November 2024 arXiv papers — page 94

Riku Imamura, Shun Fujii, Keigo Nagashima, Takasumi Tanabe

This study explores sol-gel methods for fabricating erbium-doped silica microtoroid resonators, addressing the limitations of conventional doping techniques and enhancing device scalability. We develop a reproducible sol-gel process that yields defect-free films for photonic applications, and detail common defects and troubleshooting strategies. Two fabricat

Duc-Ly Vu, Trevor Dunlap, Karla Obermeier-Velazquez, Thanh-Cong Nguyen

Malicious attacks on open-source software packages are a growing concern. The discovery of the XZ Utils backdoor intensified these concerns because of the potential widespread impact. This study, therefore, explores the challenges of preventing and detecting malware in Linux distribution package repositories. To do so, we ask two research questions: (1) What

Ziyue Zeng, Haoyuan Liu, Dingjie Peng, Luoxu Jing

Currently, high-fidelity text-to-image models are developed in an accelerating pace. Among them, Diffusion Models have led to a remarkable improvement in the quality of image generation, making it vary challenging to distinguish between real and synthesized images. It simultaneously raises serious concerns regarding privacy and security. Some methods are pro

Kunle Adegoke, Robert Frontczak

Two classes of infinite series involving harmonic numbers and the binomial coefficient $C(3n,n)$ are evaluated in closed form using integrals. Several remarkable integral values and difficult series identities are stated as special cases of the main results.

Dominik Strutz, Andrew Curtis

Monitoring the seismic activity of volcanoes is crucial for hazard assessment and eruption forecasting. The layout of each seismic network determines the information content of recorded data about volcanic earthquakes, and experimental design methods optimise sensor locations to maximise that information. We provide a code package that implements Bayesian ex

Neftalem Negussie, Addis Yesserie, Chinchu Harris, Abou Keita

Over the past few decades, floods have become one of the costliest natural hazards and losses have sharply escalated. Floods are an increasing problem in urban areas due to increased residential settlement along the coastline and climate change is a contributing factor to this increased frequency. In order to analyze flood risk, a model is proposed to identi

Jianfeng Hou, Shufei Wu, Yuanyuan Zhong

A bisection of a graph is a bipartition of its vertex set such that the two resulting parts differ in size by at most 1, and its size is the number of edges that connect vertices in the two parts. The perfect matching condition and forbidden even cycles subgraphs are essential in finding large bisections of graphs. In this paper, we show that the perfect mat

Max Grody, Sandeep Bansal, Huthaifa I. Ashqar

Daily fantasy baseball has shortened the life cycle of an entire fantasy season into a single day. As of today, it has become familiar with more than 10 million people around the world who participate in online fantasy. As daily fantasy continues to grow, the importance of selecting a winning lineup becomes more valuable. The purpose of this paper is to dete

Kunwei Lv, Ruobing Wu, Suyang Chen, Ping Lan

Fire incidents in urban and forested areas pose serious threats,underscoring the need for more effective detection technologies. To address these challenges, we present CCi-YOLOv8n, an enhanced YOLOv8 model with targeted improvements for detecting small fires and smoke. The model integrates the CARAFE up-sampling operator and a context-guided module to reduc

Aria Yom, Nathan E. Lewis

Transposons are small, self-replicating DNA sequences found in every branch of life. Often, one transposon will parasitize another, forming a tiny intracellular ecosystem. In some species these ecosystems thrive, while in others they go extinct, yet little is known about when or why this occurs. Here, we present a stochastic model for these ecosystems and di

Feng Tian, Yilan Wang, Wendi Huang, Xuan Fang

Density-integrated, multi-wavelength nanoscale lasers with ultra-low power consumption and ultra-compact footprints are essential for energy-efficient, fast and high-throughput data processing. Currently, on-chip multi-wavelength lasers predominantly rely on arrays of discrete large-scale conventional semiconductor lasers that are susceptible to the fabricat

Tianyi Liang, Dong Wu, Lifeng Wang, Lianqiang Shan

In laser-driven indirect inertial confinement fusion (ICF), a hohlraum--a cavity constructed from high-Z materials--serves the purpose of converting laser energy into thermal x-ray energy. This process involves the interaction of low-density ablated plasmas, which can give rise to weakly collisional shock waves characterized by a Knudsen number $K_n$ on the

Evangelos Koutsonas, Alexandros-Apostolos A. Boulogeorgos, Stylianos E. Trevlakis, Tanweer Ali

