December 2024 arXiv papers — page 185

Martin Klimeš

This paper contributes to the theory of singularities of meromorphic linear ODEs in traceless $2\times2$ cases, focusing on their deformations and confluences. It is divided into two parts: The first part addresses individual singularities without imposing restrictions on their type or degeneracy. The main result establishes a correspondence between local fo

Xudong Hong, Sharid Loáiciga, Asad Sayeed

Active Curriculum Language Modeling (ACLM; Hong et al., 2023) is a learner directed approach to training a language model. We proposed the original version of this process in our submission to the BabyLM 2023 task, and now we propose an updated ACLM process for the BabyLM 2024 task. ACLM involves an iteratively- and dynamically-constructed curriculum informe

Wenyi Liu, Ziqi Zhang, Xinshi Li, Jiacheng Hu

This paper addresses key challenges in enhancing recommendation systems by leveraging Graph Neural Networks (GNNs) and addressing inherent limitations such as over-smoothing, which reduces model effectiveness as network hierarchy deepens. The proposed approach introduces three GNN-based recommendation models, specifically designed to mitigate over-smoothing

Huiying Cao, Yiqun Zhang, Shi Feng, Xiaocui Yang

Empathetic conversation is a crucial characteristic in daily conversations between individuals. Nowadays, Large Language models (LLMs) have shown outstanding performance in generating empathetic responses. Knowledge bases like COMET can assist LLMs in mitigating illusions and enhancing the understanding of users' intentions and emotions. However, models rema

Amir Ahmad Ghods, Mohammadreza Doostmohammadian

Mobile target tracking is crucial in various applications such as surveillance and autonomous navigation. This study presents a decentralized tracking framework utilizing a Consensus-Based Estimation Filter (CBEF) integrated with the Nearly-Constant-Velocity (NCV) model to predict a moving target's state. The framework facilitates agents in a network to coll

Emmanuel Chetcuti, Curt Healey

Recently, the question of whether surjective maps preserving the norm of a symmetric Kubo-Ando mean can be extended to Jordan $\ast$-isomorphisms has been tackled. The question was affirmatively answered for surjective maps between $C^{*}$-algebras for certain specific classes of symmetric Kubo-Ando means. Here, we give a comprehensive answer to this questio

Sungheon Jeong, Hanning Chen, Sanggeon Yun, Suhyeon Cho

This paper introduces a powerful encoder that transfers CLIP`s capabilities to event-based data, enhancing its utility and expanding its applicability across diverse domains. While large-scale datasets have significantly advanced image-based models, the scarcity of comprehensive event datasets has limited performance potential in event modality. To address t

Peiyan Zhang, Haibo Jin, Leyang Hu, Xinnuo Li

Recent advancements in large language models (LLMs) have significantly enhanced the ability of LLM-based systems to perform complex tasks through natural language processing and tool interaction. However, optimizing these LLM-based systems for specific tasks remains challenging, often requiring manual interventions like prompt engineering and hyperparameter

Ruy Coimbra Charão, Ryo Ikehata

We consider damped wave equations with a potential and rotational inertia terms. We study the Cauchy problem for this model in the one dimensional Euclidean space and we obtain fast energy decay and L^2-decay of the solution itself as time goes to infinity. Since we are considering this problem in the one dimensional space, we have no useful tools such as th

Chuanxin Wang, Tomoya Naito, Jian Li, Haozhao Liang

We extend the method from [Naito, Naito, and Hashimoto, Phys. Rev. Research 5, 033189 (2023)] to solve the Dirac equation not only for the ground state but also for low-lying excited states using a deep neural network and the unsupervised machine learning technique. The variational method fails because of the Dirac sea, which is avoided by introducing the in

Benyamin Khezeli, Hamid Reza Zarandi, Elham Cheshmikhani

Resistive Random-Access Memory (ReRAM) crossbar arrays are promising candidates for in-situ matrix-vector multiplication (MVM), a frequent operation in Deep Learning algorithms. Despite their advantages, these emerging non-volatile memories are susceptible to errors due to non-idealities such as immature fabrication processes and runtime errors, which lead t

Krishnaswami Alladi, Ankush Goswami

Let $\nu_y(n)$ denote the number of distinct prime factors of $n$ that are $<y$. For $k$ a positive integer, and for $k+2\leq y\leq x$, let $S_{-k}(x,y)$ denote the sum \begin{eqnarray*} S_{-k}(x,y):=\sum_{n\leq x}(-k)^{\nu_y(n)}. \end{eqnarray*} In this paper, we describe our recent results on the asymptotic behavior of $S_{-k}(x,y)$ for $k+2\leq y\leq x$,

