November 2024 arXiv papers — page 10

Vu-Anh Le, Mehmet Dik

We propose an algorithmic framework for dataset normalization in data augmentation pipelines that preserves topological stability under non-uniform scaling transformations. Given a finite metric space \( X \subset \mathbb{R}^n \) with Euclidean distance \( d_X \), we consider scaling transformations defined by scaling factors \( s_1, s_2, \ldots, s_n > 0 \).

Ling Li, Wiktor Zuba, Grigorios Loukides, Solon P. Pissis

Time series are ubiquitous in domains ranging from medicine to marketing and finance. Frequent Pattern Mining (FPM) from a time series has thus received much attention. Recently, it has been studied under the order-preserving (OP) matching relation stating that a match occurs when two time series have the same relative order on their elements. Here, we propo

Hongji Yang, Yiru Li, Yingying Zhu

Information retrieval techniques have demonstrated exceptional capabilities in identifying semantic similarities across diverse domains through robust feature representations. However, their potential in guiding synthesis tasks, particularly cross-view image synthesis, remains underexplored. Cross-view image synthesis presents significant challenges in estab

Tianqi Li, Ruobing Zheng, Minghui Yang, Jingdong Chen

Recent advances in diffusion models have endowed talking head synthesis with subtle expressions and vivid head movements, but have also led to slow inference speed and insufficient control over generated results. To address these issues, we propose Ditto, a diffusion-based talking head framework that enables fine-grained controls and real-time inference. Spe

Md Imran Hossen, Xiali Hei

The advent of instruction-tuned Large Language Models designed for coding tasks (Code LLMs) has transformed software engineering practices. However, their robustness against various input challenges remains a critical concern. This study introduces DegradePrompter, a novel method designed to systematically evaluate the robustness of instruction-tuned Code LL

Kunyang Han, Yibo Hu, Mengxue Qu, Hailin Shi

Advances in CLIP and large multimodal models (LMMs) have enabled open-vocabulary and free-text segmentation, yet existing models still require predefined category prompts, limiting free-form category self-generation. Most segmentation LMMs also remain confined to sparse predictions, restricting their applicability in open-set environments. In contrast, we pr

Limin Wang, Toyotaro Suzumura, Hiroki Kanezashi

Electroencephalography (EEG) signals provide critical insights for applications in disease diagnosis and healthcare. However, the scarcity of labeled EEG data poses a significant challenge. Foundation models offer a promising solution by leveraging large-scale unlabeled data through pre-training, enabling strong performance across diverse tasks. While both t

Abhijith Gandrakota

We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger (GT) test crate FPGAs during LHC Run 3. The GT makes the final decision whether to readout or discard the data from each LHC collision, which occur at a rate of 40 MHz, within a 50 ns latency. The

Keitaro Anai, Yasunari Suzuki, Yuuki Tokunaga, Yuichiro Matsuzaki

Continuous-variable (CV) quantum computing is a promising candidate for quantum computation because it can, even with one mode, utilize infinite-dimensional Hilbert spaces and can efficiently handle continuous values. Although photonic platforms have been considered as a leading platform for CV computation, hybrid systems that use both qubits and bosonic mod

Seongeun Yun, Won Bo Lee

Retrosynthesis is essential for designing synthetic pathways for complex molecules and can be revolutionized by AI to automate and accelerate chemical synthesis planning for drug discovery and materials science. Here, we propose a hierarchical framework for retrosynthesis prediction that systematically integrates reaction center identification, action predic

Ruimin Peng, Jiayu An, Dongrui Wu

Electroencephalogram (EEG)-based seizure subtype classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two c

Rafael López

We give necessary and sufficient conditions on the curvature and the torsion of a regular curve of the space forms $\h^3$ and $\s^3$ to be contained in a totally umbilical surface. In case that the curve has constant torsion, we obtain the value of the curvature of the curve. Also numerical pictures of these curves are shown.

