December 2024 arXiv papers — page 98

Gian Oré, Jhonnatan Yepes, Juliana A. Góes, Luciano P. Oliveira

The paper introduces a novel approach for estimating soil moisture in vegetated surfaces, specifically focusing on sugarcane crops throughout various growth stages in agriculture applications. While existing models typically address bare soil scenarios, this model utilizes data from P-, L-, and C-band Synthetic Aperture Radar (SAR) to estimate soil moisture.

Cyrille Armel Sayou Ngomsi, Sai Krishna Narayanan, Pratibha Dev

Silicon carbide (SiC) hosts a number of point defects that are being explored as single-photon emitters for quantum applications. Unfortunately, these quantum emitters lose their photostability when placed in proximity to the surface of the host semiconductor. In principle, a uniform passivation of the surface's dangling bonds by simple adsorbates, such as h

José M. Campos-Salazar, Juan L. Aguayo-Lazcano, Roya Rafiezadeh

This study presents a comprehensive analysis of a two-level battery charger for electric vehicles, focusing on modeling, simulation, and performance evaluation. The proposed charger topology employs two switches operating complementarily, along with essential components such as inductors, capacitors, and batteries. Detailed modeling in steady-state and dynam

Tian Tang, Yu Duan, Yu-qiang Ma

We study a binary metric-free Vicsek model involving two species of self-propelled particles aligning with their Voronoi neighbors, focusing on a weakly nonreciprocal regime, where species $A$ aligns with both $A$ and $B$, but species $B$ does not align with either. Using agent-based simulations, we find that even with a small fraction of $B$ particles, the

Anna Logioti

We consider a Kuramoto-Shivashinsky like equation close to the threshold of instability with additive white noise and spatially periodic boundary conditions which simultaneously exhibit Turing bifurcations with a spatial 1:3 resonance of the critical wave numbers. For the description of the bifurcating solutions we derive a system of coupled stochastic Landa

Zengyang Li, Xiaoyong Zhang, Wenshuo Wang, Peng Liang

Nowadays, most DL frameworks (DLFs) use multilingual programming of Python and C/C++, facilitating the flexibility and performance of the DLF. However, inappropriate interlanguage interaction may introduce design smells involving multiple programming languages (PLs), i.e., Inter-Language Design Smells (ILDS). Despite the negative impact of ILDS on multi-lang

Yakoub Salhi

In this paper, we introduce a syntactic framework for analyzing and handling inconsistencies in propositional bases. Our approach focuses on examining the relationships between variable occurrences within conflicts. We propose two dual concepts: Minimal Inconsistency Relation (MIR) and Maximal Consistency Relation (MCR). Each MIR is a minimal equivalence rel

Alex F. Spies, William Edwards, Michael I. Ivanitskiy, Adrians Skapars

Recent studies in interpretability have explored the inner workings of transformer models trained on tasks across various domains, often discovering that these networks naturally develop highly structured representations. When such representations comprehensively reflect the task domain's structure, they are commonly referred to as "World Models" (WMs). In t

Xiaopeng Lin, Hongwei Ren, Yulong Huang, Zunchang Liu

Conventional frame-based cameras inevitably produce blurry effects due to motion occurring during the exposure time. Event camera, a bio-inspired sensor offering continuous visual information could enhance the deblurring performance. Effectively utilizing the high-temporal-resolution event data is crucial for extracting precise motion information and enhanci

Gian Oré, Alexandre Santos, Daniele Ukan, Ronald Zanetti

Leaf-cutting ants, notorious for causing defoliation in commercial forest plantations, significantly contribute to biomass and productivity losses, impacting forest producers in Brazil. These ants construct complex underground nests, highlighting the need for advanced monitoring tools to extract subsurface information across large areas. Synthetic Aperture R

Effrosyni Sokli, Pranav Kasela, Georgios Peikos, Gabriella Pasi

While Dense Retrieval Models (DRMs) have advanced Information Retrieval (IR), one limitation of these neural models is their narrow generalizability and robustness. To cope with this issue, one can leverage the Mixture-of-Experts (MoE) architecture. While previous IR studies have incorporated MoE architectures within the Transformer layers of DRMs, our work

Renqiu Xia, Mingsheng Li, Hancheng Ye, Wenjie Wu

Despite their proficiency in general tasks, Multi-modal Large Language Models (MLLMs) struggle with automatic Geometry Problem Solving (GPS), which demands understanding diagrams, interpreting symbols, and performing complex reasoning. This limitation arises from their pre-training on natural images and texts, along with the lack of automated verification in

