December 2023 arXiv papers — page 113

Peng Liu, Fanyi Wang, Jingwen Su, Yanhao Zhang

Existing saliency object detection (SOD) methods struggle to satisfy fast inference and accurate results simultaneously in high resolution scenes. They are limited by the quality of public datasets and efficient network modules for high-resolution images. To alleviate these issues, we propose to construct a saliency object matting dataset HRSOM and a lightwe

Raphael Danchin, Piotr Boguslaw Mucha

We here investigate a modification of the compressible barotropic Euler system with friction, involving a fuzzy nonlocal pressure term in place of the conventional one. This nonlocal term is parameterized by $\epsilon$ > 0 and formally tends to the classical pressure when $\epsilon$ approaches zero. The central challenge is to establish that this system is a

Neha Elizabeth Thomas, K Vishnu Namboothiri

The near orthgonality of certain $k$-vectors involving the Ramanujan sums were studied by E. Alkan in [J. Number Theory, 140:147--168 (2014)]. Here we undertake the study of similar vectors involving a generalization of the Ramanujan sums defined by E. Cohen in [Duke Math. J., 16(2):85--90 (1949)]. We also prove that the weighted average $\frac{1}{k^{s(r+1)}

Louis Dupaigne, Hatem Hajlaoui, Marius Ghergu

We consider the Lane-Emden system-$\Delta$u = |v| p-1 v,-$\Delta$v = |u| q-1 u in R d. When p $\ge$ q $\ge$ 1, it is known that there exists a positive radial stable solution (u, v) $\in$ C 2 (R d) if and only if d $\ge$ 11 and (p, q) lies on or above the so-called Joseph-Lundgren curve introduced in [5]. In this paper, we prove that for d $\le$ 10, there is

Dan Crisan, Arturo Kohatsu-Higa

We introduce a probabilistic representation of the derivative of the semigroup associated to a multidimensional killed diffusion process defined on the half-space. The semigroup derivative is expressed as a functional of a process that is normally reflected when it hits the hyperplane. The representation of the derivative also involves a matrix-valued proces

Xudong Wang, Tingting Xiang

In this paper, we consider the concept of $C$-star body in a fixed pointed closed convex cone $C$ and study the dual mixed volume for $C$-star bodies. For $C$-star bodies, we establish the corresponding dual Brunn-Minkowski inequality, the dual Minkowski inequality and the dual Aleksandrov-Fenchel inequality. Our dual Brunn-Minkowski inequality for $C$-star

Ravindra Bandara, Andrus Giraldo, Neil G. R. Broderick, Bernd Krauskopf

We focus on the existence and persistence of families of saddle periodic orbits in a four-dimensional Hamiltonian reversible ordinary differential equation derived using a travelling wave ansatz from a generalised nonlinear Schr{\"o}dinger equation (GNLSE) with quartic dispersion. In this way, we are able to characterise different saddle periodic orbits with

Michael Unterkalmsteiner

Traditional trace links are established directly between source and target artefacts. This requires that the target artefact exists when the trace is established. We introduce the concept of indirect trace links between a source artefact and a knowledge organization structure, e.g. a taxonomy. This allows the creation of links (we call them taxonomic traces)

Filippo Boni, Simone Dovetta, Enrico Serra

We investigate the existence of normalized ground states for Schr\"odinger equations on noncompact metric graphs in presence of nonlinear point defects, described by nonlinear $\delta$-interactions at some of the vertices of the graph. For graphs with finitely many vertices, we show that ground states exist for every mass and every $L^2$-subcritical power. F

V. Grisoni, C. Chiappini, A. Miglio, K. Brogaard

The origin of apparently young $\alpha$-rich stars in the Galaxy is still a matter of debate in Galactic archaeology, whether they are genuinely young or might be products of binary evolution and merger/mass accretion. We aim to shed light on the nature of young $\alpha$-rich stars in the Milky Way by studying their distribution in the Galaxy thanks to an un

Aitor Arjona, Arnau Gabriel-Atienza, Sara Lanuza-Orna, Xavier Roca-Canals

With the escalating complexity and volume of genomic data, the capacity of biology institutions' HPC faces limitations. While the Cloud presents a viable solution for short-term elasticity, its intricacies pose challenges for bioinformatics users. Alternatively, serverless computing allows for workload scalability with minimal developer burden. However, port