Quentin Houbre, Roel Pieters

The autonomous learning of new goals in robotics remains a complex issue to address. Here, we propose a model where curiosity influence learning flexibility. To do so, this paper proposes to root curiosity and attention together by taking inspiration from the Locus Coeruleus-Norepinephrine system along with various cognitive processes such as cognitive persi

Abhinav Joshi, Areeb Ahmad, Ashutosh Modi

Large Language Models (LLMs) have shown state-of-the-art performance in a variety of tasks, including arithmetic and reasoning; however, to gauge the intellectual capabilities of LLMs, causal reasoning has become a reliable proxy for validating a general understanding of the mechanics and intricacies of the world similar to humans. Previous works in natural

Seher Kaya, Rafael López

Spacelike intrinsic rotational surfaces with constant mean curvature in the Lorentz-Minkowski space $\E_1^3$ have been recently investigated by Brander et al., extending the known Smyth's surfaces in Euclidean space. Assuming that the surface is intrinsic rotational with coordinates $(u,v)$ and conformal factor $\rho(u)^2$, we replace the constancy of the me

Lubin Meng, Xue Jiang, Tianwang Jia, Dongrui Wu

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG-based BCIs have been successfully used in many applications, such as neurological rehabilitation, text input, games, and so on. However,

Gourang Pathak, Abhay Kumar, Sannidhya Rawat, Shikha Gupta

This paper addresses the challenge of automated violence detection in video frames captured by surveillance cameras, specifically focusing on classifying scenes as "fight" or "non-fight." This task is critical for enhancing unmanned security systems, online content filtering, and related applications. We propose an approach using a 3D Convolutional Neural Ne

Rahath Malladi, Amol Harsh, Arshia Sangwan, Sunita Chauhan

This paper introduces SANGO (Socially Aware Navigation through Grouped Obstacles), a novel method that ensures socially appropriate behavior by dynamically grouping obstacles and adhering to social norms. Using deep reinforcement learning, SANGO trains agents to navigate complex environments leveraging the DBSCAN algorithm for obstacle clustering and Proxima

Debapriya Roy

Clustering is a long-standing problem area in data mining. The centroid-based classical approaches to clustering mainly face difficulty in the case of high dimensional inputs such as images. With the advent of deep neural networks, a common approach to this problem is to map the data to some latent space of comparatively lower dimensions and then do the clus

Michael Ruderman

A standard motion control with feedback of the output displacement cannot handle unforeseen contact with environment without penetrating into the soft, i.e. viscoelastic, materials or even damaging the fragile materials. Robotics and mechatronics with tactile and haptic capabilities, and in particular medical robotics for example, place special demands on th

Rafael López

We introduce the Frenet theory of curves in dual space $\d^3$. After defining the curvature and the torsion of a curve, we classify all curves in dual plane with constant curvature. We also establish the fundamental theorem of existence in the theory of dual curves, proving that there is a dual curve with prescribed curvature and torsion. Finally we classify

Xinyu Yuan, Yan Qiao, Zhenchun Wei, Zeyu Zhang

Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Al

Qirui Wu, Denys Iliash, Daniel Ritchie, Manolis Savva

Reconstructing structured 3D scenes from RGB images using CAD objects unlocks efficient and compact scene representations that maintain compositionality and interactability. Existing works propose training-heavy methods relying on either expensive yet inaccurate real-world annotations or controllable yet monotonous synthetic data that do not generalize well

Ahmad Mohammadshirazi, Pinaki Prasad Guha Neogi, Ser-Nam Lim, Rajiv Ramnath

Document Visual Question Answering (VQA) demands robust integration of text detection, recognition, and spatial reasoning to interpret complex document layouts. In this work, we introduce DLaVA, a novel, training-free pipeline that leverages Multimodal Large Language Models (MLLMs) for zero-shot answer localization in order to improve trustworthiness, interp

Zhong-Ying Fan, Fan Zhou, Yuehang Li, Minyong Guo

We examine the physical implications of the centrifugal and gravitational electromotive forces on magnetic reconnection in a Kerr black hole background. We find that both forces increase the reconnection rate, though the underlying mechanisms differ substantially. The gravitational force leads to a separation of charge density, breaking the quasi-neutrality

Tianlong Zhong, Gaoxia Zhu, Kang You Lim, Yew Soon Ong

The increasing use of Generative Artificial Intelligence (GAI) tools in education highlights the need to understand their influence on individuals' thinking processes and agency. This research explored 20 university students' interaction with GAI during programming. Participants completed surveys, recorded their screens during an hour-long programming sessio

Rong Liu, Tie Liu, Izaskun Jiménez-Serra, Jin-Zeng Li

The production of silicon monoxide (SiO) can be considered as a fingerprint of shock interaction. In this work, we use high-sensitivity observations of the SiO (2-1) and H$^{13}$CO$^{+}$ (1-0) emission to investigate the broad and narrow SiO emission toward 146 massive star-forming regions in the ATOMS survey. We detected SiO emission in 136 regions and dist