While the wireless word moves towards higher frequency bands, new challenges arises, due to the inherent characteristics of the transmission links, such as high path and penetration losses. Penetration losses causes blockages that in turn can significantly reduce the signal strength at the receiver. Most published contributions consider a binary blockage sta

Haiyang Yu, Tian Xie, Jiaping Gui, Pengyang Wang

Over the past few years, the emergence of backdoor attacks has presented significant challenges to deep learning systems, allowing attackers to insert backdoors into neural networks. When data with a trigger is processed by a backdoor model, it can lead to mispredictions targeted by attackers, whereas normal data yields regular results. The scope of backdoor

Ishan Pandey, Gokul A, Varun Raghunathan

In Measurement-Device-Independent Quantum Key Distribution (MDI-QKD), key rates are significantly reduced due to dispersion in asymmetric channels. This work addresses this issue by utilizing intensity and phase modulators for dispersion compensation, thus avoiding the additional losses associated with dispersion-compensating fibers (DCFs). This approach enh

Wanli Xing, Shijie Lin, Linhan Yang, Zeqing Zhang

This paper presents EROAM, a novel event-based rotational odometry and mapping system that achieves real-time, accurate camera rotation estimation. Unlike existing approaches that rely on event generation models or contrast maximization, EROAM employs a spherical event representation by projecting events onto a unit sphere and introduces Event Spherical Iter

Erkut Akdag, Egor Bondarev, Peter H. N. De With

Anomaly detection in video surveillance has recently gained interest from the research community. Temporal duration of anomalies vary within video streams, leading to complications in learning the temporal dynamics of specific events. This paper presents a temporal-granularity method for an anomaly detection model (TeG) in real-world surveillance, combining

Nayeon Kim, Hongje Seong, Daehyun Ji, Sujin Jang

Predicting and constructing road geometric information (e.g., lane lines, road markers) is a crucial task for safe autonomous driving, while such static map elements can be repeatedly occluded by various dynamic objects on the road. Recent studies have shown significantly improved vectorized high-definition (HD) map construction performance, but there has be

Hongrui Jia, Chaoya Jiang, Haiyang Xu, Wei Ye

As language models continue to scale, Large Language Models (LLMs) have exhibited emerging capabilities in In-Context Learning (ICL), enabling them to solve language tasks by prefixing a few in-context demonstrations (ICDs) as context. Inspired by these advancements, researchers have extended these techniques to develop Large Multimodal Models (LMMs) with IC

Humam Kourani, Alessandro Berti, Daniel Schuster, Wil M. P. van der Aalst

Large Language Models (LLMs) are rapidly transforming various fields, and their potential in Business Process Management (BPM) is substantial. This paper assesses the capabilities of LLMs on business process modeling using a framework for automating this task, a comprehensive benchmark, and an analysis of LLM self-improvement strategies. We present a compreh

Praveen Murali, Sadhana Dash, Basanta Kumar Nandi

A deep learning based method with Convolutional Neural Network (CNN) algorithm is developed for simultaneous determination of the Elliptic Flow coefficient ($v_{2}$) and the Impact Parameter in Heavy-Ion Collisions at relativistic energies. The proposed CNN is trained on Pb$-$Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV with minimum biased events simulated wi

Jing Lu, Brandon L. Barker, Jared Goldberg, Wolfgang E. Kerzendorf

Stripped-envelope supernovae (SESNe) represent a significant fraction of core-collapse supernovae, arising from massive stars that have shed their hydrogen and, in some cases, helium envelopes. The origins and explosion mechanisms of SESNe remain a topic of active investigation. In this work, we employ radiative-transfer simulations to model the light curves

Chuwen Ma, Shi Jin, Nana Liu, Kezhen Wang

The Schr\"odingerisation method combined with the autonomozation technique in \cite{cjL23} converts general non-autonomous linear differential equations with non-unitary dynamics into systems of autonomous Schr\"odinger-type equations, via the so-called warped phase transformation that maps the equation into two higher dimension. Despite the success of Schr\

Yanran Wang, Yichun Tang, Jing Du, Mike Hillman

Stress distributions and the corresponding fracture patterns and evolutions in the microstructures strongly influence the load-carrying capabilities of composite structures. This work introduces an enhanced phase-field fracture model incorporating interface decohesion to simulate fracture propagation and interactions at material interfaces and within the con

Wanxin Gao, Ioanis Nikolaidis, Janelle Harms

We consider the problem of learning models of spatial density functions, representing the steady-state density of mobile nodes moving on a two-dimensional terrain. Deriving such models can assist in network design and optimization problems, e.g., by accelerating the computation of the density function during a parameter sweep. We address the question of appl