A. V. Parafilo, Meng Sun, K. Sonowal, V. M. Kovalev

We propose a concept of a superconducting photodiode - a device that transforms the energy and `spin' of an external electromagnetic field into the rectified steady-state supercurrent and develop a microscopic theory describing its properties. For this, we consider a two-dimensional thin film cooled down below the temperature of superconducting transition wi

Luis Angel González-Serrano, Egor A. Maximenko

We consider polynomials of the form $\operatorname{h}_m(y_1^{[\varkappa_1]},\ldots,y_n^{[\varkappa_n]})$, where $\operatorname{h}_m$ is the complete homogeneous polynomial of degree $m$ and $y_j^{[\varkappa_j]}$ denotes $y_j$ repeated $\varkappa_j$ times. Using the decomposition of the generating function into partial fractions we represent such polynomials

Shuai Tan, Biao Gong, Yutong Feng, Kecheng Zheng

Text serves as the key control signal in video generation due to its narrative nature. To render text descriptions into video clips, current video diffusion models borrow features from text encoders yet struggle with limited text comprehension. The recent success of large language models (LLMs) showcases the power of decoder-only transformers, which offers t

Ajinkya Deshpande, Deep Gupta, Ankit Bhurane, Nisha Meshram

Hepatocellular carcinoma (HCC) is a common type of liver cancer whose early-stage diagnosis is a common challenge, mainly due to the manual assessment of hematoxylin and eosin-stained whole slide images, which is a time-consuming process and may lead to variability in decision-making. For accurate detection of HCC, we propose a hybrid deep learning-based arc

Giacomo Belli, Marco Mordacci, Michele Amoretti

In this work, a novel quantum neural network is introduced as a means to approximate any unitary evolution through the Standard Recursive Block Basis (SRBB) and is subsequently redesigned with the number of CNOTs asymptotically reduced by an exponential contribution. This algebraic approach to the problem of unitary synthesis exploits Lie algebras and their

Shinji Miwa, Tatsuya Yamamoto, Takashi Nagata, Shoya Sakamoto

Chirality pervades multiple scientific domains-physics, chemistry, biology, and astronomy-and profoundly influences their foundational principles. Recently, the chirality-induced spin selectivity (CISS) phenomenon has captured significant attention in physical chemistry due to its potential applications and intriguing underlying physics. Despite its prominen

Joshua Lee, Wyatt Fong, Alexander Le, Sur Shah

Sarcasm detection is a significant challenge in sentiment analysis due to the nuanced and context-dependent nature of verbiage. We introduce Pragmatic Metacognitive Prompting (PMP) to improve the performance of Large Language Models (LLMs) in sarcasm detection, which leverages principles from pragmatics and reflection helping LLMs interpret implied meanings,

Hong Hui Yeoh, Fredrik Strand, Raphaël Phan, Kartini Rahmat

To facilitate early detection of breast cancer, there is a need to develop short-term risk prediction schemes that can prescribe personalized/individualized screening mammography regimens for women. In this study, we propose a new deep learning architecture called TRINet that implements time-decay attention to focus on recent mammographic screenings, as curr

Yunhi Cho, Hyounggyu Choi

G.A. Galperin introduced the axiomatic mass center system for finite point sets in spherical and hyperbolic spaces, proving the uniqueness of the mass center system. In this paper, we revisit this system and provide a significantly simpler proof of its uniqueness. Furthermore, we extend the axiomatic mass center system to manifolds. As an application of our

Jiahao Lu, Tianyu Huang, Peng Li, Zhiyang Dou

Recent developments in monocular depth estimation methods enable high-quality depth estimation of single-view images but fail to estimate consistent video depth across different frames. Recent works address this problem by applying a video diffusion model to generate video depth conditioned on the input video, which is training-expensive and can only produce

Chandra Shekhar Saraf, David Parkinson

The ability to differentiate between different models of inflation through the imprint of primordial non-Gaussianity (PNG) requires stringent constraints on the local PNG parameter $f_{\text{NL}}^{\text{loc}}$. Upcoming data from the large scale structure surveys like \textit{Euclid}, Vera C. Rubin Observatory, and the Spectro-Photometer for the History of t

Junmyeong Lee, Hoseung Choi, Yoonwoo Jeong, Minsu Cho

4D reconstruction from casually captured monocular videos is challenging due to inherent ambiguity in reconstructing dynamic 3D geometry. To address this challenge, we introduce Robust Dynamic Gaussian Splatting (RoDyGS), a method that reconstructs dynamic scene representation from casual monocular videos. RoDyGS explicitly separates static and dynamic scene