M. S. Bahovadinov, R. O. Sharipov, B. L. Altshuler, G. V. Shlyapnikov

In one dimension density-density interactions of particles reduce their mobility and hence the Drude weight, which controls the divergence of the optical conductivity at zero frequency, decreases. We study effects of pair hopping events on this result in a 1D system of spinless fermions. The considered model consists of the usual single-particle hopping and

Nantao Zheng, Siyu Long, Xinyu Dai

Canonical relation extraction aims to extract relational triples from sentences, where the triple elements (entity pairs and their relationship) are mapped to the knowledge base. Recently, methods based on the encoder-decoder architecture are proposed and achieve promising results. However, these methods cannot well utilize the entity information, which is m

Hwanjun Song, Minseok Kim, Jae-Gil Lee

Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, named BalanceMix, to address these challenges. Our approach includes two samplers for imbalanced labels, generating minority-augmented instances with high diversity. It also refines multi-labels at the label-wise gra

Robert Ganian, Thekla Hamm, Dušan Knop, Sanjukta Roy

Social distance games have been extensively studied as a coalition formation model where the utilities of agents in each coalition were captured using a utility function $u$ that took into account distances in a given social network. In this paper, we consider a non-normalized score-based definition of social distance games where the utility function $u^s$ d

David Ziemkiewicz, Sylwia Zielińska-Raczyńska

A novel, copper-based plasmonic system is presented to provide optical to microwave photon conversion. The process uses highly excited levels in Cu2O Rydberg excitons and takes advantage of spoof plasmons, which allow for significant enhancement of the transition probability between specific excitonic energy levels. The theoretical results are verified with

Dan Crisan, Arturo Kohatsu-Higa

We provide a probabilistic representation for the derivative of the semigroup corresponding to a diffusion process killed at the boundary of a half interval. In particular, we show that the derivative of the semi-group can be expressed as the expected value of a functional of a reflected diffusion process. Furthermore, as an application, we obtain a Bismut-E

Amir H. Gandomi, Mohammad Nabi Omidvar, Rohit Salgotra, Kalyanmoy Deb

As optimization challenges continue to evolve, so too must our tools and understanding. To effectively assess, validate, and compare optimization algorithms, it is crucial to use a benchmark test suite that encompasses a diverse range of problem instances with various characteristics. Traditional benchmark suites often consist of numerous fixed test function

Yi Sun, Xin Xu, Jian Li, Guanglei Xie

It's challenging to balance the networks stability and plasticity in continual learning scenarios, considering stability suffers from the update of model and plasticity benefits from it. Existing works usually focus more on the stability and restrict the learning plasticity of later tasks to avoid catastrophic forgetting of learned knowledge. Differently, we

Jiawei Sun, Bin Yang, Nektarios Koukourakis, Jochen Guck

Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution an

Jun Okamoto, Ru-Pan Wang, Yen-Yi Chu, Hung-Wei Shiu

X-ray circular dichroism, arising from the contrast in X-ray absorption between opposite photon helicities, serves as a spectroscopic tool to measure the magnetization of ferromagnetic materials and identify the handedness of chiral crystals. Antiferromagnets with crystallographic chirality typically lack X-ray magnetic circular dichroism because of time-rev

Meng Chen, Leevan Ling, Dongfang Yun

Motivated by the need for the rigorous analysis of the numerical stability of variational least-squares kernel-based methods for solving second-order elliptic partial differential equations, we provide previously lacking stability inequalities. This fills a significant theoretical gap in the previous work [Comput. Math. Appl. 103 (2021) 1-11], which provided

Ziqiang Zhang, Yan Yan, Jing-Hao Xue, Hanzi Wang

Most existing GAN inversion methods either achieve accurate reconstruction but lack editability or offer strong editability at the cost of fidelity. Hence, how to balance the distortioneditability trade-off is a significant challenge for GAN inversion. To address this challenge, we introduce a novel spatial-contextual discrepancy information compensationbase

Theofanis P. Raptis, Chiara Boldrini, Marco Conti, Andrea Passarella

We present a computational modelling approach which targets capturing the specifics on how to virtually augment a Metaverse user's available social time capacity via using an independent and autonomous version of her digital representation in the Metaverse. We motivate why this is a fundamental building block to model large-scale social networks in the Metav

Guillermo Diaz, Iker Sobron, Inaki Eizmendi, Iratxe Landa

The cross-domain capability of wireless sensing is currently one of the major challenges on human activity recognition (HAR) based on the channel state information (CSI) of wireless signals. The difficulty of labeling samples from new domains has encouraged the use of few and zero shot strategies. In this context, prototype networks have attracted attention

