December 2023 arXiv papers — page 104

Mona Schirmer, Dan Zhang, Eric Nalisnick

Knowing if a model will generalize to data 'in the wild' is crucial for safe deployment. To this end, we study model disagreement notions that consider the full predictive distribution - specifically disagreement based on Hellinger distance, Jensen-Shannon and Kullback-Leibler divergence. We find that divergence-based scores provide better test error estimat

Shanghua Liu, Anna Hedström, Deepak Hanike Basavegowda, Cornelia Weltzien

Grasslands are known for their high biodiversity and ability to provide multiple ecosystem services. Challenges in automating the identification of indicator plants are key obstacles to large-scale grassland monitoring. These challenges stem from the scarcity of extensive datasets, the distributional shifts between generic and grassland-specific datasets, an

Mohammed Bazirha

This thesis addressed the HHCRSP, which is a class of workforce scheduling problems. The HHCRSP is an extension of the VRPTW to which the constraints related to the HHC context are added. It aims to provide care services to patients at their homes instead of going to the hospital. We dealt with three different problems from the optimization viewpoint. In the

Leena Elneel, M. Sami Zitouni, Husamuldin Mukhtar, Paolo Galli

Sea Level Rise (SLR) is one of the most pressing challenges of climate change and has drawn noticeable research interest over the past few decades. Factors induced by global climate change such as temperature increase, have resulted in both direct and indirect changes in sea levels at different spatial scales. A number of climatic and non-climatic events dri

Tilman Daab, Noémie Jaquier, Christian Dreher, Andre Meixner

Movement primitives (MPs) are compact representations of robot skills that can be learned from demonstrations and combined into complex behaviors. However, merely equipping robots with a fixed set of innate MPs is insufficient to deploy them in dynamic and unpredictable environments. Instead, the full potential of MPs remains to be attained via adaptable, la

Jie Yan, Jing Liu, Zhong-yuan Zhang

Variational autoencoder (VAE) and generative adversarial networks (GAN) have found widespread applications in clustering and have achieved significant success. However, the potential of these approaches may be limited due to VAE's mediocre generation capability or GAN's well-known instability during adversarial training. In contrast, denoising diffusion prob

Philip Scherer, Christiane Weis, Thorsten Strufe

Onion routing and mix networks are fundamental concepts to provide users with anonymous access to the Internet. Various corresponding solutions rely on the efficient Sphinx packet format. However, flaws in Sphinx's underlying proof strategy were found recently. It is thus currently unclear which guarantees Sphinx actually provides, and, even worse, there is

Jinta Weng, Jiarui Zhang, Yue Hu, Daidong Fa

Large language models (LLMs) can be used as accessible and intelligent chatbots by constructing natural language queries and directly inputting the prompt into the large language model. However, different prompt' constructions often lead to uncertainty in the answers and thus make it hard to utilize the specific knowledge of LLMs (like ChatGPT). To alleviate

Wang Zheng, Zhang Jintao, Feng Xiaojuan, Xing Li

The zero field splitting (ZFS) quantifies the energy difference for the ground electron spin-triplet of a nitrogen-vacancy center in the absence of external fields. The values of the ZFS play a key role in determining the Larmor precession of the Bloch sphere and the Rabi oscillation of a spin system. The ZFS is generally detected using coherent spin manipul

Yuxuan Liu, Qian Chen, Yi Ling, Cheng Peng

In the framework of double holography, we investigate the entanglement behavior of a brane subregion in AdS spacetime coupled to a bath on its boundary and also extract the contribution from the quantum matter within this subregion. From the boundary perspective, the brane subregion serves as the entanglement wedge of a subsystem on the conformal defect. In

Sergio Cruz-Blázquez

In this paper we consider a doubly critical nonlinear elliptic problem with Neumann boundary conditions. The existence of blow-up solutions for this problem is related to the blow-up analysis of the classical geometric problem of prescribing negative scalar curvature $K=-1$ on a domain of $\R^n$ and mean curvature $H=D(n(n-1))^{-1/2}$, for some constant $D>1

Spiros Cotsakis

The crease flow, replacing the Hamiltonian system used for the evolution of crease sets on black hole horizons, is introduced and its bifurcation properties for null hypersurfaces are discussed. We state the conditions of nondegeneracy and typicality for the crease submanifolds, and find their normal forms and versal unfoldings (codimension 3). The allowed b

