March 2024 arXiv papers — page 147

Binyu Pang, Abdusalam Abdukerim, Zihao Bo, Wei Chen

Neutrinos from core-collapse supernovae are essential for the understanding of neutrino physics and stellar evolution. The dual-phase xenon dark matter detectors can provide a way to track explosions of galactic supernovae by detecting neutrinos through coherent elastic neutrino-nucleus scatterings. In this study, a variation of progenitor masses as well as

C-Y. Jean Chan, I-Chiau Huang, Jung-Chen Liu

We study affine semigroup rings as algebras over subsemigroup rings. From this relative viewpoint with respect to a given subsemigroup ring, the fibered sum of two affine semigroup algebras is constructed. Such a construction is compared to the tensor product and to the classical gluings of affine semigroup rings as defined in Rosales (1997). While fibered s

Andres E. Tomas, Enrique S. Quintana-Orti, Hartwig Anzt

We investigate the solution of low-rank matrix approximation problems using the truncated SVD. For this purpose, we develop and optimize GPU implementations for the randomized SVD and a blocked variant of the Lanczos approach. Our work takes advantage of the fact that the two methods are composed of very similar linear algebra building blocks, which can be a

Brian Harvie, Ye-Kai Wang

We prove that equality within the Minkowski inequality for asymptotically flat static manifolds is achieved only by slices of Schwarzschild space.

Peigen Li, Jihai Zhang, Di Zhu, Cui-Qun Chen

Low-dimensional magnetic structures coupled with superconductors are promising platforms for realizing Majorana zero modes, which have potential applications in topological quantum computing. Here, we report a two-dimensional (2D) magnetic-superconducting heterostructure consisting of single-layer chromium diiodide (CrI2) on a niobium diselenide (NbSe2) supe

Haozhen Situ, Zhimin He, Shenggen Zheng, Lvzhou Li

Variational quantum algorithms, inspired by neural networks, have become a novel approach in quantum computing. However, designing efficient parameterized quantum circuits remains a challenge. Quantum architecture search tackles this by adjusting circuit structures along with gate parameters to automatically discover high-performance circuit structures. In t

Roy Miles, Ismail Elezi, Jiankang Deng

Knowledge distillation is an effective method for training small and efficient deep learning models. However, the efficacy of a single method can degenerate when transferring to other tasks, modalities, or even other architectures. To address this limitation, we propose a novel constrained feature distillation method. This method is derived from a small set

Anant Vijay Varma, Amichay Vardi, Doron Cohen

Generic low-dimensional Hamiltonian systems feature a structured, mixed classical phase-space. The traditional Percival classification of quantum spectra into regular states supported by quasi-integrable regions and irregular states supported by quasi-chaotic regions turns out to be insufficient to capture the richness of the Hilbert space. Berry's conjectur

Jianrong Zhou, Jiyao He, Kun He

The problem of packing unequal circles into a circular container stands as a classic and challenging optimization problem in computational geometry. This study introduces a suite of innovative and efficient methods to tackle this problem. Firstly, we present a novel layout-graph transformation method that represents configurations as graphs, together with an

Alberta Longhini, Michael C. Welle, Zackory Erickson, Danica Kragic

We present AdaFold, a model-based feedback-loop framework for optimizing folding trajectories. AdaFold extracts a particle-based representation of cloth from RGB-D images and feeds back the representation to a model predictive control to replan folding trajectory at every time step. A key component of AdaFold that enables feedback-loop manipulation is the us

Xiao-Song Wang

We suppose that a Cooper pair (CP) will experience a damping force exerted by the condensed matter. A Langevin equation of a CP in two dimensional condensed matter is established. Following a method similar to Nelson's stochastic mechanics, generalized Schr\"{o}dinger equation of a CP in condensed matter is derived. If the CPs move with a constant velocity,

Konomi Furuki, Hiroshi Tamaru

A quandle is an algebraic system originated in knot theory, and can be regarded as a generalization of symmetric spaces. The inner automorphism group of a quandle is defined as the group generated by the point symmetries (right multiplications). In this paper, starting from any simple graphs, we construct quandles whose inner automorphism groups are abelian.