Rajneil Baruah, Arghya Choudhury, Kirtiman Ghosh, Subhadeep Mondal

Probing higgsinos remains a challenge at the LHC owing to their small production cross-sections and the complexity of the decay modes of the nearly mass degenerate higgsino states. The existing limits on higgsino mass are much weaker compared to its bino and wino counterparts. This leaves a large chunk of sub-TeV supersymmetric parameter space unexplored so

Tobias A. Russell, Neva Dobric, Nathan J. Adams, Christopher J. Conselice

We present a detailed investigation into the abundance and morphology of high redshift quenched galaxies at $3 < z < 7$ using James Webb Space Telescope data in the NEP, CEERS and JADES fields. Within these fields, we identify 90 candidate passive galaxies using specific star formation rates modelled with the BAGPIPES SED fitting code, which is more effectiv

Chen Qian, Siqi Xu, Yang-Guang Yang, Xingbo Zhao

Given that the wave function of a proton can be derived relativistically and nonperturbatively from a light-front quantized Hamiltonian, investigating the quantum correlation between quarks and gluons offers a novel perspective on the internal structure of partons within a proton. In this work, we address this topic by computing the spin and longitudinal mom

Sonia Rani, Xi Cao, Alejandro E. Baptista, Axel Hoffmann

Magnons can endow quantum devices with new functionalities. Assessing their potential requires precise characterization of magnon properties. Here, we use a superconducting qubit to probe magnons in a ferrimagnet over a range of about 2000 excitations. Using qubit control and parametrically induced qubit-magnon interactions we demonstrate few-excitation sens

Michael Tsopanopoulos

The model problem of a plane angle for a second-order elliptic system subject to Dirichlet, mixed, and Neumann boundary conditions is analyzed. For each boundary condition, the existence of solutions of the form $r^\lambda v$ is reduced to spectral analysis of a particular matrix. Focusing on Dirichlet and mixed boundary conditions, optimal bounds on $|\Re \

Van-Phuc Bui, Shashi Raj Pandey, Israel Leyva-Mayorga, Petar Popovski

The immense volume of data generated by Earth observation (EO) satellites presents significant challenges in transmitting it to Earth over rate-limited satellite-to-ground communication links. This paper presents an efficient downlink framework for multi-spectral satellite images, leveraging adaptive transmission techniques based on pixel importance and link

K. Yu. Povarov, Y. Skourskii, J. Wosnitza, D. E. Graf

We present comprehensive high magnetic field studies of the alternating weakly coupled ferro-antiferromagnetic (FM-AFM) spin-$1/2$ chain compound Cu$_2$(OH)$_3$Br, with the structure of the natural mineral botallackite. Our measurements reveal a broad magnetization plateau at about half of the saturation value, strongly suggesting that the FM chain sublattic

Heng Zhang, Guifei Jiang, Donghui Quan

There has been a longstanding dispute over which formalism is the best for representing knowledge in AI. The well-known "declarative vs. procedural controversy" is concerned with the choice of utilizing declarations or procedures as the primary mode of knowledge representation. The ongoing debate between symbolic AI and connectionist AI also revolves around

Michael Shen, Muhammad Umar, Kiwan Maeng, G. Edward Suh

The rapid increase in the number of parameters in large language models (LLMs) has significantly increased the cost involved in fine-tuning and retraining LLMs, a necessity for keeping models up to date and improving accuracy. Retrieval-Augmented Generation (RAG) offers a promising approach to improving the capabilities and accuracy of LLMs without the neces

Donsung Lee

In 1974, Birman posed the problem of identifying the conditions under which a matrix with Laurent polynomial entries lies in the image of the Burau representation. Building on this, Salter, in 2021, refined the inquiry to ask whether the central quotient of the Burau image group coincides with the central quotient of a specific subgroup of the unitary group.

