December 2024 arXiv papers — page 153

Congxi Zhang, Yongchun Xie

When intelligent spacecraft or space robots perform tasks in a complex environment, the controllable variables are usually not directly available and have to be inferred from high-dimensional observable variables, such as outputs of neural networks or images. While the dynamics of these observations are highly complex, the mechanisms behind them may be simpl

Zhen Wan, Chenyang Qi, Zhiheng Liu, Tao Gui

In this paper, we present UniPaint, a unified generative space-time video inpainting framework that enables spatial-temporal inpainting and interpolation. Different from existing methods that treat video inpainting and video interpolation as two distinct tasks, we leverage a unified inpainting framework to tackle them and observe that these two tasks can mut

Yu-En Chien, Marina Fernández-Galán, Ming-Shian Tsai, An-Yuan Liang

Isolated attosecond pulses (IAPs) generated by few-cycle femtosecond lasers are essential for capturing ultrafast dynamics in atoms, molecules, and solids. Nonetheless, the advancement of attosecond science critically depends on achieving stable, high-temporal-contrast IAPs. Our study reveals a universal scenario in which self-compression of the infrared dri

A. A. S. Amad, F. F. Deppisch, M. Fleck, J. Gallop

Next generation tritium decay experiments to determine the absolute neutrino mass require high-precision measurements of $\beta$-decay electron energies close to the kinematic end point. To achieve this, the development of high phase-space density sources of atomic tritium is required, along with the implementation of methods to control the motion of these a

Yirong Cai, Zikai Tang, Hanyuan Deng

Let $G=(V,E)$ be a simple and connected graph. A $h$-order invariant of $G$ based on the path sequence is defined from a set of real numbers ${f(x_{0},x_{1},\cdots,x_{h})}$ as $^{h}I_f(G)=\sum\limits_{v_{0}v_{1}v_{2}\cdots v_{h}}f\left(d_{0},d_{1},\cdots,d_{h}\right)$, where the sum runs over all paths $v_{0}v_{1}v_{2}\cdots v_{h}$ of length $h$ and $d_{i}$

Boyang Zhang, Daning Cheng, Yunquan Zhang, Fangming Liu

Low-rank factorization is a popular model compression technique that minimizes the error $\delta$ between approximated and original weight matrices. Despite achieving performances close to the original models when $\delta$ is optimized, a performance discrepancy remains due to the separate optimization processes for low-rank factorization and model performan

Maryam Ostadsharif Memar, Navid Ziaei, Behzad Nazari

Intracranial EEG (iEEG) recording, characterized by high spatial and temporal resolution and superior signal-to-noise ratio (SNR), enables the development of precise brain-computer interface (BCI) systems for neural decoding. However, the invasive nature of the procedure significantly limits the availability of iEEG datasets in terms of both the number of pa

Jiexi Zhan, Yu Chen, Peng Cheng, Lei Chen

Ridesharing services play an essential role in modern transportation, which significantly reduces traffic congestion and exhaust pollution. In the ridesharing problem, improving the sharing rate between riders can not only save the travel cost of drivers but also utilize vehicle resources more efficiently. The existing online-based and batch-based methods fo

Ilya A. Petrov, Riccardo Marin, Julian Chibane, Gerard Pons-Moll

Modeling 3D human-object interaction (HOI) is a problem of great interest for computer vision and a key enabler for virtual and mixed-reality applications. Existing methods work in a one-way direction: some recover plausible human interactions conditioned on a 3D object; others recover the object pose conditioned on a human pose. Instead, we provide the firs

F. Bredell, H. A. Engelbrecht, J. C. Schoeman

The card game Hanabi is considered a strong medium for the testing and development of multi-agent reinforcement learning (MARL) algorithms, due to its cooperative nature, partial observability, limited communication and remarkable complexity. Previous research efforts have explored the capabilities of MARL algorithms within Hanabi, focusing largely on advanc

Jiawen Kang, Junan Li, Jinchao Li, Xixin Wu

Automatic Speech Recognition (ASR) plays an important role in speech-based automatic detection of Alzheimer's disease (AD). However, recognition errors could propagate downstream, potentially impacting the detection decisions. Recent studies have revealed a non-linear relationship between word error rates (WER) and AD detection performance, where ASR transcr

Ibrahim Delibasoglu, Sanjay Chakraborty, Fredrik Heintz

Time series forecasting is an important challenge with significant applications in areas such as weather prediction, stock market analysis, scientific simulations and industrial process analysis. In this work, we introduce LMS-AutoTSF, a novel time series forecasting architecture that incorporates autocorrelation while leveraging dual encoders operating at m