Xinyu Mao, Guangxu Yang, Jiapeng Zhang

We prove an \Omega(n/k+k) communication lower bound on (k-1)-round distributional complexity of the k-step pointer chasing problem under uniform input distribution, improving the \Omega(n/k - k log n) lower bound due to Yehudayoff (Combinatorics Probability and Computing, 2020). Our lower bound almost matches the upper bound of O(n/k + k) communication by Ni

Francesco Schiliro

The paper examines the trajectory of crime, tracing its evolution from traditional forms to digital manifestations in cybercrime, and proposes "Hypercrime" as the latest frontier. Leveraging insights from Michael McGuire's "Hypercrime: The New Geometry of Harm," the study calls for a paradigm shift in law enforcement strategies to meet the challenges posed b

Akitoshi Shiotari, Shuyi Liu, George Trenins, Toshiki Sugimoto

We report on tip-enhanced Raman spectroscopy of H2 and D2 molecules physisorbed within a plasmonic picocavity at 10 K. The intense Raman peaks resulting from the rotational and vibrational transitions are observed at subnanometer gap distances of the junction formed by an Ag tip and an Ag(111) surface, where a picocavity-enhanced field plays a crucial role.

Akira Dohi, Nobuya Nishimura, Ryosuke Hirai, Tomoshi Takeda

In February and March 2024, a series of many Type I X-ray bursts from the accreting neutron star SRGA J144459.2$-$604207, which has been identified by multiple X-ray satellites, with the first reports coming from INTEGRAL and NinjaSat. These observations reveal that after exhibiting very regular behavior as a ``clocked'' burster, the peak luminosity of the S

Tomoshi Takeda, Toru Tamagawa, Teruaki Enoto, Takao Kitaguchi

The CubeSat X-ray observatory NinjaSat was launched on 2023 November 11 and has provided opportunities for agile and flexible monitoring of bright X-ray sources. On 2024 February 23, the NinjaSat team started long-term observation of the new X-ray source SRGA J144459.2$-$604207 as the first scientific target, which was discovered on 2024 February 21 and reco

Qinchen Yang, Zhiqing Hong, Dongjiang Cao, Haotian Wang

Textual description of a physical location, commonly known as an address, plays an important role in location-based services(LBS) such as on-demand delivery and navigation. However, the prevalence of abnormal addresses, those containing inaccuracies that fail to pinpoint a location, have led to significant costs. Address rewriting has emerged as a solution t

Zahra Koulaeizadeh, Erhan Oztop

A random recurrent neural network, called a reservoir, can be used to learn robot movements conditioned on context inputs that encode task goals. The Learning is achieved by mapping the random dynamics of the reservoir modulated by context to desired trajectories via linear regression. This makes the reservoir computing (RC) approach computationally efficien

Xiangfei Hu, Yuyang Ye, Tinghuan Chen, Hao Yan

With the shrinking technology nodes, timing optimization becomes increasingly challenging. Approximate logic synthesis (ALS) can perform local approximate changes (LACs) on circuits to optimize timing with the cost of slight inaccuracy. However, existing ALS methods that focus solely on critical path depth reduction (depth-driven methods) or area minimizatio

Ting-Ting Duan, Sahanaa Büriechin, Hai-Ling Lao, Fu-Hu Liu

Utilizing a partitioning method based on equal (or unequal) probabilities -- without incorporating the alpha-cluster ($\alpha$-cluster) model -- allows for the derivation of diverse topological configurations of nuclear fragments resulting from fragmentation. Subsequently, we predict the multiplicity distribution of nuclear fragments for specific excited nuc

Fatemeh Omidian, Athena Abdi

This paper presents a multi-level approximate computing approach for real-time traffic sign recognition in autonomous vehicles called AppSign. Since autonomous vehicles are real-time systems, they must gather environmental information and process them instantaneously to respond properly. However, due to the limited resources of these systems, executing compu

Qiming Fang, Sihong Shao

We establish a geometric framework by transforming a graph $G$ into a $(d-1)$-dimensional CW complex $U^{d-1}(G)$. This construction is achieved by systematically attaching $i$-spheres ($2 \le i \le d-1$) to $G$ according to specific rules, ensuring that the $j$-th homotopy group of $U^{d-1}(G)$ are trivial for $j = 0, 1, \dots, d-2$. Building upon this cons

Alok Shukla, Prakash Vedula

This paper presents a quantum algorithm for efficiently computing partial sums and specific weighted partial sums of quantum state amplitudes. Computation of partial sums has important applications, including numerical integration, cumulative probability distributions, and probabilistic modeling. The proposed quantum algorithm uses a custom unitary construct