You Zhang, Jin Wang, Liang-Chih Yu, Dan Xu

Effectively and efficiently adapting a pre-trained language model (PLM) for human-centered text understanding (HCTU) is challenging since user tokens are million-level in most personalized applications and do not have concrete explicit semantics. A standard and parameter-efficient approach (e.g., LoRA) necessitates memorizing numerous suits of adapters for e

Sven Jacobs, Timo Hardebusch, Esther Franke, Henning Peters

This work-in-progress paper presents the current development of a new collaborative remote laboratory platform. The results are intended to serve as a foundation for future research on collaborative work in remote laboratories. Our platform, standing out with its adaptive and collaborative capabilities, integrates a distributed web-application for streamline

Jiefeng Zhou, Zhen Li, Kang Hao Cheong, Yong Deng

The Random Permutation Set (RPS) is a new type of set proposed recently, which can be regarded as the generalization of evidence theory. To measure the uncertainty of RPS, the entropy of RPS and its corresponding maximum entropy have been proposed. Exploring the maximum entropy provides a possible way of understanding the physical meaning of RPS. In this pap

Xingyi Li, Zhiguo Cao, Yizheng Wu, Kewei Wang

Current 3D stylization methods often assume static scenes, which violates the dynamic nature of our real world. To address this limitation, we present S-DyRF, a reference-based spatio-temporal stylization method for dynamic neural radiance fields. However, stylizing dynamic 3D scenes is inherently challenging due to the limited availability of stylized refer

Wanqian Bao, Uri Hasson

The question of whether people's experience in the world shapes conceptual representation and lexical semantics is longstanding. Word-association, feature-listing and similarity rating tasks aim to address this question but require a subjective interpretation of the latent dimensions identified. In this study, we introduce a supervised representational-align

Koya Murakami, Kenji Kadota, Atsushi J. Nishizawa, Kentaro Nagamine

We apply the convolutional neural networks (CNNs) to the mock 21cm maps from the post-reionization epoch to show that the $\Lambda$ cold dark matter and warm dark matter (WDM) model can be distinguished for WDM particle masses $m_{FD}<3$\,keV, under the assumption of thermal production of WDM following the Fermi-Dirac (FD) distribution. We demonstrate that t

Rui Yan, Shuai Mi, Xiaoming Duan, Jintao Chen

This paper studies a multiplayer reach-avoid differential game in the presence of general polygonal obstacles that block the players' motions. The pursuers cooperate to protect a convex region from the evaders who try to reach the region. We propose a multiplayer onsite and close-to-goal (MOCG) pursuit strategy that can tell and achieve an increasing lower b

Huanqi Yang, Sijie Ji, Rucheng Wu, Weitao Xu

There is a burgeoning discussion around the capabilities of Large Language Models (LLMs) in acting as fundamental components that can be seamlessly incorporated into Artificial Intelligence of Things (AIoT) to interpret complex trajectories. This study introduces LLMTrack, a model that illustrates how LLMs can be leveraged for Zero-Shot Trajectory Recognitio

Maxence Boels, Yang Liu, Prokar Dasgupta, Alejandro Granados

Intra-operative recognition of surgical phases holds significant potential for enhancing real-time contextual awareness in the operating room. However, we argue that online recognition, while beneficial, primarily lends itself to post-operative video analysis due to its limited direct impact on the actual surgical decisions and actions during ongoing procedu

Minjie Zhu, Yichen Zhu, Xin Liu, Ning Liu

Multimodal Large Language Models (MLLMs) have showcased impressive skills in tasks related to visual understanding and reasoning. Yet, their widespread application faces obstacles due to the high computational demands during both the training and inference phases, restricting their use to a limited audience within the research and user communities. In this p

Marc Jornet

We show how a rescaling of fractional operators with bounded kernels may help circumvent their documented deficiencies, for example, the inconsistency at zero or the lack of inverse integral operator. On the other hand, we build a novel class of linear operators with memory effects to extend the L-fractional and the ordinary derivatives, using probability to

Wenfang Yao, Kejing Yin, William K. Cheung, Jia Liu

The combination of electronic health records (EHR) and medical images is crucial for clinicians in making diagnoses and forecasting prognosis. Strategically fusing these two data modalities has great potential to improve the accuracy of machine learning models in clinical prediction tasks. However, the asynchronous and complementary nature of EHR and medical

Nian Hong Zhou

In this paper, we refine a result of Andrews and Merca on truncated pentagonal number series. Subsequently, we establish some positivity results involving Andrews--Gordon--Bressoud identities and $d$-regular partitions. In particular, we prove several conjectures of Merca and Krattenthaler--Merca--Radu on truncated pentagonal number series.