Qianqian Liu, Yaxian Zhang, Heping Zhang

Klein and Randic (1985) proposed the concept of forcing number, which has an application in chemical resonance theory. Let $G$ be a graph with a perfect matching $M$. The forcing number of $M$ is the smallest cardinality of a subset of $M$ that is contained only in one perfect matching $M$. The maximum forcing number of $G$ is the maximum value of forcing nu

Dexuan Kong, Zaiming Fu, Yujie Deng, Ruiqi Wang

This paper proposes a high-speed transceiver-based method for implementing a digital-to-time converter (DTC). A real-time decoding technique is introduced to inject time information into high-speed pattern data. The stability of the high-speed clock ensures the high precision of the synthesized timing signal without the need for calibration. The reconfigurab

Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran, David Berthelot

Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable

Massimiliano Luca, Bruno Lepri, Riccardo Gallotti, Stefania Paolazzi

We introduce Civic Digital Twin (CDT), an evolution of Urban Digital Twins designed to support a citizen-centric transformative approach to urban planning and governance. CDT is being developed in the scope of the Bologna Digital Twin initiative, launched one year ago by the city of Bologna, to fulfill the city's political and strategic goal of adopting inno

Diego Gutiérrez-Oribio, Ioannis Stefanou

Induced seismicity caused by fluid extraction or injection in underground reservoirs is a major challenge for safe energy production and storage. This paper presents a robust output-feedback controller for induced seismicity mitigation in geological reservoirs described by a coupled 3D PDE-ODE model. The controller is nonlinear and robust (MIMO Super-Twistin

Kevin Zambello, Massimo D'Elia, Lorenzo Maio, Giuseppe Zanichelli

In this work we discuss our preliminary results regarding the so-called Roberge-Weiss (RW) transition, which is found for imaginary values of the baryon chemical potential, in the presence of a background magnetic field. We perform lattice QCD simulations on $N_t = 6, 8$ lattices with $2+1$ flavors of stout-staggered fermions at physical quark masses and the

Jianming Lin, Hui Li, Hongjian Xing, Runhuai Huang

Due to the rapid development of quantum computing, many classical blockchain technologies are now considered insecure. The emergence of quantum blockchain holds promise for addressing this issue. Various quantum consensus algorithms have been proposed so far, but there has not yet been a quantum consensus algorithm tailored specifically for consortium blockc

Mingliang Zhai, Cheng Li, Zengyuan Guo, Ningrui Yang

The Multi-modal Large Language Models (MLLMs) with extensive world knowledge have revitalized autonomous driving, particularly in reasoning tasks within perceivable regions. However, when faced with perception-limited areas (dynamic or static occlusion regions), MLLMs struggle to effectively integrate perception ability with world knowledge for reasoning. Th

Florian Strohm, Mihai Bâce, Andreas Bulling

We present HAIFAI - a novel two-stage system where humans and AI interact to tackle the challenging task of reconstructing a visual representation of a face that exists only in a person's mind. In the first stage, users iteratively rank images our reconstruction system presents based on their resemblance to a mental image. These rankings, in turn, allow the

Mingjie Xu, Mengyang Wu, Yuzhi Zhao, Jason Chun Lok Li

Scene Graph Generation (SGG) converts visual scenes into structured graph representations, providing deeper scene understanding for complex vision tasks. However, existing SGG models often overlook essential spatial relationships and struggle with generalization in open-vocabulary contexts. To address these limitations, we propose LLaVA-SpaceSGG, a multimoda

Andrea Belano, Yvan Tortorella, Angelo Garofalo, Luca Benini

Transformer-based generative Artificial Intelligence (GenAI) models achieve remarkable results in a wide range of fields, including natural language processing, computer vision, and audio processing. However, this comes at the cost of increased complexity and the need of sophisticated non-linearities such as softmax and GELU. Even if Transformers are computa

Fen Zuo

We introduce a quantum strategy from nonlocal games to improve the stabilizer approximation we proposed previously. The resulting approach turns out to be a qubit-by-qubit gauging procedure for standard stabilizers, which could involve discrete or continuous gauge parameters. We take examples from many-body physics and quantum chemistry to show such a proced

Qi Deng, Guanghui Lan, Zhenwei Lin

This paper presents new first-order methods for achieving optimal oracle complexities in convex optimization with convex functional constraints. Oracle complexities are measured by the number of function and gradient evaluations. To achieve this, we enable first-order methods to utilize computational oracles for solving diagonal quadratic programs in subprob

Michele Caselli

For $s \in (0,1)$ small, we show that the only cones in $\mathbb{R}^2$ stationary for the $s$-perimeter and stable in $\mathbb{R}^2 \setminus \{0\}$ are half-planes. This is in direct contrast with the case of the classical perimeter or the regime $s$ close to $1$, where nontrivial cones as $\{xy>0\} \subset \mathbb{R}^2$ are stable for inner variations.