Tatsuya Kaneko, Yukinori Ohta

The fundamental idea of the excitonic insulator (EI) driven by electron-hole correlations in narrow-gap semiconductors or semimetals was originally proposed in the 1960s, and only theoretical studies had been advanced for a long time. However, the rise of new candidate materials and recent developments in measurement techniques have enabled us to discuss the

I. Brevik, Maxim Khlopov, S. D. Odintsov, Alexander V. Timoshkin

Interest on the possible future scenarios the universe could have has grew substantially with breakthroughs on late-time acceleration. Holographic dark energy (HDE) presents a very interesting approach towards addressing late-time acceleration, presenting an intriguing interface of ideas from quantum gravity and cosmology. In this work we present an extensiv

Ayan Banerjee, Payal Kamboj, Sandeep Gupta

Precision medicine is a promising approach for accessible disease diagnosis and personalized intervention planning in high-mortality diseases such as coronary artery disease (CAD), drug-resistant epilepsy (DRE), and chronic illnesses like Type 1 diabetes (T1D). By leveraging artificial intelligence (AI), precision medicine tailors diagnosis and treatment sol

Yueyang Shen, Agus Sudjianto, Arun Prakash R, Anwesha Bhattacharyya

We propose and study a minimalist approach towards synthetic tabular data generation. The model consists of a minimalistic unsupervised SparsePCA encoder (with contingent clustering step or log transformation to handle nonlinearity) and XGboost decoder which is SOTA for structured data regression and classification tasks. We study and contrast the methodolog

Taehyun Kim, Minjin Kim, Luis C. Ho, Yang A. Li

Future satellite missions are expected to perform all-sky surveys, thus providing the entire sky near-infrared spectral data and consequently opening a new window to investigate the evolution of galaxies. Specifically, the infrared spectral data facilitate the precise estimation of stellar masses of numerous low-redshift galaxies. We utilize the synthetic sp

Namhwa Lee, Shujie Ma

Estimating treatment effects using observation data often relies on the assumption of no unmeasured confounders. However, unmeasured confounding variables may exist in many real-world problems. It can lead to a biased estimation without incorporating the unmeasured confounding effect. To address this problem, this paper proposes a new mixed-effects joint mod

Yolo Y. Tang, Junjia Guo, Hang Hua, Susan Liang

The advancement of Multimodal Large Language Models (MLLMs) has enabled significant progress in multimodal understanding, expanding their capacity to analyze video content. However, existing evaluation benchmarks for MLLMs primarily focus on abstract video comprehension, lacking a detailed assessment of their ability to understand video compositions, the nua

Fujio Sako, Ryo Ohshima, Yuichiro Ando, Naoya Morioka

Two-dimensional (2D) carrier systems exhibit various significant physical phenomena for electronics and spintronics, where one of the most promising traits is efficient spin-to-charge conversion stemming from their Rashba-type spin-orbit interaction. Meanwhile, a nuisance in quests of promising materials for spintronics application is that vast majority of t

Yingzhe Ban, Jie Chen, Ying Zhang

In this paper, we continue the study of the local well-posedness theory for the Schr\"{o}dinger-KdV system in the Sobolev space $H^{s_1}\times H^{s_2}$. We show the local well-posedness in $H^{-3/16}\times H^{-3/4}$ for $\beta = 0$. Combining our work \cite{banchenzhang}, we also have the local well-posedness for $\max\{-3/4,s_1-3\}\leq s_2\leq \min\{4s_1,s_

A. Karmakar, Nazira Nazir, P. Datta, J. A. Sheikh

The majority of atomic nuclei have deformed shapes and nearly all these shapes are symmetric with respect to reflection. There are only a few reflection asymmetric pear-shaped nuclei that have been found in actinide and lanthanide regions, which have static octupole deformation. These nuclei possess an intrinsic electric dipole moment due to the shift betwee

Yingzhe Ban, Jie Chen, Ying Zhang

In this paper, we study local well-posedness theory of the Cauchy problem for Schr\"{o}dinger-KdV system in Sobolev spaces $H^{s_1}\times H^{s_2}$. We obtain the local well-posedness when $s_1\geq 0$, $\max\{-3/4,s_1-3\}\leq s_2\leq \min\{4s_1,s_1+2\}$. The result is sharp in some sense and improves previous one by Corcho-Linares \cite{corcho2007well}. The e

Mateus Valverde Gasparino, Vitor Akihiro Hisano Higuti, Arun Narenthiran Sivakumar, Andres Eduardo Baquero Velasquez