Yang Zhan, Yuan Yuan, Zhitong Xiong

We introduce a novel task of 3D visual grounding in monocular RGB images using language descriptions with both appearance and geometry information. Specifically, we build a large-scale dataset, Mono3DRefer, which contains 3D object targets with their corresponding geometric text descriptions, generated by ChatGPT and refined manually. To foster this task, we

Nitin Agarwal, Ashish Kumar, Kiran R, Manish Gupta

Azure Cognitive Search (ACS) has emerged as a major contender in "Search as a Service" cloud products in recent years. However, one of the major challenges for ACS users is to improve the relevance of the search results for their specific usecases. In this paper, we propose a novel method to find the optimal ACS configuration that maximizes search relevance

Yuyang Sun, Huy H. Nguyen, Chun-Shien Lu, ZhiYong Zhang

The growing diversity of digital face manipulation techniques has led to an urgent need for a universal and robust detection technology to mitigate the risks posed by malicious forgeries. We present a blended-based detection approach that has robust applicability to unseen datasets. It combines a method for generating synthetic training samples, i.e., recons

Zhiyuan Ma, Guoli Jia, Bowen Zhou

With the great success of text-conditioned diffusion models in creative text-to-image generation, various text-driven image editing approaches have attracted the attentions of many researchers. However, previous works mainly focus on discreteness-sensitive instructions such as adding, removing or replacing specific objects, background elements or global styl

Bingbing Ding, Shijie Dong, Gang Xu

We are interested in coupled semi-linear wave equations satisfying the null condition in two space dimensions, a basic model in nonlinear wave equations. Our aim is to establish global existence of smooth solutions to this system with large initial data of short pulse type. Major difficulties arise due to the largeness of initial data and the slow decay natu

Weilong Chai, DanDan Zheng, Jiajiong Cao, Zhiquan Chen

Text-to-image diffusion models (SD) exhibit significant advancements while requiring extensive computational resources. Existing acceleration methods usually require extensive training and are not universally applicable. LCM-LoRA, trainable once for diverse models, offers universality but rarely considers ensuring the consistency of generated content before

Walaa Elmetenawee

A precise and efficient tracking is one of the critical components of the CMS physics program as it impacts the ability to reconstruct the physics objects needed to understand proton-proton collisions at the LHC. The CMS detector has undergone extensive improvements in preparation for Run 3 of the LHC to operate efficiently at the increased luminosity and pi

Xin Hao, Phee Lep Yeoh, Changyang She, Branka Vucetic

This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for {data management and} resource allocation in decentralized {wireless mobile edge computing (MEC)} networks. In our framework, {we design a low-latency reputation-based proof-of-stake (RPoS) consensus protocol to select highly reliable blockchain-enabled B

Andreas Zmija, Naomi Vogel, Frederik Wohlleben, Gisela Anton

Intensity interferometry for astrophysical observations has gained increasing interest in the last decade. The method of correlating photon fluxes at different telescopes for high resolution astronomy without access to the phase of the incoming light is insensitive to atmospheric turbulence and doesn't require high-precision optical path control. The necessa

Jacob Morgan, Simon Cox

Aqueous foams coarsen with time due to gas diffusion through the liquid. The mean bubble size grows, and small bubbles vanish. However, coarsening is little understood for foams with an intermediate liquid content, particularly in the presence of surfactant-induced attractive forces between the bubbles, measured by the contact angle. Rigorous bubble growth l

Xin Hao, Phee Lep Yeoh, Yuhong Liu, Changyang She

This paper designs a graph neural network (GNN) to improve bandwidth allocations for multiple legitimate wireless users transmitting to a base station in the presence of an eavesdropper. To improve the privacy and prevent eavesdropping attacks, we propose a user scheduling algorithm to schedule users satisfying an instantaneous minimum secrecy rate constrain

Michał Lipiński, Konstantin Mischaikow, Marian Mrozek

Motivated by the study of the recurrent orbits in a Morse set of a Morse decomposition, we introduce the concept of Morse predecomposition of an isolated invariant set in the setting of combinatorial and classical dynamical systems. We prove that a Morse predecomposition indexed by a poset is a Morse decomposition and we show how a Morse predecomposition may

Vsevolod Skorokhodov, Darya Drozdova, Dmitry Yudin

Recently, there has been an increased interest in NeRF methods which reconstruct differentiable representation of three-dimensional scenes. One of the main limitations of such methods is their inability to assess the confidence of the model in its predictions. In this paper, we propose a new neural network model for the formation of extended vector represent

