November 2024 arXiv papers — page 92

Na Ta, Fang Jiao, Cong Lin, Cuihua Shen

The (re)creation and distribution of cultural products such as music are increasingly shaped by digital platforms. This study explores how TikTok and Spotify, situated in different governance and user contexts, could influence digital music production and reception within each platform and between each other. Focusing on daily hit song charts as the embodime

Tristan Goodwill, Charles L. Epstein

We introduce a new class of computationally tractable scattering problems in unbounded domains, which we call decomposable problems. In these decomposable problems, the computational domain can be split into a finite collection of subdomains in which the scatterer has a "simple" structure. A subdomain is simple if the domain Green's function for this subdoma

Yangxinyu Xie, Xiang Li, Tanwi Mallick, Weijie J. Su

Watermarking language models is essential for distinguishing between human and machine-generated text and thus maintaining the integrity and trustworthiness of digital communication. We present a novel green/red list watermarking approach that partitions the token set into ``green'' and ``red'' lists, subtly increasing the generation probability for green to

Carlos E. Budde, Ranindya Paramitha, Fabio Massacci

Software security mainly studies vulnerability detection: is my code vulnerable today? This hinders risk estimation, so new approaches are emerging to forecast the occurrence of future vulnerabilities. While useful, these approaches are coarse-grained and hard to employ for project-specific technical decisions. We introduce a model capable of vulnerability f

Iris Y. Shi

Let $p$ be an odd prime and $k$ be an algebraically closed field with characteristic $p$. Booher and Cais showed that the $a$-number of a $\mathbb Z/p \mathbb Z$-Galois cover of curves $\phi: Y \to X$ must be greater than a lower bound determined by the ramification of $\phi$. In this paper, we provide evidence that the lower bound is optimal by finding exam

Kealan Dunnett, Reza Arablouei, Dimity Miller, Volkan Dedeoglu

The widespread adoption of deep learning across various industries has introduced substantial challenges, particularly in terms of model explainability and security. The inherent complexity of deep learning models, while contributing to their effectiveness, also renders them susceptible to adversarial attacks. Among these, backdoor attacks are especially con

Ge Gao, Adrian Azzarelli, Ho Man Kwan, Nantheera Anantrasirichai

The advances in immersive technologies and 3D reconstruction have enabled the creation of digital replicas of real-world objects and environments with fine details. These processes generate vast amounts of 3D data, requiring more efficient compression methods to satisfy the memory and bandwidth constraints associated with data storage and transmission. Howev

Can Cui, Zichong Yang, Yupeng Zhou, Juntong Peng

Personalized driving refers to an autonomous vehicle's ability to adapt its driving behavior or control strategies to match individual users' preferences and driving styles while maintaining safety and comfort standards. However, existing works either fail to capture every individual preference precisely or become computationally inefficient as the user base

José Oscar González Cervantes, Juan Bory-Reyes

This work presents the basic elements and results of a Clifford algebra valued fractional slice monogenic functions theory defined from the null-solutions of a suitably fractional Cauchy-Riemann operator in the Riemann-Liouville and Caputo sense with respect to a pair of real valued functions on certain domains of Euclidean spaces.

Minhua Lin, Enyan Dai, Junjie Xu, Jinyuan Jia

Graph Neural Networks (GNNs) have shown promising results in modeling graphs in various tasks. The training of GNNs, especially on specialized tasks such as bioinformatics, demands extensive expert annotations, which are expensive and usually contain sensitive information of data providers. The trained GNN models are often shared for deployment in the real w

Vincent van der Brugge, Marc Pollefeys, Joshua B. Tenenbaum, Ayush Tewari

Reconstructing compositional 3D representations of scenes, where each object is represented with its own 3D model, is a highly desirable capability in robotics and augmented reality. However, most existing methods rely heavily on strong appearance priors for object discovery, therefore only working on those classes of objects on which the method has been tra

Ruoxi Sun, Jiamin Chang, Hammond Pearce, Chaowei Xiao

Multimodal foundation models (MFMs) integrate diverse data modalities to support complex and wide-ranging tasks. However, this integration also introduces distinct safety and security challenges. In this paper, we unify the concepts of safety and security in the context of MFMs by identifying critical threats that arise from both model behavior and system-le

Gabriel K. Gegenhuber, Maximilian Günther, Markus Maier, Aljosha Judmayer

With over 3 billion users globally, mobile instant messaging apps have become indispensable for both personal and professional communication. Besides plain messaging, many services implement additional features such as delivery and read receipts informing a user when a message has successfully reached its target. This paper highlights that delivery receipts

David A. Ross

The S-measure construction from nonstandard analysis is used to prove an extension of a result on the intersection of sets in a finitely-additive measure space. This is then used to give a density-limit version of a representation theorem of Banach.

