December 2024 arXiv papers — page 107

Jih-Hsin Cheng, Chin-Yu Hsiao, I-Hsun Tsai

For a complex manifold $\Sigma $ with $\mathbb{C}^{\ast }$-action, we define the $m$-th $\mathbb{C}^{\ast }$ Fourier-Dolbeault cohomology group and consider the $m$-index on $\Sigma $. By applying the method of transversal heat kernel asymptotics, we obtain a local index formula for the $m$-index. We can reinterpret Kawasaki's Hirzebruch-Riemann-Roch formula

Rajdeep Dutta, T Venkatesh Varma, Saikat Sarkar, Mariya Mamajiwala

Many global optimization algorithms of the memetic variety rely on some form of stochastic search, and yet they often lack a sound probabilistic basis. Without a recourse to the powerful tools of stochastic calculus, treading the fine balance between exploration and exploitation could be tricky. In this work, we propose an evolutionary algorithm (EA) compris

Seyed Reza Nabavi, Mohammad Javad Jafari, Zhiyuan Wang

Background: Multilayer perceptron (MLP) aided multi-objective particle swarm optimization algorithm (MOPSO) is employed in the present article to optimize the liquefied petroleum gas (LPG) thermal cracking process. This new approach significantly accelerated the multi-objective optimization (MOO), which can now be completed within one minute compared to the

Xudong Zhou, Wenhao He

We propose SAM-IF, a novel method for incremental few-shot instance segmentation leveraging the Segment Anything Model (SAM). SAM-IF addresses the challenges of class-agnostic instance segmentation by introducing a multi-class classifier and fine-tuning SAM to focus on specific target objects. To enhance few-shot learning capabilities, SAM-IF employs a cosin

Yusheng Zhao, Xiao Luo, Haomin Wen, Zhiping Xiao

Traffic flow forecasting aims to predict future traffic flows based on the historical traffic conditions and the road network. It is an important problem in intelligent transportation systems, with a plethora of methods been proposed. Existing efforts mainly focus on capturing and utilizing spatio-temporal dependencies to predict future traffic flows. Though

Huijun Han, Yongqing Liang, Yuanlong Zhou, Wenping Wang

Creating realistic VR experiences is challenging due to the labor-intensive process of accurately replicating real-world details into virtual scenes, highlighting the need for automated methods that maintain spatial accuracy and provide design flexibility. In this paper, we propose AURORA, a novel method that leverages RGB-D images to automatically generate

Wenjie Chen, Zichang Lin, Xinxin Zhang, Hao Zhou

Magnesium-ion batteries hold promise as future energy storage solution, yet current Mg cathodes are challenged by low voltage and specific capacity. Herein, we present an AI-driven workflow for discovering high-performance Mg cathode materials. Utilizing the common characteristics of various ionic intercalation-type electrodes, we design and train a Crystal

Chowdhury Mofizur Rahman, Mahbub E Sobhani, Anika Tasnim Rodela, Swakkhar Shatabda

Text compression shrinks textual data while keeping crucial information, eradicating constraints on storage, bandwidth, and computational efficacy. The integration of lossless compression techniques with transformer-based text decompression has received negligible attention, despite the increasing volume of English text data in communication. The primary bar

Luc Vinet, Alexei Zhedanov

We show that the Jacobi polynomials that are orthogonal on the unit circle (the Jacobi OPUC) are CMV bispectral. This means that the corresponding Laurent polynomials in the CMV basis satisfy two dual ordinary eigenvalue problems: a recurrence relation and a differential equation of Dunkl type. This is presumably the first nontrivial explicit example of CMV

Cheng-To Lin, Chung-Han Tsai, Baowen Zhang, Qingyue Deng

With the development of intelligence, the combination of big data and judicial practice has become a hot research topic. There are fewer studies on credit card contract disputes related to big data, which makes it difficult to respond to the trend of Big data era. This paper uses the data source of credit card disputes related to the protection of personal i

Elham Kiyani, Manav Manav, Nikhil Kadivar, Laura De Lorenzis

Phase-field modeling reformulates fracture problems as energy minimization problems and enables a comprehensive characterization of the fracture process, including crack nucleation, propagation, merging, and branching, without relying on ad-hoc assumptions. However, the numerical solution of phase-field fracture problems is characterized by a high computatio

Cong Wang

This is a continuation of the previous work (arXiv:2403.10128). Additional aspects such as linear combinations of projections and hash-table canonicalizations are described. Implementations of the general-order partial-spin adaptation (PSA) coupled-cluster (CC) method are outlined. Numerical results are reported.