Francesc Wilhelmi, Boris Bellalta, Szymon Szott, Katarzyna Kosek-Szott

Multi-Access Point Coordination (MAPC) and Artificial Intelligence and Machine Learning (AI/ML) are expected to be key features in future Wi-Fi, such as the forthcoming IEEE 802.11bn (Wi-Fi~8) and beyond. In this paper, we explore a coordinated solution based on online learning to drive the optimization of Spatial Reuse (SR), a method that allows multiple de

Victor Junqiu Wei, Weicheng Wang, Di Jiang, Yuanfeng Song

Automatic speech Recognition (ASR) is a fundamental and important task in the field of speech and natural language processing. It is an inherent building block in many applications such as voice assistant, speech translation, etc. Despite the advancement of ASR technologies in recent years, it is still inevitable for modern ASR systems to have a substantial

Joonyong Park, Daisuke Saito, Nobuaki Minematsu

We examine the text-free speech representations of raw audio obtained from a self-supervised learning (SSL) model by analyzing the synthesized speech using the SSL representations instead of conventional text representations. Since raw audio does not have paired speech representations as transcribed texts do, obtaining speech representations from unpaired sp

Sachira Karunasena, Erfan Khordad, Thomas Drummond, Rajitha Senanayake

Meeting the high data rate demands of modern applications necessitates the utilization of high-frequency spectrum bands, including millimeter-wave and sub-terahertz bands. However, these frequencies require precise alignment of narrow communication beams between transmitters and receivers, typically resulting in significant beam training overhead. This paper

Xinyu Qiao, Yudong Hu, Congying Han, Weiyan Wu

Strategy learning in game environments with multi-agent is a challenging problem. Since each agent's reward is determined by the joint strategy, a greedy learning strategy that aims to maximize its own reward may fall into a local optimum. Recent studies have proposed the opponent modeling and shaping methods for game environments. These methods enhance the

Motoko Qiu Kawakita

We find that non-hyperelliptic generalised Howe curves and their twists of genus 5 attain the Hasse-Weil-Serre bound over some finite fields of order p, p^2 or p^3 for a prime p. We are able to decompose their Jacobians completely under certain assumptions and to determine the precise condition on the finite field over which they attain the Hasse-Weil-Serre

Zongxia Liang, Keyu Zhang, Yaqi Zhuang

We investigate stochastic utility maximization games under relative performance concerns in both finite-agent and infinite-agent (graphon) settings. An incomplete market model is considered where agents with power (CRRA) utility functions trade in a common risk-free bond and individual stocks driven by both common and idiosyncratic noise. The Nash equilibriu

Liao Qu, Huichao Zhang, Yiheng Liu, Xu Wang

We present TokenFlow, a novel unified image tokenizer that bridges the long-standing gap between multimodal understanding and generation. Prior research attempt to employ a single reconstruction-targeted Vector Quantization (VQ) encoder for unifying these two tasks. We observe that understanding and generation require fundamentally different granularities of

Xiangkai Ma, Xiaobin Hong, Mingkai Lin, Han Zhang

Conventional deep models have achieved unprecedented success in time series forecasting. However, facing the challenge of cross-domain generalization, existing studies utilize statistical prior as prompt engineering fails under the huge distribution shift among various domains. In this paper, a novel time series generalization diffusion model (TimeControl) t

Csilla Bujtás, Sandi Klavžar, Jing Tian

Mutual-visibility sets were motivated by visibility in distributed systems and social networks, and intertwine with several classical mathematical areas. Monotone properties of the variety of mutual-visibility sets, and restrictions of such sets to convex and isometric subgraphs are studied. Dual mutual-visibility sets are shown to be intrinsically different

Sai Swagat Mishra, N. S. Kavya, P. K. Sahoo, V. Venkatesha

Cosmography has been extensively utilized to constrain the kinematic state of the Universe using measured distances. In this work, we propose a new method to reconstruct coupling theories using the first kind of Chebyshev polynomial for two variables in which the functional form of the $f(Q,T)$ theory has been obtained. Further, the unknowns that appeared in

Ruixu Geng, Jianyang Wang, Yuqin Yuan, Fengquan Zhan

Wireless sensing technology has become prevalent in healthcare, smart homes, and autonomous driving due to its non-contact operation, penetration capabilities, and cost-effectiveness. As its applications expand, the technology faces mounting security challenges: sensing systems can be attack targets, signals can be weaponized, or signals can function as secu

Z. L. Yang, J. L. Han, T. Wang, P. F. Wang

Binary millisecond pulsars with a massive white dwarf (WD) companion are intermediate-mass binary pulsars (IMBPs). They are formed via the Case BB Roche-lobe overflow evolution channel if they are in compact orbits with an orbital period of less than 1 day. They are fairly rare in the known pulsar population; only five such IMBPs have been discovered before,