Jun Gao, Yongqi Li, Ziqiang Cao, Wenjie Li

Chain-of-Thought (CoT) prompting elicits large language models (LLMs) to produce a series of intermediate reasoning steps before arriving at the final answer. However, when transitioning to vision-language models (VLMs), their text-only rationales struggle to express the fine-grained associations with the original image. In this paper, we propose an image-in

Minseo Kim, Wei Shu, Mohsen Amini Salehi

The high computational, memory, and energy demands of Deep Learning (DL) applications often exceed the capabilities of battery-powered edge devices, creating difficulties in meeting task deadlines and accuracy requirements. Unlike previous solutions that optimize a single metric (e.g., accuracy or energy efficiency), HE2C framework is designed to holisticall

Zhen Zheng, Xin Ji, Taosong Fang, Fanghao Zhou

Large language models (LLMs) increasingly play an important role in a wide range of information processing and management tasks in industry. Many of these tasks are performed in large batches or even offline, and the performance indicator for which is throughput. These tasks usually show the characteristic of prefix sharing, where different prompt input can

Jeongsoo Choi, Ji-Hoon Kim, Jinyu Li, Joon Son Chung

In this paper, we introduce V2SFlow, a novel Video-to-Speech (V2S) framework designed to generate natural and intelligible speech directly from silent talking face videos. While recent V2S systems have shown promising results on constrained datasets with limited speakers and vocabularies, their performance often degrades on real-world, unconstrained datasets

Akiharu Esashi, Pawissanutt Lertpongrujikorn, Shinji Kato, Mohsen Amini Salehi

Function as a Service (FaaS) is poised to become the foundation of the next generation of cloud systems due to its inherent advantages in scalability, cost-efficiency, and ease of use. However, challenges such as the need for specialized knowledge, platform dependence, and difficulty in scalability in building functional workflows persist for cloud-native ap

Ryan J. Turner

Peculiar velocities are the motions of galaxies due to the gravitational influence of large-scale structure, and thus are an important cosmological probe of the underlying matter density field. In recent years the number of surveys designed to measure peculiar velocities has increased, to the point that it is plausible that we will have completely mapped out

Zeyu Peng, Farhad Farokhi, Ye Pu

In this paper, we study distributed optimization with smooth non-convex local objectives. We propose a novel variant of the well-known EXact firsT-ordeR Algorithm (EXTRA), called Two-timescale EXTRA, by introducing two distinct step-sizes. Leveraging the two-timescale strategy, we construct a Lyapunov function and establish the sub-linear convergence of Two-

Baolai Liao, Fan Wang

As we all know, the $k$-ary $n$-cube is a highly efficient interconnect network topology structure. It is also a concept of great significance, with a broad range of applications spanning both mathematics and computer science. In this paper, we study the existence of Hamiltonian cycles passing through prescribed matchings in $k$-ary $n$-cubes, and obtain the

Samanwaya Mukherjee

Astrophysical observations point toward strong evidence for the existence of black holes (BHs). Nevertheless, it is yet to be established or ruled out with confidence whether some exotic compact objects (ECOs), capable of mimicking black holes from an observational point of view, are indeed doing so. In classical General Relativity (GR), a horizon is the def

M. Homma, K. Nomura

We analyze parameter sensitivities of the mapped interacting boson model (IBM) and boson-fermion-fermion model (IBFFM) in the description of $\beta$-decay properties of the even-mass neutron-deficient Ge and As, and neutron-rich Zr and Mo isotopes. Based on the self-consistent mean-field calculations with a given energy density functional and a pairing inter

Linshan Hou, Wei Luo, Zhongyun Hua, Songhua Chen

Deep neural networks (DNNs) are susceptible to backdoor attacks, where adversaries poison datasets with adversary-specified triggers to implant hidden backdoors, enabling malicious manipulation of model predictions. Dataset purification serves as a proactive defense by removing malicious training samples to prevent backdoor injection at its source. We first

Guangxin He, Zonghong Dai, Jiangcheng Zhu, Binqiang Zhao

Retrieval augmented generation has emerged as an effective method to enhance large language model performance. This approach typically relies on an internal retrieval module that uses various indexing mechanisms to manage a static pre-processed corpus. However, such a paradigm often falls short when it is necessary to integrate the most up-to-date informatio