Pavle Pandžić, Ana Prlić, Gordan Savin, Vladimír Souček

In our previous paper, we gave a complete classification of the unitary highest weight modules for the universal covers of the Lie groups $Sp(2n, \mathbb{R}), SO^{*}(2n)$ and $SU(p, q)$, using the Dirac inequality and the so called PRV product. In this paper, we complete the classification of the unitary highest weight modules for the remaining cases; i.e.,

Kevin Buchin, Carolin Rehs, Torben Scheele

Given a point set $P$ in the Euclidean space, a geometric $t$-spanner $G$ is a graph on $P$ such that for every pair of points, the shortest path in $G$ between those points is at most a factor $t$ longer than the Euclidean distance between those points. The value $t\geq 1$ is called the dilation of $G$. Commonly, the aim is to construct a $t$-spanner with a

G. A. Diamandis, K. Kaskavelis, A. B. Lahanas, G. Pavlopoulos

We consider gravitationally induced corrections to inflaton potentials driven by supersymmetry breaking in a five-dimensional supergravity, compactified on a $ S_1/Z_2 $ orbifold. The supersymmetry breaking takes place on the hidden brane and is transmitted to the visible brane through finite one loop graphs giving rise to an inflaton potential which include

Yijie Dang, Weijun Ma, Xiaohu Luo, Huaizhu Wang

Since the outbreak of the COVID-19 pandemic in 2019, medical imaging has emerged as a primary modality for diagnosing COVID-19 pneumonia. In clinical settings, the segmentation of lung infections from computed tomography images enables rapid and accurate quantification and diagnosis of COVID-19. Segmentation of COVID-19 infections in the lungs poses a formid

Junqiao Wang, Zhongliang Yu, Dong Zhou, Jiaqi Shi

The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to-end policy designed to address the chal

Shenghao Zhu, Yifei Chen, Shuo Jiang, Weihong Chen

Neurogliomas are among the most aggressive forms of cancer, presenting considerable challenges in both treatment and monitoring due to their unpredictable biological behavior. Magnetic resonance imaging (MRI) is currently the preferred method for diagnosing and monitoring gliomas. However, the lack of specific imaging techniques often compromises the accurac

Carla Davesa Sureda, Joan Espasa Arxer, Ian Miguel, Mateu Villaret Auselle

Planning is a fundamental activity, arising frequently in many contexts, from daily tasks to industrial processes. The planning task consists of selecting a sequence of actions to achieve a specified goal from specified initial conditions. The Planning Domain Definition Language (PDDL) is the leading language used in the field of automated planning to model

Jinhong Li, Jicai Liu, Jinhong You, Riquan Zhang

We propose a new class of metrics, called the survival independence divergence (SID), to test dependence between a right-censored outcome and covariates. A key technique for deriving the SIDs is to use a counting process strategy, which equivalently transforms the intractable independence test due to the presence of censoring into a test problem for complete

Damiano Lombardi, Cecilia Pagliantini

Nonconservative evolution problems describe irreversible processes and dissipative effects in a broad variety of phenomena. Such problems are often characterised by a conservative part, which can be modelled as a Hamiltonian term, and a nonconservative part, in the form of gradient flow dissipation. Traditional numerical approximations of this class of probl

Qingwei Jiang, Shun Han, Mingliang Xiong, Mengyuan Xu

Meeting the large bandwidth demands of wireless communication for mobile Internet of Things (IoT) devices while enhancing their endurance is a significant challenge. Simultaneous lightwave information and power transfer (SLIPT) technology offers the potential to realize wireless charging and signal transfer, making it suitable for supporting autonomous vehic

Chonggang Song, Chunxu Shen, Hao Gu, Yaoming Wu

Real-world recommendation systems commonly offer diverse content scenarios for users to interact with. Considering the enormous number of users in industrial platforms, it is infeasible to utilize a single unified recommendation model to meet the requirements of all scenarios. Usually, separate recommendation pipelines are established for each distinct scena