David A. Miranda, Carlos J. Paéz

In this book, we study Statistical Physics under conditions of thermodynamic equilibrium, starting from the definition of statistical ensembles. The book is divided into five chapters: First, a brief introduction to statistical methods. Second, the statistical description of isolated systems, corresponding to microcanonical ensembles. Third, the statistical

Houssem Boulahbal

Perception of the environment is a critical component for enabling autonomous driving. It provides the vehicle with the ability to comprehend its surroundings and make informed decisions. Depth prediction plays a pivotal role in this process, as it helps the understanding of the geometry and motion of the environment. This thesis focuses on the challenge of

Alexander V. Kochin

The general circulation of the atmosphere (GCA) carries out a constant and unidirectional transfer of air masses, therefore its influence is manifested in the distribution of precipitation around the globe due to the occurrence of rain shadow behind mountain barriers. Over the territory of Russia, GCA manifests itself in the form of westerly winds, which cau

Jianhai Bao, Jiaqing Hao

In this paper, concerning SDEs with H\"older continuous drifts, which are merely dissipative at infinity, and SDEs with piecewise continuous drifts, we investigate the strong law of large numbers and the central limit theorem for underlying additive functionals and reveal the corresponding rates of convergence. To establish the limit theorems under considera

Fanhao Kong, Haiyi Wang, Weijun Xu

We consider a class of weakly asymmetric continuous microscopic growth models with polynomial smoothing mechanisms, general nonlinearities and a Poisson type noise. We show that they converge to the KPZ equation after proper rescaling and re-centering, where the coupling constant depends nontrivially on all details of the smoothing and growth mechanisms in t

Yuchen Li, Zongxia Liang, Shunzhi Pang

We use the martingale method to discuss the relationship between mean-variance (MV) and monotone mean-variance (MMV) portfolio selections. We propose a unified framework to discuss the relationship in general financial markets without any specific setting or completeness requirement. We apply this framework to a semimartingale market and find that MV and MMV

Yuqin Dai, Wanlu Zhu, Ronghui Li, Zeping Ren

Creating group choreography from music is crucial in cultural entertainment and virtual reality, with a focus on generating harmonious movements. Despite growing interest, recent approaches often struggle with two major challenges: multi-dancer collisions and single-dancer foot sliding. To address these challenges, we propose a Trajectory-Controllable Diffus

Mücahit Aygün, Fabio Bellini, Roger J. A. Laeven

Geometrically convex functions constitute an interesting class of functions obtained by replacing the arithmetic mean with the geometric mean in the definition of convexity. As recently suggested, geometric convexity may be a sensible property for financial risk measures ([7,13,4]). We introduce a notion of GG-convex conjugate, parallel to the classical noti

Edward Olex, Alexander Knebe, Noam I. Libeskind, Dmitry I. Makarov

We present a new method - called HINORA (HIgh-NOise RANdom SAmple Consensus) - for the identification of regular structures in 3D point distributions. Motivated by the possible existence of the so called Council of Giants, i.e. a ring of twelve massive galaxies surrounding the Local Group in the Local Sheet with a radius of 3.75 Mpc, we apply HINORA to the L

Yuchong Zhang, Nona Rajabi, Farzaneh Taleb, Andrii Matviienko

Brain-robot interaction (BRI) empowers individuals to control (semi-)automated machines through their brain activity, either passively or actively. In the past decade, BRI systems have achieved remarkable success, predominantly harnessing electroencephalogram (EEG) signals as the central component. This paper offers an up-to-date and exhaustive examination o

Zheyu Wu, Ya-Feng Liu, Wei-Kun Chen, Christos Masouros

Both dual-functional radar-communication (DFRC) and massive multiple-input multiple-output (MIMO) have been recognized as enabling technologies for 6G wireless networks. This paper considers the advanced waveform design for hardware-efficient massive MIMO DFRC systems. Specifically, the transmit waveform is imposed with the quantized constant-envelope (QCE)

M. Anwar, Mustafa Ismail, H. M. Bahig

In this paper, we present attacks on three types of RSA modulus when the least significant bits of the prime factors of RSA modulus satisfy some conditions. Let $p,$ and $q$ be primes of the form $p=a^{m_1}+r_p$ and $q=b^{m_2}+r_q$ respectively, where $a,b,m_{1},m_{2} \in \mathbb{Z^+}$ $r_p,$ and $ r_q$ are known. The first attack is when the RSA modulus is