Small robots that can operate under the plant canopy can enable new possibilities in agriculture. However, unlike larger autonomous tractors, autonomous navigation for such under canopy robots remains an open challenge because Global Navigation Satellite System (GNSS) is unreliable under the plant canopy. We present a hybrid navigation system that autonomous

Kansei Kanayama, Kazuaki Toyoura

The machine learning potential (MLP) based molecular dynamics (MD) method was applied for constructing the pressure-temperature phase diagram in the barium titanate (BaTiO3) crystals. The nuclear quantum effects (NQEs) on the phase transitions were quantitatively evaluated from the difference in the phase transition pressures between the NQEs-incorporated an

Michael J. Plank, Matthew J. Simpson, Ruth E. Baker

Random walks and related spatial stochastic models have been used in a range of application areas including animal and plant ecology, infectious disease epidemiology, developmental biology, wound healing, and oncology. Classical random walk models assume that all individuals in a population behave independently, ignoring local physical and biological interac

Alysha M De Livera, Luke Prendergast, Udara Kumaranathunga

In meta-analysis with continuous outcomes, the use of effect sizes based on the means is the most common. It is often found, however, that only the quantile summary measures are reported in some studies, and in certain scenarios, a meta-analysis of the quantiles themselves are of interest. We propose a novel density-based approach to support the implementati

Shota Goto, Takenobu Nakamura, Davide Michieletto, Kang Kim

Ring polymers are characterized by topology-specific entanglements called threadings. In the limit of large rings, it is conjectured that a "topological glass" should emerge due to the proliferation of threadings. In this study, we used persistent homology to quantify threading structures of ring polymers with different chain stiffness and elucidate mechanis

Yuji Hirota, Noriaki Ikeda

Reduction theorem for Poisson manifolds with Hamiltonian Lie algebroids is presented. The notion of compatibility of a momentum section is introduced to the category of Hamiltonian Lie algebroids over Poisson manifolds. It is shown that a compatible momentum section is a Lie algebra homomorphism, and then the quotient space of the zero level set of a compati

Suchismito Chattopadhyay, Ranjeev Misra, Soma Mandal, Akash Garg

We present the broadband spectral and timing properties of the atoll source 4U 1702-429 using two observations of AstroSat with the second one having simultaneous NICER data. For both observations, the spectra can be represented by a Comptonizing medium with a black body seed photon source which can be identified with the surface of the neutron star. A disk

Lance J. Dixon, Anthony Morales

We show how curing an anomaly of the twistor uplift of self-dual Yang-Mills theory implies linear relations among one-loop, $n$-gluon, color-ordered subamplitudes in QCD, when all $n$ gluon helicities are positive, or when exactly one is negative. We compute the number of linearly independent subamplitudes as determined by these relations, in terms of unsign

Mengyu Ji, Jiahao Shen, Huazi Cao, Shiyu Zhao

Aerial manipulators, composed of multirotors and robotic arms, have a structure and function highly reminiscent of avian species. This paper studies the tracking control problem for aerial manipulators. This paper studies the tracking control problem for aerial manipulators. We propose an avian-inspired aerial manipulation system, which includes an avian-ins

Romeo Ortega, Alexey Bobtsov, Jose Guadalupe Romero, Leyan Fang

Generalized parameter estimation-based observers have proven very successful to deal with systems described in state-affine form. In this paper, we enlarge the domain of applicability of this method proposing an algebraic procedure to immerse} an $n$-dimensional general nonlinear system into and $n_z$-dimensional system in state affine form, with $n_z>n$. Fi

Yongquan Hu, Dongsheng Zheng, Kexin Nie, Junyan Zhang

Content sharing across multiple Augmented Reality (AR) displays is becoming commonplace, enhancing team communication and collaboration through devices like smartphones and AR glasses. However, this practice raises significant privacy concerns, especially concerning the physical environment visible in AR, which may include sensitive personal details like fac

Keito Kikuchi, Jun Ishihara, Miari Hiyama, Sota Yamamoto

Precise control of spatial spin structures, such as spin helices, is critical for advancing spintronic devices, particularly in non-volatile, low-power information storage and processing. Conventional techniques, including transient spin grating spectroscopy and spatial- and time-resolved Kerr rotation microscopy, are limited by fixed optical grating periods

Lei Yang, Xinyu Zhang, Jun Li, Chen Wang

Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby enhancing the safety of autonomous driving. In recent years, a series of cooperative perception datasets h