Alexandr Afanasiev, Nicolas Wijsen, Rami Vainio

Gradual solar energetic particle (SEP) events are generally attributed to the particle acceleration in shock waves driven by coronal mass ejections (CMEs). Space-weather effects of such events are important, so there has been continuous effort to develop models able to forecast their various characteristics. Here we present the first version of a new such mo

Simon Hachmeier, Robert Jäschke

Detecting music entities such as song titles or artist names is a useful application to help use cases like processing music search queries or analyzing music consumption on the web. Recent approaches incorporate smaller language models (SLMs) like BERT and achieve high results. However, further research indicates a high influence of entity exposure during p

Zhenyu Wang, Yifan Hu, Peter Bühlmann, Zijian Guo

Identifying the causal relationship among variables from observational data is an important yet challenging task. This work focuses on identifying the direct causes of an outcome and estimating their magnitude, i.e., learning the causal outcome model. Data from multiple environments provide valuable opportunities to uncover causality by exploiting the invari

Ramy A. Zeineldin, Franziska Mathis-Ullrich

Motivated by the need for advanced solutions in the segmentation and inpainting of glioma-affected brain regions in multi-modal magnetic resonance imaging (MRI), this study presents an integrated approach leveraging the strengths of ensemble learning with hybrid transformer models and convolutional neural networks (CNNs), alongside the innovative application

Shohreh Gholizadeh Siahmazgi

The late-time behaviors of the modes in the Unruh state for various eternal two-dimensional black holes are discussed. The Unruh state is designed to be a state that mimics the late-time behaviors of quantized fields that Hawking predicted for the black holes that form from collapse. Evidence is provided that the late-time behaviors of some of the modes of t

Shailesh Kumar, Tieguang Zi

We analyze a rotating regular black hole spacetime with an asymptotically Minkowski core, focusing on extreme mass-ratio inspiral (EMRIs) where a stellar-mass object inspirals a supermassive black hole under consideration. Such spacetimes are also called Kerr-like spacetimes, which motivate the investigation of black holes beyond general relativity and the t

Tiantian Liang, Zhe Yang

Session-based recommendation seeks to forecast the next item a user will be interested in, based on their interaction sequences. Due to limited interaction data, session-based recommendation faces the challenge of limited data availability. Traditional methods enhance feature learning by constructing complex models to generate positive and negative samples.

Joackim Bernier, Benoit Grébert, Tristan Robert

We prove that nonlinear Schr\"odinger equations on the circle, without external parameters, admits plenty of almost periodic solutions. Indeed, we prove that arbitrarily close to most of the finite dimensional KAM tori constructed by Kuksin--Poschel in 1996, there exist infinite dimensional non resonant Kronecker tori, i.e. rotational invariant tori. This re

Marta Molero, Laura Magrini, Marco Palla, Gabriele Cescutti

Chemical clocks based on [s-process elements/alpha-elements] ratios are widely used to estimate ages of Galactic stellar populations. However, the [s/alpha] vs. age relations are not universal, varying with metallicity, location in the Galactic disc, and specific s-process elements. Current Galactic chemical evolution models struggle to reproduce the observe

Trung Chau, Huy Tai Ha, Aryaman Maithani

We identify several classes of monomial ideals that possess minimal generalized Barile-Macchia resolutions. These classes of ideals include generic monomial ideals, monomial ideals with linear quotients, and edge ideals of hypertrees. We also characterize connected unicyclic graphs whose edge ideals are bridge-friendly and, in particular, have minimal Barile

Cécile Mailler, Bruno Schapira

We show the existence of a phase transition between a localisation and a non-localisation regime for a branching random walk with a catalyst at the origin. More precisely, we consider a continuous-time branching random walk that jumps at rate one, with simple random walk jumps on $\mathbb Z^d$, and that branches (with binary branching) at rate $\lambda>0$ ev

Bo Wang, Zhizhang Wang

In this paper, we consider the Hessian equations in some exterior domain with prescribed asymptotic behavior at infinity and Dirichlet-Neumann conditions on its interior boundary. We obtain that there exists a unique bounded domain such that the over-determined problem admits a unique strictly convex solution.

Martin Aubard, Ana Madureira, Luís Teixeira, José Pinto

With the growing interest in underwater exploration and monitoring, Autonomous Underwater Vehicles (AUVs) have become essential. The recent interest in onboard Deep Learning (DL) has advanced real-time environmental interaction capabilities relying on efficient and accurate vision-based DL models. However, the predominant use of sonar in underwater environme

Lucas José da Costa, Vinicius Ruiz Uemoto, Mariana F. N. de Marchi, Renato de Aguiar Hortegal

Use real word data to evaluate the performance of the electrocardiographic markers of GEH as features in a machine learning model with Standard ECG features and Risk Factors in Predicting Outcome of patients in a population referred to a tertiary cardiology hospital. Patients forwarded to specific evaluation in a cardiology specialized hospital performed an

Marcin Wrona, Andrej Prša

In modern astronomy, the quantity of data collected has vastly exceeded the capacity for manual analysis, necessitating the use of advanced artificial intelligence (AI) techniques to assist scientists with the most labor-intensive tasks. AI can optimize simulation codes where computational bottlenecks arise from the time required to generate forward models.

