April 2024 arXiv papers — page 8

Haresh Raval

Recently it has been shown that the theory in the quadratic gauge on 4-sphere, $\mathbb{S}^{4}$ consists of two phases namely, the confined and the deconfined phases. A suitable finite field dependent BRST (FFBRST) transformation interrelates two different gauge fixed theories. In this paper, we use the FFBRST technique on the curved space for the first time

Isabela Rocha

This study details an approach for the analysis of social media collected political data through the lens of Topological Data Analysis, with a specific focus on Persistent Homology and the political processes they represent by proposing a set of mathematical generalizations using Gaussian functions to define and analyze these Persistent Homology categories.

Nandhini Swaminathan, David Danks

One strategy in response to pluralistic values in a user population is to personalize an AI system: if the AI can adapt to the specific values of each individual, then we can potentially avoid many of the challenges of pluralism. Unfortunately, this approach creates a significant ethical issue: if there is an external measure of success for the human-AI team

Maryam Allahbakhshi, Aylar Sadri, Seyed Omid Shahdi

Parkinson's disease is a widespread neurodegenerative condition necessitating early diagnosis for effective intervention. This paper introduces an innovative method for diagnosing Parkinson's disease through the analysis of human EEG signals, employing a Support Vector Machine (SVM) classification model. this research presents novel contributions to enhance

Liam MacDonald, Rua Murray, Rachael Tappenden

This paper studies the convergence properties of a family of Relaxed $\ell$-Minimal Gradient Descent methods for quadratic optimization; the family includes the omnipresent Steepest Descent method, as well as the Minimal Gradient method. Simple proofs are provided that show, in an appropriately chosen norm, the gradient and the distance of the iterates from

Vaishak Narayanan, Prabin Raj KP, Saifudheen Nouphal

Current evaluation benchmarks for question answering (QA) in Indic languages often rely on machine translation of existing English datasets. This approach suffers from bias and inaccuracies inherent in machine translation, leading to datasets that may not reflect the true capabilities of EQA models for Indic languages. This paper proposes a new benchmark spe

Liam Roy, Dana Kulic, Elizabeth Croft

Collaborative robots must effectively communicate their internal state to humans to enable a smooth interaction. Nonverbal communication is widely used to communicate information during human-robot interaction, however, such methods may also be misunderstood, leading to communication errors. In this work, we explore modulating the acoustic parameter values (

Cuong Nhat Vo, Khanh Bao Huynh, Son T. Luu, Trong-Hop Do

The growth of social networks makes toxic content spread rapidly. Hate speech detection is a task to help decrease the number of harmful comments. With the diversity in the hate speech created by users, it is necessary to interpret the hate speech besides detecting it. Hence, we propose a methodology to construct a system for targeted hate speech detection f

Arinta Auza, Akram Youssry, Gerardo Paz-Silva, Alberto Peruzzo

Controlling quantum systems under correlated non-Markovian noise, particularly when strongly coupled, poses significant challenges in the development of quantum technologies. Traditional quantum control strategies, heavily reliant on precise models, often fail under these conditions. Here, we address the problem by utilizing a data-driven graybox model, whic

Jeonghoon Park, Chaeyeon Chung, Juyoung Lee, Jaegul Choo

In the image classification task, deep neural networks frequently rely on bias attributes that are spuriously correlated with a target class in the presence of dataset bias, resulting in degraded performance when applied to data without bias attributes. The task of debiasing aims to compel classifiers to learn intrinsic attributes that inherently define a ta

Guanghui Hu, Hehu Xie, Fei Xu, Gang Zhao

In this paper, a novel adaptive finite element method is proposed to solve the Kohn-Sham equation based on the moving mesh (nonnested mesh) adaptive technique and the augmented subspace method. Different from the classical self-consistent field iterative algorithm which requires to solve the Kohn-Sham equation directly in each adaptive finite element space,

Junghyup Lee, Jeimin Jeon, Dohyung Kim, Bumsub Ham

Quantization-aware training (QAT) simulates a quantization process during training to lower bit-precision of weights/activations. It learns quantized weights indirectly by updating latent weights,i.e., full-precision inputs to a quantizer, using gradient-based optimizers. We claim that coupling a user-defined learning rate (LR) with these optimizers is sub-o

Wei Huang, Bingyang Zhang, Kaituo Zhang, Hua Gao

The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep convolutional autoencoder (CAE) network and deep supporting vector data description (SVDD) model have been universally employed and have demonstrated significant success in detecting anomalies. However, the over-reconstruction ability of CAE netwo