Guiyang Cui, Ruihao Xia, Xin Jin, Yang Tang

Morphing quadrotors with four external actuators can adapt to different restricted scenarios by changing their geometric structure. However, previous works mainly focus on the improvements in structures and controllers, and existing planning algorithms don't consider the morphological modifications, which leads to safety and dynamic feasibility issues. In th

Dong Liu, Yi Yang, Zheng-Wen Long

Variations at the event horizon structure of a black hole will emit the signals of the gravitational wave echoes associated with the ringdown of the binary black hole merger. In this work, combining mass model of M87 and Einasto profile for dark matter halo, we construct one formal solution of black holes in dark matter halo, and this solution includes the r

Qinglong Cao, Zhengqin Xu, Yuntian Chen, Chao Ma

Prompt learning has emerged as an effective and data-efficient technique in large Vision-Language Models (VLMs). However, when adapting VLMs to specialized domains such as remote sensing and medical imaging, domain prompt learning remains underexplored. While large-scale domain-specific foundation models can help tackle this challenge, their concentration on

Ajaharul Islam

We present a technique to represent anisotropic heavy-quark potentials as effective potentials. This involves employing an effective screening mass linked to the quantum numbers $l$ and $m$ of a specific state. Our approach utilizes the resulting 1D effective potential model, enabling the solution of a 1D Schr\"odinger equation. Remarkably, this model accura

Mehmet Öz, Elif Aydoğan

We study a $d$-dimensional branching Brownian motion inside subdiffusively expanding balls, where the boundary of the ball is deactivating in the sense that once a particle hits the moving boundary, it is instantly deactivated but is reactivated later if and when its ancestral line becomes fully inside the expanding ball at that later time. That is, at each

Mete Şeref Ahunbay, Martin Bichler, Teodora Dobos, Johannes Knörr

Electricity market operators worldwide use mixed-integer linear programming to solve the allocation problem in wholesale electricity markets. Prices are typically determined based on the duals of relaxed versions of this optimization problem. The resulting outcomes are efficient, but market operators must pay out-of-market uplifts to some market participants

Jianhui Lian, Li Luo

Although thick disk is a structure prevalent in local disk galaxies and also present in our home Galaxy, its formation and evolution is still unclear. Whether the thick disk is born thick and/or gradually heated to be thick after formation is under debate. To disentangle these two scenarios, one effective approach is to inspect the thickness of young disk ga

Xiang Li, Haoran Tang, Siyu Chen, Ziwei Wang

In this paper, we explore the challenges inherent to Large Language Models (LLMs) like GPT-4, particularly their propensity for hallucinations, logic mistakes, and incorrect conclusions when tasked with answering complex questions. The capacity of LLMs to present erroneous answers in a coherent and semantically rigorous manner further complicates the detecti

Sarah Safi, Marzieh Farhang

Several ongoing and upcoming large scale structure surveys aim to explore the nonlinear regime of structure formation with high precision. Making reliable cosmological inferences from these observations necessitates precise theoretical modeling of the mildly nonlinear regime. In this work we explore how the choice of nonlinear prescription would impact param

Qian Li, Yuxiao Hu, Yinpeng Dong, Dongxiao Zhang

Adversarial training is often formulated as a min-max problem, however, concentrating only on the worst adversarial examples causes alternating repetitive confusion of the model, i.e., previously defended or correctly classified samples are not defensible or accurately classifiable in subsequent adversarial training. We characterize such non-ignorable sample

Shengguang Wu, Mei Yuan, Qi Su

Recent advances in image and video creation, especially AI-based image synthesis, have led to the production of numerous visual scenes that exhibit a high level of abstractness and diversity. Consequently, Visual Storytelling (VST), a task that involves generating meaningful and coherent narratives from a collection of images, has become even more challengin

Roberto Savona, Cristina Maria Alberini, Lucia Alessi, Iacopo Baussano

A major gap exists between the conceptual suggestion of how much a nation should invest in science, innovation, and technology, and the practical implementation of what is done. We identify 4 critical challenges that must be address in order to develop an environment conducive to collaboration across organizations and governments, while also preserving comme

Philippe Rufin, Sherrie Wang, Sá Nogueira Lisboa, Jan Hemmerling

Transfer learning allows for resource-efficient geographic transfer of pre-trained field delineation models. However, the scarcity of labeled data for complex and dynamic smallholder landscapes, particularly in Sub-Saharan Africa, remains a major bottleneck for large-area field delineation. This study explores opportunities of using sparse field delineation