Xunpeng Huang, Hanze Dong, Difan Zou, Tong Zhang

Understanding the dimension dependency of computational complexity in high-dimensional sampling problem is a fundamental problem, both from a practical and theoretical perspective. Compared with samplers with unbiased stationary distribution, e.g., Metropolis-adjusted Langevin algorithm (MALA), biased samplers, e.g., Underdamped Langevin Dynamics (ULD), perf

Sze-Man Ngai, Lei Ouyang

For an open set in a compact smooth oriented Riemannian n-manifold and a positive finite Borel measure with support contained in the closure of the open set, we define an associated Krein-Feller operator on k-forms by assuming the Poincare inequality. Krein-Feller operators on Euclidean space have been studied extensively in fractal geometry. Using results e

Pietro Carlo Boldini, Benjamin Bugeat, Jurriaan W. R. Peeters, Markus Kloker

In the region close to the thermodynamic critical point and in the proximity of the pseudo-boiling (Widom) line, strong property variations substantially alter the growth of modal instabilities, as revealed in Ren et al. (J. Fluid Mech., vol. 871, 2019, pp. 831-864). Here, we study non-modal disturbances in the spatial framework using an eigenvector decompos

Gang Cao, Xiongbang Yang, Li Zhang

We review the recent advances in the pulsar high-energy $\gamma$-ray observation and the electrodynamics of the pulsar magnetospheres from the early vacuum model to the recent plasma-filled models by the numerical simulations. The numerical simulations have made the significant progresses toward the self-consistent modeling of the plasma-filled magnetosphere

Gang Cao, Xiongbang Yang

We study the pulsar energy-dependent $\gamma$-ray light curves and spectra from curvature radiation in the dissipative magnetospheres. The dissipative magnetospheres with the combined force-free (FFE) and Aristotelian (AE) are computed by a pseudo-spectral method with the high-resolution simulation in the rotating coordinate system, which produces a near for

Jessica N. Lopez Sanchez, Erick Munive Villa, Ana A. Avilez Lopez, Oscar M. Martinez Bravo

The estimation of the bulge and disk massses, the main baryonic components of a galaxy, can be performed using various approaches, but their implementation tend to be challenging as they often rely on strong assumptions about either the baryon dynamics or the dark matter model. In this work, we present an alternative method for predicting the masses of galac

Yinwei Wu

Graph Convolutional Neural Networks (GCNs) possess strong capabilities for processing graph data in non-grid domains. They can capture the topological logical structure and node features in graphs and integrate them into nodes' final representations. GCNs have been extensively studied in various fields, such as recommendation systems, social networks, and pr

Andrés Gallardo, Ignacio Viglizzo

In this paper we define a class of polynomial functors suited for constructing coalgebras representing processes in which uncertainty plays an important role. In these polynomial functors we include upper and lower probability measures, finitely additive probability measures, plausibilty measures (and their duals, belief functions), and possibility measures.

Aparup Ghosh, Pawan Kumar, Amrita Prasad, Bidya Binay Karak

The solar dynamo is essentially a cyclic process in which the toroidal component of the magnetic field is converted into the poloidal one and vice versa. This cyclic loop is disturbed by some nonlinear and stochastic processes mainly operating in the toroidal to poloidal part. Hence, the memory of the polar field decreases in every cycle. On the other hand,

Hai-Bin Yu, Liang Gao, Jia-Qi Gao, Konrad Samwer

Relaxation processes are crucial in understanding the structural rearrangements of liquids and amorphous materials. However, the overarching principle that governs these processes across vastly different materials remains an open question. Substantial analysis has been carried out based on the motions of individual particles. Here, alternatively, we propose

Jianting Chen, Ling Ding, Yunxiao Yang, Zaiyuan Di

Domain generalization models aim to learn cross-domain knowledge from source domain data, to improve performance on unknown target domains. Recent research has demonstrated that diverse and rich source domain samples can enhance domain generalization capability. This paper argues that the impact of each sample on the model's generalization ability varies. De

Johann Huber, François Hélénon, Mathilde Kappel, Elie Chelly

Recent advances in AI have led to significant results in robotic learning, including natural language-conditioned planning and efficient optimization of controllers using generative models. However, the interaction data remains the bottleneck for generalization. Getting data for grasping is a critical challenge, as this skill is required to complete many man

Jiaxun Cao, Abhinaya SB, Anupam Das, Pardis Emami-Naeini

Recent years have seen a sharp increase in the number of underage users in virtual reality (VR), where security and privacy (S\&P) risks such as data surveillance and self-disclosure in social interaction have been increasingly prominent. Prior work shows children largely rely on parents to mitigate S\&P risks in their technology use. Therefore, understandin

Yurii Burman, Raphaël Fesler

We provide a direct correspondence between the $b$-Hurwitz numbers with $b=1$ from \cite{ChapuyDolega}, and twisted Hurwtiz numbers from \cite{TwistedHurwitz}. This provides a description of real coverings of the sphere with ramification on the real line in terms of monodromy.