P. F. Wang, J. L. Han, Z. L. Yang, T. Wang

Finding pulsars in binaries are important for measurements of the masses of neutron stars, for tests of gravity theories, and for studies of star evolution. We are carrying out the Galactic Plane Pulsar Snapshot survey (GPPS) by using the the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Here we present the Keplerian parameters for 116 newly

Kazuki Kotoyori, Shota Hirose, Heming Sun, Jiro Katto

Accurate video prediction by deep neural networks, especially for dynamic regions, is a challenging task in computer vision for critical applications such as autonomous driving, remote working, and telemedicine. Due to inherent uncertainties, existing prediction models often struggle with the complexity of motion dynamics and occlusions. In this paper, we pr

Yuechun Jiao, Oliver D. W. Hughes, Max Z. Festenstein, Zhengyang Bai

We report on the coherent creation, control and read-out of a single photonic qutrit in a Rydberg ensemble. In each measurement, an optical photon is stored as a Rydberg polariton through electromagnetically induced transparency. Employing two microwave fields, the polariton is driven into an arbitrary superposition of three collective states, each encoded i

Runjian Chen, Hang Zhang, Avinash Ravichandran, Hyoungseob Park

Unsupervised 3D representation learning reduces the burden of labeling multimodal 3D data for fusion perception tasks. Among different pre-training paradigms, differentiable-rendering-based methods have shown most promise. However, existing works separately conduct pre-training for each modalities due to computational costs of processing large point clouds w

Yifan Wu, Xichen Ye, Songmin Dai, Dengye Pan

Out-of-distribution (OOD) detection is an essential approach to robustifying deep learning models, enabling them to identify inputs that fall outside of their trained distribution. Existing OOD detection methods usually depend on crafted data, such as specific outlier datasets or elaborate data augmentations. While this is reasonable, the frequent mismatch b

Yunsong Gan, Weijun Liu, Binzhou Xia

A regular bipartite graph $\Gamma$ is called semisymmetric if its full automorphism group $\mathrm{Aut}(\Gamma)$ acts transitively on the edge set but not on the vertex set. For a subgroup $G$ of $\mathrm{Aut}(\Gamma)$ that stabilizes the biparts of $\Gamma$, we say that $\Gamma$ is $G$-biprimitive if $G$ acts primitively on each part. In this paper, we firs

Marzieh Mohammadi, Amir Salarpour

This paper introduces Point-GN, a novel non-parametric network for efficient and accurate 3D point cloud classification. Unlike conventional deep learning models that rely on a large number of trainable parameters, Point-GN leverages non-learnable components-specifically, Farthest Point Sampling (FPS), k-Nearest Neighbors (k-NN), and Gaussian Positional Enco

Jun Dong, Jintao Cheng, Jin Wu, Chengxi Zhang

In the fifth-generation (5G) era, eliminating communication interference sources is crucial for maintaining network performance. Interference often originates from unauthorized or malfunctioning antennas, and radio monitoring agencies must address numerous sources of such antennas annually. Unmanned aerial vehicles (UAVs) can improve inspection efficiency. H

Runjian Chen, Hyoungseob Park, Bo Zhang, Wenqi Shao

Labeling LiDAR point clouds is notoriously time-and-energy-consuming, which spurs recent unsupervised 3D representation learning methods to alleviate the labeling burden in LiDAR perception via pretrained weights. Almost all existing work focus on a single frame of LiDAR point cloud and neglect the temporal LiDAR sequence, which naturally accounts for object

Mrinmoy Sarker Turja, Tae Ho Kwon, Hyoungkeun Kim, Ki Doo Kim

Glycated hemoglobin (HbA1c) is the most important factor in diabetes control. Since HbA1c reflects the average blood glucose level over the preceding three months, it is unaffected by the patient's activity level or diet before the test. Noninvasive HbA1c measurement reduces both the pain and complications associated with fingertip piercing to collect blood.