Markus Borg, Amogha Udayakumar, Adam Tornhill

Software maintainability is essential for long-term success in the software industry. Despite widespread evidence of the high costs associated with poor maintainability, market pressure drives many organizations to prioritize short-term releases. This focus leads to accumulating technical debt worldwide. In this preliminary work, we propose maintainability g

Zi-Wei Sun, Ze-Xi hua, Heng-Chao Li, Yan Li

In order to avoid the impact of hard samples on the training process of the Flying Bird Object Detection model (FBOD model, in our previous work, we designed the FBOD model according to the characteristics of flying bird objects in surveillance video), the Self-Paced Learning strategy with Easy Sample Prior Based on Confidence (SPL-ESP-BC), a new model train

Ishwar B Balappanawar, Bhargav Srinivas Kommireddy

An exercise in implementing Scale Invariant Feature Transform using CKKS Fully Homomorphic encryption quickly reveals some glaring limitations in the current FHE paradigm. These limitations include the lack of a standard comparison operator and certain operations that depend on it (like array max, histogram binning etc). We also observe that the existing sol

Yuzhong Cheng, Hiroki Masuda

This paper considers estimating the parameters in a regime-switching stochastic differential equation(SDE) driven by Normal Inverse Gaussian(NIG) noise. The model under consideration incorporates a continuous-time finite state Markov chain to capture regime changes, enabling a more realistic representation of evolving market conditions or environmental facto

Boyang Zhang, Daning Cheng, Yunquan Zhang, Jiake Tian

Post-Training Quantization (PTQ) converts pre-trained Full-Precision (FP) models into quantized versions without training. While existing methods reduce size and computational costs, they also significantly degrade performance and quantization efficiency at extremely low settings due to quantization noise. We introduce a deep model series expansion framework

K. Topolnicki, R. Skibiński, J. Golak

We present a novel approach to calculating theoretical uncertainties in few-nucleon calculations, making use of automatic differentiation via backpropagation, which is particularly efficient when there are many input variables but only a few outputs. The methods described in this paper constitute tools that can be used to investigate the properties of scalar

Lincan Li, Jiaqi Li, Catherine Chen, Fred Gui

In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection. Meanwhile, the need to systematically understand how LLMs can further revolutionize the field also becomes urgent. In this work, we--a multidisciplinary team

Hyowon Cho, Soonwon Ka, Daechul Park, Jaewook Kang

Large language models (LLMs) often struggle to objectively identify latent characteristics in large datasets due to their reliance on pre-trained knowledge rather than actual data patterns. To address this data grounding issue, we propose Data Scientist AI (DSAI), a framework that enables unbiased and interpretable feature extraction through a multi-stage pi

Takaaki Nomura, Kei Yagyu

We discuss triple $Z'$ boson signatures via the decay chain of $Z \to Z' \phi \to Z' Z' Z'$, with a new light scalar $\phi$, at future Z factories such as CEPC and FCC-ee. These new bosons $\phi$ and $Z'$ naturally appear in models with a new $U(1)$ gauge symmetry which is spontaneously broken and introduced in various new physics scenarios. The branching ra

Qiyuan Gao, Dan Lin, Hongkai Liu, Teng Ma

The Electron-Ion Collider in China (EicC), a proposed high-luminosity facility with advanced charged particle and photon detection capabilities, provides unique opportunities to uncover new physics beyond the Standard Model. We analyze its sensitivity to dark photons produced through electron bremsstrahlung in coherent scattering. Thanks to its beam energy s

Jinbo Yan, Rui Peng, Luyang Tang, Ronggang Wang

Reconstructing dynamic scenes from video sequences is a highly promising task in the multimedia domain. While previous methods have made progress, they often struggle with slow rendering and managing temporal complexities such as significant motion and object appearance/disappearance. In this paper, we propose SaRO-GS as a novel dynamic scene representation

Joscha Knolle

Two recent searches for heavy neutral leptons (HNLs) performed with proton-proton collision data recorded at 13 TeV by the CMS experiment are presented. A prompt search in the trilepton final state analyses events with exactly three charged leptons originating from the primary proton-proton interaction vertex, targeting HNL masses between 10 GeV and 1.5 TeV.