Yanxi Chen, Xuchen Pan, Yaliang Li, Bolin Ding

We propose two simple, principled and practical algorithms that enjoy provable scaling laws for the test-time compute of large language models (LLMs). The first one is a two-stage knockout-style algorithm: given an input problem, it first generates multiple candidate solutions, and then aggregate them via a knockout tournament for the final output. Assuming

Yeonguk Yu, Minhwan Ko, Sungho Shin, Kangmin Kim

Deep neural networks have demonstrated remarkable performance in various vision tasks, but their success heavily depends on the quality of the training data. Noisy labels are a critical issue in medical datasets and can significantly degrade model performance. Previous clean sample selection methods have not utilized the well pre-trained features of vision f

Ta-Yu Mu, Ching-Chi Lin

The domination problem and its variants represent a classical domain within algorithmic graph theory. Among these variants, the paired-domination problem holds particular prominence due to its real-world implications in security and surveillance domains. Given an input graph $G$, the paired-domination problem involves identifying a minimum dominating set $D$

Ruoqi Wang, Haitao Wang, Qiong Luo

Galaxy morphology analysis involves studying galaxies based on their shapes and structures. For such studies, fundamental tasks include identifying and classifying galaxies in astronomical images, as well as retrieving visually or structurally similar galaxies through similarity search. Existing methods either directly train domain-specific foundation models

Nikhil Behari, Aaron Young, Siddharth Somasundaram, Tzofi Klinghoffer

3D surface reconstruction is essential across applications of virtual reality, robotics, and mobile scanning. However, RGB-based reconstruction often fails in low-texture, low-light, and low-albedo scenes. Handheld LiDARs, now common on mobile devices, aim to address these challenges by capturing depth information from time-of-flight measurements of a coarse

Ta-Yu Mu, Ching-Chi Lin

A set $D \subseteq V$ is a dominating set of a graph $G$ if every vertex in $V - D$ is adjacent to at least one vertex in $D$. A dominating set $D$ is a paired-dominating set if the subgraph of $G$ induced by $D$ contains a perfect matching. In this paper, we prove that determining the minimum paired-dominating set in circle graphs is NP-complete. We further

Nabson Silva, Eriky Rodrigues, Tayana Conte

Micro frontend (MFE) architectures have gained significant popularity for promoting independence and modularity in development. Despite their widespread adoption, the field remains relatively unexplored, especially concerning identifying problems and documenting best practices. Drawing on both established microservice (MS) anti-patterns and the analysis of r

Chen Zhang, Sushil Varma

This research presents a Python-based simulation framework designed to model electric vehicle (EV) on-demand transportation systems, with a focus on optimizing urban fleet operations. Built on a process-driven architecture, the system efficiently simulates EV fleet dynamics, including passenger matching, vehicle dispatching, and charging strategies, while en

Soonchul Choi, Kyungil Kim, Zhenyu He, Youngman Kim

Most nuclei are deformed, and these deformations play an important role in various nuclear and astrophysical phenomena. Microscopic nuclear mass models have been developed based on covariant density functional theory to explore exotic nuclear properties. Among these, we adopt mass models based on the relativistic continuum Hartree-Bogoliubov theory (RCHB) wi

Xulei Sun, Yongcheng Wu, Xiaorong Zhou

This study investigates the intrinsic electric dipole moment (EDM) of the $\tau$ lepton, which is an important quantity in the search for physics beyond the Standard Model (BSM). In preparation for future measurements at the Super Tau-Charm Facility (STCF), we employ Monte Carlo simulations of the $e^+e^- \rightarrow \tau^+\tau^-$ process and optimize the an

Jiawei Chu, King-Yeung Lam, Boyu Wang, Tong Wang

Bird migration is an adaptive behavior ultimately aiming at optimizing survival and reproductive success. We propose an optimal switching model to study bird migration, where birds' migration behaviors can be efficiently modeled as switching between different stochastic differential equations. For individuals with perfect information regarding the environmen

Zhihao Sun, Haoran Jiang, Haoran Chen, Yixin Cao

Multimodal large language models have unlocked new possibilities for various multimodal tasks. However, their potential in image manipulation detection remains unexplored. When directly applied to the IMD task, M-LLMs often produce reasoning texts that suffer from hallucinations and overthinking. To address this, we propose ForgerySleuth, which leverages M-L

Saloni Deepak, J. Christopher Howk, Nicolas Lehner, Céline Péroux

We present a census of the mass density of metals and their evolution with cosmic time on a global scale throughout the Universe, synthesizing robust estimates of metals in stars, hot intra-cluster gas, and gaseous absorbers tracing neutral gas as well as ionized gas in the circumgalactic and intergalactic media. We observe an order of magnitude increase in