Xiaoran Liu, Jong-Woo Kim, Yao Wang, Michael Terilli

The pyrochlore iridates have become ideal platforms to unravel fascinating correlated and topolog?ical phenomena that stem from the intricate interplay among strong spin-orbit coupling, electronic correlations, lattice with geometric frustration, and itinerancy of the 5d electrons. The all-in-all?out antiferromagnetic state, commonly considered as the magnet

F. Colombo, I. Sabadini, D. C. Struppa, A. Yger

Superoscillations have roots in various scientific disciplines, including optics, signal processing, radar theory, and quantum mechanics. This intriguing mathematical phenomenon permits specific functions to oscillate at a rate surpassing their highest Fourier component. A more encompassing concept, supershifts, extends the idea of superoscillations to funct

Xiaobin Hu, Xu Peng, Donghao Luo, Xiaozhong Ji

Due to the difficulty and labor-consuming nature of getting highly accurate or matting annotations, there only exists a limited amount of highly accurate labels available to the public. To tackle this challenge, we propose a DiffuMatting which inherits the strong Everything generation ability of diffusion and endows the power of "matting anything". Our Diffu

Jiawei Tang, Yuxing Zhong, Pengyu Wang, Xingzhou Chen

Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programming solvers. However, conventional direct shooting raises a contradictory dynamics issue when using an augmented state to handle {high-order} sys

Zhili Chen, Kien T. Pham, Maosheng Ye, Zhiqiang Shen

We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. Traditional point-based 3D object detectors often employ architectures that rely on a progressive downsampling of points. While this method effectively reduces computational demands and increases receptive fields, it will compromise the pre

Mohammed Sayyad, Jan Kopaczek, Carmem M. Gilardoni, Weiru Chen

Two-dimensional (2D) Janus Transition Metal Dichalcogenides (TMDs) have attracted much interest due to their exciting quantum properties arising from their unique two-faced structure, broken-mirror symmetry, and consequent colossal polarisation field within the monolayer. While efforts have been made to achieve high-quality Janus monolayers, the existing met

Paweł A. Pierzchlewicz, Caio O. da Silva, R. James Cotton, Fabian H. Sinz

Single camera 3D pose estimation is an ill-defined problem due to inherent ambiguities from depth, occlusion or keypoint noise. Multi-hypothesis pose estimation accounts for this uncertainty by providing multiple 3D poses consistent with the 2D measurements. Current research has predominantly concentrated on generating multiple hypotheses for single frame st

Xiaofan Zhang, Fan Zhang, Kunpeng Jia, Yunfeng Liu

Self-injection locking scheme has the potential to narrow the linewidth of lasers in a compact setup. Here, we report a narrow linewidth laser source near 1 {\mu}m by self-injection locking scheme using a Fabry-Perot (FP) hollow resonator with a high-quality factor (Q>10^8). The measured fundamental linewidth of the laser is 41 Hz, and a coarse tuning range

Dagne Wordofa Tola, Mulugeta Bekele

The study of nonequilibrium steady-state (NESS) in the Ising model offers rich insights into the properties of complex systems far from equilibrium. This paper explores the nature of NESS phase transitions in two-dimensional (2D) ferromagnetic Ising model on a square lattice under effective interactions using Monte Carlo (MC) algorithms. This requires extens

Tiemo Pedergnana, Abel Faure-Beaulieu, Romain Fleury, Nicolas Noiray

Breaking the reciprocity of wave propagation is a problem of fundamental interest, and a mucht-sought functionality in practical applications, both in photonics and phononics. Although it has been achieved using resonant linear scattering from cavities with broken time-reversal symmetry, such realizations have remained inescapably plagued by inherent passivi

Stefan Schoder

A handy technique for the Finite Element Method (FEM) is presented that uses the null space for the implementation of Dirichlet and constraint boundary conditions. The focus of this method is to present an illustrative approach to modeling boundary constraints within FEM simulations for teaching. It presents a consistent way of including the boundary terms i