Paolo Minelli, Athanasios Sourmelidis

We study lower bounds for the Riemann zeta function $\zeta(s)$ along vertical arithmetic progressions in the right-half of the critical strip. We show that the lower bounds obtained in the discrete case coincide, up to the constants in the exponential, with the ones known for the continuous case, that is when the imaginary part of $s$ ranges on a given inter

Shahzad Ahmad, Sukalpa Chanda, Yogesh S Rawat

Recent advancements in large-scale pre-training of visual-language models on paired image-text data have demonstrated impressive generalization capabilities for zero-shot tasks. Building on this success, efforts have been made to adapt these image-based visual-language models, such as CLIP, for videos extending their zero-shot capabilities to the video domai

Kewei Wang, Yizheng Wu, Zhiyu Pan, Xingyi Li

Class-agnostic motion prediction methods aim to comprehend motion within open-world scenarios, holding significance for autonomous driving systems. However, training a high-performance model in a fully-supervised manner always requires substantial amounts of manually annotated data, which can be both expensive and time-consuming to obtain. To address this ch

Reda Ouhamma, Maryam Kamgarpour

We address payoff-based decentralized learning in infinite-horizon zero-sum Markov games. In this setting, each player makes decisions based solely on received rewards, without observing the opponent's strategy or actions nor sharing information. Prior works established finite-time convergence to an approximate Nash equilibrium under strong reachability and

Wenxuan Wang, Tongtian Yue, Yisi Zhang, Longteng Guo

Referring expression segmentation (RES) aims at segmenting the foreground masks of the entities that match the descriptive natural language expression. Previous datasets and methods for classic RES task heavily rely on the prior assumption that one expression must refer to object-level targets. In this paper, we take a step further to finer-grained part-leve

Melven Röhrig-Zöllner, Manuel Joey Becklas, Jonas Thies, Achim Basermann

Tensor networks are a class of algorithms aimed at reducing the computational complexity of high-dimensional problems. They are used in an increasing number of applications, from quantum simulations to machine learning. Exploiting data parallelism in these algorithms is key to using modern hardware. However, there are several ways to map required tensor oper

Wojciech Porowski

We consider sections of the \'etale homotopy exact sequence of a hyperbolic curve over a number field. We prove that two sections whose restrictions to decomposition groups are conjugate on a set of valuations of density one are globally conjugate, which establishes the local-global principle for the conjugacy classes of sections. In fact, we obtain this res

Yang Jiao, Zequn Jie, Shaoxiang Chen, Lechao Cheng

Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field. Under such a paradigm, accurate BEV representation construction relies on reliable depth estimation for multi-camera images. However, existing approaches exhaustively predict depths for every pixel without prioritizing objects, which are precis

Rishabh Sharma

Light nuclei might be formed in heavy-ion collisions by the coalescence of produced (anti-)nucleons or transported nucleons. Due to their low binding energies, they are more likely to form at later stages of the hadronic fireball. In this proceedings, we report the transverse momentum and centrality dependence of elliptic ($v_{2}$) and triangular ($v_{3}$) f

Joe Myers Hill

We prove the Bernoulli property for a class of counter-twisting linked twist maps. These compose orthogonal linear shears on the torus, orientated in the opposite sense to their co-twisting counterparts (where the shears reinforce one another). Compared to previous studies we focus on the parameter space corresponding to weak shears, near the critical parame

Huizi Wu, Cong Geng, Hui Fang

Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions. Most existing studies on SR adopt advanced deep learning methods. However, the majority only consider a special behavior type (e.g., click), while those few considering multi-typed behaviors ignore to take full advant

Kateryna Korshynska, Sebastian Ulbricht

We investigate a non-interacting many-particle bosonic system, placed in an asymmetric double-well potential. We first consider the dynamics of a single particle and determine its time-dependent probabilities to be in the left or the right well of the potential. These probabilities obey the standard Josephson equations, which in their many-particle interpret

Kai Ma, Jintao Huang, Ningyu He, Zhuo Wang

Non-fungible tokens (NFTs) drive the prosperity of the Web3 ecosystem. By November 2023, the total market value of NFT projects reached approximately 16 billion USD. Accompanying the success of NFTs are various security issues, i.e., attacks and scams are prevalent in the ecosystem. While NFTs have attracted significant attentions from both industry and acad

Sudharsan Sundar, Eric Gao, Trevor Chow, Matthew Ding

It is well known that Random Serial Dictatorship is strategy-proof and leads to a Pareto-Efficient outcome. We show that this result breaks down when individuals are allowed to make transfers, and adapt Random Serial Dictatorship to encompass trades between individuals. Strategic analysis of play under the new mechanisms we define is given, accompanied by si

Raheam A. Al-Saphory, Abdullah A. Al-Hayani, Alaa A. Auad

The aim of this work is to introduced the concept of the best one-sided approximation of unbounded functions in weighted space by using algebraic operators in terms the average modulus of smoothness. We also show an estimate of the degree of best one-sided approximation of unbounded functions in the terms of average modulus of smoothness.