Joscha Knolle for the ATLAS, CMS Collaborations

Recent results from the ATLAS and CMS experiments in searches for prompt signatures of feebly interacting particles are presented. All presented results are based on the 2015-2018 data set of $13\,\mathrm{TeV}$ proton-proton collisions, corresponding to an integrated luminosity of about $140\,\mathrm{fb}^{-1}$. The discussed models include dark mesons, heavy

Sifei Li, Binxin Yang, Chunji Yin, Chong Sun

Video-to-music generation presents significant potential in video production, requiring the generated music to be both semantically and rhythmically aligned with the video. Achieving this alignment demands advanced music generation capabilities, sophisticated video understanding, and an efficient mechanism to learn the correspondence between the two modaliti

Yunpeng Liu, Boxiao Liu, Yi Zhang, Xingzhong Hou

Significant advances have been made in the sampling efficiency of diffusion models and flow matching models, driven by Consistency Distillation (CD), which trains a student model to mimic the output of a teacher model at a later timestep. However, we found that the learning complexity of the student model varies significantly across different timesteps, lead

Louis Milliken, Sungmin Kang, Shin Yoo

Many works have recently proposed the use of Large Language Model (LLM) based agents for performing `repository level' tasks, loosely defined as a set of tasks whose scopes are greater than a single file. This has led to speculation that the orchestration of these repository-level tasks could lead to software engineering agents capable of performing almost i

Qifan Yu, Zhebei Shen, Zhongqi Yue, Yang Wu

Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to handle real-world tasks. However, the rapid expansion of visual instruction datasets introduces data redundancy, leading to excessive computational costs. We propose a collaborative framework, DataTailor, which leverages three key principles--informativeness, uniqueness, a

Bingchen Gong, Diego Gomez, Abdullah Hamdi, Abdelrahman Eldesokey

We propose a novel zero-shot approach for keypoint detection on 3D shapes. Point-level reasoning on visual data is challenging as it requires precise localization capability, posing problems even for powerful models like DINO or CLIP. Traditional methods for 3D keypoint detection rely heavily on annotated 3D datasets and extensive supervised training, limiti

Jian-Guo Liu, Yuliang Wang

In many real-world scenarios, the underlying random fluctuations are non-Gaussian, particularly in contexts where heavy-tailed data distributions arise. A typical example of such non-Gaussian phenomena calls for L\'evy noise, which accommodates jumps and extreme variations. We propose the Random Batch Method for interacting particle systems driven by L\'evy

Yanbiao Li, Hui Zou, Yuxuan Chen, Yinbo Xu

On top of the Resource Public Key Infrastructure (RPKI), the Route Origin Authorization (ROA) creates a cryptographically verifiable binding of an autonomous system to a set of IP prefixes it is authorized to originate. By their design, ROAs can protect the inter-domain routing system against prefix and sub-prefix hijacks. However, it is hard for the state-o

Naser Sadeghnezhad

In the present work, we seek for static spherically symmetric solutions representing wormhole configurations in generalized Rastall gravity (GRG). In this theory, a varying coupling parameter could act as dark energy (DE) and thus, it can be considered as responsible for the current accelerated expansion of the universe. We consider an anisotropic energy mom

Xinyu Yang, Jixuan Leng, Geyang Guo, Jiawei Zhao

Current PEFT methods for LLMs can achieve either high quality, efficient training, or scalable serving, but not all three simultaneously. To address this limitation, we investigate sparse fine-tuning and observe a remarkable improvement in generalization ability. Utilizing this key insight, we propose a family of Structured Sparse Fine-Tuning (S$^{2}$FT) met

Diksha Goel, Hussain Ahmad, Ankit Kumar Jain, Nikhil Kumar Goel

The increasing reliance on smartphones for communication, financial transactions, and personal data management has made them prime targets for cyberattacks, particularly smishing, a sophisticated variant of phishing conducted via SMS. Despite the growing threat, traditional detection methods often struggle with the informal and evolving nature of SMS languag

Qian Zhang, Panfeng Chen, Jiali Li, Linkun Feng

The emergence of Large Language Models (LLMs) in the medical domain has stressed a compelling need for standard datasets to evaluate their question-answering (QA) performance. Although there have been several benchmark datasets for medical QA, they either cover common knowledge across different departments or are specific to another department rather than pe

Patrick Ramos, Nicolas Gonthier, Selina Khan, Yuta Nakashima

Object detection in art is a valuable tool for the digital humanities, as it allows for faster identification of objects in artistic and historical images compared to humans. However, annotating such images poses significant challenges due to the need for specialized domain expertise. We present NADA (no annotations for detection in art), a pipeline that lev

Meng Zhang, Jun Li

Achieving efficient, high-fidelity, high-resolution garment simulation is challenging due to its computational demands. Conversely, low-resolution garment simulation is more accessible and ideal for low-budget devices like smartphones. In this paper, we introduce a lightweight, learning-based method for garment dynamic super-resolution, designed to efficient