Aakash Agrawal, Stanislas Dehaene

Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate highly similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a large range of sizes and absolute positions. How neural circuits achieve invariant word recognition remain

Pere-Pau Vázquez

Generative models have received a lot of attention in many areas of academia and the industry. Their capabilities span many areas, from the invention of images given a prompt to the generation of concrete code to solve a certain programming issue. These two paradigmatic cases fall within two distinct categories of requirements, ranging from "creativity" to "

Ying Wang, Gautam Rai, Chris Matsumura, Anuradha Jagannathan

Superconductivity was recently reported in several quasicrystalline systems. These are materials which are structurally ordered, but since they are not translationally invariant, the usual BCS theory does not apply. At the present time, the underlying mechanism and the properties of the superconducting phase are insufficiently understood. To gain a better un

Shigeki Matsutani

It is known that the elliptic function solutions of the nonlinear Schr\"odinger equation are reduced to the algebraic differential relation in terms of the Weierstrass sigma function, $\displaystyle{ \left[-{\frak{i}}\frac{\partial}{\partial t} +\alpha \frac{\partial}{\partial u}\right]\Psi -\frac{1}{2} \frac{\partial^2}{\partial u^2}\Psi +(\Psi^* \Psi) \Psi

P. Veysi, M. Adeli, N. Peirov Naziri

The expanding applications, utilized by more users, enhance hardware performance and further develop cloud systems for big data processing. This leads to numerous unexplored deep learning applications, especially in advanced computer vision for object recognition. Deep learning in image processing encompasses varied tasks from recognizing elements with diver

Chokri Manai, Simone Warzel

We establish three equivalent versions of a Parisi formula for the free energy of mean-field spin glasses in a transversal magnetic field. These results are derived from available results for classical vector spin glasses by an approximation method using the functional integral representation of the partition function. In this approach, the order parameter i

Huaxin Zhang, Xiang Wang, Xiaohao Xu, Xiaonan Huang

In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling. Despite significant progress, these methods still suffer from unsatisfactory results such as numerous false alarms, primarily due to the absence of precise temporal anomaly annotation. In this p

Jeeva Keshav Sattianarayanin, Anil Kumar Yerrapragada, Radha Krishna Ganti

Accurate decoding of Uplink Control Information (UCI) on the Physical Uplink Control Channel (PUCCH) is essential for enabling 5G wireless links. This paper explores an AI/ML-based receiver design for PUCCH Format 0. Format 0 signaling encodes the UCI content within the phase of a known base waveform and even supports multiplexing of up to 12 users within th

Herbert Batte, Florian Luca

Let $ \{T_n\}_{n\geq 0} $ be the sequence of Tribonacci numbers. In this paper, we study the exponential Diophantine equation $T_n-2^x3^y=c$, for $n,x,y\in \mathbb{Z}_{\ge0}$. In particular, we show that there is no integer $c$ with at least six representations of the form $T_n-2^x3^y$.

John Hood, Aaron Schein

This paper introduces AL$\ell_0$CORE, a new form of probabilistic non-negative tensor decomposition. AL$\ell_0$CORE is a Tucker decomposition where the number of non-zero elements (i.e., the $\ell_0$-norm) of the core tensor is constrained to a preset value $Q$ much smaller than the size of the core. While the user dictates the total budget $Q$, the location

Ben Sprenger, Giulia De Pasquale, Raffaele Soloperto, John Lygeros

A closed-loop control model to analyze the impact of recommendation systems on opinion dynamics within social networks is introduced. The core contribution is the development and formalization of model-free and model-based approaches to recommendation system design, integrating the dynamics of social interactions within networks via an extension of the Fried

Shiyu Xuan, Shiliang Zhang

Supervised Contrastive Loss (SCL) is popular in visual representation learning. Given an anchor image, SCL pulls two types of positive samples, i.e., its augmentation and other images from the same class together, while pushes negative images apart to optimize the learned embedding. In the scenario of long-tailed recognition, where the number of samples in e

Zhang Xu, Mingsheng Zhang, Wei Zhao

This paper examines how data inputs shape competition among artificial intelligences (AIs) in pricing games. The dataset assigns labels to consumers and divides them into different market segments, thereby inducing multimarket contact among AIs. We document that AIs can adapt to tacit collusion via market allocation. Under symmetric segmentation, each algori