Md Meraz, Md Afzal Ansari, Mohammed Javed, Pavan Chakraborty

In recent years, the challenge of 3D shape analysis within point cloud data has gathered significant attention in computer vision. Addressing the complexities of effective 3D information representation and meaningful feature extraction for classification tasks remains crucial. This paper presents Point-GR, a novel deep learning architecture designed explicit

Junchao Fan, Xuyang Lei, Xiaolin Chang, Jelena Mišić

Despite significant advancements in deep reinforcement learning (DRL)-based autonomous driving policies, these policies still exhibit vulnerability to adversarial attacks. This vulnerability poses a formidable challenge to the practical deployment of these policies in autonomous driving. Designing effective adversarial attacks is an indispensable prerequisit

Shivanagouda Biradar, Deepak U Patil

We consider the problem of recovering the topology and the edge conductance value, as well as characterizing a set of electrical networks that satisfy the limitedly available Thevenin impedance measurements. The measurements are obtained from an unknown electrical network, which is assumed to belong to a class of circular planar passive electrical network. T

Jun Yin, Amilcare Porporato

Understanding the complex interactions between land surface and atmosphere is essential to improve weather and climate predictions. Various numerical experiments have suggested that regions of strong coupling strength (hotspots) are located in the transitional climate zones. However, atmospheric processes in these hotspots are found to have different respons

Xin Zhong, Naihong Hu

In this study, we focus on the positive part $U_q^{+}$ of the quantum affine superalgebra $U_q(C(2)^{(2)})$. This algebra admits a presentation with two two generators $e_{\alpha}$ and $e_{\delta-\alpha}$, which satisfy the cubic $q$-Serre relations. According to the work of Khoroshkin-Lukierski-Tolstoy, the Damiani and the Beck $PBW$ bases exist for this su

S. Kaur, Smriti Bhatia, Pooja, Kshitij Sharma

We have grown (111)- and (001)-oriented NiO thin films on (0001)-Sapphire and (001)-MgO substrates using pulsed laser deposition (PLD), respectively. DC magnetic susceptibility measurements underline that the N\'eel temperatures of the samples are beyond room-temperature. This is further confirmed by the presence of two-magnon Raman scattering modes in these

Ammar Shaikh, Raj Abhijit Dandekar, Sreedath Panat, Rajat Dandekar

Rapid advancements in Large Language models (LLMs) has significantly enhanced their reasoning capabilities. Despite improved performance on benchmarks, LLMs exhibit notable gaps in their cognitive processes. Additionally, as reflections of human-generated data, these models have the potential to inherit cognitive biases, raising concerns about their reasonin

Yuchen Sun, Anup Teejo Mathew, Imran Afgan, Federico Renda

Inspired by the embodied intelligence of biological creatures like the octopus, the soft robotic arm utilizes its highly flexible structure to perform various tasks in the complex environment. While the classic Cosserat rod theory investigates the bending, twisting, shearing, and stretching of the soft arm, it fails to capture the in-plane deformation that o

Soheli Mukherjee, Naftali R. Smith

We calculate the steady state distribution $P_{\text{SSD}}(\boldsymbol{X})$ of the position of a Brownian particle under an intermittent confining potential that switches on and off with a constant rate $\gamma$. We assume the external potential $U(\boldsymbol{x})$ to be smooth and have a unique global minimum at $\boldsymbol{x} = \boldsymbol{x}_0$, and in d

Xiaofeng Tan, Hongsong Wang, Xin Geng, Liang Wang

Video anomaly detection (VAD) is a vital yet complex open-set task in computer vision, commonly tackled through reconstruction-based methods. However, these methods struggle with two key limitations: (1) insufficient robustness in open-set scenarios, where unseen normal motions are frequently misclassified as anomalies, and (2) an overemphasis on, but restri

Zehai Pang, Omar Abdelghani, Marin Soljačić, Yi Yang

We theoretically introduce synthetic non-Abelian gauge fields for topological quantum walks. The photonic mesh lattice configuration is generalized with polarization multiplexing to achieve a four-dimensional Hilbert space, based on which we provide photonic building blocks for realizing various quantum walks in non-Abelian gauge fields. It is found that SU(

Annabel Webb, Nan Zou, Serigne Lo, Jun Ma

This article considers the joint modeling of longitudinal covariates and partly-interval censored time-to-event data. Longitudinal time-varying covariates play a crucial role in obtaining accurate clinically relevant predictions using a survival regression model. However, these covariates are often measured at limited time points and may be subject to measur

Yuki Watanabe, Dominik Krengel, Hans-Georg Matuttis

We report our experiences for the development of a neighborhood algorithm implemented via tree-codes to optimize the performance of a discrete element method (DEM) for convex polytopes. Our implementation of the two-dimensional tree code needs $N\log N$, as does the sort and sweep approach. For our choice of boundary conditions (a rotating drum) and system s

Zarullo Rakhmonov

For a nonprincipal character $\chi$ modulo $D$, when $x\ge D^{\frac56+\varepsilon}$, $(l,D) = 1$, we prove a nontrivial estimate of the form $\sum_{n\le x}\Lambda (n)\chi (n-l)\ll x\exp\left(-0.6\sqrt{\ln D}\right)$ for the sum of values of $\chi$ over a sequence of shifted primes. Bibliography: 41 references.