Vitali Pirau

Stochastic saddle point (SSP) problems are, in general, less studied compared to stochastic minimization problems. However, SSP problems emerge from machine learning (adversarial training, e.g., GAN, AUC maximization), statistics (robust estimation), and game theory (stochastic matrix games). Notwithstanding the existing results on the convergence of stochas

Chentao Tan, Zhun Lu

We study the gluon Wigner distributions of the proton which are the phase-space distributions containing the most general one-parton information. Using the proton wave functions deduced from a light-cone spectator model that contains the gluonic degree of freedom, we calculate the Wigner distributions of the unpolarized and longitudinally polarized gluon ins

Sebastian Emmert, Peter Lunkenheimer, Alois Loidl

We present a detailed study on the temperature-dependent THz spectra of the polycrystalline amino acids L-serine and L-cysteine for wave numbers from 20 to 120 cm-1 and temperatures from 4 to 300 K. Even though the structure of these two amino acids is very similar, with a sulfur atom in the side chain of cysteine instead of an oxygen atom in serine, the exc

Michael Goldman, Martin Huesmann, Felix Otto

In this note we prove estimates for the average cost in the quadratic optimal transport problem on the two-dimensional flat torus which are optimal up to a double logarithm. We also prove sharp estimates on the displacement. This is based on the combination of a post-processing of our quantitative linearization result together with a quasi-orthogonality prop

Yvan Castin

We present simple arguments suggesting that H. Biss et al [PRL 128, 100401 (2022)] did not measure with the required accuracy the low-wavenumber curvature of the acoustic excitation branch of the ground-state unitary Fermi gas. This difficult-to-calculate quantity is crucial for the relaxation dynamics of the gas at low temperature.

Zeynep G. Saribatur, Stefan Woltran

Answer Set Programming (ASP) is a prominent rule-based language for knowledge representation and reasoning with roots in logic programming and non-monotonic reasoning. The aim to capture the essence of removing (ir)relevant details in ASP programs led to the investigation of different notions, from strong persistence (SP) forgetting, to faithful abstractions

Qiongxiu Li, Lixia Luo

The privacy concern in federated clustering has attracted considerable attention in past decades. Many privacy-preserving clustering algorithms leverage cryptographic techniques like homomorphic encryption or secure multiparty computation, to guarantee full privacy, i.e., no additional information is leaked other than the final output. However, given the ite

Alon Mor, Yonatan Belinkov, Benny Kimelfeld

Local explanation methods highlight the input tokens that have a considerable impact on the outcome of classifying the document at hand. For example, the Anchor algorithm applies a statistical analysis of the sensitivity of the classifier to changes in the token. Aggregating local explanations over a dataset provides a global explanation of the model. Such a

Hiroyuki Sakai, Hideaki Iiduka

Novel convergence analyses are presented of Riemannian stochastic gradient descent (RSGD) on a Hadamard manifold. RSGD is the most basic Riemannian stochastic optimization algorithm and is used in many applications in the field of machine learning. The analyses incorporate the concept of mini-batch learning used in deep learning and overcome several problems

Mikhail Muzychuk, Grigory Ryabov

In the present paper, we study relative difference sets (RDSs) and linked systems of them. It is shown that a closed linked system of RDSs is always graded by a group. Based on this result, we also define a product of RDS linked systems sharing the same grading group. Further, we generalize the Davis-Polhill-Smith construction of a linked system of RDSs. Fin

A. A. Dzhioev, A. V. Yudin, N. V. Dunina-Barkovskaya, A. I. Vdovin

We propose a new method for calculating spectra and luminosities for (anti)neutrinos produced in the pre-supernova environment by weak processes with hot nuclei. It is based on the thermal quasiparticle random phase approximation (TQRPA), that allows microscopic thermodynamically consistent calculations of the weak-interaction response of nuclei at finite te