Tsung-Han Yang, Shang Gao, Yuanpeng Zhang, Daniel Olds

CoSn and FeSn, two kagome-lattice metals, have recently attracted significant attention as hosts of electronic flat bands and emergent physical properties. However, current understandings of their physical properties are limited to the knowledge of the average crystal structure. Here, we report the Fe-doping induced co-emergence of the antiferromagentic (AFM

Peter Bradshaw, Tianyue Cao, Atlas Chen, Braden Dean

We study the paintability, an on-line version of choosability, of complete multipartite graphs. We do this by considering an equivalent chip game introduced by Duraj, Gutowski, and Kozik. We consider complete multipartite graphs with $ n $ parts of size at most 3. Using a computational approach, we establish upper bounds on the paintability of such graphs fo

Herbert Wright, Weiming Zhi, Martin Matak, Matthew Johnson-Roberson

Constructing 3D representations of object geometry is critical for many robotics tasks, particularly manipulation problems. These representations must be built from potentially noisy partial observations. In this work, we focus on the problem of reconstructing a multi-object scene from a single RGBD image using a fixed camera. Traditional scene representatio

Hosu Lee, Junho Kim, Hyunjun Kim, Yong Man Ro

With the growing scale and complexity of video data, efficiently processing long video sequences poses significant challenges due to the quadratic increase in memory and computational demands associated with existing transformer-based Large Multi-modal Models (LMMs). To address these issues, we introduce Video-Ma$^2$mba, a novel architecture that incorporate

Yuhang Zhang, Yuan Zhou, Zeyu Liu, Yuxuan Cai

Current methods for generating human motion videos rely on extracting pose sequences from reference videos, which restricts flexibility and control. Additionally, due to the limitations of pose detection techniques, the extracted pose sequences can sometimes be inaccurate, leading to low-quality video outputs. We introduce a novel task aimed at generating hu

Yang You, Yixin Li, Congyue Deng, Yue Wang

Vision foundation models, particularly the ViT family, have revolutionized image understanding by providing rich semantic features. However, despite their success in 2D comprehension, their abilities on grasping 3D spatial relationships are still unclear. In this work, we evaluate and enhance the 3D awareness of ViT-based models. We begin by systematically a

Bo Qu, Zhurong Wang, Minghao Gu, Daisuke Yagi

The burgeoning e-Commerce sector requires advanced solutions for the detection of transaction fraud. With an increasing risk of financial information theft and account takeovers, deep learning methods have become integral to the embedding of behavior sequence data in fraud detection. However, these methods often struggle to balance modeling capabilities and

Yujin Han, Lei Xu, Sirui Chen, Difan Zou

Large language models (LLMs) have shown remarkable capability in natural language tasks, yet debate persists on whether they truly comprehend deep structure (i.e., core semantics) or merely rely on surface structure (e.g., presentation format). Prior studies observe that LLMs' performance declines when intervening on surface structure, arguing their success

Fusheng Liu, Qianxiao Li

Current methods for initializing state space model (SSM) parameters primarily rely on the HiPPO framework \citep{gu2023how}, which is based on online function approximation with the SSM kernel basis. However, the HiPPO framework does not explicitly account for the effects of the temporal structures of input sequences on the optimization of SSMs. In this pape

Jiepeng Wang, Yuan Liu, Peng Wang, Cheng Lin

3D Gaussian Splatting has achieved impressive performance in novel view synthesis with real-time rendering capabilities. However, reconstructing high-quality surfaces with fine details using 3D Gaussians remains a challenging task. In this work, we introduce GausSurf, a novel approach to high-quality surface reconstruction by employing geometry guidance from

Masato Shinoda

The classic game of Nim has been well-known for many years, inspiring numerous variations. One such variant is Delete Nim, where players take turns eliminating one pile of stones and splitting the remaining pile into two smaller piles. In this paper we generalize the game to include the case of n piles. On each turn, a player eliminates one pile and splits o

Eggon Viana

In this work, we investigate how the marginal beta deformation of the ${N}=4$ super-Yang-Mills theory manifests within the context of the topological B-model in the twistor space $\mathbb{CP}^{3|4}$. We begin by identifying the beta deformation as states living in a specific irreducible representation of the superconformal algebra. Then, we compute the ghost