Daniel Mallory

We investigate the K-stability of certain blow-ups of $\mathbb{P}^1$-bundles over a Fano variety $V$, where the $\mathbb{P}^1$-bundle is the projective compactification of a line bundle $L$ proportional to $-K_V$ and the center of the blow-up is the image along a positive section of a divisor $B$ also proportional to $L$. When $V$ and $B$ are smooth, we show

Yaoxuan Zeng, Malte F. Jansen

Symmetric instability has broad applications in geophysical and planetary fluid dynamics. It plays a crucial role in the formation of mesoscale rainbands at mid-latitudes on Earth, instability in the ocean's mixed layer, and slantwise convection on gas giants and icy moon oceans. Here, we apply linear instability analysis to an arbitrary zonally symmetric Bo

Hang Zhang, Zhuoling Li, Jun Liu

Dynamic scenes contain intricate spatio-temporal information, crucial for mobile robots, UAVs, and autonomous driving systems to make informed decisions. Parsing these scenes into semantic triplets <Subject-Predicate-Object> for accurate Scene Graph Generation (SGG) is highly challenging due to the fluctuating spatio-temporal complexity. Inspired by the reas

Xinran Wang, Muxi Diao, Baoteng Li, Haiwen Zhang

The Controllable Image Captioning Agent (CapAgent) is an innovative system designed to bridge the gap between user simplicity and professional-level outputs in image captioning tasks. CapAgent automatically transforms user-provided simple instructions into detailed, professional instructions, enabling precise and context-aware caption generation. By leveragi

Zeeshan Patel, James DeLoye, Lance Mathias

In this paper, we present a comprehensive theoretical comparison of diffusion and flow matching under the Generator Matching framework. Despite their apparent differences, both diffusion and flow matching can be viewed under the unified framework of Generator Matching. By recasting both diffusion and flow matching under the same generative Markov framework,

Ben Kang, Xin Chen, Simiao Lai, Yang Liu

Contextual information at the video level has become increasingly crucial for visual object tracking. However, existing methods typically use only a few tokens to convey this information, which can lead to information loss and limit their ability to fully capture the context. To address this issue, we propose a new video-level visual object tracking framewor

A. J. Foraci, R. P. Woodard

A previous calculation of the 1-loop photon contribution to the graviton self-energy on de Sitter background is considered. We first show that there is no local obstacle to conservation, unlike the contribution from a loop of massless, minimally coupled scalars. This is correlated to the absence of an Eddington ($R^2$) counterterm and to the vanishing of the

Xiaobing Ni, Mengke Ge, Jiaheng Ruan, Song Chen

Streaming coarse-grained reconfgurable array (CGRA) is a promising architecture for data/computing-intensive applications because of its fexibility, high throughput and efcient memory system. However,when accelerating sparse CNNs, the irregular input data demands inside sparse CNNs would cause excessive caching operations (COPs) and multi-cycle internal depe

Tong-Xing Zheng, Xin Chen, Lan Lan, Ying Ju

Despite the enhanced spectral efficiency brought by the integrated radar and communication technique, it poses significant risks to communication security when confronted with malicious radar targets. To address this issue, a reconfigurable intelligent surface (RIS)-aided transmission scheme is proposed to improve secure communication in two systems, i.e., t

Siqiao Zhao, Dan Wang, Raphael Douady

The domain of hedge fund investments is undergoing significant transformation, influenced by the rapid expansion of data availability and the advancement of analytical technologies. This study explores the enhancement of hedge fund investment performance through the integration of machine learning techniques, the application of PolyModel feature selection, a

Jack H. Koolen, Chenhui Lv, Alexander L. Gavrilyuk

We prove that the Grassmann graphs $J_2(2D+3,D)$, $D\geq 3$, are characterized by their intersection numbers, which settles one of the few remaining cases.