Róbert Csordás, Piotr Piękos, Kazuki Irie, Jürgen Schmidhuber

Despite many recent works on Mixture of Experts (MoEs) for resource-efficient Transformer language models, existing methods mostly focus on MoEs for feedforward layers. Previous attempts at extending MoE to the self-attention layer fail to match the performance of the parameter-matched baseline. Our novel SwitchHead is an effective MoE method for the attenti

Ahmet Daşdemir

In this paper, we deal with Diophantine equations $N = {F_k}^3 + {F_\ell }^3 = {F_m}^3 + {F_n}^3$ and $M = {L_k}^3 + {L_\ell }^3 = {L_m}^3 + {L_n}^3$. In other words, we discover the Fibonacci and Lucas numbers that are also Hardy-Ramanujan numbers.

Sombuddha Bhattacharyya, Katya Krupchyk, Suman Kumar Sahoo, Gunther Uhlmann

We study inverse boundary problems for third-order nonlinear tensorial perturbations of biharmonic operators on a bounded domain in $\mathbb{R}^n$, where $n\geq 3$. By imposing appropriate assumptions on the nonlinearity, we demonstrate that the Dirichlet-to-Neumann map, known on the boundary of the domain, uniquely determines the genuinely nonlinear tensori

Aurélien Fabre, Sylvain Nascimbene

The realization of topological states of matter in ultracold atomic gases is currently the subject of intense experimental activity. Using a synthetic dimension, encoded in a non-spatial degree of freedom, can greatly simplify the simulation of gauge fields and give access to exotic topological states. We review here recent advances in the field and discuss

Xiaobo Zhu, Yan Wu, Zhipeng Li, Hailong Su

Recently, representation learning over graph networks has gained popularity, with various models showing promising results. Despite this, several challenges persist: 1) most methods are designed for static or discrete-time dynamic graphs; 2) existing continuous-time dynamic graph algorithms focus on a single evolving perspective; and 3) many continuous-time

Fulvio Gesmundo, Hanieh Keneshlou

In this article, we introduce the $k$-th collineation variety of a third order tensor. This is the closure of the image of the rational map of size $k$ minors of a matrix of linear forms associated to the tensor. We classify such varieties in the case of pencils of matrices, and nets of matrices of small size. We discuss the natural stratification of tensor

Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le

Multi-modality promises to unlock further uses for large language models. Recently, the state-of-the-art language model GPT-4 was enhanced with vision capabilities. We carry out a prompting evaluation of GPT-4V and five other baselines on structured reasoning tasks, such as mathematical reasoning, visual data analysis, and code generation. We show that visua

Haiming Yi, Lei Hou, Yuhong Jin, Nasser A. Saeed

Diffusion models have demonstrated powerful data generation capabilities in various research fields such as image generation. However, in the field of vibration signal generation, the criteria for evaluating the quality of the generated signal are different from that of image generation and there is a fundamental difference between them. At present, there is

Marta Irene Bracco, Jonas Peter Eiberg, Ulver Spangsberg Lorenzen, Stephane Avril

OBJECTIVE: Assessing the biomechanical behaviour of the aortic wall in vivo can potentially improve the diagnosis and prognosis of patients with abdominal aortic aneurysms (AAA). With ultrasound, AAA wall stiffness can be estimated as the diameter change in response to blood pressure. However, this measurement has shown limited reproducibility, possibly infl

Prameela Madambakam, Shathanaa Rajmohan, Himangshu Sharma, Tummepalli Anka Chandrahas Purushotham Gupta

Legal Judgment Prediction (LJP) is a judicial assistance system that recommends the legal components such as applicable statues, prison term and penalty term by analyzing the given input case document. Indian legal system is in the need of technical assistance such as artificial intelligence to solve the crores of pending cases in various courts for years an

Reuven Segev

This paper offers an informal instructive introduction to some of the main notions of geometric continuum mechanics for the case of smooth fields. We use a metric invariant stress theory of continuum mechanics to formulate a simple generalization of the fields of electrodynamics and Maxwell's equations to general differentiable manifolds of any dimension, th

Dipesh Niraula, Issam El Naqa, Jack Adam Tuszynski, Robert A. Gatenby

Virtually all cells use energy and ion-specific membrane pumps to maintain large transmembrane gradients of Na$^+$, K$^+$, Cl$^-$, Mg$^{++}$, and Ca$^{++}$. Although they consume up to 1/3 of a cell's energy budget, the corresponding evolutionary benefit of transmembrane ion gradients remain unclear. Here, we propose that ion gradients enable a dynamic and v