Chunlin Tian, Zhan Shi, Zhijiang Guo, Li Li

Adapting Large Language Models (LLMs) to new tasks through fine-tuning has been made more efficient by the introduction of Parameter-Efficient Fine-Tuning (PEFT) techniques, such as LoRA. However, these methods often underperform compared to full fine-tuning, particularly in scenarios involving complex datasets. This issue becomes even more pronounced in com

Shannon Smith, Melissa Tate, Keri Freeman, Anne Walsh

Generative Artificial Intelligence (generative AI) poses both opportunities and risks for the integrity of research. Universities must guide researchers in using generative AI responsibly, and in navigating a complex regulatory landscape subject to rapid change. By drawing on the experiences of two Australian universities, we propose a framework to help inst

Youxi Wu, Zhen Wang, Yan Li, Yingchun Guo

Recently, order-preserving pattern (OPP) mining has been proposed to discover some patterns, which can be seen as trend changes in time series. Although existing OPP mining algorithms have achieved satisfactory performance, they discover all frequent patterns. However, in some cases, users focus on a particular trend and its associated trends. To efficiently

Xin Ma, Puchen Zhu, Xiao Li, Xiaoyin Zheng

Depth position highly affects lens distortion, especially in close-range photography, which limits the measurement accuracy of existing stereo vision systems. Moreover, traditional depth-dependent distortion models and their calibration methods have remained complicated. In this work, we propose a minimal set of parameters based depth-dependent distortion mo

Yuya Hikima, Yasunori Akagi, Hideaki Kim

Stochastic matching is the stochastic version of the well-known matching problem, which consists in maximizing the rewards of a matching under a set of probability distributions associated with the nodes and edges. In most stochastic matching problems, the probability distributions inherent in the nodes and edges are set a priori and are not controllable. Ho

Zhirong Xin, Junpeng Cao, Wen-Li Yang, Yupeng Wang

We study the thermodynamic limit of the anisotropic XYZ spin chain with non-diagonal integrable open boundary conditions. Although the $U(1)$-symmetry is broken, by using the new parametrization scheme, we exactly obtain the surface energy and the excitation energy of the system, which has solved the difficulty in the inhomogeneous $T-Q$ relation. With the b

Hengyun Yang, Yang Zhang

We construct central elements of the degenerate quantum general linear group introduced by Cheng, Wang and Zhang. In particular, we give an explicit formula for the quantum Casimir element. Our method is based on the explicit $L$ operators. Moreover, we construct a universal $L$ operator, which is a spectral parameter-dependent solution of the quantum Yang-B

Muhammad Kamran Saeed, Ashfaq Khokhar, Shakil Ahmed

Massive multiple input multiple output (M-MIMO) technology plays a pivotal role in fifth-generation (5G) and beyond communication systems, offering a wide range of benefits, from increased spectral efficiency (SE) to enhanced energy efficiency and higher reliability. However, these advantages are contingent upon precise channel state information (CSI) availa

Chun Wang Chau, Shuai A. Chen, K. T. Law

Superconductivity is observed in rhombohedral trilayer graphene in a narrow regime between the flavor-symmetric state and the symmetry breaking phase, which cannot be described by the conventional Bardeen-Cooper-Schrieffer theory. The measured coherence length, for instance, is roughly two orders of magnitude shorter than the value predicted by the Bardeen-C

Lihui Yi, Ermin Wei

Nash equilibrium is a common solution concept that captures strategic interaction in electricity market analysis. However, it requires a fundamental but impractical assumption that all market participants are fully rational, implying unlimited computational resources and cognitive abilities. To tackle the limitation, level-k reasoning is proposed and studied

Weiyi Wang, Shailendra Sawleshwarkar, Mahendra Piraveenan

Human papillomavirus (HPV) infection is the most common sexually transmitted infection in the world. Persistent oncogenic Human papillomavirus infection has been a leading threat to global health and can lead to serious complications such as cervical cancer. Prevention interventions including vaccination and screening have been proved effective in reducing t

Abir Chakraborty

Knowledge graphs (KGs) are large datasets with specific structures representing large knowledge bases (KB) where each node represents a key entity and relations amongst them are typed edges. Natural language queries formed to extract information from a KB entail starting from specific nodes and reasoning over multiple edges of the corresponding KG to arrive

Gabriel Currier, Kenneth Moore, Chi Hoi Yip

We provide a general framework to construct colorings avoiding short monochromatic arithmetic progressions in Euclidean Ramsey theory. Specifically, if $\ell_m$ denotes $m$ collinear points with consecutive points of distance one apart, we say that $\mathbb{E}^n \not \to (\ell_r,\ell_s)$ if there is a red/blue coloring of $n$-dimensional Euclidean space that