Xiaodong Liu, Yucheng Xing, Xin Wang

Reliable motion forecasting of surrounding agents is essential for ensuring the safe operation of autonomous vehicles. Many existing trajectory prediction methods rely heavily on high-definition (HD) maps as strong driving priors. However, the availability and accuracy of these priors are not guaranteed due to substantial costs to build, localization errors

Ziwei Liu, Wen Chen, Qingqing Wu, Zhendong Li

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered distributed cooperative integrated sensing and communication (ISAC) network to enhance coverage as well as to enhance wireless environment understanding. Based on the network requirements, the users are categorized into cooperative users (CUEs) and

Jintao Zhang, Haofeng Huang, Pengle Zhang, Jia Wei

Although quantization for linear layers has been widely used, its application to accelerate the attention process remains limited. To further enhance the efficiency of attention computation compared to SageAttention while maintaining precision, we propose SageAttention2, which utilizes significantly faster 4-bit matrix multiplication (Matmul) alongside addit

Sejun Park, Joo Young Park, Hyunwoo Park

This paper addresses domain adaptation challenges in graph data resulting from chronological splits. In a transductive graph learning setting, where each node is associated with a timestamp, we focus on the task of Semi-Supervised Node Classification (SSNC), aiming to classify recent nodes using labels of past nodes. Temporal dependencies in node connections

Guixin Xu, Meirong Zhang, Zhe Zhou

This paper focuses on the asymptotic stability of the spectra of generalized indefinite strings (GISs). A unitarily equivalent linear relation is introduced for GISs. It is shown that the solutions of the corresponding differential equations are continuously dependent on distributions and measures under certain conditions. Using these results, the convergenc

Hanwool Lee, Heehwan Park

This paper presents a new approach to volume ratio prediction in financial markets, specifically targeting the execution of Volume-Weighted Average Price (VWAP) strategies. Recognizing the importance of accurate volume profile forecasting, our research leverages the Transformer architecture to predict intraday volume ratio at a one-minute scale. We diverge f

Da-Chen Lian, Shu-Kai Hsieh

This study aims to fill the gap by constructing a topic-aware comparable corpus of Mainland Chinese Mandarin and Taiwanese Mandarin from the social media in Mainland China and Taiwan, respectively. Using Dcard for Taiwanese Mandarin and Sina Weibo for Mainland Chinese, we create a comparable corpus that updates regularly and reflects modern language use on s

Fahim Faisal, Md Mushfiqur Rahman, Antonios Anastasopoulos

There has been little systematic study on how dialectal differences affect toxicity detection by modern LLMs. Furthermore, although using LLMs as evaluators ("LLM-as-a-judge") is a growing research area, their sensitivity to dialectal nuances is still underexplored and requires more focused attention. In this paper, we address these gaps through a comprehens

Bin Sun, Shaowen Lan, Yi Cao, Jie Liu

We investigate the dynamics of a relativistic spin-$\frac{1}{2}$ particle governed by a one-dimensional time-periodic kicking Dirac equation. We observe distinct oscillatory behavior in the momentum space and quantum tunneling in the vicinity of zero momentum, which is found to be equivalent to the Bloch oscillations and Landau-Zener tunneling, i.e., Bloch-L

Fatih Berkay Sarpkaya, Fraida Fund, Shivendra Panwar

With few exceptions, the path to deployment for any Internet technology requires that there be some benefit to unilateral adoption of the new technology. In an Internet where the technology is not fully deployed, is an individual better off sticking to the status quo, or adopting the new technology? This question is especially relevant in the context of the

Xin Su, Chen Wu, Zhuoran Zheng

Ultra-high-definition (UHD) image restoration is vital for applications demanding exceptional visual fidelity, yet existing methods often face a trade-off between restoration quality and efficiency, limiting their practical deployment. In this paper, we propose TSFormer, an all-in-one framework that integrates \textbf{T}rusted learning with \textbf{S}parsifi

Zeping Yu, Sophia Ananiadou

Understanding the mechanisms behind Large Language Models (LLMs) is crucial for designing improved models and strategies. While recent studies have yielded valuable insights into the mechanisms of textual LLMs, the mechanisms of Multi-modal Large Language Models (MLLMs) remain underexplored. In this paper, we apply mechanistic interpretability methods to ana

Thibaut Horel, Yaron Singer

We study the problem of maximizing a function that is approximately submodular under a cardinality constraint. Approximate submodularity implicitly appears in a wide range of applications as in many cases errors in evaluation of a submodular function break submodularity. Say that $F$ is $\varepsilon$-approximately submodular if there exists a submodular func