Lingxiao Luo, Xuanzhong Chen, Bingda Tang, Xinsheng Chen

Recent advancements in foundation models, typically trained with self-supervised learning on large-scale and diverse datasets, have shown great potential in medical image analysis. However, due to the significant spatial heterogeneity of medical imaging data, current models must tailor specific structures for different datasets, making it challenging to leve

Manuel Röder, Leon Heller, Maximilian Münch, Frank-Michael Schleif

With the advent of interconnected and sensor-equipped edge devices, Federated Learning (FL) has gained significant attention, enabling decentralized learning while maintaining data privacy. However, FL faces two challenges in real-world tasks: expensive data labeling and domain shift between source and target samples. In this paper, we introduce a privacy-pr

Xianghui Xie, Bharat Lal Bhatnagar, Jan Eric Lenssen, Gerard Pons-Moll

Reconstructing human-object interaction in 3D from a single RGB image is a challenging task and existing data driven methods do not generalize beyond the objects present in the carefully curated 3D interaction datasets. Capturing large-scale real data to learn strong interaction and 3D shape priors is very expensive due to the combinatorial nature of human-o

Guanxing Lu, Ziwei Wang, Changliu Liu, Jiwen Lu

Embodied Instruction Following (EIF) requires agents to complete human instruction by interacting objects in complicated surrounding environments. Conventional methods directly consider the sparse human instruction to generate action plans for agents, which usually fail to achieve human goals because of the instruction incoherence in action descriptions. On

Jingyang Xiang, Siqi Li, Junhao Chen, Zhuangzhi Chen

N:M sparsity has received increasing attention due to its remarkable performance and latency trade-off compared with structured and unstructured sparsity. However, existing N:M sparsity methods do not differentiate the relative importance of weights among blocks and leave important weights underappreciated. Besides, they directly apply N:M sparsity to the wh

Faizuddin Ahmed, Abdelmalek Bouzenada

In our investigation, we explore the quantum dynamics of charge-free scalar particles through the Klein-Gordon equation within the framework of rainbow gravity's, considering the Bonnor-Melvin-Lambda (BML) space-time background. The BML solution is characterized by the magnetic field strength along the axis of symmetry direction which is related with the cos

Guangfeng Yan, Tan Li, Tian Lan, Kui Wu

Next-generation wireless networks, such as edge intelligence and wireless distributed learning, face two critical challenges: communication efficiency and privacy protection. In this work, our focus is on addressing these issues in a distributed learning framework. We consider a new approach that simultaneously achieves communication efficiency and privacy p

Praveen Damacharla, Hamid Rajabalipanah, Mohammad Hosein Fakheri

There are multiple applications to automatically count people and specify their gender at work, exhibitions, malls, sales, and industrial usage. Although current speech detection methods are supposed to operate well, in most situations, in addition to genders, the number of current speakers is unknown and the classification methods are not suitable due to ma

Gergo Igneczi, Tamas Dobay

The rapid development of automated driving systems in recent years has led to improvements in road safety and travel comfort. One typical function of these systems is Lane Keep Assist, which generally does not take human driving preferences into account. In our previous work, we have demonstrated that it is possible to implement a Lane Keep Assist function t

Eduard Eiben, Robert Ganian, Iyad Kanj, Sebastian Ordyniak

We study two variants of the fundamental problem of finding a cluster in incomplete data. In the problems under consideration, we are given a multiset of incomplete $d$-dimensional vectors over the binary domain and integers $k$ and $r$, and the goal is to complete the missing vector entries so that the multiset of complete vectors either contains (i) a clus

Ayoub Arous, Andres F Lopez-Lopera, Nael Abu-Ghazaleh, Ihsen Alouani

In this paper, we investigate the following question: Can we obtain adversarially-trained models without training on adversarial examples? Our intuition is that training a model with inherent stochasticity, i.e., optimizing the parameters by minimizing a stochastic loss function, yields a robust expectation function that is non-stochastic. In contrast to rel

Yusuke Sakurai, Chul-Moon Yoo, Atsushi Naruko, Daisuke Yamauchi

We consider axion cloud decay due to the axion-photon conversion with multi-pole background magnetic fields. We focus on the $\ell=m=1$ and $n=2$ mode for the axion field configuration since it has the largest growth rate associated with superradiant instability. Under the existence of a background multi-pole magnetic field, the axion field can be converted