Xinzhe Li, Ming Liu, Shang Gao

Retrieval-Augmented Generation (RAG) systems are widely used across various industries for querying closed-domain and in-house knowledge bases. However, evaluating these systems presents significant challenges due to the private nature of closed-domain data and a scarcity of queries with verifiable ground truths. Moreover, there is a lack of analytical metho

Zehuan Tang, Tingfeng Ma, Hui Chen, Yuanwen Gao

Based on the Maxwell-Beatty reciprocity theorem, static non-reciprocity has been realized by using nonlinearity, but this non-reciprocity has strict restrictions on input amplitude and structure size (number of units). Here, we propose a robotic metamaterial with two components of displacement and rotation, which uses active control to add external forces on

Odin Zhang, Haitao Lin, Hui Zhang, Huifeng Zhao

The idea of using deep-learning-based molecular generation to accelerate discovery of drug candidates has attracted extraordinary attention, and many deep generative models have been developed for automated drug design, termed molecular generation. In general, molecular generation encompasses two main strategies: de novo design, which generates novel molecul

Kuan-Wen Chen

Let $f:X \rightarrow \Delta $ be a one-parameter semistable degeneration of $m$-dimensional compact complex manifolds. Assume that each component of the central fiber $X_0$ is K\"ahler. Then, we provide a criterion for a general fiber to satisfy the $\partial\overline{\partial}$-lemma and a formula to compute the Hodge index on the middle cohomology of the g

Toshimitsu Uesaka, Taiji Suzuki, Yuhta Takida, Chieh-Hsin Lai

In typical multimodal contrastive learning, such as CLIP, encoders produce one point in the latent representation space for each input. However, one-point representation has difficulty in capturing the relationship and the similarity structure of a huge amount of instances in the real world. For richer classes of the similarity, we propose the use of weighte

Anudeep Das, Vasisht Duddu, Rui Zhang, N. Asokan

Diffusion based text-to-image models are trained on large datasets scraped from the Internet, potentially containing unacceptable concepts (e.g., copyright-infringing or unsafe). We need concept removal techniques (CRTs) which are i) effective in preventing the generation of images with unacceptable concepts, ii) utility-preserving on acceptable concepts, an

Bridget Nyirongo, Yanjie Jiang, He Jiang, Hui Liu

Refactoring is one of the most important activities in software engineering which is used to improve the quality of a software system. With the advancement of deep learning techniques, researchers are attempting to apply deep learning techniques to software refactoring. Consequently, dozens of deep learning-based refactoring approaches have been proposed. Ho

Wenfu Cao, Xin Wu, Jun Lyu

The Wald vector potential is an exact solution of the source-less Maxwell equations regarding an electromagnetic field of a vacuum uncharged black hole like the Kerr background black hole in an asymptotically uniform magnetic field. However, it is not if the black hole is a nonvacuum solution in a theory of modified gravity with extra fields or a charged Ker

Bobin Li

Under the framework of thermodynamics, the phase transition of the black hole is a general issue in general relativity. In this work, the phase transition of charged black holes is discussed carefully. The metric tensor of thermodynamics is redefined in the charged black hole, based on the Ruppeiner geometry. With the well-defined metric tensor of thermodyna

Leonardo Cella, Ryan Martin

Inferential models (IMs) offer provably reliable, data-driven, possibilistic statistical inference. But despite the IM framework's theoretical and foundational advantages, efficient computation is a challenge. This paper presents a simple yet powerful numerical strategy for approximating the IM's possibility contour, or at least its $\alpha$-cut for a specif

Adnane Saoud, Pushpak Jagtap, Sadegh Soudjani

We consider the notion of resilience for cyber-physical systems, that is, the ability of the system to withstand adverse events while maintaining acceptable functionality. We use finite temporal logic to express the requirements on the acceptable functionality and define the resilience metric as the maximum disturbance under which the system satisfies the te

Meike Hatzel, Stephan Kreutzer, Marcelo Garlet Milani, Irene Muzi

In 1996, Reed, Robertson, Seymour and Thomas [Combinatorica 1996] proved Younger's Conjecture, which states that, for all directed graphs $D$, there exists a function $f$ such that, if $D$ does not contain $k$ disjoint cycles, then $D$ contains a feedback vertex set, i.e.~a subset of vertices whose deletion renders the graph acyclic, of size bounded by $f(k)