Hannes Westermann, Jaromir Savelka

Interacting with the legal system and the government requires the assembly and analysis of various pieces of information that can be spread across different (paper) documents, such as forms, certificates and contracts (e.g. leases). This information is required in order to understand one's legal rights, as well as to fill out forms to file claims in court or

Dhruv Sharma, Chhavi Dhiman, Dinesh Kumar

Image captioning is the generation of natural language descriptions of images which have increased immense popularity in the recent past. With this different deep-learning techniques are devised for the development of factual and stylized image captioning models. Previous models focused more on the generation of factual and stylized captions separately provi

Tim Menzner, Jochen L. Leidner

We present improved models for the granular detection and sub-classification news media bias in English news articles. We compare the performance of zero-shot versus fine-tuned large pre-trained neural transformer language models, explore how the level of detail of the classes affects performance on a novel taxonomy of 27 news bias-types, and demonstrate how

Jingze Shi, Bingheng Wu

In order to make the foundation model more efficient and effective, our idea is combining sequence transformation and state transformation. First, we prove the availability of rotary position embedding in the state space duality algorithm, which reduces the perplexity of the hybrid quadratic causal self-attention and state space duality by more than 4%, to e

Tingting Luo, Jiayu Liu, Cairong Chen, Qun Wang

In this paper, we proposed a monotone block coordinate descent method for solving absolute value equation (AVE). Under appropriate conditions, we analyzed the global convergence of the algorithm and conduct numerical experiments to demonstrate its feasibility and effectiveness.

Tian-Yi Che, Xian-Ling Mao, Chun Xu, Cheng-Xin Xin

Numerous retrieval models, including sparse, dense and llm-based methods, have demonstrated remarkable performance in predicting the relevance between queries and corpora. However, the preliminary effectiveness analysis experiments indicate that these models fail to achieve satisfactory performance on the majority of queries and corpora, revealing their effe

Leonidas Zotos, Hedderik van Rijn, Malvina Nissim

In an educational setting, an estimate of the difficulty of multiple-choice questions (MCQs), a commonly used strategy to assess learning progress, constitutes very useful information for both teachers and students. Since human assessment is costly from multiple points of view, automatic approaches to MCQ item difficulty estimation are investigated, yielding

Anka Van de Walle, Markus Schmitt, Annabelle Bohrdt

Simulating the dynamics of many-body quantum systems is a significant challenge, especially in higher dimensions where entanglement grows rapidly. Neural quantum states (NQS) offer a promising tool for representing quantum wavefunctions, but their application to time evolution faces scaling challenges. We introduce the time-dependent neural quantum state (t-

Teng Xue, Amirreza Razmjoo, Suhan Shetty, Sylvain Calinon

Contact-rich manipulation plays an important role in daily human activities. However, uncertain physical parameters often pose significant challenges for both planning and control. A promising strategy is to develop policies that are robust across a wide range of parameters. Domain adaptation and domain randomization are widely used, but they tend to either

Sergey Zinchenko, Denis Ponomaryov

View materialization, index selection, and plan caching are well-known techniques for optimization of query processing in database systems. The essence of these tasks is to select and save a subset of the most useful candidates (views/indexes/plans) for reuse within given space/time budget constraints. In this paper, we propose a unified view on these select

Arkaprava Saha, Bogdan Cautis, Xiaokui Xiao, Laks V. S. Lakshmanan

We study the problem of robust influence maximization in dynamic diffusion networks. In line with recent works, we consider the scenario where the network can undergo insertion and removal of nodes and edges, in discrete time steps, and the influence weights are determined by the features of the corresponding nodes and a global hyperparameter. Given this, ou

Laura J. Johnston, Jim E. Griffin, Ioanna Manolopoulou, Takoua Jendoubi

Understanding and enhancing student engagement through digital platforms is critical in higher education. This study introduces a methodology for quantifying engagement across an entire module using virtual learning environment (VLE) activity log data. Using study session frequency, immediacy, and diversity, we create a cumulative engagement metric and model