Jianhua Yao, Yuxin Dong, Jiajing Wang, Bingxing Wang

This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectively. The paper discusses the construction

Semyon Petrov, Alexander Okhotin

This paper establishes a lower bound on the number of states necessary in the worst case to simulate an $n$-state two-way nondeterministic finite automaton (2NFA) by a one-way unambiguous finite automaton (UFA). It is proved that for every $n$, there is a language recognized by an $n$-state 2NFA that requires a UFA with at least $\sum_{k=1}^{n} (k - 1)! \cdo

Yu-Xuan Chen, Jing Sun, Bo-Qi Meng

Conventional electromagnetic induction-based current transformers suffer from issues such as bulky and complex structures, slow response times, and low safety levels. Consequently, researchers have explored combining various sensing technologies with optical fibers to develop optical current transformers that could become the primary choice for power systems

Jeyapradhap Thirisangu, Anjan Mahapatra, Karthick Subramani

In the field of acoustic suspension or levitation of droplets against gravity, the application of Gorkov's acoustic radiation force for small particles (within the Rayleigh limit) or its extensions to larger ones (beyond the Rayleigh limit) is limited to predicting the suspension position of the droplet. Since this approach treats the droplet as a rigid part

Nguyen Thi Anh Hang, Le Thanh Nhan

Let $(R, \frak m)$ be a Noetherian local ring. This paper deals with the annihilator of Artinian local cohomology modules $H^i_{\frak m}(M)$ in the relation with the structure of the base ring $R$, for non negative integers $i$ and finitely generated $R$-modules $M$. Firstly, the catenarity and the unmixedness of local rings are characterized via the compati

Qian Li, Ziang Yang, Dou Li, Hongliang Zhang

Distributed phased Multiple-Input Multiple-Output (phased-MIMO) radar systems have attracted wide attention in target detection and tracking. However, the phase-shifting circuits in phased subarrays contribute to high power consumption and hardware cost. To address this issue, an energy-efficient and cost-efficient metamaterial antenna array, i.e., reconfigu

Jing Zhang, Tianchen Sun, Mai Ji, Anirudh R. Ramaseshan

Integrated photonics platforms offer a compact and scalable solution for developing next-generation optical technologies. For precision applications involving weak signals, the responsivity as well as the accurate calibration of the integrated photodetectors at low optical powers become increasingly important. It remains challenging to perform a calibration

Yanjun Guo, Nicholas Storm, Maria Bergemann, Jianhui Lian

Accurate measurements of europium abundances in cool stars are essential for an enhanced understanding of the r-process mechanisms. We measure the abundance of Eu in solar spectra and a sample of metal-poor stars in the Galactic halo and metal-poor disk, with the metallicities ranging from \GG{$-2.4$} to $-0.5$ dex, using non-local thermodynamic equilibrium

Yuanyang Zhou, Huaxin He, Fengtao Pang, Hao Lyu

Semiconductor quantum dots offer a promising platform for controlling spin qubits and realizing quantum logic gates, essential for scalable quantum computing. In this work, we utilize a variational quantum compiling algorithm to design efficient three-qubit gates using a time-independent Hamiltonian composed of only physical interaction terms. The resulting

Guanghui Song, Meiru Gao, Ying Li, Bin Dai

A novel framework for performance analysis and code design is proposed to address the sneak path (SP) problem in resistive random-access memory (ReRAM) arrays. The main idea is to decompose the ReRAM channel, which is both non-ergodic and data-dependent, into multiple stationary memoryless channels. A finite-length performance bound is derived by analyzing t

Patrick Rein, Stefan Ramson, Tom Beckmann, Robert Hirschfeld

Live programming features can be found in a range of programming environments, from individual prototypes to widely used environments. While liveness is generally considered a useful property, there is little empirical evidence on when and how liveness can be beneficial. Even though there are few experimental studies, their results are largely inconclusive.