Jiading Fang, Xiangshan Tan, Shengjie Lin, Igor Vasiljevic

If robots are to work effectively alongside people, they must be able to interpret natural language references to objects in their 3D environment. Understanding 3D referring expressions is challenging -- it requires the ability to both parse the 3D structure of the scene and correctly ground free-form language in the presence of distraction and clutter. We i

Yin-Jen Chen, Minh Tang

We study the matrix-variate regression problem $Y_i = \sum_{k} \beta_{1k} X_i \beta_{2k}^{\top} + E_i$ for $i=1,2\dots,n$ in the high dimensional regime wherein the response $Y_i$ are matrices whose dimensions $p_{1}\times p_{2}$ outgrow both the sample size $n$ and the dimensions $q_{1}\times q_{2}$ of the predictor variables $X_i$ i.e., $q_{1},q_{2} \ll n

Dapeng Yao, Takehito Yokoyama

We theoretically propose chiral magnon in ferromagnetic chiral crystals. We show that the crystal chirality is imprinted in orbital angular momentum of magnons which exhibits the opposite signs for opposite chiralities of the crystal. We also show that a finite magnon orbital angular momentum can be induced by a temperature gradient which is a magnonic analo

Qinzhi Hao, Jiali Zhang, Tengyu Jing, Wei Wang

Aiming at the problem of low accuracy of flight trajectory prediction caused by the high speed of fighters, the diversity of tactical maneuvers, and the transient nature of situational change in close range air combat, this paper proposes an enhanced CNN-LSTM network as a fighter flight trajectory prediction method. Firstly, we extract spatial features from

Yongqiang Zhao, Kun Qian, Boyi Duan, Shan Luo

Simulation is a widely used tool in robotics to reduce hardware consumption and gather large-scale data. Despite previous efforts to simulate optical tactile sensors, there remain challenges in efficiently synthesizing images and replicating marker motion under different contact loads. In this work, we propose a fast optical tactile simulator, named FOTS, fo

Qianqian Liu, Tonghua Liu, Cuihong Wen, Jieci Wang

From the perspective of quantum information theory, the effect of Unruh radiation on a two-level accelerated detector can be modeled as a quantum channel. In this work, we employ the tools of channel-position finding to locate Unruh channels. The signal-idler and idler-free protocols are explored to determine the position of the target Unruh channel within a

Yiming Tang

We consider a discrete one-dimensional random interface on the half-space whose height at any positive point is composed of a function of the heights at its two closest neighbours and an independent random noise background. In [AC24], Adhikari and Chatterjee proved for the full-space model that the height function of such a Markov process converges to the Co

Jianzong Wang, Ziqi Liang, Xulong Zhang, Ning Cheng

In recent years, Transformer networks have shown remarkable performance in speech recognition tasks. However, their deployment poses challenges due to high computational and storage resource requirements. To address this issue, a lightweight model called EfficientASR is proposed in this paper, aiming to enhance the versatility of Transformer models. Efficien

Hequn Zhang, Xiao Zhang

We show that the Dirac equation is separated into four differential equations for time-period Majarana fermions in Kerr-Newman, Kerr-Newman-(A)dS spacetimes. Although they can not be transformed into radial and angular equations, the four differential equations yield two algebraic identities. When the electric or magnetic charge is nonzero, they conclude tha

Ziqi Liang, Jianzong Wang, Xulong Zhang, Yong Zhang

Using unsupervised learning to disentangle speech into content, rhythm, pitch, and timbre for voice conversion has become a hot research topic. Existing works generally take into account disentangling speech components through human-crafted bottleneck features which can not achieve sufficient information disentangling, while pitch and rhythm may still be mix

Robbie King, David Gosset, Robin Kothari, Ryan Babbush

Given copies of a quantum state $\rho$, a shadow tomography protocol aims to learn all expectation values from a fixed set of observables, to within a given precision $\epsilon$. We say that a shadow tomography protocol is triply efficient if it is sample- and time-efficient, and only employs measurements that entangle a constant number of copies of $\rho$ a

Zheng Lin, Bin Guo, Sicong Liu, Wentao Zhou

Deep neural network (DNN) has driven extensive applications in mobile technology. However, for long-running mobile apps like voice assistants or video applications on smartphones, energy efficiency is critical for battery-powered devices. The rise of heterogeneous processors in mobile devices today has introduced new challenges for optimizing energy efficien

N. Morrell, M. M. Phillips, G. Folatelli, M. D. Stritzinger

We present the second and final release of optical spectroscopy of Type Ia Supernovae (SNe Ia) obtained during the first and second phases of the Carnegie Supernova Project (CSP-I and CSP-II). The newly released data consist of 148 spectra of 30 SNe Ia observed in the course of the CSP-I, and 234 spectra of 127 SNe Ia obtained during the CSP-II. We also pres

A. -K. Gallagher

It is shown that the $p$-Poincar\'e inequality holds on an open set $\Omega$ in $\mathbb{R}^n$ if and only if the strict $p$-capacitary inradius of $\Omega$ is finite. To that end, new upper and lower bounds for the infimum for the associated nonlinear Rayleigh quotients are derived.