Hideo Furugori, Naoki Ogawa, Sotaro Sugishita, Takahiro Waki

A naive celestial dictionary causes massless two-point functions to take the delta-function forms in the celestial conformal field theory (CCFT). We rectify the dictionary, involving the shadow transformation so that the two-point functions follow the standard power-law. In this new definition, we can smoothly take the massless limit of the massive dictionar

Marcin Markiewicz, Marek Żukowski

We discuss the status of relative facts - the central concept of Relational Quantum Mechanics (RQM) - in the context of the new amendment to RQM called cross-perspective links postulate. The new axiom states that by a proper measurement one learns the value of the relative outcome/fact earlier obtained by another observer-system. We discuss a Wigner-Friend-t

Quentin Hillebrand, Vorapong Suppakitpaisarn, Tetsuo Shibuya

We suggest the use of hash functions to cut down the communication costs when counting subgraphs under edge local differential privacy. While various algorithms exist for computing graph statistics, including the count of subgraphs, under the edge local differential privacy, many suffer with high communication costs, making them less efficient for large grap

Özgür Martin, Rebecca Sanders

In the present note, we solve two open questions posed by Salas in [H. Salas, The strong disjoint blow-up/collapse property, J. Funct. Spaces Appl., 2013, Article ID 146517, 6 pages] about disjoint hypercyclic operators. First, we show that given any family $T_1, \dots, T_N$ of disjoint hypercyclic operators, one can always select an operator $T$ such that t

Xiao Ma, Zetian Zhang, Zexuan Ji, Kun Huang

Myopia is a manifestation of visual impairment caused by an excessively elongated eyeball. Image data is critical material for studying high myopia and pathological myopia. Measurements of spherical equivalent and axial length are the gold standards for identifying high myopia, but the available image data for matching them is scarce. In addition, the criter

Yuchen Yang, Yu Qiao, Xiao Sun

Automatic estimation of 3D human pose from monocular RGB images is a challenging and unsolved problem in computer vision. In a supervised manner, approaches heavily rely on laborious annotations and present hampered generalization ability due to the limited diversity of 3D pose datasets. To address these challenges, we propose a unified framework that levera

Akatsuki Nishioka, Yoshihiro Kanno

Smoothing accelerated gradient methods achieve faster convergence rates than that of the subgradient method for some nonsmooth convex optimization problems. However, Nesterov's extrapolation may require gradients at infeasible points, and thus they cannot be applied to some structural optimization problems. We introduce a variant of smoothing accelerated pro

Xingwei He, Qianru Zhang, A-Long Jin, Jun Ma

Factual error correction (FEC) aims to revise factual errors in false claims with minimal editing, making them faithful to the provided evidence. This task is crucial for alleviating the hallucination problem encountered by large language models. Given the lack of paired data (i.e., false claims and their corresponding correct claims), existing methods typic

Yuke Zhu, Yumeng Ruan, Zihua Xiong, Sheng Guo

Regression loss design is an essential topic for oriented object detection. Due to the periodicity of the angle and the ambiguity of width and height definition, traditional L1-distance loss and its variants have been suffered from the metric discontinuity and the square-like problem. As a solution, the distribution based methods show significant advantages

Naser Ahmadiniaz, Misha A. Lopez-Lopez, Christian Schubert

While the QED photon amplitudes at full momentum so far have been calculated only up to the six-photon level, in the low-energy limit there are explicit formulas for all helicity components even at the N-photon level, obtained by Martin et al. in 2002. Here we use the worldline formalism to extend that result to the N-photon amplitudes in a generic constant

Arnav Chavan, Nahush Lele, Deepak Gupta

Due to the substantial scale of Large Language Models (LLMs), the direct application of conventional compression methodologies proves impractical. The computational demands associated with even minimal gradient updates present challenges, particularly on consumer-grade hardware. This paper introduces an innovative approach for the parametric and practical co

Laurent Stolovitch, Zhiyan Zhao

In this article, we consider analytic perturbations of isometries of an analytic Riemannian manifold M. We prove that, under some conditions, a finitely presented group of such small enough perturbations is analytically conjugate on M to the same group of isometry it is a perturbation of. Our result relies on a "Diophantine-like" condition, relating the acti

Chenghao Huang, Siyang Li, Ruohong Liu, Hao Wang

Foundation models, such as Large Language Models (LLMs), can respond to a wide range of format-free queries without any task-specific data collection or model training, creating various research and application opportunities for the modeling and operation of large-scale power systems. In this paper, we outline how such large foundation model such as GPT-4 ar

Argyrios Deligkas, Eduard Eiben, Robert Ganian, Thekla Hamm

We consider the problem of fairly dividing a set of heterogeneous divisible resources among agents with different preferences. We focus on the setting where the resources correspond to the edges of a connected graph, every agent must be assigned a connected piece of this graph, and the fairness notion considered is the classical envy freeness. The problem is

Ariel Neufeld, Tuan Anh Nguyen

In this paper we prove that rectified deep neural networks do not suffer from the curse of dimensionality when approximating McKean--Vlasov SDEs in the sense that the number of parameters in the deep neural networks only grows polynomially in the space dimension $d$ of the SDE and the reciprocal of the accuracy $\epsilon$.