Dongxu Wei, Zhiqi Li, Peidong Liu

Prior works employing pixel-based Gaussian representation have demonstrated efficacy in feed-forward sparse-view reconstruction. However, such representation necessitates cross-view overlap for accurate depth estimation, and is challenged by object occlusions and frustum truncations. As a result, these methods require scene-centric data acquisition to mainta

Jiuzhou Han, Paul Burgess, Ehsan Shareghi

Large Language Models (LLMs) have demonstrated strong potential across legal tasks, yet the problem of legal citation prediction remains under-explored. At its core, this task demands fine-grained contextual understanding and precise identification of relevant legislation or precedent. We introduce the AusLaw Citation Benchmark, a real-world dataset comprisi

Deepthy Rose Jose, Venkataseshan Sundaram, M Manivannan

Neonatal echocardiography is vital for early detection of heart anomalies in newborns, enabling timely, non-invasive interventions where 4D ultrasound, adds the dimension of time to 3D imaging, enhances diagnostic capabilities by visualizing real-time heart dynamics. However, training for 4D neonatal echocardiography is limited by the lack of simulators that

Boburjon Mukhamedov, Ferenc Tasnadi, Igor A. Abrikosov

Traditionally, alloying and thermal treatment are considered as the main tools for design of new materials. Application of first-principles simulations can significantly accelerate the process of materials design, however, to account for both, multicomponent chemical disorder and finite temperature effects in theoretical simulations is a challenging task. In

Jonathan Edwards, Tomas Petricek, Tijs van der Storm, Geoffrey Litt

Many improvements to programming have come from shortening feedback loops, for example with Integrated Development Environments, Unit Testing, Live Programming, and Distributed Version Control. A barrier to feedback that deserves greater attention is Schema Evolution. When requirements on the shape of data change then existing data must be migrated into the

Jiuyi Xu, Meida Chen, Andrew Feng, Zifan Yu

In the domain of the U.S. Army modeling and simulation, the availability of high quality annotated 3D data is pivotal to creating virtual environments for training and simulations. Traditional methodologies for 3D semantic and instance segmentation, such as KpConv, RandLA, Mask3D, etc., are designed to train on extensive labeled datasets to obtain satisfacto

Jiachen Zhong, Xinliang Xu

When placed in flows with local shear, flagellated bacteria commonly display reorientations towards the local vorticity direction, a chirality-induced rheotactic behavior of great importance for many biological functions. As the observed reorientational dynamics arises from the interplay between the Jeffery dynamics controlled by the cell aspect ratio and th

Shi-fan Qi, Jun Jing

Quantum battery concerns about population redistribution and energy dispatch over controllable quantum systems. Under unitary transformation, ergotropy rather than energy plays an essential role in describing the accumulated useful work. Thus, the charging and recharging of quantum batteries are distinct from the electric-energy input and reuse of classical

Kentaroh Toyoda, Xiao Wang, Mingzhe Li, Bo Gao

Blockchain data analysis is essential for deriving insights, tracking transactions, identifying patterns, and ensuring the integrity and security of decentralized networks. It plays a key role in various areas, such as fraud detection, regulatory compliance, smart contract auditing, and decentralized finance (DeFi) risk management. However, existing blockcha

Seungeun Lee, Kihwan Lee, Subin Bae, Sangjun Lee

Deep tabular models should ideally balance predictive performance, parameter efficiency, and robustness to imperfect learning signals---properties that are rarely considered jointly. We present Table2Image, a lightweight tabular learning model built around a learned generation pathway that maps tabular inputs into intermediate, structured proxy representatio

Yaron Lipman, Marton Havasi, Peter Holderrieth, Neta Shaul

Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including image, video, audio, speech, and biological structures. This guide offers a comprehensive and self-contained review of FM, covering its mathematical foundations, design choices, and extensions. By also providing a

Lianyu Hu, Liqing Gao, Fanhua Shang, Liang Wan

Recent methods have made notable progress in accelerating Large Vision-Language Models (LVLMs) by exploiting the inherent redundancy in visual inputs. Most existing approaches, however, focus narrowly on reducing image tokens before or within the Large Language Model (LLM) stage to lower computational cost. This overlooks other major bottlenecks, particularl

Quansong He, Xiaojun Yao, Jun Wu, Zhang Yi

In recent years, advanced U-like networks have demonstrated remarkable performance in medical image segmentation tasks. However, their drawbacks, including excessive parameters, high computational complexity, and slow inference speed, pose challenges for practical implementation in scenarios with limited computational resources. Existing lightweight U-like n

Mingyu Chen, Yu Zhang, Zhaoyu Zheng, Yongshang Li

Quantum circuit optimization is essential for improving the performance of quantum algorithms, particularly on Noisy Intermediate-Scale Quantum (NISQ) devices with limited qubit connectivity and high error rates. Pattern matching has proven to be an effective technique for identifying and optimizing subcircuits by replacing them with functionally equivalent,