Watheq Mansour, Salam Albatarni, Sohaila Eltanbouly, Tamer Elsayed

Although several methods were proposed to address the problem of automated essay scoring (AES) in the last 50 years, there is still much to desire in terms of effectiveness. Large Language Models (LLMs) are transformer-based models that demonstrate extraordinary capabilities on various tasks. In this paper, we test the ability of LLMs, given their powerful l

Jiahao Chen, Enhui Zheng, Ming Dai, Yifu Chen

The geo-localization and navigation technology of unmanned aerial vehicles (UAVs) in denied environments is currently a prominent research area. Prior approaches mainly employed a two-stream network with non-shared weights to extract features from UAV and satellite images separately, followed by related modeling to obtain the response map. However, the two-s

Ekaterina O. Pozdeeva, Maria A. Skugoreva, Alexey V. Toporensky, Sergey Yu. Vernov

We propose new slow-roll approximations for inflationary models with the Gauss-Bonnet term. We find more accurate expressions of the standard slow-roll parameters as functions of the scalar field. To check the accuracy of approximations considered we construct inflationary models with quadratic and quartic monomial potentials and the Gauss-Bonnet term. Numer

Yungang Lu

We introduce the (q,2)-Fock space over a given Hilbert space, calculate the explicit form of a product of the creation and annihilation operators acting on the vacuum vector, demonstrate that this explicit form involves a specific subset of the set of all pair partitions, and provide a detailed characterization of this subset.

Changhee Han, Kyohei Shibano, Wataru Ozaki, Keishiro Osaki

Deep Learning is advancing medical imaging Research and Development (R&D), leading to the frequent clinical use of Artificial Intelligence/Machine Learning (AI/ML)-based medical devices. However, to advance AI R&D, two challenges arise: 1) significant data imbalance, with most data from Europe/America and under 10% from Asia, despite its 60% global populatio

Frank Tian-fang Ye, Xiaozi Gao

This study presents a framework for conducting psychological and linguistic research through simulated conversations using large language models (LLMs). The proposed methodology offers significant advantages, particularly for simulating human interactions involving potential unethical language or behaviors that would be impermissible in traditional experimen

Xincheng Li, Jianting Ning, Geong Sen Poh, Leo Yu Zhang

Federated learning (FL) facilitates collaborative training of machine learning models among a large number of clients while safeguarding the privacy of their local datasets. However, FL remains susceptible to vulnerabilities such as privacy inference and inversion attacks. Single-server secure aggregation schemes were proposed to address these threats. Nonet

David T. Hughes

When a liquid flows down a vertical tube at a low flowrate, it may not be able to completely cover the surface as a film, and the flow will comprise a number of rivulets separated by unwetted areas. Predictions of the minimum film thickness and corresponding minimum flowrate below which films cannot be sustained are needed in many industrial heat and mass tr

Xinyu Li, Jinyang Huang, Xiang Zhang, Peng Zhao

Modeling the information dissemination process in social networks is a challenging problem. Despite numerous attempts to address this issue, existing studies often assume that user attitudes have only one opportunity to alter during the information dissemination process. Additionally, these studies tend to consider the transformation of user attitudes as sol

Nand Sharma

Axonal damage is the primary pathological correlate of long-term impairment in multiple sclerosis (MS). Previous work has demonstrated a strong, quantitative relationship between decrease in axial diffusivity and axonal damage. In the present work, we develop an extension of diffusion basis spectrum imaging (DBSI) which can be used to quantify the fraction o

Xin Zhang, Linhai Zhang, Deyu Zhou, Guoqiang Xu

Due to the sparsity of user data, sentiment analysis on user reviews in e-commerce platforms often suffers from poor performance, especially when faced with extremely sparse user data or long-tail labels. Recently, the emergence of LLMs has introduced new solutions to such problems by leveraging graph structures to generate supplementary user profiles. Howev

Yaoyao Zhu, Xiuding Cai, Xueyao Wang, Xiaoqing Chen

Data augmentation is a crucial regularization technique for deep neural networks, particularly in medical image classification. Mainstream data augmentation (DA) methods are usually applied at the image level. Due to the specificity and diversity of medical imaging, expertise is often required to design effective DA strategies, and improper augmentation oper

Maxim V. Kulesh, Aleksandra E. Samirkhanova, Giovanni Carraro, Joao Sales Silva

We use a Kernel Density Estimator method to evaluate the stellar velocity dispersion in the open cluster NGC 2571. We derive the 3-D velocity dispersion using both proper motions as extracted from Gaia DR3 and single epoch radial velocities as obtained with the instrument FLAMES at ESO VLT. The mean-square velocity along the line-of-sight is found to be larg