Gioni Mexi, Somayeh Shamsi, Mathieu Besançon, Pierre Le Bodic

Strong Branching (SB) is a cornerstone of all modern branching rules used in the Branch-and-Bound (BnB) algorithm, which is at the center of Mixed-Integer Programming solvers. In its full form, SB evaluates all variables to branch on and then selects the one producing the best relaxation, leading to small trees, but high runtimes. State-of-the-art branching

Jonggyu Jang, Hyeonsu Lyu, Hyun Jong Yang

Model inversion (MI) attacks aim to infer or reconstruct the training dataset through reverse-engineering from the target model's weights. Recently, significant advancements in generative models have enabled MI attacks to overcome challenges in producing photo-realistic replicas of the training dataset, a technique known as generative MI. The generative MI p

Weiguang Zhao, Guanyu Yang, Rui Zhang, Chenru Jiang

With the explosive 3D data growth, the urgency of utilizing zero-shot learning to facilitate data labeling becomes evident. Recently, methods transferring language or language-image pre-training models like Contrastive Language-Image Pre-training (CLIP) to 3D vision have made significant progress in the 3D zero-shot classification task. These methods primari

Lukas Fuerst, Adrian Kirchner, Alexander Eber, Florian Siegrist

The highly energetic photons of ultraviolet light drive electronic and rovibronic transitions in all molecular species. This radiation is thus a prime tool for strongly selective spectroscopic fingerprinting and real-time environmental monitoring if broad spectral coverage, short acquisition times and high spectral resolution is achieved - requirements that

Jiyeon Kim, Junhyuk Kim, Changhoon Lee

With the recent rapid developments in machine learning (ML), several attempts have been made to apply ML methods to various fluid dynamics problems. However, the feasibility of ML for predicting turbulence dynamics has not yet been explored in detail. In this study, PredictionNet, a data-driven ML framework based on generative adversarial networks (GANs), wa

Hu Zhang, Jianhua Xu, Tao Tang, Haiyang Sun

Traditional LiDAR-based object detection research primarily focuses on closed-set scenarios, which falls short in complex real-world applications. Directly transferring existing 2D open-vocabulary models with some known LiDAR classes for open-vocabulary ability, however, tends to suffer from over-fitting problems: The obtained model will detect the known obj

Jiyuan Yang, Yue Ding, Yidan Wang, Pengjie Ren

Sequential recommendation (SR) models are typically trained on user-item interactions which are affected by the system exposure bias, leading to the user preference learned from the biased SR model not being fully consistent with the true user preference. Exposure bias refers to the fact that user interactions are dependent upon the partial items exposed to

Giang Do, Khiem Le, Quang Pham, TrungTin Nguyen

By routing input tokens to only a few split experts, Sparse Mixture-of-Experts has enabled efficient training of large language models. Recent findings suggest that fixing the routers can achieve competitive performance by alleviating the collapsing problem, where all experts eventually learn similar representations. However, this strategy has two key limita

Amir H. Gandomi, Danial Yazdani, Mohammad Nabi Omidvar, Kalyanmoy Deb

This document introduces a set of 24 box-constrained numerical global optimization problem instances, systematically constructed using the Generalized Numerical Benchmark Generator (GNBG). These instances cover a broad spectrum of problem features, including varying degrees of modality, ruggedness, symmetry, conditioning, variable interaction structures, bas

Keishi Yang, Feng-Yueh Chan, Hiroki Watanabe, Shingo Yoshioka

High-speed atomic force microscopy (HS-AFM) is an indispensable technique in the biological field owing to its excellent imaging capability for the real-time observation of biomolecules with high spatial resolution. Furthermore, recent developments have established a tip-scan stand-alone HS-AFM that can be combined with an optical microscope, drastically imp

Yun Liao, Junfan Li, Shizhong Liao, Qinghua Hu

In this paper, we study the mistake bound of online kernel learning on a budget. We propose a new budgeted online kernel learning model, called Ahpatron, which significantly improves the mistake bound of previous work and resolves the open problem posed by Dekel, Shalev-Shwartz, and Singer (2005). We first present an aggressive variant of Perceptron, named A