Jukka Ruohonen, Paul Timmers

The Cyber Resilience Act (CRA) of the European Union (EU) imposes many new cyber security requirements practically to all network-enabled information technology products, whether hardware or software. The paper examines and elaborates the CRA's new requirements for vulnerability coordination, including vulnerability disclosure. Although these requirements ar

N. Salor-Iguiñiz, J. M. Benlloch-Rodríguez, R. Esteve, C. Romo-Luque

This work explores the combination of liquid xenon as a scintillating medium and silicon photomultipliers as a readout in Positron Emission Tomography (PET) for enhanced Time-Of-Flight resolution. We present the results of our first prototype optimized to maximize light collection using high-PDE, VUV-sensitive sensors and to minimize time fluctuations. We re

Li Yin, Calvin Yeung, Qingrui Hu, Jun Ichikawa

Multi-object tracking (MOT) is crucial for various multi-agent analyses such as evaluating team sports tactics and player movements and performance. While pedestrian tracking has advanced with Tracking-by-Detection MOT, team sports like basketball pose unique challenges. These challenges include players' unpredictable movements, frequent close interactions,

Shi Qiu, Binzhu Xie, Qixuan Liu, Pheng-Ann Heng

3D Gaussian Splatting (3DGS) has attracted significant attention for its potential to revolutionize 3D representation, rendering, and interaction. Despite the rapid growth of 3DGS research, its direct application to Extended Reality (XR) remains underexplored. Although many studies recognize the potential of 3DGS for XR, few have explicitly focused on or dem

Qi Jia, Onri J. Benally, Brandon Zink, Delin Zhang

Superparamagnetic tunnel junctions (sMTJs) are emerging as promising components for stochastic units in neuromorphic computing, owing to their tunable random switching behavior. Conventional MTJ control methods, such as spin-transfer torque (STT) and spin-orbit torque (SOT), often require substantial power. Here, we introduce the voltage-controlled exchange

Omer Sen, Bozhidar Ivanov, Christian Kloos, Christoph Zol_

The power grid is a critical infrastructure essential for public safety and welfare. As its reliance on digital technologies grows, so do its vulnerabilities to sophisticated cyber threats, which could severely disrupt operations. Effective protective measures, such as intrusion detection and decision support systems, are essential to mitigate these risks. M

Omer Sen, Yanico Aust, Martin Neumuller, Immanuel Hacker

The modernization of power grid infrastructures necessitates the incorporation of decision support systems to effectively mitigate cybersecurity threats. This paper presents a comprehensive framework based on integrating Attack-Defense Trees and the Multi-Criteria Decision Making method to enhance smart grid cybersecurity. By analyzing risk attributes and op

Sergey Masaev, Georgiy Dorrer, Valentina Vingert, Elena Yakimova

Dublin descriptors are under consideration. It is part of one of the global integration processes between European countries and Russia, which began in 1999. It causes a lot of controversy and approval from different sides. For the sake of clarity, an assessment is being made of the industrial application of the Dublin Descriptors. The assessment is based on

Mrinnoy M. Gohain, Kalyan Bhuyan, Rajnandini Borgohain, Tonmoyee Gogoi

The Frolov black hole (BH) is a charged extension of the Hayward BH, having regularity at the central point $r = 0$ and an asymptotically Schwarzschild form for large values of $r$. Such a BH is parameterized by a length scale parameter, \( \alpha_0 \). In this paper, we analyze the thermodynamic properties, null and timelike geodesics, and shadows of a Frol

Mohan Cui, Penglei Mao, Shuran Sun, Yangfan Zhou

Rust, a popular systems-level programming language, has garnered widespread attention due to its features of achieving run-time efficiency and memory safety. With an increasing number of real-world projects adopting Rust, understanding how to assist programmers in correctly writing unsafe code poses a significant challenge. Based on our observations, the cur

Zheng Chen, Chenming Wu, Zhelun Shen, Chen Zhao

Wide-baseline panoramic images are frequently used in applications like VR and simulations to minimize capturing labor costs and storage needs. However, synthesizing novel views from these panoramic images in real time remains a significant challenge, especially due to panoramic imagery's high resolution and inherent distortions. Although existing 3D Gaussia

Nicolas Bougie, Narimasa Watanabe

The peer review process is fundamental to scientific progress, determining which papers meet the quality standards for publication. Yet, the rapid growth of scholarly production and increasing specialization in knowledge areas strain traditional scientific feedback mechanisms. In light of this, we introduce Generative Agent Reviewers (GAR), leveraging LLM-em