Kai Zhao, Chang Xu, Bailu Si

Visual abstract reasoning tasks present challenges for deep neural networks, exposing limitations in their capabilities. In this work, we present a neural network model that addresses the challenges posed by Raven's Progressive Matrices (RPM). Inspired by the two-stream hypothesis of visual processing, we introduce the Dual-stream Reasoning Network (DRNet),

Ammar Fayad

Gravitational waves (GW), predicted by Einstein's General Theory of Relativity, provide a powerful probe of astrophysical phenomena and fundamental physics. In this work, we propose an unsupervised anomaly detection method using variational autoencoders (VAEs) to analyze GW time-series data. By training on noise-only data, the VAE accurately reconstructs noi

Jason Li, Connor Mowry

We present a simplified algorithm for solving the Negative-Weight Single-Source Shortest Paths (SSSP) problem, focusing on enhancing clarity and practicality over prior methods. Our algorithm uses graph diameter as a recursive parameter, offering greater robustness to the properties of the decomposed graph compared to earlier approaches. Additionally, we ful

Tonglin Zhang, Huyunting Huang

Compared to supervised variable selection, the research on unsupervised variable selection is far behind. A forward partial-variable clustering full-variable loss (FPCFL) method is proposed for the corresponding challenges. An advantage is that the FPCFL method can distinguish active, redundant, and uninformative variables, which the previous methods cannot

Zhi Li, Kai Zhao, Yaqi Wang, Shuai Wang

In medical image analysis, achieving fast, efficient, and accurate segmentation is essential for automated diagnosis and treatment. Although recent advancements in deep learning have significantly improved segmentation accuracy, current models often face challenges in adaptability and generalization, particularly when processing multi-modal medical imaging d

Jackson A. Mickley, Chris Allton, Ryan Bignell, Derek B. Leinweber

Evidence for the existence of a second finite-temperature transition in quantum chromodynamics (QCD) is obtained through the study of centre vortex geometry and its evolution with temperature. The dynamical anisotropic ensembles of the FASTSUM Collaboration are utilised to conduct a comprehensive analysis at eight temperatures beyond the established chiral t

Zhi Li, Wenhui Zhou, Xin Yuan, Weiwei Cai

Phase retrieval is a fundamental technique of advanced optical technologies, enabling precise control over wavefront properties. A persistent challenge in diffractive optical element (DOE) design is that a single hologram typically operates within a single wavelength or color channel, limiting it to monochromatic image generation. This limitation in channel

Tian Yu, Shaolei Zhang, Yang Feng

Iterative retrieval refers to the process in which the model continuously queries the retriever during generation to enhance the relevance of the retrieved knowledge, thereby improving the performance of Retrieval-Augmented Generation (RAG). Existing work typically employs few-shot prompting or manually constructed rules to implement iterative retrieval. Thi

Ali Hojjat, Janek Haberer, Olaf Landsiedel

The rapid growth of camera-based IoT devices demands the need for efficient video compression, particularly for edge applications where devices face hardware constraints, often with only 1 or 2 MB of RAM and unstable internet connections. Traditional and deep video compression methods are designed for high-end hardware, exceeding the capabilities of these co

Zhizhen Li, Xuanhao Luo, Mingzhe Chen, Chenhan Xu

Due to the exponential increase in wireless devices and a diversification of network services, unprecedented challenges, such as managing heterogeneous data traffic and massive access demands, have arisen in next-generation wireless networks. To address these challenges, there is a pressing need for the evolution of multiple access schemes with advanced tran

Luca Visinelli, Bradley Johnson, Bradley J. Kavanagh, David J. E. Marsh

The QCD axion, originally proposed to solve the strong CP problem in QCD, is a prominent candidate for dark matter (DM). In the presence of strong magnetic fields, such as those around neutron stars, axions can theoretically convert into photons, producing detectable electromagnetic signals. This axion-photon coupling provides a unique experimental pathway t

Divya Anand Sinha, Ruijie Du, Yezi Liu, Athina Markopolou

Graph federated learning is of essential importance for training over large graph datasets while protecting data privacy, where each client stores a subset of local graph data, while the server collects the local gradients and broadcasts only the aggregated gradients. Recent studies reveal that a malicious attacker can steal private image data from the gradi

Călin A. Georgescu, Merel A. Schalkers, Matthias Möller

We present qlbm, a Python software package designed to facilitate the development, simulation, and analysis of Quantum Lattice Boltzmann Methods (QBMs). qlbm is a modular framework that introduces a quantum component abstraction hierarchy tailored to the implementation of novel QBMs. The framework interfaces with state-of-the-art quantum software infrastruct