Mengying Qiu, Bao-Jun Cai, Lie-Wen Chen, Cen-Xi Yuan

The data-driven Bayesian model averaging is a rigorous statistical approach to combining multiple models for a unified prediction. Compared with the individual model, it provides more reliable information, especially for problems involving apparent model dependence. In this work, within both the non-relativistic Skyrme energy density functional and the nonli

Chunyan Cao, Gang Li

Agricultural production heavily exploits the soil, resulting in high erosion in cultivated land, which poses a threat to food security and environmental sustainability. To address this issue, we stabilize the soil using polyvinyl alcohol (PVA). PVA strongly adheres to the soil after mixing and annealing, enhancing the cohesive strength of the soil. The PVA-s

Kazuki Maezawa, Shun Fujii, Kazuto Yamanoi, Yukio Nozaki

Surface acoustic waves (SAWs) coupled to magnons have attracted much attention because they allow for the long-range transport of magnetic information which cannot be achieved by magnon alone. We employed pulsed laser interferometry to visualize the entire spatiotemporal dynamics of a SAW that travels on a nickel (Ni) thin film and is coupled to magnons. It

Zhengyang Geng, Ashwini Pokle, J. Zico Kolter

Diffusion models excel at producing high-quality samples but naively require hundreds of iterations, prompting multiple attempts to distill the generation process into a faster network. However, many existing approaches suffer from a variety of challenges: the process for distillation training can be complex, often requiring multiple training stages, and the

Ibtihel Amara, Vinija Jain, Aman Chadha

We tackle the challenging issue of aggressive fine-tuning encountered during the process of transfer learning of pre-trained language models (PLMs) with limited labeled downstream data. This problem primarily results in a decline in performance on the subsequent task. Inspired by the adaptive boosting method in traditional machine learning, we present an eff

Kotaro Oda, Shugo Yasuda

The effect of shear flows on the thermal conductivity of polymer melts is investigated using a reversed nonequilibrium molecular dynamics (RNEMD) method. We extended the original RNEMD method to simultaneously produce spatial gradients of temperature and flow velocity in a single direction. This method enables accurate measurement of the thermal conductivity

John Metzcar, Catherine R. Jutzeler, Paul Macklin, Alvaro Köhn-Luque

Mechanistic learning, the synergistic combination of knowledge-driven and data-driven modeling, is an emerging field. In particular, in mathematical oncology, the application of mathematical modeling to cancer biology and oncology, the use of mechanistic learning is growing. This review aims to capture the current state of the field and provide a perspective

Wei Geng, Baidi Xiao, Rongpeng Li, Ning Wei

Generally, Reinforcement Learning (RL) agent updates its policy by repetitively interacting with the environment, contingent on the received rewards to observed states and undertaken actions. However, the environmental disturbance, commonly leading to noisy observations (e.g., rewards and states), could significantly shape the performance of agent. Furthermo

Aditi Aggarwal, Deepika Varshney, Saurabh Patel

Social media has created a global network where people can easily access and exchange vast information. This information gives rise to a variety of opinions, reflecting both positive and negative viewpoints. GIFs stand out as a multimedia format offering a visually engaging way for users to communicate. In this research, we propose a multimodal framework tha

Xiao Wu, Zhenzhong Hao, Li Zhang, Di Jia

Thin-film lithium niobate (TFLN) enables extremely high-efficiency second-order nonlinear optical effects due to large nonlinear coefficient d33 and strong optical field localization. Here, we first designed and fabricated a pulley-waveguide-coupled microring resonator with an intrinsic quality factor above 9.4 x10^5 on the reverse-polarized double-layer X-c

Tao Zhang, Shiru Li, Yong Xia

We consider finding a zero point of the maximally monotone operator $T$. First, instead of using the proximal point algorithm (PPA) for this purpose, we employ PPA to solve its Yosida regularization $T_{\lambda}$. Then, based on an $O(a_{k+1})$ ($a_{k+1}\geq \varepsilon>0$) resolvent index of $T$, it turns out that we can establish a convergence rate of $O (

Hiroya Kato, Kento Hasegawa, Seira Hidano, Kazuhide Fukushima

Graph Contrastive Learning (GCL) is unsupervised graph representation learning that can obtain useful representation of unknown nodes. The node representation can be utilized as features of downstream tasks. However, GCL is vulnerable to poisoning attacks as with existing learning models. A state-of-the-art defense cannot sufficiently negate adverse effects