Pengfei Fang, Yongchun Qin, Hui Xue

Few-shot Class-Incremental Learning (FSCIL) addresses the challenges of evolving data distributions and the difficulty of data acquisition in real-world scenarios. To counteract the catastrophic forgetting typically encountered in FSCIL, knowledge distillation is employed as a way to maintain the knowledge from learned data distribution. Recognizing the limi

Haji Gul, Abdul Ghani Naim, Ajaz A. Bhat

Knowledge graphs (KGs) are valuable for representing structured, interconnected information across domains, enabling tasks like semantic search, recommendation systems and inference. A pertinent challenge with KGs, however, is that many entities (i.e., heads, tails) or relationships are unknown. Knowledge Graph Completion (KGC) addresses this by predicting t

Tanjung Krisnanda, Pengtao Song, Adrian Copetudo, Clara Yun Fontaine

Quantum machine learning is a rapidly advancing discipline that leverages the features of quantum mechanics to enhance the performance of computational tasks. Quantum reservoir processing, which allows efficient optimization of a single output layer without precise control over the quantum system, stands out as one of the most versatile and practical quantum

Zhendong Mi, Renming Zheng, Haowen Zhong, Yue Sun

Recent advances in agentic LLMs have demonstrated great capabilities in Verilog code generation. However, existing approaches either use LLM-assisted single-agent prompting or cooperation-only multi-agent learning, which will lead to: (i) Degeneration issue for single-agent learning: characterized by diminished error detection and correction capabilities; (i

Spencer Daugherty

The quasisymmetric functions, $QSym$, are generalized for a finite alphabet $A$ by the colored quasisymmetric functions, $QSym_A$, in partially commutative variables. Their dual, $NSym_A$, generalizes the noncommutative symmetric functions, $NSym$, through a relationship with a Hopf algebra of trees. We define an algebra $Sym_A$, contained within $QSym_A$, t

Chuanxin Cui, Moshe Rozali

We present two discussions of firewalls in JT gravity. First we present an alternative, arguably simpler, derivation of the gray hole conjecture, applying uniformly to all probes of the firewall probability previously discussed. This derivation is based on the wormhole shortening picture using the handle-disk geometry. However we modifies Saad's story utiliz

Rodrigo Santos Monteiro

Convergence spaces are a generalization of topological spaces. The category of convergence spaces is well-suited for Algebraic Topology, one of the reasons is the existence of exponential objects provided by continuous convergence. In this work, we use a net-theoretic approach to convergence spaces. The goal is to simplify the description of continuous conve

Zaijun Ye, Wansheng Wang

We propose a novel framework for solving a class of Partial Integro-Differential Equations (PIDEs) and Forward-Backward Stochastic Differential Equations with Jumps (FBSDEJs) through a deep learning-based approach. This method, termed the Forward-Backward Stochastic Jump Neural Network (FBSJNN), is both theoretically interpretable and numerically effective.

Shenxiong Li, Huaxia Rui

We conducted three experiments to investigate how large language models (LLMs) evaluate posterior probabilities. Our results reveal the coexistence of two modes in posterior judgment among state-of-the-art models: a normative mode, which adheres to Bayes' rule, and a representative-based mode, which relies on similarity -- paralleling human System 1 and Syst

Fangwei Hao, Ji Du, Weiyun Liang, Jing Xu

Image restoration (IR) is a long-standing task to recover a high-quality image from its corrupted observation. Recently, transformer-based algorithms and some attention-based convolutional neural networks (CNNs) have presented promising results on several IR tasks. However, existing convolutional residual building modules for IR encounter limited ability to

Jinliang Shi, Shigang Li, Youxuan Xu, Rongtian Fu

Sparse Matrix-matrix Multiplication (SpMM) and Sampled Dense-dense Matrix Multiplication (SDDMM) are important sparse operators in scientific computing and deep learning. Tensor Core Units (TCUs) enhance modern accelerators with superior computing power, which is promising to boost the performance of matrix operators to a higher level. However, due to the ir

Shubham Agarwal, Gaurav Sahu, Abhay Puri, Issam H. Laradji

Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, especially due to the recent influx of research papers. This paper explores the zero-shot abilities of recent Large Language Models (LLMs) in assisting with the writing of literature reviews based on an abstract. We decompose the tas

Hanning Zhang, Pengcheng Wang, Shizhe Diao, Yong Lin

Large language models (LLMs) have shown promise in performing complex multi-step reasoning, yet they continue to struggle with mathematical reasoning, often making systematic errors. A promising solution is reinforcement learning (RL) guided by reward models, particularly those focusing on process rewards, which score each intermediate step rather than solel

Wenting Huang, Ying Sun, Xiaojing Xu

Rotation significantly influences the stability characteristics of both laminar and turbulent shear flows. This study examines the stability threshold of the three-dimensional Navier-Stokes equations with rotation, in the vicinity of the Couette flow at high Reynolds numbers ($\mathbf{Re}$) in the periodical domain $\mathbb{T} \times \mathbb{R} \times \mathb