Ji-Bing Yuan, Hai-Fei Liu, Ya-Ju Song, Shi-Qing Tang

We examine a system in which an impurity qubit is immersed in a quasi-two-dimensional dipolar Bose-Einstein condensate whose collective excitations act as a depasing reservoir for the qubit. The relative dipole-dipole interaction strength is estimated by the probe qubit dephasing. The ultimate precision of this estimation is quantified by the quantum Fisher

Fathiyya Izzatun Az-zahra, Shinji Takeda, Takeshi Yamazaki

We present a spectroscopy scheme using transfer matrix and tensor network. With this method, the energy spectrum is obtained from the eigenvalues of the transfer matrix which is estimated by coarse grained tensor network of a lattice model, and the quantum number is classified from the matrix elements of a proper operator that can be represented as an impuri

Qiqi Li, Wei Wang, Yiying Zhang

In economic analysis, rational decision-makers often take actions to reduce their risk exposure. These actions include purchasing market insurance and implementing prevention measures to modify the shape of the loss distribution. Under the assumption that the insureds' actions are fully observed by the insurer, this paper investigates the interaction between

Saleh Ahmed Khan, Ahmed Ibreljic, Stephen Margiotta, A F M Anhar Uddin Bhuiyan

We systematically investigated the growth of Si-doped $\beta$-Ga$_2$O$_3$ films using LPCVD system, achieving high electron mobilities of 162 cm$^2$/V.s and 149 cm$^2$/V.s at carrier concentrations of $1.51 \times 10^{17}$ cm$^{-3}$ and $1.15 \times 10^{17}$ cm$^{-3}$, respectively, for homoepitaxial (010) $\beta$-Ga$_2$O$_3$ films grown on $\beta$-Ga$_2$O$_

Sepideh Bakhoda, Yongge Ma

Loop Quantum Gravity faces challenges in constructing a well-defined Hamiltonian constraint and understanding the quantum notion of time. In this paper these issues are studied by quantizing the $U(1)^3$ model, a simplified system exhibiting features similar to general relativity. By isolating a holonomy component within the Hamiltonian constraint, a discret

Safaa Abdullahi Moallim Mohamud, Ho-Young Jung

In this paper, we introduce the Actions and Objects Pathways (AOPath) for out-of-domain generalization in video question answering tasks. AOPath leverages features from a large pretrained model to enhance generalizability without the need for explicit training on the unseen domains. Inspired by human brain, AOPath dissociates the pretrained features into act

Javad A. Asadzade, Nazim I. Mahmudov

This paper investigates the well-posedness of singular mean-field backward stochastic Volterra integral equations (MF-BSVIEs) in infinite-dimensional spaces. We consider the equation: \[X(t) = \Psi(t) + \int_t^b P\big(t, s, X(s), \aleph(t, s), \aleph(s, t), \mathbb{E}[X(s)], \mathbb{E}[\aleph(t, s)], \mathbb{E}[\aleph(s, t)]\big) ds - \int_t^b \aleph(t, s) d

Karran Pandey, Matheus Gadelha, Yannick Hold-Geoffroy, Karan Singh

Predicting diverse object motions from a single static image remains challenging, as current video generation models often entangle object movement with camera motion and other scene changes. While recent methods can predict specific motions from motion arrow input, they rely on synthetic data and predefined motions, limiting their application to complex sce

Rajko Nenadov

Given a family of hypergraphs $\mathcal{H}$, we say that a hypergraph $\Gamma$ is $\mathcal{H}$-universal if it contains every $H \in \mathcal{H}$ as a subgraph. For $D, r \in \mathbb{N}$, we construct an $r$-uniform hypergraph with $\Theta\left(n^{r - r/D} \log^{r/D}(n)\right)$ edges which is universal for the family of all $r$-uniform hypergraphs with $n$

Xueke Zhu, Wenjie Lin, Yanyu Lin, Yunhao Ma

With the increasing application scope of spiking neural networks (SNN), the complexity of SNN models has surged, leading to an exponential growth in demand for AI computility. As the new generation computing architecture of the neural networks, the efficiency and power consumption of distributed storage and parallel computing in the many-core near-memory com

Xia Zhang, A. Louise Bradley

Bound states in the continuum (BIC) are trapped eigenmodes with infinite $Q$ factors that are confined in the system. In this work, we propose a simple design for engineering a Friedrich-Wintgen BIC through the interference between a symmetry protected BIC and a surface lattice mode in a dimerized dielectric metasurface. The meta-atoms are comprised a symmet