Yuwei Guo, Wenhao Zhang, Licheng Jiao, Shuang Wang

Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian of interest across visible and infrared modalities. Existing models mainly focus on compensating for modality-specific information to reduce modality variation. However, these methods often lead to a higher computational overhead and may introduce interfering information w

Jayeon Yoo, Dongkwan Lee, Inseop Chung, Donghyun Kim

It is a well-known fact that the performance of deep learning models deteriorates when they encounter a distribution shift at test time. Test-time adaptation (TTA) algorithms have been proposed to adapt the model online while inferring test data. However, existing research predominantly focuses on classification tasks through the optimization of batch normal

Armin Rainer, Gerhard Schindl

Interpolation inequalities for $C^m$ functions allow to bound derivatives of intermediate order $0 < j<m$ by bounds for the derivatives of order $0$ and $m$. We review various interpolation inequalities for $L^p$-norms ($1 \le p \le \infty$) in arbitrary finite dimensions. They allow us to study ultradifferentiable regularity by lacunary estimates in a compr

Sixu Li, Mohammad Anis, Dominique Lord, Hao Zhang

This paper presents a generic analytical framework tailored for surrogate safety measures (SSMs) that is versatile across various highway geometries, capable of encompassing vehicle dynamics of differing dimensionality and fidelity, and suitable for dynamic, real-world environments. The framework incorporates a generic vehicle movement model, accommodating a

Zhi Luo, Hao Yu, Jin Li

In this paper, we study the observational appearance of the asymmetric thin-shell wormhole (ATW) in Horndeski theory by employing the ray-tracing method. We first calculate the effective potential and null geodesic of the ATW, and then we obtain the deflection angle of the photon in the ATW spacetime. Based on the impact parameter of the photon, the trajecto

Jing Tan, Baoxiang Wang

In this paper, we derive a 90\% credible lower bound on the modified parameter of scalar-tensor theories as $\varphi_{-2}>-7.94\times10^{-4}$ by using dominant-mode correction. Specific to BD theory, we have the constraint $\omega_{\rm BD}>4.75$. Asymmetric binary systems usually have a significant mass ratio; in such cases, higher harmonic modes cannot be n

Yo-Yu Lai, Chia-Hsiang Lin, Zi-Chao Leng

The deep learning model Transformer has achieved remarkable success in the hyperspectral image (HSI) restoration tasks by leveraging Spectral and Spatial Self-Attention (SA) mechanisms. However, applying these designs to remote sensing (RS) HSI restoration tasks, which involve far more spectrums than typical HSI (e.g., ICVL dataset with 31 bands), presents c

Murat Can Sarihan, Alperen Govdeli, Zhihao Lan, Yildirim Batuhan Yilmaz

Amorphous photonic structures are mesoscopic optical structures described by electrical permittivity distributions with underlying spatial randomness. They offer a unique platform for studying a broad set of electromagnetic phenomena, including transverse Anderson localization, enhanced wave transport, and suppressed diffusion in random media. Despite this,

G. Jackson, M. Laine

Motivated by precision computations of neutrino decoupling at MeV temperatures, we show how QED corrections to the thermal neutrino interaction rate can be related to the electron-positron spectral function as well as an effective $\bar{\nu}\nu\gamma$ vertex. The spectral function is needed both in a timelike and in a spacelike domain, and for both of its ph

Gourab Kumar Sar, Kevin O'Keeffe, Dibakar Ghosh

We present a case study of swarmalators (mobile oscillators) which move on a 1D ring and are subject to pinning. Previous work considered the special case where the pinning in space and the pinning in the phase dimension were correlated. Here we study the general case where the space and phase pinning are uncorrelated, both being chosen uniformly at random.

Muzamil Shah, Muhammad Sabieh Anwar, Reza Asgari, Gao Xianlong

The photonic spin Hall effect of light beams reflected from the surfaces of various two-dimensional hexagonal crystalline structures, considering their associated time-reversal $\mathcal{T}$ and inversion $\mathcal{I}$ symmetries, is investigated. Employing the Haldane model with tunable parameters as a generic model, we examine the longitudinal and transver

Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li

Within the complex neuroarchitecture of the brain, astrocytes play crucial roles in development, structure, and metabolism. These cells regulate neural activity through tripartite synapses, directly impacting cognitive processes such as learning and memory. Despite the growing recognition of astrocytes' significance, traditional Spiking Neural Network (SNN)