Richard A. Wolf, Sho Araiba

In this paper we provide an overview of the programmed instructions approach for the purpose of quantum software education. The article presents the programmed instructions method and recent successes in STEM fields before describing its operating mode. Elements tackled include the core components of programmed instructions, its behavioural roots and early u

Rabah Labbas, Stéphane Maingot, Alexandre Thorel

The purpose of this article (composed of two parts) is the study of the generalized dispersal operator of a reaction-diffusion equation in $L^p$-spaces set in the finite conical domain $S_{\omega,\rho}$ of angle $\omega>0$ and radius $\rho$ > 0 in $\mathbb{R}^2$. This first part is devoted to the behavior of the solution near the top of the cone which is com

T. Kim, H. Jeon, Y. Lim

Recently, as many studies of autonomous vehicles have been achieved for levels 4 and 5, there has been also increasing interest in the advancement of perception, decision, and control technologies, which are the three major aspects of autonomous vehicles. As for the perception technologies achieving reliable maneuvering of autonomous vehicles, object detecti

Marc Castella

We deal with a model where a set of observations is obtained by a linear superposition of unknown components called sources. The problem consists in recovering the sources without knowing the linear transform. We extend the well-known Independent Component Analysis (ICA) methodology. Instead of assuming independent source components, we assume that the sourc

Bogdan Matioc, Christoph Walker

Well-posedness in time-weighted spaces of certain quasilinear (and semilinear) parabolic evolution equations $u'=A(u)u+f(u)$ is established. The focus lies on the case of strict inclusions $\mathrm{dom}(f)\subsetneq \mathrm{dom}(A)$ of the domains of the nonlinearities $u\mapsto f(u)$ and $u\mapsto A(u)$. Based on regularizing effects of parabolic equations

Yu. A. Fadeyev

Evolutionary sequences of AGB stars with initial masses on the main sequence $M_\mathrm{ZAMS}=1.5M_\odot$, $2M_\odot$ and $3M_\odot$ were computed for the initial metallicity $Z=0.014$. Selected models of evolutionary sequences with envelopes under thermal equilibrium were used as initial conditions for calculation of nonlinear stellar pulsations. The hydrod

Florent Berthelin

In this paper, we prove particle approximations of initial data for systems of conservation laws in two dimensions. This involves approaching the density but also all the additional quantities that could be verified by the model considered. We prove that according to the hypothesis of regularity or support, the speed of convergence is of form C/N or C/N^2 .

Zhiyuan Ma, zhihuan yu, Jianjun Li, Bowen Zhou

As a class of fruitful approaches, diffusion probabilistic models (DPMs) have shown excellent advantages in high-resolution image reconstruction. On the other hand, masked autoencoders (MAEs), as popular self-supervised vision learners, have demonstrated simpler and more effective image reconstruction and transfer capabilities on downstream tasks. However, t

Yanling Tian, Di Chen, Yunan Liu, Jian Yang

Large-scale pre-training has proven to be an effective method for improving performance across different tasks. Current person search methods use ImageNet pre-trained models for feature extraction, yet it is not an optimal solution due to the gap between the pre-training task and person search task (as a downstream task). Therefore, in this paper, we focus o

Shiyun Chen, Li Lin, Pujin Cheng, Xiaoying Tang

Liver tumor segmentation is essential for computer-aided diagnosis, surgical planning, and prognosis evaluation. However, obtaining and maintaining a large-scale dataset with dense annotations is challenging. Semi-Supervised Learning (SSL) is a common technique to address these challenges. Recently, Segment Anything Model (SAM) has shown promising performanc

François Parreau

We extend the Foia\c{s} and Stratila theorem to the case of $L^2$-functions whose spectral measure is continuous and concentrated on an independent Helson set, and to ergodic actions of locally compact second countable abelian groups. We first prove it for functions satisfying Carleman's condition for the Hamburger moment problem, without the assumption that

Claudio Quadrelli

We prove that a strengthened version of Minac-Tan's Massey Vanishing Conjecture holds true for fields with a finite number of square classes whose maximal pro-$2$ Galois group is of elementary type (as defined by I. Efrat). In particular, this proves Minac-Tan's Massey Vanishing Conjecture for Pythagorean fields with a finite number of square classes and the