Arnbjörg Soffía Árnadóttir, Alexey Gordeev, Sabrina Lato, Tovohery Randrianarisoa

Evra, Feigon, Maurischat, and Parzanchevski (2023) introduced a biregular extension of Cayley graphs. In this paper, we reformulate their definition and provide some basic properties. We also show how these Cayley incidence graphs relate to various notions of Cayley hypergraphs. We further establish connections between Cayley incidence graphs and certain geo

Michail Patsakis, Kimonas Provatas, Ioannis Mouratidis, Ilias Georgakopoulos-Soares

Motivation: With the rapid expansion of large-scale biological datasets, DNA and protein sequence alignments have become essential for comparative genomics and proteomics. These alignments facilitate the exploration of sequence similarity patterns, providing valuable insights into sequence conservation, evolutionary relationships and for functional analyses.

Arianna Dolfi, Facundo A. Gomez, Antonela Monachesi, Patricia B. Tissera

Current studies of large-scale asymmetries (i.e. lopsidedness) in the stellar density distribution of disk galaxies have mainly focused on the local Universe. Recent observations have found a significant fraction (over 60%) of lopsided galaxies at high-redshift ($1.5 < z < 3$), which is significantly larger than the fraction (~30%) observed in the nearby Uni

Alvaro Alexander Burbano Moreno, Ronaldo Dias

This study presents a Bayesian hierarchical model for analyzing spatially correlated functional data and handling irregularly spaced observations. The model uses Bernstein polynomial (BP) bases combined with autoregressive random effects, allowing for nuanced modeling of spatial correlations between sites and dependencies of observations within curves. Moreo

Akio Kawasaki

Technologies for manipulating single atoms have advanced drastically in the past decades. Due to their excellent controllability of internal states, atoms serve as one of the ideal platforms as quantum systems. One major research direction in atomic systems is the precise determination of physical quantities using atoms, which is included in the field of pre

Yuichiro Kasahara, Kota Akinari, Tomoya Kouno, Noriko Sano

In recent years, labor shortages due to the declining birthrate and aging population have become significant challenges at construction sites in developed countries, including Japan. To address these challenges, we are developing an open platform called ROS2-TMS for Construction, a Cyber-Physical System (CPS) for construction sites, to achieve both efficienc

Pedram Emami, John C. Bowman

Analytic bounds on the projection of the global attractor of 2D incompressible turbulence in the palinstrophy--enstrophy plane [Dascaliuc, Foias, and Jolly 2005, 2010] are observed to vastly overestimate the values obtained from numerical simulations. This is due to the lack of a good estimate for the inner product $(\cal{B}(u,u),A^2u)$ of the advection term

Abhishek Mishra, Suman Kumar, Anush Lingamoorthy, Anup Das

In integrated circuit design, the analysis of wafer map patterns is critical to improve yield and detect manufacturing issues. We develop Wafer2Spike, an architecture for wafer map pattern classification using a spiking neural network (SNN), and demonstrate that a well-trained SNN achieves superior performance compared to deep neural network-based solutions.

Dohyun Kim, Boyan Stefanov Lazarov, Thomas M. Surowiec, Brendan Keith

We introduce a novel method for solving density-based topology optimization problems: Sigmoidal Mirror descent with a Projected Latent variable (SiMPL). The SiMPL method (pronounced as ``the simple method'') optimizes a design using only first-order derivative information of the objective function. The bound constraints on the density field are enforced with

Lihao Zhang, Haijian Sun, Samuel Berweger, Camillo Gentile

Precisely modeling radio propagation in complex environments has been a significant challenge, especially with the advent of 5G and beyond networks, where managing massive antenna arrays demands more detailed information. Traditional methods, such as empirical models and ray tracing, often fall short, either due to insufficient details or because of challeng

Zan Chaudhry

We introduce an algorithm for efficiently representing convolution with zero-padding and stride as a sparse transformation matrix, applied to a vectorized input through sparse matrix-vector multiplication (SpMV). We provide a theoretical contribution with an explicit expression for the number of non-zero multiplications in convolutions with stride and paddin

Siddhant Agarwal, Harshit Sikchi, Peter Stone, Amy Zhang

Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment without additional interactions. Referred to as "zero-shot learning", this ability remains elusive for general-purpose reinforcement learning algorithms. While recent works have attempted to produce zero-shot RL agent