Wenjin Guo, Donglai Liu, Weiying Xie, Yunsong Li

Neural network training is a memory- and compute-intensive task. Quantization, which enables low-bitwidth formats in training, can significantly mitigate the workload. To reduce quantization error, recent methods have developed new data formats and additional pre-processing operations on quantizers. However, it remains quite challenging to achieve high accur

Haoyu Wu, Meher Gitika Karumuri, Chuhang Zou, Seungbae Bang

Current image-to-3D approaches suffer from high computational costs and lack scalability for high-resolution outputs. In contrast, we introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D depth and RGB images along with 3D Gaussian features using a repurposed Stable Diffusion model. We introduce a depth br

Mathew George, Bo Guan

Over many decades fully nonlinear PDEs, and the complex Monge-Amp\`ere equation in particular played a central role in the study of complex manifolds. Most previous works focused on problems that can be expressed through equations involving real $(1, 1)$ forms. As many important questions, especially those linked to higher cohomology classes in complex geome

Mohammad Kachuee, Sarthak Ahuja, Vaibhav Kumar, Puyang Xu

Using tools by Large Language Models (LLMs) is a promising avenue to extend their reach beyond language or conversational settings. The number of tools can scale to thousands as they enable accessing sensory information, fetching updated factual knowledge, or taking actions in the real world. In such settings, in-context learning by providing a short list of

Inpyo Song, Sanghyeon Lee, Minjun Joo, Jangwon Lee

Recent advancements in computer vision have led to a renewed interest in developing assistive technologies for individuals with visual impairments. Although extensive research has been conducted in the field of computer vision-based assistive technologies, most of the focus has been on understanding contexts in images, rather than addressing their physical s

Arnaldo J. Vargas

A model for the Lorentz- and CPT-violating frequency shift for the antihydrogen $1S$-$2P$ transition in the presence of an external magnetic field is derived. Using the recent measurement of the $1S$-$2P$ transition frequency in antihydrogen by the ALPHA collaboration, which they demonstrated agrees with predictions from the Standard Model of particle physic

Minghe Gao, Wendong Bu, Bingchen Miao, Yang Wu

In this paper, we introduce the Generalist Virtual Agent (GVA), an autonomous entity engineered to function across diverse digital platforms and environments, assisting users by executing a variety of tasks. This survey delves into the evolution of GVAs, tracing their progress from early intelligent assistants to contemporary implementations that incorporate

Gergő Nemes

Simple asymptotic expansions for the Jacobi functions $P_\nu^{(\alpha, \beta)}(z)$ and $Q_\nu^{(\alpha, \beta)}(z)$ for large degree $\nu$, with fixed parameters $\alpha$ and $\beta$, are surprisingly rare in the literature, with only a few special cases covered. This paper addresses this notable gap by deriving simple (inverse) factorial expansions for thes

Derek Fan, David A. Copp

Online trajectory optimization and optimal control methods are crucial for enabling sustainable unmanned aerial vehicle (UAV) services, such as agriculture, environmental monitoring, and transportation, where available actuation and energy are limited. However, optimal controllers are highly sensitive to model mismatch, which can occur due to loaded equipmen

Wei-Hsiang Lien, Benedictus Kent Chandra, Robin Fischer, Ya-Hui Tang

In recent years, with the rapid development of augmented reality (AR) technology, there is an increasing demand for multi-user collaborative experiences. Unlike for single-user experiences, ensuring the spatial localization of every user and maintaining synchronization and consistency of positioning and orientation across multiple users is a significant chal

Hanna Wallach, Meera Desai, Nicholas Pangakis, A. Feder Cooper

Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in mea

Xunmeng Wu, Zai Yang, Zongben Xu

This paper studies the problem of multichannel spectral super-resolution with either constant amplitude (CA) or not. We propose two optimization problems based on low-rank Hankel-Toeplitz matrix factorization. The two problems effectively leverage the multichannel and CA structures, while also enabling the design of low-complexity gradient descent algorithms

Wenjun Hou, Yi Cheng, Kaishuai Xu, Yan Hu

Comprehensively understanding surgical scenes in Surgical Visual Question Answering (Surgical VQA) requires reasoning over multiple objects. Previous approaches address this task using cross-modal fusion strategies to enhance reasoning ability. However, these methods often struggle with limited scene understanding and question comprehension, and some rely on

Ni Ou, Zhuo Chen, Xinru Zhang, Junzheng Wang

Cameras and LiDAR are essential sensors for autonomous vehicles. Camera-LiDAR data fusion compensate for deficiencies of stand-alone sensors but relies on precise extrinsic calibration. Many learning-based calibration methods predict extrinsic parameters in a single step. Driven by the growing demand for higher accuracy, a few approaches utilize multi-range