Yuncheng Yang, Chuyan Zhang, Zuopeng Yang, Yuting Gao

Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks. However, the prompts that are independently optimized along a single modality path, may sacrifice the vision-language alignment of pre-trained models in return for improved performance on specific tasks and classes, leading to

Shilin Lu, Zilan Wang, Leyang Li, Yanzhu Liu

The rapid expansion of large-scale text-to-image diffusion models has raised growing concerns regarding their potential misuse in creating harmful or misleading content. In this paper, we introduce MACE, a finetuning framework for the task of mass concept erasure. This task aims to prevent models from generating images that embody unwanted concepts when prom

Fatemeh Hossein Khani, Omid Akbari, Muhammad Shafique

Reliability management is one of the primary concerns in manycore systems design. Different aging mechanisms such as Negative-Bias Temperature Instability (NBTI), Electromigration (EM), and thermal cycling can reduce the reliability of these systems. However, state-of-the-art works mainly focused on NBTI and EM, whereas a few works have considered the therma

Ying Gao, Kai-Bao Chen, Yu-Kun Song, Shu-Yi Wei

The transverse polarization of $\Lambda$ hyperon within reconstructed jets in hadronic collisions offers a complementary platform to probe the polarized fragmentation function $D_{1T}^\perp$. We illustrate that by performing a global analysis of the transverse polarization of $\Lambda$ hyperons produced in different kinematic regions and in different hadroni

S. R. Musawi, S. H. Jafari

For a simple graph $G$, the $3$-distance graph, $D_3(G)$, is a graph with the vertex set $V(G)$ and two vertices are adjacent if and only if their distance is $3$ in the graph $G$. For a connected graph $G$, we provide some conditions for the connectedness of $D_3(G)$. Also, we characterize all trees and unicyclic graphs with connected $3$-distance graph.

Zhuo Zhang, Jingyuan Zhang, Jintao Huang, Lizhen Qu

Instruction tuning has been identified as a crucial technique for optimizing the performance of large language models (LLMs) in generating human-aligned responses. Nonetheless, gathering diversified and superior-quality instruction data for such tuning presents notable obstacles, especially in domains with rigid privacy provisions. Federated instruction tuni

Pinxue Guo, Lingyi Hong, Xinyu Zhou, Shuyong Gao

Video Object Segmentation (VOS) task aims to segment objects in videos. However, previous settings either require time-consuming manual masks of target objects at the first frame during inference or lack the flexibility to specify arbitrary objects of interest. To address these limitations, we propose the setting named Click Video Object Segmentation (ClickV

Qiong Wu, Le Kuai, Pingyi Fan, Qiang Fan

In Internet of Things (IoT) networks, the amount of data sensed by user devices may be huge, resulting in the serious network congestion. To solve this problem, intelligent data compression is critical. The variational information bottleneck (VIB) approach, combined with machine learning, can be employed to train the encoder and decoder, so that the required

Zhihao Chen, Tao Chen, Chenhui Wang, Chuang Niu

While various deep learning methods were proposed for low-dose computed tomography (CT) denoising, they often suffer from over-smoothing, blurring, and lack of explainability. To alleviate these issues, we propose a plug-and-play Language-Engaged Dual-space Alignment loss (LEDA) to optimize low-dose CT denoising models. Our idea is to leverage large language

Matthias Hamann

Based on a notion by Gray and Kambites of hyperbolicity in the setting of semimetric spaces like digraphs or semigroups, we will construct (under a small additional geometric assumption) a boundary based on quasi-geodesic rays and anti-rays that is preserved by quasi-isometries and, in the case of locally finite digraphs and right cancellative semigroups, re

Junhui Yin, Xinyu Zhang, Lin Wu, Xiaojie Wang

Current pre-trained vision-language models, such as CLIP, have demonstrated remarkable zero-shot generalization capabilities across various downstream tasks. However, their performance significantly degrades when test inputs exhibit different distributions. In this paper, we explore the concept of test-time prompt tuning (TTPT), which facilitates the adaptat

Hongyao Yu, Zhi Hu, Siqi Xu, Chandan Mondal

Gluon transverse-momentum dependent distributions (TMDs) are very important for revealing the internal structure of the proton. They correspond to a variety of experiments and are among the major tasks of the future electron-ion colliders. In this paper, we calculate the T-even gluon TMDs at the leading twist within the Basis Light-front Quantization framewo