Sansrit Paudel

SightGlow is a web extension tailored to improve color perception accuracy for individuals with red-green color blindness. The research was focused on evaluating whether personalized color adjustment and selective zoom enhance user interaction and satisfaction for individuals with low vision and color vision impairment. The system was developed as an iterati

Changyu Gao, Andrew Lowy, Xingyu Zhou, Stephen J. Wright

Machine learning algorithms in high-dimensional settings are highly susceptible to the influence of even a small fraction of structured outliers, making robust optimization techniques essential. In particular, within the $\epsilon$-contamination model, where an adversary can inspect and replace up to an $\epsilon$-fraction of the samples, a fundamental open

Karim Saifullin, Hussein Al-Shatri, Mohamed-Slim Alouini

The utilization of unlicensed spectrum presents a promising solution to the issue of spectrum scarcity in densely populated areas, while also offering a cost-effective means to connect underserved regions. In response to this potential, both academia and industry are actively exploring innovative applications of unlicensed spectrum. This work offers a thorou

Wenlong Jiang

Supercooled liquid state is a particularly interesting state in that it exhibits several unusual physical properties. To illustrate, the liquid displays a single peak relaxation frequency at high temperatures, which splits into $\alpha$ relaxation and $\beta$ relaxation in the moderately supercooled regime, with relaxation a disappearing at the glass transit

Aron W. Cummings, Simon M. -M. Dubois, Pedro Alcázar Guerrero, Jean-Christophe Charlier

We use a combination of molecular dynamics and quantum transport simulations to investigate the upper limit of spin transport in suspended graphene. We find that thermally-induced atomic-scale corrugations are the dominant factor, limiting spin lifetimes to ~10 ns by inducing a strongly-varying local spin-orbit coupling. These extremely short-range corrugati

Xiang Li, Qiaomin Xie

The convergence behavior of Stochastic Gradient Descent (SGD) crucially depends on the stepsize configuration. When using a constant stepsize, the SGD iterates form a Markov chain, enjoying fast convergence during the initial transient phase. However, when reaching stationarity, the iterates oscillate around the optimum without making further progress. In th

S. A. Larin

We suggest a version of renormalizable Quantum Field Theory which does not contain non-perturbative effects. This is otained by the proper use of the boundary conditions in the functional integral of the generating functional of Green functions. It is well known which boundary conditions are applied to the fields of the functional integral to get correct per

Sachin Vaidya

In this article, we study Self-Similar configurations of non-relativistic Bose-Einstein condensate (BEC) described by the Gross-Pitaevskii Equation (GPE). To be precise, we discuss singular Self-similar solutions of the Gross-Pitaevskii equation in 2D (with circular symmetry) and 3D (with spherical symmetry). We use these solutions to check for the crossover

Daniel Mastropietro, Georgios Korpas, Vyacheslav Kungurtsev, Jakub Marecek

Inspired by the Fleming-Viot stochastic process, we propose a parallel implementation of variational quantum algorithms with the aim of reducing the time spent by the algorithm in barren plateaus, where optimization direction is unclear. In the Fleming-Viot tradition, parallel searches are called particles. In the proposed approach, the search by a Fleming-V

K. J. Kevin Feng, Kevin Pu, Matt Latzke, Tal August

As AI agents take on increasingly long-running tasks involving sophisticated planning and execution, there is a corresponding need for novel interaction designs that enable deeper human-agent collaboration. However, most prior works leverage human interaction to fix "autonomous" workflows that have yet to become fully autonomous or rigidly treat planning and

Sanu Mishra, Caitlin S. Kengle, Joe D. Thompson, Allen O. Scheie

The intersection between nonsymmorphic symmetry and electronic correlations has emerged as a platform for topological Kondo semimetallic states and unconventional spin textures. Here we report the synthesis of nonsymmorphic UPd$_{0.65}$Bi$_2$ single crystals and their structural, electronic, magnetic, and thermodynamic properties. UPd$_{0.65}$Bi$_2$ orders a

Lichun Zhang, Steve Ran Zhou, Moon Hyung Choi, Jeong Hoon Lee

Prostate cancer is a leading cause of cancer-related deaths among men. The recent development of high frequency, micro-ultrasound imaging offers improved resolution compared to conventional ultrasound and potentially a better ability to differentiate clinically significant cancer from normal tissue. However, the features of prostate cancer remain subtle, wit