Qi Zheng, Yibo Fan, Leilei Huang, Tianyu Zhu

Video quality assessment (VQA) is an important processing task, aiming at predicting the quality of videos in a manner highly consistent with human judgments of perceived quality. Traditional VQA models based on natural image and/or video statistics, which are inspired both by models of projected images of the real world and by dual models of the human visua

Hyojeong Lee, Youngwan Jo, Inpyo Hong, Sanghyun Park

We propose a Multifaceted Resilient Network(MRNet), a novel architecture developed for medical image-to-image translation that outperforms state-of-the-art methods in MRI-to-CT and MRI-to-MRI conversion. MRNet leverages the Segment Anything Model (SAM) to exploit frequency-based features to build a powerful method for advanced medical image transformation. T

Liwei Deng, Tianfu Wang, Yan Zhao, Kai Zheng

Portfolio management is an important yet challenging task in AI for FinTech, which aims to allocate investors' budgets among different assets to balance the risk and return of an investment. In this study, we propose a general Multi-objectIve framework with controLLable rIsk for pOrtfolio maNagement (MILLION), which consists of two main phases, i.e., return-

Jay Tharwani, Arnab A Purkayastha

Cloud computing has become the cornerstone of modern IT infrastructure, offering a wide range of general-purpose instances optimized for diverse workloads. This paper provides a comparative analysis of cost and performance for general-purpose compute instances across four major cloud providers: AWS, Azure, Google Cloud Platform (GCP), and Oracle Cloud Infras

Jehan Taraporewalla, Arun KP, Sugata Ghosh, Abhishek Agarwal

In application-specific designs, owing to the trade-off between power consumption and speed, optimization of various circuit parameters has become a challenging task. Several of the performance metrics, viz. energy efficiency, gain, performance, and noise immunity, are interrelated and difficult to tune. Such efforts may result in a great deal of manual iter

Shin Kim

Computed Tomography (CT) is a technology that reconstructs cross-sectional images using X-ray images taken from multiple directions. In CT, hundreds of X-ray images acquired as the X-ray source and detector rotate around a central axis, are used for precise reconstruction. In security baggage inspection, X-ray imaging is also widely used; however, unlike the

Aditya Shah, Aditya Challa, Sravan Danda, Archana Mathur

Stochastic Gradient Descent (SGD) is the main approach to optimizing neural networks. Several generalization properties of deep networks, such as convergence to a flatter minima, are believed to arise from SGD. This article explores the causality aspect of gradient descent. Specifically, we show that the gradient descent procedure has an implicit granger-cau

Alia Hamieh, Peng-Jie Wong

In this article, we study the density conjecture of Katz and Sarnak for $L$-functions of ad\'elic Hilbert modular forms and their convolutions. In particular, under the generalised Riemann hypothesis, we establish several instances supporting the conjecture and extending the works of Iwaniec-Luo-Sarnak and many others. For applications, we obtain an upper bo

Divya Sharma

The paper presents findings from a comprehensive study examining the saving and credit behaviors of low-income households residing in unauthorized colonies within a metropolitan area. Utilizing a dual approach, the study engaged in prolonged fieldwork, including repeated fortnightly interviews with selected households and a one-time primary survey with a lar

Shahidullah Kaiser, Ali Saman Tosun, Turgay Korkmaz

Edge computing is emerging as a key enabler of low-latency, high-efficiency processing for the Internet of Things (IoT) and other real-time applications. To support these demands, containerization has gained traction in edge computing due to its lightweight virtualization and efficient resource management. However, there is currently no established framework

Guohao Ying, Shinya Kanemura, Yushi Mura

We present a new viable benchmark scenario under the current experimental data for the model which can explain tiny mass of active neutrinos and dark matter, as a summary of our results. Majorana masses of right-handed neutrinos are given by the spontaneous breaking of the $\mathrm{U(1)}_{B-L}$ gauge symmetry above the electroweak scale, and tiny neutrino ma

Shahidullah Kaiser, Ali Saman Tosun, Turgay Korkmaz

The rapid expansion of IoT devices and their real-time applications have driven a growing need for edge computing. To meet this need, efficient and secure solutions are required for running such applications on resource-constrained devices with limited power, CPU, and memory. Unikernel, with its minimalistic design and application-specific approach, offers a

Yi Kang, Joseph F. Hennawi, Jan-Torge Schindler, John Tamanas

Density estimation is a fundamental problem that arises in many areas of astronomy, with applications ranging from selecting quasars using color distributions to characterizing stellar abundances. Astronomical observations are inevitably noisy; however, the density of a noise-free feature is often the desired outcome. The extreme-deconvolution (XD) method ca