Cenk Demir, Mamadou Diagne, Miroslav Krstic

Exploring novel strategies for the regulation of axon growth, we introduce a periodic event-triggered control (PETC) to enhance the practical implementation of the associated PDE backstepping control law. Neurological injuries may impair neuronal function, but therapies like Chondroitinase ABC (ChABC) have shown promise in improving axon elongation by influe

Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama

This study proposes social navigation metrics for autonomous agents in air combat, aiming to facilitate their smooth integration into pilot formations. The absence of such metrics poses challenges to safety and effectiveness in mixed human-autonomous teams. The proposed metrics prioritize naturalness and comfort. We suggest validating them through a user stu

Yoonsik Kim, Moonbin Yim, Ka Yeon Song

In this paper, we establish a benchmark for table visual question answering, referred to as the TableVQA-Bench, derived from pre-existing table question-answering (QA) and table structure recognition datasets. It is important to note that existing datasets have not incorporated images or QA pairs, which are two crucial components of TableVQA. As such, the pr

Benet Oriol Sabat, Alessandro Achille, Matthew Trager, Stefano Soatto

We propose NeRF-Insert, a NeRF editing framework that allows users to make high-quality local edits with a flexible level of control. Unlike previous work that relied on image-to-image models, we cast scene editing as an in-painting problem, which encourages the global structure of the scene to be preserved. Moreover, while most existing methods use only tex

Arani Mukhopadhyay, Anish Pal, Mohamad Jafari Gukeh, Constantine M. Megaridis

The ever-increasing need for power consumption in electronic devices, coupled with the requirement for thinner size, calls for the development of efficient heat spreading components. Vapor chambers (VCs), because of their ability to effectively spread heat over a large area by two-phase heat transfer, seem ideal for such applications. However, creating thin

Zhao-Xuan Chen, Hui Dong, Shu-Yi Wei

The helicity correlation of two back-to-back hadrons is a powerful tool that makes it possible to probe the longitudinal spin transfer, $G_{1L}$, in unpolarized hadronic collisions. In this work, we investigate the helicity correlation of back-to-back dihadrons produced in photon-nucleus collisions with both space-like and quasireal photons and explore its p

Yao Gao, Qi Jiang, Shaohua Gao, Lei Sun

Recently, joint design approaches that simultaneously optimize optical systems and downstream algorithms through data-driven learning have demonstrated superior performance over traditional separate design approaches. However, current joint design approaches heavily rely on the manual identification of initial lenses, posing challenges and limitations, parti

Luiz L. Lopes, H. C. Das

Using the Bower-Liang model, we discuss how pressure anisotropies affect the microscopic and macroscopic properties of hybrid stars. We find that anisotropies affect the maximum mass, central density, and radius of the canonical stars. Anisotropies also affect the minimum neutron star mass that presents quarks in their core, as well as the total amount of qu

Shannon Overbay, Samuel Joslin, Paul C. Kainen

We show that a cyclic vertex order due to Yu, Shao and Li gives a dispersable book embedding for any bipartite circulant.

Yufeng Ye, Jeremy B. Kline, Alec Yen, Gregory Cunningham

The interaction between an atom and an electromagnetic mode of a resonator is of both fundamental interest and is ubiquitous in quantum technologies. Most prior work studies a linear light-matter coupling of the form $g \widehat{\sigma}_x (\widehat{a} + \widehat{a}^\dagger)$, where $g$ measured relative to photonic ($\omega_a$) and atomic ($\omega_b$) mode f

Da-Cheng Yan, Yan Yan, Zhou Rui

In this work, we investigate six helicity amplitudes of the four-body $B_{(s)} \to (\pi\pi)(K\bar{K})$ decays in the perturbative QCD (PQCD) approach. The $\pi\pi$ invariant mass spectrum is dominated by the vector resonance $\rho(770)$ together with scalar resonance $f_0(980)$, while the vector resonance $\phi(1020)$ and scalar resonance $f_0(980)$ are expe