Mabel Osorio-Archila, María Magdalena González, J. Rodrigo Sacahui

The gamma-ray emission in blazars can be attributed to the leptonic Synchrotron Self-Compton (SSC) model, photo-hadronic interactions, or a combination thereof. While evidence supports both models, their specific contributions remain uncertain. One supportive piece of evidence for the SSC model is the correlation between synchrotron and SSC fluxes in some bl

Mustafa Munir, Md Mostafijur Rahman, Radu Marculescu

Vision transformers (ViTs) have dominated computer vision in recent years. However, ViTs are computationally expensive and not well suited for mobile devices; this led to the prevalence of convolutional neural network (CNN) and ViT-based hybrid models for mobile vision applications. Recently, Vision GNN (ViG) and CNN hybrid models have also been proposed for

Thi-Nga Do, Godfrey Gumbs

In this work, we construct a Wannier tight-binding model for TaS$_2$ under a superlattice potential modulation, based on the Joint Automated Repository for Various Integrated Simulations database established by the U.S. National Institute of Standards and Technology, so as to study the electronic properties of the structure. Our computational method enables

Phuong Duy Huynh, Son Hoang Dau, Nicholas Huppert, Joshua Cervenjak

We explored the ubiquitous phenomenon of serial scammers, each of whom deployed dozens to thousands of addresses to conduct a series of similar Rug Pulls on popular decentralized exchanges. We first constructed two datasets of around 384,000 scammer addresses behind all one-day Simple Rug Pulls on Uniswap (Ethereum) and Pancakeswap (BSC), and identified dist

Nicolas Alder, Shivam Nitin Kajale, Milin Tunsiricharoengul, Deblina Sarkar

(Pseudo)random sampling, a costly yet widely used method in (probabilistic) machine learning and Markov Chain Monte Carlo algorithms, remains unfeasible on a truly large scale due to unmet computational requirements. We introduce an energy-efficient algorithm for uniform Float16 sampling, utilizing a room-temperature stochastic magnetic tunnel junction devic

Tamal Mukhopadhyay, Banadipa Chakraborty, Ujjal Debnath, Anirudh Pradhan

This paper investigates a newly proposed fluid description of dark energy within the framework of the late-time accelerated expansion of the universe. Our primary objective is to explore the theoretical foundation of the proposed equation of state by establishing its correspondence with well-known scalar field models such as quintessence, k-essence, and DBI-

Christian Remling

I am interested in canonical systems and Dirac operators that are reflectionless on an open set. In this situation, the half line $m$ functions are holomorphic continuations of each other and may be combined into a single function. By passing to the universal cover of its domain, we then obtain a one-to-one correspondence of these operators with Herglotz fun

Ahmed Haj Ahmed, Rui-Jie Yew, Xerxes Minocher, Suresh Venkatasubramanian

Social media platforms have become central to global communication, yet they also facilitate the spread of hate speech. For underrepresented dialects like Levantine Arabic, detecting hate speech presents unique cultural, ethical, and linguistic challenges. This paper explores the complex sociopolitical and linguistic landscape of Levantine Arabic and critica

Jonathan Holland, George Sparling

This article examines the structure of plane wave spacetimes (of signature $(1,n+1)$, $n\ge 2$) that are homogeneous (the isometry group is transitive) and geodesically complete -- which we call microcosms. In general, a plane wave is shown to determine a smooth positive curve in the Lagrangian Grassmannian associated with the $2n$ dimensional symplectic vec

Yang Liu, Li Wan, Yiteng Huang, Ming Sun

Deep learning models like Convolutional Neural Networks and transformers have shown impressive capabilities in speech verification, gaining considerable attention in the research community. However, CNN-based approaches struggle with modeling long-sequence audio effectively, resulting in suboptimal verification performance. On the other hand, transformer-bas

Paul Bekima

Oral Glucose Tolerance Test (OGTT) is one of many way to produce data in the study of the diabetes dynamic. In a recent paper [1.]:\textit{ Estimating insulin sensitivity and $ \beta $-cell function from the oral glucose tolerance test: validation of a new insulin sensitivity and secretion (ISS) model, \textit{J. American Physiological Society },(2024)},Ha J

Maqsood Hussain Shah, Ji Li, Mingming Liu

In the rapidly evolving landscape of urban transportation, shared e-mobility services have emerged as a sustainable solution to meet growing demand for flexible, eco-friendly travel. However, the existing literature lacks a comprehensive multi-modal optimization framework with focus on user preferences and real-world constraints. This paper presents a multi-