Zhi Zhou, Lan-Zhe Guo, Peng-Xiao Song, Yu-Feng Li

Deep generative models have achieved promising results in image generation, and various generative model hubs, e.g., Hugging Face and Civitai, have been developed that enable model developers to upload models and users to download models. However, these model hubs lack advanced model management and identification mechanisms, resulting in users only searching

Thu Le, Dinh-Liem Nguyen

This paper addresses the inverse scattering problem for Maxwell's equations. We first show that a bianisotropic scatterer can be uniquely determined from multi-static far-field data through the factorization analysis of the far-field operator. Next, we investigate a modified version of the orthogonality sampling method, as proposed in Le [2022 Inverse Proble

Valeriy Novikov, Jun Jia, Túlio Brito Brasil, Andrea Grimaldi

Ultimate limits for sensing of fields and forces are set by the quantum noise of a sensor. Entanglement allows for suppression of such noise and for achieving sensitivity beyond standard quantum limits. Applicability of quantum optical sensing is often restricted by fixed wavelengths of available photonic quantum sources. Another ubiquitous limitation is ass

Md Iftekhar Islam Tashik, Abdullah Khondoker, Enam Ahmed Taufik, Antara Firoz Parsa

The domain of Natural Language Processing (NLP) has experienced notable progress in the evolution of Bangla Question Answering (QA) systems. This paper presents a comprehensive review of seven research articles that contribute to the progress in this domain. These research studies explore different aspects of creating question-answering systems for the Bangl

Weizhao Liang, Jin Yan, Hua Zhu

In this paper, we primarily study the Pogorelov-type $C^2$ estimates for $(k-1)$-convex solutions of the sum Hessian equation under the assumption of semi-convexity, and apply these estimates to obtain a rigidity theorem for global solutions satisfying the corresponding conditions. Furthermore, we investigate the Pogorelov estimates and rigidity theorems for

Michael Senatore, Daniel L. Campbell, James A. Williams, Matthew D. LaHaye

Environmental noise that couples longitudinally to a quantum system dephases that system and can limit its coherence lifetime. Performance using quantum superposition in clocks, information processors, communication networks, and sensors depends on careful state and external field selection to lower sensitivity to longitudinal noise. In many cases time varyi

Zikang Chen, Tao Jiang, Xiaowan Hu, Wang Zhang

Self-supervised video denoising aims to remove noise from videos without relying on ground truth data, leveraging the video itself to recover clean frames. Existing methods often rely on simplistic feature stacking or apply optical flow without thorough analysis. This results in suboptimal utilization of both inter-frame and intra-frame information, and it a

Ba Hung Ngo, Doanh C. Bui, Nhat-Tuong Do-Tran, Tae Jong Choi

The enhanced representational power and broad applicability of deep learning models have attracted significant interest from the research community in recent years. However, these models often struggle to perform effectively under domain shift conditions, where the training data (the source domain) is related to but exhibits different distributions from the

Simon Hachmeier, Robert Jäschke

YouTube is a rich source of cover songs. Since the platform itself is organized in terms of videos rather than songs, the retrieval of covers is not trivial. The field of cover song identification addresses this problem and provides approaches that usually rely on audio content. However, including the user-generated video metadata available on YouTube promis

Gergely Molnár, Etienne Barthel

The difference between free surface energy and fracture toughness in amorphous silica is studied via multi-scale simulations. We combine the homogenization of a molecular dynamics fracture model with a phase-field approach to track and quantify the various energy contributions. We clearly separate free surface energy localized as potential energy on the surf

Yuhao Du, Shunian Chen, Wenbo Zan, Peizhao Li

The application of Large Language Models (LLMs) in Computer-Aided Design (CAD) remains an underexplored area, despite their remarkable advancements in other domains. In this paper, we present BlenderLLM, a novel framework for training LLMs specifically for CAD tasks leveraging a self-improvement methodology. To support this, we developed a bespoke training d

Angelo Sanfelice, Luigi Costanzo, Alessandro Lo Schiavo, Alessandro Sarracino

We present an experimental and numerical study of a piezoelectric energy harvester driven by broadband vibrations. This device can extract power from random fluctuations and can be described by a stochastic model, based on an underdamped Langevin equation with white noise, which mimics the dynamics of the piezoelectric material. A crucial point in the modeli