Gervais Nazaire Chendjou Beukam, Jean Pierre Nguenang, Stefano Ruffo, Andrea Trombettoni

The Homotopy Analysis Method (HAM) is a powerful technique which allows to derive approximate solutions of both ordinary and partial differential equations. We propose to use a variational approach based on the Least Action Principle (LAP) in order to improve the efficiency of the HAM when applied to Lagrangian systems. The extremization of the action is ach

Isha Chaudhary, Shuyi Lin, Cheng Tan, Gagandeep Singh

Specifications - precise mathematical representations of correct domain-specific behaviors - are crucial to guarantee the trustworthiness of computer systems. With the increasing development of neural networks as computer system components, specifications gain more importance as they can be used to regulate the behaviors of these black-box models. Traditiona

Xiran Chen, Sha Lin, Xiaofeng Chen, Weikai Li

Timestamp automatic annotation (TAA) is a crucial procedure for analyzing time-series ScRNA-seq data, as they unveil dynamic biological developments and cell regeneration process. However, current TAA methods heavily rely on manual timestamps, often overlooking their reliability. This oversight can significantly degrade the performance of timestamp automatic

Junchao Zhu, Ruining Deng, Tianyuan Yao, Juming Xiong

Spatial transcriptomics (ST) is an emerging technology that enables medical computer vision scientists to automatically interpret the molecular profiles underlying morphological features. Currently, however, most deep learning-based ST analyses are limited to two-dimensional (2D) sections, which can introduce diagnostic errors due to the heterogeneity of pat

Sergio E. Zanotto, Segun Aroyehun

The rapid advancements in large language models (LLMs) have significantly improved their ability to generate natural language, making texts generated by LLMs increasingly indistinguishable from human-written texts. Recent research has predominantly focused on using LLMs to classify text as either human-written or machine-generated. In our study, we adopt a d

Jinghan Xu, Zhiyuan Li

The broadcast model is widely used to describe the process of information dissemination from a single node to all nodes within an interconnected network. In this model, a graph represents the network, where vertices correspond to nodes and edges to communication links. The efficiency of this broadcasting process is evaluated by the broadcast time, the minimu

Cebajel Tanan, Sameer G. Kulkarni, Tamal Das, Manjesh K. Hanawal

In recent years, the advances in digitalisation have also adversely contributed to the significant rise in cybercrimes. Hence, building the threat intelligence to shield against rising cybercrimes has become a fundamental requisite. Internet Protocol (IP) addresses play a crucial role in the threat intelligence and prevention of cyber crimes. However, we hav

Kai Wang, Zhaohua Hou, Kaiyi Qian, Leizhen Chen

Quantum entanglement -- correlations of particles that are stronger than any classical analogue -- is the basis for research on the foundations of quantum mechanics and for practical applications such as quantum networks. Traditionally, entanglement is achieved through local interactions or via entanglement swapping, where entanglement at a distance is gener

Tianyu Chang, Xiaohao Chen, Zhichao Wei, Xuanpu Zhang

Video Virtual Try-on aims to seamlessly transfer a reference garment onto a target person in a video while preserving both visual fidelity and temporal coherence. Existing methods typically rely on inpainting masks to define the try-on area, enabling accurate garment transfer for simple scenes (e.g., in-shop videos). However, these mask-based approaches stru

Yan-Cheng Wei, Pieter-Jan Stas, Aziza Suleymanzade, Gefen Baranes

Blind quantum computing (BQC) is a promising application of distributed quantum systems, where a client can perform computations on a remote server without revealing any details of the applied circuit. While the most promising realizations of quantum computers are based on various matter qubit platforms, implementing BQC on matter qubits remains an outstandi

Huijiao Wang, Shenghao Zhao, Lei Yu, Xulei Yang

Image quality degradation caused by raindrops is one of the most important but challenging problems that reduce the performance of vision systems. Most existing raindrop removal algorithms are based on a supervised learning method using pairwise images, which are hard to obtain in real-world applications. This study proposes a deep neural network for raindro

Ignacio Puiggros T., A. Srikantha Phani

There has been increasing interest in methodologies that incorporate physics priors into neural network architectures to enhance their modeling capabilities. A family of these methodologies that has gained traction are Hamiltonian neural networks (HNN) and their variations. These architectures explicitly encode Hamiltonian mechanics both in their structure a

Lingchen Sun, Rongyuan Wu, Zhiyuan Ma, Shuaizheng Liu

Diffusion prior-based methods have shown impressive results in real-world image super-resolution (SR). However, most existing methods entangle pixel-level and semantic-level SR objectives in the training process, struggling to balance pixel-wise fidelity and perceptual quality. Meanwhile, users have varying preferences on SR results, thus it is demanded to d