Mathieu Schumann, Quentin Reynaud, François Sempé, Julien Guibourdenche

To address the major issues associated with using Time-Use Survey (TUS) for simulating residential load curves, we present the SMACH approach, which combines qualitative and quantitative data with agent-based simulation. Our model consists of autonomous agents assigned with daily tasks. The agents try to accomplish their assigned tasks to the best of their a

Alhassan Mabrouk, Rebeca P. Díaz Redondo, Abdelghani Dahou, Mohamed Abd Elaziz

Pneumonia is a life-threatening lung infection resulting from several different viral infections. Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity to other pulmonary diseases. Thus, the existing methods for predicting pneumonia cannot attain substantial levels of accuracy. Therefore, this paper presents a comput

Xinpeng Wang, Xiaoyuan Yi, Han Jiang, Shanlin Zhou

Warning: this paper includes model outputs showing offensive content. Recent large-scale Visual-Language Generative Models (VLGMs) have achieved unprecedented improvement in multimodal image/text generation. However, these models might also generate toxic content, e.g., offensive text and pornography images, raising significant ethical risks. Despite exhaust

Jingwei Yang, Bohuan Xue, Yi Feng, Deming Wang

This article introduces three-filters-to-normal+ (3F2N+), an extension of our previous work three-filters-to-normal (3F2N), with a specific focus on incorporating discontinuity discrimination capability into surface normal estimators (SNEs). 3F2N+ achieves this capability by utilizing a novel discontinuity discrimination module (DDM), which combines depth cu

Cong Xu, Qing-Hua Zhang, Shao-Ming Fei

Uncertainty principle is one of the most essential features in quantum mechanics and plays profound roles in quantum information processing. We establish tighter summation form uncertainty relations based on metric-adjusted skew information via operator representation of observables, which improve the existing results. By using the methodologies of sampling

Édouard Bonnet, Jędrzej Hodor, Tuukka Korhonen, Tomáš Masařík

A graph $G$ contains a graph $H$ as an induced minor if $H$ can be obtained from $G$ after vertex deletions and edge contractions. We show that for every $k$-vertex planar graph $H$, every graph $G$ excluding $H$ as an induced minor and $K_{t,t}$ as a subgraph has treewidth at most $\Delta(G)^{f(k,t)}$ where $\Delta(G)$ denotes the maximum degree of $G$. Wit

Xiaojun Xue, Chunxia Zhang, Tianxiang Xu, Zhendong Niu

Few-shot named entity recognition (NER) aims to recognize novel named entities in low-resource domains utilizing existing knowledge. However, the present few-shot NER models assume that the labeled data are all clean without noise or outliers, and there are few works focusing on the robustness of the cross-domain transfer learning ability to textual adversar

Markus Schwagenscheidt

We study meromorphic modular forms associated with positive definite binary quadratic forms and their cycle integrals along closed geodesics in the modular curve. We show that suitable linear combinations of these meromorphic modular forms have rational cycle integrals. Along the way, we evaluate the cycle integrals of the Siegel theta function associated wi

Hussein Albazzal, Alexei Lozinski, Roberta Tittarelli

This work is motivated by the need of efficient numerical simulations of gas flows in the serpentine channels used in proton-exchange membrane fuel cells. In particular, we consider the Poisson problem in a 2D domain composed of several long straight rectangular sections and of several bends corners. In order to speed up the resolution, we propose a 0D model

Ziyang You, Jiheng Duan, Wenhui Huang, Libo Zhang

Superconducting qubits have emerged as a premier platform for large-scale quantum computation, yet the fidelity of state readout is often hindered by random noise and crosstalk, especially in multi-qubit systems. While neural networks trained on labeled data have shown promise in reducing crosstalk effects during readout, their current capabilities are limit

W. K. Kim, H. Y. Lee, K. W. Kim, Y. J. Ko

The scintillation characteristics of 1 g undoped CsI crystal were studied by directly coupling two silicon photomultipliers(SiPMs) over a temperature range from room temperature to 86 K. The scintillation decay time and light output were measured using x-ray and gamma-ray peaks from a 109Cd radioactive source. An increase in decay time was observed as the te

Qiongxiu Li, Wenrui Yu, Changlong Ji, Richard Heusdens

Decentralized Federated Learning (FL) has attracted significant attention due to its enhanced robustness and scalability compared to its centralized counterpart. It pivots on peer-to-peer communication rather than depending on a central server for model aggregation. While prior research has delved into various factors of decentralized FL such as aggregation