Yu Deng, Yiyang Xu, Linglong Qian, Charlène Mauger

Cardiac Magnetic Resonance (CMR) imaging is widely used for heart model reconstruction and digital twin computational analysis because of its ability to visualize soft tissues and capture dynamic functions. However, CMR images have an anisotropic nature, characterized by large inter-slice distances and misalignments from cardiac motion. These limitations res

Nayra A. Álvarez Pari, Rodrigo Jaeschke-Ubiergo, Atasi Chakraborty, Libor Šmejkal

Motivated by the ongoing interest in understanding the actual magnetic ground state of the promising axion insulator candidate EuIn2As2, we present here a spin symmetry analysis and ab-initio calculations, aiming to identify specific exchange-dominated physics that could offer insights into the current debate. We investigate two non-collinear coplanar magnet

John Bally, Samuel Crowe, Rubén Fedriani, Adam Ginsburg

We present JWST-NIRCam narrow-band, 4.05 $\mu$m Brackett-$\alpha$ images of the Sgr C HII region, located in the Central Molecular Zone (CMZ) of the Galaxy. Unlike any HII region in the Solar vicinity, the Sgr C plasma is dominated by filamentary structure in both Brackett-$\alpha$ and the radio continuum. Some bright filaments, which form a fractured arc wi

Gabriel R. Rosenbaum, Lavender Yao Jiang, Ivaxi Sheth, Jaden Stryker

Large language models (LLMs) have recently emerged as powerful tools, finding many medical applications. LLMs' ability to coalesce vast amounts of information from many sources to generate a response-a process similar to that of a human expert-has led many to see potential in deploying LLMs for clinical use. However, medicine is a setting where accurate reas

Daniel M. Benjamin, Fred Morstatter, Ali E. Abbas, Andres Abeliuk

Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we developed SAGE, a hybrid forecasting system that combines human and machine generated forecasts. The system provides a platform where users can interact with machine models and thus an

Erman Isik

In this article we study the Iwasawa theory for Hecke characters associated with CM abelian varieties and Hilbert modular forms at ordinary primes. We formulate and prove a result concerning the anticyclotomic Iwasawa main conjecture for CM Hilbert modular forms. Additionally, we obtain a result towards the study of the Mordell-Weil ranks of the CM abelian v

Ying Wang, Jian Guo, Yanlong Zhao, Ji-feng Zhang

This paper addresses distributed parameter estimation in stochastic dynamic systems with quantized measurements, constrained by quantized communication and Markovian switching directed topologies. To enable accurate recovery of the original signal from quantized communication signal, a persistent excitation-compliant linear compression encoding method is int

Nir Daniel, Florian Klaus Kaiser, Shay Giladi, Sapir Sharabi

Analysts in Security Operations Centers (SOCs) are often occupied with time-consuming investigations of alerts from Network Intrusion Detection Systems (NIDS). Many NIDS rules lack clear explanations and associations with attack techniques, complicating the alert triage and the generation of attack hypotheses. Large Language Models (LLMs) may be a promising

Fatima Zahra Iguenfer, Achraf Hsain, Hiba Amissa, Yousra Chtouki

Reconstructing meshes from point clouds is a fundamental task in computer vision with applications spanning robotics, autonomous systems, and medical imaging. Selecting an appropriate learning-based method requires understanding trade-offs between computational efficiency, geometric accuracy, and output constraints. This paper categorizes over fifteen method

Yunqiao Hu, Shunqiao Sun, Yimin D. Zhang

This paper tackles the challenge of one-bit off-grid direction of arrival (DOA) estimation in a single snapshot scenario based on a learning-based Bayesian approach. Firstly, we formulate the off-grid DOA estimation model, utilizing the first-order off-grid approximation, incorporating one-bit data quantization. Subsequently, we address this problem using th

Jorge Fandinno, Zachary Hansen

This paper shows that the semantics of programs with aggregates implemented by the solvers clingo and dlv can be characterized as extended First-Order formulas with intensional functions in the logic of Here-and-There. Furthermore, this characterization can be used to study the strong equivalence of programs with aggregates under either semantics. We also pr

Siyuan He

The competitive pressures in China's primary and secondary education system have persisted despite decades of policy interventions aimed at reducing academic burdens and alleviating parental anxiety. This paper develops a game-theoretic model to analyze the strategic interactions among families in this system, revealing how competition escalates into a socia