Junhao Zhuang, Xuan Ju, Zhaoyang Zhang, Yong Liu

Automatic black-and-white image sequence colorization while preserving character and object identity (ID) is a complex task with significant market demand, such as in cartoon or comic series colorization. Despite advancements in visual colorization using large-scale generative models like diffusion models, challenges with controllability and identity consist

Fatima AlGhamdi, Reem Mohammed, Hend Al-Khalifa, Areeb Alowisheq

This paper introduces GLARE an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews of 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the gathered reviews. We also highlight possib

Mengna Zhu, Kaisheng Zeng, Mao Wang, Kaiming Xiao

In real life, many dynamic events, such as major disasters and large-scale sports events, evolve continuously over time. Obtaining an overview of these events can help people quickly understand the situation and respond more effectively. This is challenging because the key information of the event is often scattered across multiple documents, involving compl

Hichem Sahbi

Deep neural networks (DNNs) are nowadays witnessing a major success in solving many pattern recognition tasks including skeleton-based classification. The deployment of DNNs on edge-devices, endowed with limited time and memory resources, requires designing lightweight and efficient variants of these networks. Pruning is one of the lightweight network design

Kristin M. de Payrebrune, Kathrin Flaßkamp, Tom Ströhla, Thomas Sattel

Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniques have become integral to the design p

Tianheng Qiu, Ka Lung Law, Guanghua Pan, Jufei Wang

Unsupervised domain adaptive (UDA) algorithms can markedly enhance the performance of object detectors under conditions of domain shifts, thereby reducing the necessity for extensive labeling and retraining. Current domain adaptive object detection algorithms primarily cater to two-stage detectors, which tend to offer minimal improvements when directly appli

Enrico Iurlano, Günther R. Raidl

Proposed for rapid document similarity estimation in web search engines, the celebrated property of minwise independence imposes highly symmetric constraints on a family $\mathcal{F}$ of permutations of $\{1,\ldots, n\}$: The property is fulfilled by $\mathcal{F}$ if for each $j\in \{1,\ldots,n\}$, any cardinality-$j$ subset $X\subseteq \{1,\ldots,n\}$, and

Wadjih Bencheikh, Jan Finkbeiner, Emre Neftci

Recurrent neural networks (RNNs) are valued for their computational efficiency and reduced memory requirements on tasks involving long sequence lengths but require high memory-processor bandwidth to train. Checkpointing techniques can reduce the memory requirements by only storing a subset of intermediate states, the checkpoints, but are still rarely used du

Tomáš Čelko, František Mráz, Benedikt Bergmann, Petr Mánek

The latest generation of Timepix series hybrid pixel detectors enhance particle tracking with high spatial and temporal resolution. However, their high hit-rate capability poses challenges for data processing, particularly in multidetector configurations or systems like Timepix4. Storing and processing each hit offline is inefficient for such high data throu

Simão Correia, Pedro Leite

We consider a general nonlinear dispersive equation with monomial nonlinearity of order $k$ over $\mathbb{R}^d$. We construct a rigorous theory which states that higher-order nonlinearities and higher dimensions induce sharper local well-posedness theories. More precisely, assuming that a certain positive multiplier estimate holds at order $k_0$ and in dimen

Xiaoran Xu, Jiangang Yang, Wenhui Shi, Siyuan Ding

Single-Domain Generalized Object Detection~(S-DGOD) aims to train on a single source domain for robust performance across a variety of unseen target domains by taking advantage of an object detector. Existing S-DGOD approaches often rely on data augmentation strategies, including a composition of visual transformations, to enhance the detector's generalizati

Steven Finch

Dumitru Popa found asymptotic expansions for certain nonlinear recurrences, but left open the numerical evaluation of associated constants. We address this issue. A change of variables involving reciprocals and the algorithm of Mavecha & Laohakosol play a key role in our computations.

Johannes Christensen, Sergey Neshveyev

Given an amenable second countable Hausdorff locally compact \'etale groupoid $\mathcal G$ such that each isotropy group $\mathcal G^x_x$ has local polynomial growth, we give a description of $\operatorname{Prim} C^*(\mathcal G)$ as a topological space in terms of the topology on $\mathcal G$ and representation theory of the isotropy groups and their subgrou

Ruidong Cheng, Yuling Shao, Xi Li, Li Zhang

Spinal cord injury (SCI) is a debilitating condition that often results in significant motor and sensory deficits, impacting the quality of life. Current rehabilitation methods, including physical therapy and electrical stimulation, offer variable outcomes and often require invasive procedures. Temporal interference (TI) stimulation has emerged as a novel, n

