken Ebisawa, Teruaki Enoto

Circinus X-1 (Cir X-1) is a neutron star binary with an elliptical orbit of 16.6~days. The source is unique for its extreme youth, providing a key to understanding early binary evolution. However, its X-ray variability is too complex to reach a clear interpretation. We conducted the first high cadence (every 4 hours on average) observations covering one enti

Ahmed S. Alahmed, Guido Cavraro, Andrey Bernstein, Lang Tong

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and impo

Majid Namazi, M. A. Hakim Newton, Conrad Sanderson, Abdul Sattar

A travelling thief problem (TTP) is a proxy to real-life problems such as postal collection. TTP comprises an entanglement of a travelling salesman problem (TSP) and a knapsack problem (KP) since items of KP are scattered over cities of TSP, and a thief has to visit cities to collect items. In TTP, city selection and item selection decisions need close coord

Shamik Roy, Dan Goldwasser

Social media has become a major driver of social change, by facilitating the formation of online social movements. Automatically understanding the perspectives driving the movement and the voices opposing it, is a challenging task as annotated data is difficult to obtain. We propose a weakly supervised graph-based approach that explicitly models perspectives

Yao Lyu, John M. Carroll

There has been extensive research on the experiences of individuals with visual impairments on text- and image-based social media platforms, such as Facebook and Twitter. However, little is known about the experiences of visually impaired users on short-video platforms like TikTok. To bridge this gap, we conducted an interview study with 30 BlindTokers (the

Huaxing Peng, Baojun Yan, Han Miao, Shulin Liu

Nowadays, Microchannel Plate (MCP), as a kind of electron multipliers based on the secondary electron emission, is widely used in many high-sensitive experiments, such as neutrino detection, which require the noise to be as low as possible, while the conventional straight-channel MCP will inevitably have ion feedback, resulting in the sequential after-pulses

Ziqi Zhang, Chen Gong, Yifeng Cai, Yuanyuan Yuan

On-device ML introduces new security challenges: DNN models become white-box accessible to device users. Based on white-box information, adversaries can conduct effective model stealing (MS) and membership inference attack (MIA). Using Trusted Execution Environments (TEEs) to shield on-device DNN models aims to downgrade (easy) white-box attacks to (harder)

Brice Romuald Gueyap Kounga

This paper studies identification and estimation of a semiparametric logit model in which an unobserved individual characteristic affects both a binary outcome and the formation of social links. In this setting, the endogeneity of the network is informative rather than a nuisance: because the same latent trait drives linking behavior, observed network data c

Qishen Han, Amélie Marian, Lirong Xia

An important question in elections is the determine whether a candidate can be a winner when some votes are absent. We study this determining winner with the absent votes (WAV) problem when the votes are top-truncated. We show that the WAV problem is NP-complete for the single transferable vote, Maximin, and Copeland, and propose a special case of positional

Hye-Seong Hong, Abhishek Kumar, Dong-Gyu Lee

The generalization capability of unsupervised domain adaptation can mitigate the need for extensive pixel-level annotations to train semantic segmentation networks by training models on synthetic data as a source with computer-generated annotations. Entropy-based adversarial networks are proposed to improve source domain prediction; however, they disregard s

Yifeng Zheng, Weibo Wang, Songlei Wang, Zhongyun Hua

The proliferation of cloud computing has greatly spurred the popularity of outsourced database storage and management, in which the cloud holding outsourced databases can process database queries on demand. Among others, skyline queries play an important role in the database field due to its prominent usefulness in multi-criteria decision support systems. To