Yonghong Jiang, Ender Ayanoglu

We investigate the use of conventional angle of arrival (AoA) algorithms the Bartlett's algorithm, the Minimum Variance Distortion Response (MVDR or Capon) algorithm, and the Minimum Norm algorithm for estimating the AoA $\theta$ together with our previously introduced algorithms linear regression (LR), inverse of the root sum squares of channel coefficients

Wade Marquette, Kyle Schultz, Vamsi Jonnalagadda, Benjamin Wong

Visual inspection of confined spaces such as aircraft wings is ergonomically challenging for human mechanics. This work presents a novel crane robot that can travel the entire span of the aircraft wing, enabling mechanics to perform inspection from outside of the confined space. However, teleoperation of the crane robot can still be a challenge due to the ne

Luis Wiedmann, Luca Wiehe, David Rozenberszki

Open-set 3D segmentation represents a major point of interest for multiple downstream robotics and augmented/virtual reality applications. We present a decoupled 3D segmentation pipeline to ensure modularity and adaptability to novel 3D representations as well as semantic segmentation foundation models. We first reconstruct a scene with 3D Gaussians and lear

Tuncay Aktosun, Abdon E. Choque-Rivero, Ivan Toledo, Mehmet Unlu

Explicit solutions to the related integrable nonlinear evolution equations are constructed by solving the inverse scattering problem in the reflectionless case for the third-order differential equation $d^3\psi/dx^3+Q\,d\psi/dx+P\psi =k^3\psi,$ where $Q$ and $P$ are the potentials in the Schwartz class and $k^3$ is the spectral parameter. The input data set

Martin Dostál, Miroslav Ježek, Jaromír Fiurášek, Jan Bílek

Probabilistic heralded Gaussification of quantum states of light is an important ingredient of protocols for distillation of continuous variable entanglement and squeezing. An elementary step of heralded Gaussification protocol consists of interference of two copies of the state at a balanced beam splitter, followed by conditioning on outcome of a suitable G

Tracy Bui, Kari Noe, Marissa Halim, Nurit Kirshenbaum

Creating engaging and immersive data visualization tools has become increasingly significant for a wide range of users who want to display their data in a meaningful way. However, this can be limiting for individuals with varying levels of coding expertise. There are specific needs, such as visualizing complex data in easily understandable ways, highlighting

Eleanor Row, György Fazekas

We present a study that explores the role of user-centred design in developing Generative AI (GenAI) tools for music composition. Through semi-structured interviews with professional composers, we gathered insights on a novel generative model for creating variations, highlighting concerns around trust, transparency, and ethical design. The findings helped fo

Mohammad R. Salmanpour, Sajad Amiri, Sara Gharibi, Ahmad Shariftabrizi

We investigate the connection between visual semantic features defined in PI-RADS and associated risk factors, moving beyond abnormal imaging findings, establishing a shared framework between medical and AI professionals by creating a standardized dictionary of biological/radiological RFs. Subsequently, 6 interpretable and seven complex classifiers, linked w

Alex Morehead, Jianlin Cheng

Powerful generative AI models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically relevant drug targets, hindering their wi

Odysseas Giatagantzidis

We introduce the notion of radical preservation and prove that a radical-preserving homomorphism of left artinian rings of finite projective dimension with superfluous kernel reflects the finiteness of the little finitistic, big finitistic and global dimension. As an application, we prove that every bound quiver algebra with quasi-uniform Loewy length, a cla

Mattia Bianchi, Florian Dörfler

Online Feedback Optimization (OFO) is a control approach to drive a dynamical plant to an optimal steady state. By interconnecting optimization algorithms with real-time plant measurements, OFO provides all the benefits of feedback control, yet without requiring exact knowledge of plant dynamics for computing a setpoint. On the downside, existing stability g

Aziz Kharoof

Quantum measurements often exhibit non-classical features, such as contextuality, which generalizes Bell's non-locality and serves as a resource in various quantum computation models. Existing frameworks have rigorously captured these phenomena, and recently, simplicial distributions have been introduced to deepen this understanding. The geometrical structur

Sharif A. Sulaiman, Zahra Shomali

As the transistors and consequently the chips are getting smaller, the accurate investigation of heat transport at micro/nanoscale, becomes an important issue of concern. This is due to an increase in the energy consumption and the leakage currents as a result of the miniaturization which requires taking care of the thermal behavior to make sure that the dev

Mingjing Xu, Peizhong Ju, Jia Liu, Haibo Yang

Multi-objective optimization (MOO) lies at the core of many machine learning (ML) applications that involve multiple, potentially conflicting objectives (e.g., multi-task learning, multi-objective reinforcement learning, among many others). Despite the long history of MOO, recent years have witnessed a surge in interest within the ML community in the develop