Logan E. Beaver

In this article, we present a long-duration autonomy approach for the control of connected and automated vehicles (CAVs) operating in a transportation network. In particular, we focus on the performance of CAVs at traffic bottlenecks, including roundabouts, merging roadways, and intersections. We take a principled approach based on optimal control, and deriv

Boyang Xue, Fei Mi, Qi Zhu, Hongru Wang

Despite demonstrating impressive capabilities, Large Language Models (LLMs) still often struggle to accurately express the factual knowledge they possess, especially in cases where the LLMs' knowledge boundaries are ambiguous. To improve LLMs' factual expressions, we propose the UAlign framework, which leverages Uncertainty estimations to represent knowledge

Wei Luo, Haiming Yao, Wenyong Yu, Zhengyong Li

Unsupervised visual anomaly detection is crucial for enhancing industrial production quality and efficiency. Among unsupervised methods, reconstruction approaches are popular due to their simplicity and effectiveness. The key aspect of reconstruction methods lies in the restoration of anomalous regions, which current methods have not satisfactorily achieved.

Ann-Kristin Malz, Gregory Ashton, Nicolo Colombo

With the increasing use of Machine Learning (ML) algorithms in scientific research comes the need for reliable uncertainty quantification. When taking a measurement it is not enough to provide the result, we also have to declare how confident we are in the measurement. This is also true when the results are obtained from a ML algorithm, and arguably more so

Juhi Chaudhary, Hendrik Molter, Meirav Zehavi

Knockout tournaments, also known as single-elimination or cup tournaments, are a popular form of sports competitions. In the standard probabilistic setting, for each pairing of players, one of the players wins the game with a certain (a priori known) probability. Due to their competitive nature, tournaments are prone to manipulation. We investigate the compu

T. Chaumont-Frelet, A. Ern

We derive a priori and a posteriori error estimates for the discontinuous Galerkin (dG) approximation of the time-harmonic Maxwell's equations. Specifically, we consider an interior penalty dG method, and establish error estimates that are valid under minimal regularity assumptions and involving constants that do not depend on the frequency for sufficiently

J. Poorta, M. Hogerheijde, A. de Koter, L. Kaper

Our current understanding is that intermediate- to high-mass stars form in a way similar to low-mass stars, that is, through disk accretion. However, the physical conditions that play a role in disk formation, evolution, and the possibility of (sub)stellar companion formation, are significantly different. We search for the mm counterparts of four intermediat

Alexandre Ern, Johnny Guzmán, Pratyush Potu, Martin Vohralík

We investigate discrete Poincar\'e inequalities on piecewise polynomial subspaces of the Sobolev spaces H(curl) and H(div) in three space dimensions. We characterize the dependence of the constants on the continuous-level constants, the shape regularity and cardinality of the underlying tetrahedral mesh, and the polynomial degree. One important focus is on m

Xiangheng He, Junjie Chen, Zixing Zhang, Björn W. Schuller

Prosody contains rich information beyond the literal meaning of words, which is crucial for the intelligibility of speech. Current models still fall short in phrasing and intonation; they not only miss or misplace breaks when synthesizing long sentences with complex structures but also produce unnatural intonation. We propose ProsodyFM, a prosody-aware text-

Liudas Panavas, Joshua Snoke, Erika Tyagi, Claire McKay Bowen

Accessing data collected by federal statistical agencies is essential for public policy research and improving evidence-based decision making, such as evaluating the effectiveness of social programs, understanding demographic shifts, or addressing public health challenges. Differentially private interactive systems, or validation servers, can form a crucial

Xiaoxiao Ma, Changwen Liang, Rong Sha, Chao Zhou

Laser cooling, which cools atomic and molecular gases to near absolute zero, is the crucial initial step for nearly all atomic gas experiments. However, fast achievement of numerous sub-$\mu$K cold atoms is challenging. To resolve the issue, we propose and experimentally validate an intelligent polarization gradient cooling approach enhanced by optical latti

Zoie Telkamp, Ruben Fedriani, Jonathan C. Tan, Chi-Yan Law

We present $\sim8-40\,\mu$m SOFIA-FORCAST images of seven regions of ``clustered" star formation as part of the SOFIA Massive (SOMA) Star Formation Survey. We identify a total of 34 protostar candidates and build their spectral energy distributions (SEDs). We fit these SEDs with a grid of radiative transfer models based on the Turbulent Core Accretion (TCA)