Hrishikesh Sathyanarayan, Ian Abraham

In this paper, we explore an approach to actively plan and excite contact modes in differentiable simulators as a means to tighten the sim-to-real gap. We propose an optimal experimental design approach derived from information-theoretic methods to identify and search for information-rich contact modes through the use of contact-implicit optimization. We dem

Mika Hämäläinen, Jack Rueter, Khalid Alnajjar

We present a novel digital humanities method for representing our Twitch chatters as user embeddings created by a large language model (LLM). We cluster these embeddings automatically using affinity propagation and further narrow this clustering down through manual analysis. We analyze the chat of one stream by each Twitch streamer: SmallAnt, DougDoug and Po

Peter L. Guo, Zhuowei Lin, Simon C. Y. Peng

We prove a criterion of when the dual character $\chi_{D}(x)$ of the flagged Weyl module associated to a diagram $D$ in the grid $[n]\times [n]$ is zero-one, that is, the coefficients of monomials in $\chi_{D}(x)$ are either 0 or 1. This settles a conjecture proposed by M{\'e}sz{\'a}ros--St. Dizier--Tanjaya. Since Schubert polynomials and key polynomials occ

William Huang, Yifeng Jiang, Tom Van Wouwe, C. Karen Liu

Diffusion models have demonstrated significant promise in various generative tasks; however, they often struggle to satisfy challenging constraints. Our approach addresses this limitation by rethinking training-free loss-guided diffusion from an optimization perspective. We formulate a series of constrained optimizations throughout the inference process of a

Pietro M. Bonetti, Yi Jiang, Haoyu Hu, Dumitru Călugăru

The discovery of a charge density wave in FeGe extends the discussion of the nature of charge order in kagome metals to a magnetic compound. Motivated by this observation, we combine density functional theory (DFT) and functional-renormalization-group calculations to study interaction-induced Fermi-surface instabilities of the magnetic state of FeGe. We argu

Koun Shirai, Hiroyoshi Momida, Kazunori Sato, Sangil Hyun

Despite increasing demands for the thermodynamic data of liquids in a wide range of science and engineering fields, there is a still a considerable lack of reliable data over a wide range of temperature ($T$) and pressure conditions. The most significant obstacle is that there is no practical method to calculate the entropy ($S$) of liquids. This problem can

Ryan Greenough, Kohei Murakami, Jan Kleissl, Adil Khurram

Public Safety Power Shutoffs (PSPS) are a proactive strategy to mitigate fire hazards from power system infrastructure failures. System operators employ PSPS to deactivate portions of the electric grid with heightened wildfire risks to prevent wildfire ignition and redispatch generators to minimize load shedding. A measure of vegetation flammability, called

Wenke Huang, Jian Liang, Zekun Shi, Didi Zhu

Multimodal Large Language Model (MLLM) have demonstrated strong generalization capabilities across diverse distributions and tasks, largely due to extensive pre-training datasets. Fine-tuning MLLM has become a common practice to improve performance on specific downstream tasks. However, during fine-tuning, MLLM often faces the risk of forgetting knowledge ac

Jisang Park, Minu Kim, DaYoung Hong, Jongha Lee

Learners of a second language (L2) often map non-native phonemes to similar native-language (L1) phonemes, making conventional L2-focused training slow and effortful. To address this, we propose an L1-grounded pronunciation training method based on compositional phoneme approximation (CPA), a feature-based representation technique that approximates L2 sounds

Hefan Zhang, Zhiyuan Wang, Shan Zhang, Qingkai Meng

Low earth orbit (LEO) satellite networks have the potential to provide low-latency communication with global coverage. To unleash this potential, it is crucial to achieve efficient packet delivery. In this paper, we propose a Link-identified Routing (LiR) architecture for LEO satellite networks. The LiR architecture leverages the deterministic neighbor relat

Tingyu Shui, Walid Saad, Mingzhe Chen

Cellular vehicle-to-everything (C-V2X) networks provide a promising solution to improve road safety and traffic efficiency. One key challenge in such systems lies in meeting different quality-of-service (QoS) requirements of coexisting vehicular communication links, particularly under imperfect channel state information (CSI) conditions caused by the highly

Priyabrata Karmakar, Manzur Murshed, Shyh Wei Teng

Recently hyperspectral imaging (HSI)-based grain quality assessment has gained research attention. However, unlike other imaging modalities, HSI data lacks sufficient labelled samples required to effectively train deep convolutional neural network (DCNN)-based classifiers. In this paper, we present a novel approach to grain quality assessment using HSI combi