Piyapath T Spencer, Nanthipat Kongborrirak

Yes! In the present-day documenting and preserving endangered languages, the application of Large Language Models (LLMs) presents a promising approach. This paper explores how LLMs, particularly through in-context learning, can assist in generating grammatical information for low-resource languages with limited amount of data. We takes Moklen as a case study

Kartik Hosanagar, Daehwan Ahn

We examined the effectiveness of various human-AI collaboration designs on creative work. Through a human subjects experiment set in the context of creative writing, we found that while AI assistance improved productivity across all models, collaboration design significantly influenced output quality, user satisfaction, and content characteristics. Models in

Hung Truong

Is gender identity binary or nonbinary? My analysis shows that while both are possible, the latter is a more attracting equilibrium under an adaptive learning perspective. I frame the gender identity problem as a modified \textit{battle of the sexes} game, where individuals define their gender identity under pairwise matching motives. From a baseline game-th

Hao Chen, Ze Wang, Xiang Li, Ximeng Sun

Efficient image tokenization with high compression ratios remains a critical challenge for training generative models. We present SoftVQ-VAE, a continuous image tokenizer that leverages soft categorical posteriors to aggregate multiple codewords into each latent token, substantially increasing the representation capacity of the latent space. When applied to

David C. Krakauer

Exbodiment describes mind outsourced to engineered matter and how matter reeducates mind. The constraints of exbodied matter encode elements of thought, channel decision-making, and constitute an important part of an extended computational phenotype. Here I provide an introduction and brief cultural history of exbodiment in music, natural history, cognition,

T. Ssettumba, S. Mashdour, L. Landau, P. da Silva

In this letter, we propose an iterative soft interference cancellation scheme for intra-cluster (ICL) and out-of-cluster (OCL) interference mitigation in user-centric clustered cell-free massive multiple-antenna networks. We propose a minimum mean-square error receive filter with a novel modified parallel interference cancellation scheme to mitigate ICL and

Sanjana Gundapaneni, Zhuo Zhi, Miguel Rodrigues

The imperative for early detection of type 2 diabetes mellitus (T2DM) is challenged by its asymptomatic onset and dependence on suboptimal clinical diagnostic tests, contributing to its widespread global prevalence. While research into noninvasive T2DM screening tools has advanced, conventional machine learning approaches remain limited to unimodal inputs du

Radhika Prasad, Sanjana Wanare, Suman Karan, Mritunjay K. Joshi

Structured optical fields have led to several ground-breaking techniques in classical imaging and microscopy. At the same time, in the quantum domain, position-momentum entangled photon fields have been shown to have several unique features that can lead to beyond-classical imaging and microscopy capabilities. Therefore, it is natural to expect that position

Simon Torka, Sahin Albayrak

In recent years, the rise of AI-assisted code-generation tools has significantly transformed software development. While code generators have mainly been used to support conventional software development, their use will be extended to powerful and secure AI systems. Systems capable of generating code, such as ChatGPT, OpenAI Codex, GitHub Copilot, and AlphaC

Abdenacer Makhlouf, Dragoş Ştefan

We extend the classical concept of deformation of an associative algebra, as introduced by Gerstenhaber, by using monoidal linear categories and cocommutative coalgebras as foundational tools. To achieve this goal, we associate to each cocommutative coalgebra $C$ and each linear monoidal category $\M$, a $\Bbbk$-linear monoidal category $\M_{C}$. This constr

Chad Hanna, James Kennington, Wanting Niu, Shio Sakon

Matched-filtering searches for gravitational-wave signals from compact binary mergers employ template banks which are a collection of modeled waveforms described by unique intrinsic parameters. We present two banks designed for low-latency and archive sub-solar mass (SSM) searches in data from the fourth observing run of LIGO-Virgo-KAGRA, and demonstrate the

Simon Torka, Sahin Albayrak

The advancement of AI technologies has greatly increased the complexity of AI pipelines as they include many stages such as data collection, pre-processing, training, evaluation and visualisation. To provide effective and accessible AI solutions, it is important to design pipelines for different user groups such as experts, professionals from different field

Justin Le

We provide an overview of the diffusion model as a method to generate new samples. Generative models have been recently adopted for tasks such as art generation (Stable Diffusion, Dall-E) and text generation (ChatGPT). Diffusion models in particular apply noise to sample data and then "reverse" this noising process to generate new samples. We will formally d