ESSnuSB, :, M. Ghosh

In this proceedings we study the sensitivity of the ESSnuSB experiment to probe quantum decoherence. ESSnuSB is a future long-baseline neutrino oscillation experiment which aims to measure $\delta_{\rm CP}$ by probing the second oscillation maximum. Using the open quantum system formalism for decoherence, we have shown that the sensitivity of ESSnuSB to cons

Simon Christoffer Ziersen, Torben Martinussen

Treatment effect heterogeneity plays an important role in many areas of causal inference and within recent years, estimation of the conditional average treatment effect (CATE) has received much attention in the statistical community. While accurate estimation of the CATE-function through flexible machine learning procedures provides a tool for prediction of

Shun Maeta

In this paper, we rigorously analyze the scalar curvature of complete expanding gradient Yamabe solitons. We completely classify nontrivial complete expanding gradient Yamabe solitons in both cases: when the scalar curvature is greater than the soliton constant and when it is less than the soliton constant.

Shuoxi Zhang, Zijian Song, Kun He

Existing knowledge distillation (KD) methods have demonstrated their ability in achieving student network performance on par with their teachers. However, the knowledge gap between the teacher and student remains significant and may hinder the effectiveness of the distillation process. In this work, we introduce the structure of Neural Collapse (NC) into the

Seonho An, Young Yik Rhim, Min-Soo Kim

As social systems become increasingly complex, legal articles are also growing more intricate, making it progressively harder for humans to identify any potential competitions among them, particularly when drafting new laws or applying existing laws. Despite this challenge, no method for detecting such competitions has been proposed so far. In this paper, we

Zhenghui Li

Let $K$ be a finite extension of $\mathbb{Q}_p$. We prove that the arithmetic $p$-adic pro-\'etale cohomology of smooth partially proper spaces over $K$ satisfies a duality, as conjectured by Colmez, Gilles and Nizio{\l}. We derive it from the geometric duality on the Fargues-Fontaine curve by Galois descent techniques of Fontaine.

Rick Akkerman, Haiwen Feng, Michael J. Black, Dimitrios Tzionas

Predicting the dynamics of interacting objects is essential for both humans and intelligent systems. However, existing approaches are limited to simplified, toy settings and lack generalizability to complex, real-world environments. Recent advances in generative models have enabled the prediction of state transitions based on interventions, but focus on gene

Sayak Bhowmik, Arijit Saha

We propose a theoretical framework for the realization of Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) pairing in a helical Shiba chain subjected to an out-of-plane Zeeman field, analyzed through a self-consistent Bogoliubov-de-Gennes (BdG) mean-field formalism approach. A chain of magnetic adatoms with helical spin texture deposited on the surface of a common $s

Benno Lossin, Philipp Czerner, Javier Esparza, Roland Guttenberg

Population protocols are a model of distributed computation in which an arbitrary number of indistinguishable finite-state agents interact in pairs to decide some property of their initial configuration. We investigate the behaviour of population protocols under adversarial faults that cause agents to silently crash and no longer interact with other agents.

Francesco Cioni, Roberto Menta, Riccardo Aiudi, Marco Polini

The processing unit of a solid-state quantum computer consists in an array of coupled qubits, each locally driven with on-chip microwave lines that route carefully-engineered control signals to the qubits in order to perform logical operations. This approach to quantum computing comes with two major problems. On the one hand, it greatly hampers scalability t

Alireza Aghili, Nadezda Sukhorukova, Julien Ugon

The non-isothermal analysis of materials with the application of the Arrhenius equation involves temperature integration. If the frequency factor in the Arrhenius equation depends on temperature with a power-law relationship, the integral is known as the general temperature integral. This integral which has no analytical solution is estimated by the approxim

Benedict Randall Shaw

We say two spanning trees of a graph are completely independent if their edge sets are disjoint, and for each pair of vertices, the paths between them in each spanning tree do not have any other vertex in common. Pai and Chang constructed two such spanning trees in the hypercube $Q_n$ for sufficiently large $n$, while Kandekar and Mane recently showed there

Eren Onaran, Erdi Sarıtaş, Hazım Kemal Ekenel

Face alignment is a crucial step in preparing face images for feature extraction in facial analysis tasks. For applications such as face recognition, facial expression recognition, and facial attribute classification, alignment is widely utilized during both training and inference to standardize the positions of key landmarks in the face. It is well known th