Kai Wang, Chengxiu Ling

We consider a family of multivariate distributions with heavy-tailed margins and the type I elliptical dependence structure. This class of risks is common in finance, insurance, environmental and biostatistic applications. We obtain the asymptotic tail risk probabilities and characterize the multivariate regular variation property. The results demonstrate ho

Arani Mukhopadhyay, Anish Pal, Congbo Bao, Mohamad Jafari Gukeh

Efficient thermal management in high-power electronics cooling can be achieved using phase-change heat transfer devices, such as vapor chambers. Traditional vapor chambers use wicks to transport condensate for efficient thermal exchange and to prevent "dry-out" of the evaporator. However, wicks in vapor chambers present significant design challenges arising

Javier de Cruz Perez, Chan-Gyung Park, Bharat Ratra

We study 6 LCDM models, with 4 allowing for non-flat geometry and 3 allowing for a non-unity lensing consistency parameter $A_L$. We also study 6 XCDM models with a dynamical dark energy density X-fluid with equation of state $w$. For the non-flat models we use two different primordial power spectra, Planck $P(q)$ and new $P(q)$. These models are tested agai

Mengxia Sun, Xu He, Mingyao Chen, Chi Sin Tang

The formation of plasmons through the collective excitation of charge density has generated intense discussions, offering insights to fundamental sciences and potential applications. While the underlying physical principles have been well-established, the effects of many-body interactions and orbital hybridization on plasmonic dynamics remain understudied. I

Xinwei Chen, Kun Li, Tianyou Song, Jiangjian Guo

Named Entity Recognition (NER) is an essential steppingstone in the field of natural language processing. Although promising performance has been achieved by various distantly supervised models, we argue that distant supervision inevitably introduces incomplete and noisy annotations, which may mislead the model training process. To address this issue, we pro

Vsevolod Syvolap, Oleg Ruchayskiy

Dark photons, hypothetical feebly interacting massive vector bosons, appear in many extensions of the Standard Model. This study investigates their production and subsequent decay during supernova explosions. We demonstrate that the decay of dark photons, with masses ranging from 200 to 400 MeV, can lead to the emission of neutrinos with energies surpassing

Alessandro Montinaro, Yanwei Zhao, Zhilin Zhang, Shenglin Zhou

The classification of the $2$-designs with $\lambda=2$ admitting a flag-transitive automorphism groups with socle $PSL(2,q)$ is completed by settling the two open cases in \cite{ABDT}. The result is achieved by using conics and hyperovals of $PG(2,q)$.

Guoqi Ma, Prabhakar R. Pagilla, Swaroop Darbha

Ensuring safety is the most important factor in connected and autonomous vehicles, especially in emergency braking situations. As such, assessing the safety benefits of one information topology over other is a necessary step towards evaluating and ensuring safety. In this paper, we compare the safety benefits of a cooperative adaptive cruise control which ut

Chaoyu Quan, Xiaoming Wang, Pinzhong Zheng, Zhi Zhou

The energy dissipation law and the maximum bound principle are two critical physical properties of the Allen--Cahn equations. While many existing time-stepping methods are known to preserve the energy dissipation law, most apply to a modified form of energy. In this work, we demonstrate that, when the nonlinear term of the Allen--Cahn equation is Lipschitz c

Jianzong Wang, Pengcheng Li, Xulong Zhang, Ning Cheng

Singing voice beautifying is a novel task that has application value in people's daily life, aiming to correct the pitch of the singing voice and improve the expressiveness without changing the original timbre and content. Existing methods rely on paired data or only concentrate on the correction of pitch. However, professional songs and amateur songs from t

Chuanming Zong

On July 5, 2022, the National Institute of Standards and Technology announced four possible post-quantum cryptography standards, three of them are based on lattice theory and the other one is based on Hash function. It is well-known that the security of the lattice cryptography relies on the hardness of the shortest vector problem (SVP) and the closest vecto

Xian Yu, Beste Basciftci

We consider a two-stage distributionally robust optimization (DRO) model with multimodal uncertainty, where both the mode probabilities and uncertainty distributions could be affected by the first-stage decisions. To address this setting, we propose a generic framework by introducing a $\phi$-divergence based ambiguity set to characterize the decision-depend

Merneh Mandado Mana, Bereket Dalga Dana, Alemayehu Nana Koya, Boyu Ji

In this work, the plasmonic properties of nanoparticle dimers with optical responses over a wide spectral range have been investigated by varying the inter-particle gap, dimer geometry, gap morphology, nanoparticle composition, and refractive index of the surrounding medium. In particular, we have theoretically investigated the plasmonic properties of spatia

Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mahmoud Assran

There are a thousand ways to caption an image. Contrastive Language Pretraining (CLIP) on the other hand, works by mapping an image and its caption to a single vector -- limiting how well CLIP-like models can represent the diverse ways to describe an image. In this work, we introduce Llip, Latent Language Image Pretraining, which models the diversity of capt

Kazuya Kato, Chikara Nakayama, Sampei Usui

Based on the logarithmic algebraic geometry and the theory of Deligne systems, we define an abelian category of $\ell$-adic sheaves with weight filtrations on a logarithmic scheme over a finite field, which is similar to the category of variations of mixed Hodge structure. We consider asymptotic behaviors and simple cases of higher direct images of objects o

Yuqian Hu, Guozhen Zhu, Beibei Wang, K. J. Ray Liu

Proximity detection in indoor environments based on WiFi signals has gained significant attention in recent years. Existing works rely on the dynamic signal reflections and their extracted features are dependent on motion strength. To address this issue, we design a robust WiFi-based proximity detector by considering gait monitoring. Specifically, we propose

Divjyot Singh, Omar French, Fan Guo, Xiaocan Li

Relativistic magnetic turbulence has been proposed as a process for producing nonthermal particles in high-energy astrophysics. Particle energization may be contributed by both magnetic reconnection and turbulent fluctuations, but their interplay is poorly understood. It has been suggested that during magnetic reconnection the parallel electric field dominat

Bingcai Sui, Junzhong Shen, Caixia Sun, Junhui Wang

General-purpose processor vendors have integrated customized accelerator in their products due to the widespread use of General Matrix-Matrix Multiplication (GEMM) kernels. However, it remains a challenge to further improve the flexibilityand scalability of these GEMM-enhanced processors to cater to the emerging large-scale GEMM workloads. In this paper we p

Jiabao Wang, Yang Wu, Jun Wang, Ni Chen

The multi-plane phase retrieval method provides a budget-friendly and effective way to perform phase imaging, yet it often encounters alignment challenges due to shifts along the optical axis in experiments. Traditional methods, such as employing beamsplitters instead of mechanical stage movements or adjusting focus using tunable light sources, add complexit

Chenqi Guo, Shiwei Zhong, Xiaofeng Liu, Qianli Feng

Does Knowledge Distillation (KD) really work? Conventional wisdom viewed it as a knowledge transfer procedure where a perfect mimicry of the student to its teacher is desired. However, paradoxical studies indicate that closely replicating the teacher's behavior does not consistently improve student generalization, posing questions on its possible causes. Con

Yadan Duan, Yuandeng Shen, Zehao Tang, Chenrui Zhou

A solar jet can often cause coronal mass ejections (CMEs) with different morphologies in the high corona, for example, jet-like CMEs, bubble-like CMEs, and so-called twin CMEs that include a pair of simultaneous jet-like and bubble-like CMEs. However, what determines the morphology of a jet-related CME is still an open question. Using high spatiotemporal res

James A. Michaelov, Catherine Arnett, Benjamin K. Bergen

Transformers have generally supplanted recurrent neural networks as the dominant architecture for both natural language processing tasks and for modelling the effect of predictability on online human language comprehension. However, two recently developed recurrent model architectures, RWKV and Mamba, appear to perform natural language tasks comparably to or

Isolda Cardoso, Ana Cosgaya, Silvio Reggiani

A real Lie algebra is said to be characteristically solvable if its derivation algebra is solvable. We explicitly determine the moduli space of left-invariant metrics, up to isometric automorphism, for $6$-dimensional nilmanifolds whose associated Lie algebra is characteristically solvable of triangular type. We also compute the corresponding full isometry g

Nina Dörnemann, Debashis Paul

In this paper, the key objects of interest are the sequential covariance matrices $\mathbf{S}_{n,t}$ and their largest eigenvalues. Here, the matrix $\mathbf{S}_{n,t}$ is computed as the empirical covariance associated with observations $\{\mathbf{x}_1,\ldots,\mathbf{x}_{ \lfloor nt \rfloor } \}$, for $t\in [0,1]$. The observations $\mathbf{x}_1,\ldots,\math

Zhihao Zhang, Feiqi Cao, Yingbin Mo, Yiran Zhang

The dynamic nature of esports makes the situation relatively complicated for average viewers. Esports broadcasting involves game expert casters, but the caster-dependent game commentary is not enough to fully understand the game situation. It will be richer by including diverse multimodal esports information, including audiences' talks/emotions, game audio,

Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins

We introduce a lightweight and accurate architecture for resource-efficient visual correspondence. Our method, dubbed XFeat (Accelerated Features), revisits fundamental design choices in convolutional neural networks for detecting, extracting, and matching local features. Our new model satisfies a critical need for fast and robust algorithms suitable to reso

Bart van Marum, Aayam Shrestha, Helei Duan, Pranay Dugar

A necessary capability for humanoid robots is the ability to stand and walk while rejecting natural disturbances. Recent progress has been made using sim-to-real reinforcement learning (RL) to train such locomotion controllers, with approaches differing mainly in their reward functions. However, prior works lack a clear method to systematically test new rewa

Kalman Szenes, Maximilian Moerchen, Paul Fischill, Markus Reiher

Multi-configurational electronic structure theory delivers the most versatile approximations to many-electron wavefunctions, flexible enough to deal with all sorts of transformations, ranging from electronic excitations, to open-shell molecules and chemical reactions. Multi-configurational models are therefore essential to establish universally applicable, p

Cai Yu, Shan Jia, Xiaomeng Fu, Jin Liu

With the rising prevalence of deepfakes, there is a growing interest in developing generalizable detection methods for various types of deepfakes. While effective in their specific modalities, traditional detection methods fall short in addressing the generalizability of detection across diverse cross-modal deepfakes. This paper aims to explicitly learn pote

Daopeng Yin, Liquan Mei

In this work, we will give proper estimates for the discrete convolution complementary (DCC) kernels, which leads to the asymptotically compatible fractional Gr\"onwall inequality. The consequence can be applied in the analysis of the stability and pointwise-in-time error of difference-type schemes on a non-uniform mesh. The pointwise error is explicitly bou

Dongyun Lin, Yi Cheng, Shangbo Mao, Aiyuan Guo

Large vision-language models have impressively promote the performance of 2D visual recognition under zero/few-shot scenarios. In this paper, we focus on exploiting the large vision-language model, i.e., CLIP, to address zero/few-shot 3D shape recognition based on multi-view representations. The key challenge for both tasks is to generate a discriminative de

Zheren Zhu, Azaan Rehman, Xiaozhi Cao, Congyu Liao

Recent developments in low-field (LF) magnetic resonance imaging (MRI) systems present remarkable opportunities for affordable and widespread MRI access. A robust denoising method to overcome the intrinsic low signal-noise-ratio (SNR) barrier is critical to the success of LF MRI. However, current data-driven MRI denoising methods predominantly handle magnitu

Xiao-Gang He, Chia-Wei Liu

This work presents the first detailed numerical predictions of CP violation in antitriplet charmed baryon decays. Adopting the flavor $SU(3)_F$ symmetry and the final state re-scattering framework, we relate the CKM-suppressed amplitudes to the leading ones, which are proportional to $\lambda_b$ and $\lambda_d$, respectively, with $\lambda_q = V_{uq}V^*_{cq}

Julian Göltz, Jimmy Weber, Laura Kriener, Sebastian Billaudelle

Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Incorporating trainable transmission delays, alongside synaptic weights, is crucial for shaping these temporal dynamics. While recent methods have shown the benefits of training delays and weights in terms of accuracy and memory efficiency, th

Cécilia Lancien, Patrick Oliveira Santos, Pierre Youssef

We establish a central limit theorem for tensor product random variables $c_k:=a_k \otimes a_k$, where $(a_k)_{k \in \mathbb{N}}$ is a free family of variables. We show that if the variables $a_k$ are centered, the limiting law is the semi-circle. Otherwise, the limiting law depends on the mean and variance of the variables $a_k$ and corresponds to a free in

Kaiwen Yu, Renhe Fan, Gang Wu, Zhijin Qin

Semantic communication technology is regarded as a method surpassing the Shannon limit of bit transmission, capable of effectively enhancing transmission efficiency. However, current approaches that directly map content to transmission symbols are challenging to deploy in practice, imposing significant limitations on the development of semantic communication

Mateo Jalen Andrew Calderon, Lee Jun Lei Lucas, Syarifuddin Azhar Bin Rosli, Stephanie See Hui Ying

This project develops a pseudo-random number generator (PRNG) using the logistic map, implemented in Verilog HDL on an FPGA and processes its output through a Central Limit Theorem (CLT) function to achieve a Gaussian distribution. The system integrates additional FPGA modules for real-time interaction and visualisation, including a clock generator, UART int