Shengsheng Qian, Dizhan Xue, Yifei Wang, Shengjie Zhang

Self-Supervised Learning (SSL) is an effective paradigm for learning representations from unlabeled data, such as text, images, and videos. However, researchers have recently found that SSL is vulnerable to backdoor attacks. The attacker can embed hidden SSL backdoors via a few poisoned examples in the training dataset and maliciously manipulate the behavior

Huimin Zhu, Gaomin Tang, Lei Zhang, Jun Chen

In this paper, we study the near-field radiative energy, linear-momentum, and angular-momentum transfer from a current-biased graphene to nanoparticles. The electric current through the graphene sheet induces nonequilibrium fluctuations, causing energy and momentum transfer even in the absence of a temperature difference. The inherent spin-momentum locking o

Vicki Young, Jumman Hossain, Nirmalya Roy

This study presents a comparative analysis between single-objective and multi-objective reinforcement learning methods for training a robot to navigate effectively to an end goal while efficiently avoiding obstacles. Traditional reinforcement learning techniques, namely Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (T

Tomoharu Iwata, Atsutoshi Kumagai

Although Gaussian processes (GPs) with deep kernels have been successfully used for meta-learning in regression tasks, its uncertainty estimation performance can be poor. We propose a meta-learning method for calibrating deep kernel GPs for improving regression uncertainty estimation performance with a limited number of training data. The proposed method met

Zhaorui Tan, Xi Yang, Kaizhu Huang

Data augmentation has been recently leveraged as an effective regularizer in various vision-language deep neural networks. However, in text-to-image synthesis (T2Isyn), current augmentation wisdom still suffers from the semantic mismatch between augmented paired data. Even worse, semantic collapse may occur when generated images are less semantically constra

Xin Ding, Xiaoyu Liu, Zhijun Tu, Yun Zhang

Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs. However, existing PTQ methods only focus on handling the outliers within one layer or one block, which ignores the dependency of blocks and leads to severe performance degradation in low-bit settings. In this paper, we propose CBQ, a cross

Ming-Hao Wang, Hua Lu

Leveraging the extraordinary phenomena of quantum superposition and quantum correlation, quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers. This paper tackles two pivotal challenges in the realm of quantum computing: firstly, the development of an effective encoding protocol for translating cla

Ye Tao, Ehsan Javanmardi, Pengfei Lin, Jin Nakazato

Cooperative perception is crucial for connected automated vehicles in intelligent transportation systems (ITSs); however, ensuring the authenticity of perception data remains a challenge as the vehicles cannot verify events that they do not witness independently. Various studies have been conducted on establishing the authenticity of data, such as trust-base

Qiongxiu Li, Jaron Skovsted Gundersen, Milan Lopuhaa-Zwakenberg, Richard Heusdens

Privacy-preserving distributed average consensus has received significant attention recently due to its wide applicability. Based on the achieved performances, existing approaches can be broadly classified into perfect accuracy-prioritized approaches such as secure multiparty computation (SMPC), and worst-case privacy-prioritized approaches such as different

Yanhong A. Liu

Incremental computation aims to compute more efficiently on changed input by reusing previously computed results. We give a high-level overview of works on incremental computation, and highlight the essence underlying all of them, which we call incrementalization -- the discrete counterpart of differentiation in calculus. We present the gist of a systematic

Gabriele Formis, Stefano Scanzio, Gianluca Cena, Adriano Valenzano

The ability to predict the behavior of a wireless channel in terms of the frame delivery ratio is quite valuable, and permits, e.g., to optimize the operating parameters of a wireless network at runtime, or to proactively react to the degradation of the channel quality, in order to meet the stringent requirements about dependability and end-to-end latency th

Yuta Suzuki, Yuma Kitagawa, Shin-ichiro Tezuka, Hiroshi Akera

Generating a nonequilibrium spin polarization with a driving force has been first realized by the electric current in a system with broken inversion symmetry and extended to that induced by the thermal current and that appearing in an inversion-symmetric system with locally-broken inversion symmetry. This paper theoretically explores the spin polarization ge

Meng Ji

Luo, Tian and Wu [Discrete Math. 345 (4) (2022) 112788] conjectured that for any tree $T$ with bipartition $(X,Y)$, every $k$-connected bipartite graph $G$ with minimum degree at least $k+w$, where $w=\max\{|X|,|Y|\}$, contains a tree $T'\cong T$ such that $\kappa(G-V(T'))\geq k$. In the paper, we confirm the conjecture when $T$ is an odd path on $m$ vertice