Toru Tamagawa, Teruaki Enoto, Takao Kitaguchi, Wataru Iwakiri

NinjaSat is an X-ray CubeSat designed for agile, long-term continuous observations of bright X-ray sources, with the size of 6U ($100\times200\times300$ mm$^3$) and a mass of 8 kg. NinjaSat is capable of pointing at X-ray sources with an accuracy of less than $0^{\circ}\hspace{-1.0mm}.1$ (2$\sigma$ confidence level) with 3-axis attitude control. The satellit

Jiaxi Yu, Tomohiro Fukuda, Nobuyoshi Yabuki

Post-flood building damage assessment is critical for rapid response and post-disaster reconstruction planning. Current research fails to consider the distinct requirements of disaster assessment (DA) from change detection (CD) in neural network design. This paper focuses on two key differences: 1) building change features in DA satellite images are more sub

Xue Pan

Assuming the equilibrium of the QCD system, we have investigated the critical behavior of sixth-, eighth- and tenth-order susceptibilities of net-baryon number, through mapping the results in the three-dimensional Ising model to that of QCD. Both the leading critical contribution as well as sub-leading critical contribution from the Ising model are discussed

Zhiqiu Chen, Zong-Gan Chen, Yuncheng Jiang, Zhi-Hui Zhan

In recent years, multimodal multiobjective optimization algorithms (MMOAs) based on evolutionary computation have been widely studied. However, existing MMOAs are mainly tested on benchmark function sets such as the 2019 IEEE Congress on Evolutionary Computation test suite (CEC 2019), and their performance on real-world problems is neglected. In this paper,

Kaiwen Jiang, Zhen Fu, Junde Guo, Wei Zhang

In this paper, we study the whole-body loco-manipulation problem using reinforcement learning (RL). Specifically, we focus on the problem of how to coordinate the floating base and the robotic arm of a wheeled-quadrupedal manipulator robot to achieve direct six-dimensional (6D) end-effector (EE) pose tracking in task space. Different from conventional whole-

Yu Feng, Shunsi Zhang, Jian Shu, Hanfeng Zhao

Generating multi-view human images from a single view is a complex and significant challenge. Although recent advancements in multi-view object generation have shown impressive results with diffusion models, novel view synthesis for humans remains constrained by the limited availability of 3D human datasets. Consequently, many existing models struggle to pro

Ye Shen, Ho-Yun YuChih

Works on the energy extraction from a rotating black hole via magnetic reconnection attract more attentions in recent years. Discussions on this topic, however, are often based on many simplifications, such as assuming a circularly flowing bulk plasma and a fixed orientation angle. A significant gap remains between theoretical models and the magnetic reconne

Jahid Hasan, Romila Pradhan

Machine learning systems are increasingly being used in critical decision making such as healthcare, finance, and criminal justice. Concerns around their fairness have resulted in several bias mitigation techniques that emphasize the need for high-quality data to ensure fairer decisions. However, the role of earlier stages of machine learning pipelines in mi

Dongke Rong, Xiuqi Chen, Shengru Chen, Jingfeng Zhang

A wealth of remarkable behaviors is observed at the interfaces between magnetic oxides due to the coexistence of Coulomb repulsion and interatomic exchange interactions. While previous research has focused on bonded oxide heterointerfaces, studies on magnetism in van der Waals interfaces remain rare. In this study, we stacked two freestanding cobaltites with

Masayuki Kawakita

We prove the ACC for minimal log discrepancies on an arbitrary fixed threefold.

Minghao Jiang, Duohui Jing, Jason W. H. Wong

Motivation: The three-dimensional (3D) organization of the genome plays a critical role in regulating gene expression and maintaining cellular homeostasis. Disruptions in this spatial organization can result in abnormal chromatin interactions, contributing to the development of various diseases including cancer. Advances in chromosome conformation capture te

Baojuan Dong, Kai Zhao, Kenji Watanabe, Takashi Taniguchi

When charge transport occurs under conditions like topological protection or ballistic motion, the conductance of low-dimensional systems often exhibits quantized values in units of $e^{2}/h$, where $e$ and $h$ are the elementary charge and Planck's constant. Such quantization has been pivotal in quantum metrology and computing. Here, we demonstrate a novel

Jeremy Levy, Chandralekha Singh

Quantum mechanics is a notoriously abstract subject, and therefore challenging to teach at pre-college and introductory college levels. Here we introduce the Bloch Cube, a hands-on educational tool which can illustrate key quantum concepts without equations. A series of videos have been created showing how Bloch Cubes can be used to teach concepts such as qu