Philippe Martin Wyder, Riyaan Bakhda, Meiqi Zhao, Quinn A. Booth

Biological lifeforms can heal, grow, adapt, and reproduce -- abilities essential for sustained survival and development. In contrast, robots today are primarily monolithic machines with limited ability to self-repair, physically develop, or incorporate material from their environments. While robot minds rapidly evolve new behaviors through AI, their bodies r

Andrew H. Proppe, Kin Long Kelvin Lee, Weiwei Sun, Chantalle J. Krajewska

Deep neural network models can be used to learn complex dynamics from data and reconstruct sparse or noisy signals, thereby accelerating and augmenting experimental measurements. Evaluating the quantum optical properties of solid-state single-photon emitters is a time-consuming task that typically requires interferometric photon correlation experiments, such

Zhen Yuan, David Stojanovski, Lei Li, Alberto Gomez

Splenomegaly, the enlargement of the spleen, is an important clinical indicator for various associated medical conditions, such as sickle cell disease (SCD). Spleen length measured from 2D ultrasound is the most widely used metric for characterising spleen size. However, it is still considered a surrogate measure, and spleen volume remains the gold standard

Lingyun Wang, Bingjie Wang, Jay Chhablani, Jose Alain Sahel

Objective: To achieve accurate 3-D reconstruction and quantitative analysis of human retinal vasculature from a single optical coherence tomography angiography (OCTA) scan. Methods: We introduce Freqformer, a novel Transformer-based model featuring a dual-branch architecture that integrates a Transformer layer for capturing global spatial context with a comp

Jake Grigsby, Justin Sasek, Samyak Parajuli, Daniel Adebi

Language models trained on diverse datasets unlock generalization by in-context learning. Reinforcement Learning (RL) policies can achieve a similar effect by meta-learning within the memory of a sequence model. However, meta-RL research primarily focuses on adapting to minor variations of a single task. It is difficult to scale towards more general behavior

Martin Bohnert, Justus Springer

We prove a sharp upper bound on the number of boundary lattice points of a rational polygon in terms of its denominator and the number of interior lattice points, generalizing Scott's inequality. We then give sharp lower and upper bounds on the area in terms of the denominator, the number of interior lattice points, and the number of boundary lattice points,

Giampaolo Bonomi

Political messages increasingly bundle economic policy arguments with moral social policy stances. Using survey experiments with roughly 6,500 U.S. adults, I show that such bundling sharply weakens economic persuasion among respondents who disagree with the social stance: support falls by 13-20 percentage points relative to when the same economic message is

Samantha Aziz, Oleg Komogortsev

Virtual reality (VR) sensors capture large amounts of user data, including body motion and eye tracking, that contain personally identifying information. While privacy-enhancing techniques can obfuscate this data, incomplete privacy protections risk privacy leakage, which may allow adversaries to leverage unprotected data to identify users without consent. T

Gabriele Formis, Stefano Scanzio, Lukasz Wisniewski, Gianluca Cena

Predicting the behavior of a wireless link in terms of, e.g., the frame delivery ratio, is a critical task for optimizing the performance of wireless industrial communication systems. This is because industrial applications are typically characterized by stringent dependability and end-to-end latency requirements, which are adversely affected by channel qual

Yury A. Neretin

Consider the real free Lie algebra $\mathfrak{fr}_n$ with generators $\omega_1$, \dots, $\omega_n$. Since it is positively graded, it has a completion $\overline{\mathfrak{fr}}_n$ consisting of formal series. By the Campbell--Hausdorff formula, we have a corresponding Lie group $\overline{\mathrm{Fr}}_n$. It is the set $\exp\bigl(\overline{\mathfrak{fr}}_n\b

Pramit Saha, Felix Wagner, Divyanshu Mishra, Can Peng

Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (PEFT) strategies. To this end, we demonstrate the impact of two factors \textit{viz.}, client-specific layer importance score that selects the most important VLM layers for fine-tun

Nathaniel Dennler, Zhonghao Shi, Stefanos Nikolaidis, Maja Matarić

Assistive robots interact with humans and must adapt to different users' preferences to be effective. An easy and effective technique to learn non-expert users' preferences is through rankings of robot behaviors, for example, robot movement trajectories or gestures. Existing techniques focus on generating trajectories for users to rank that maximize the outc

Bartłomiej Dyda, Sven Jarohs, Firoj Sk

We introduce and study the logarithmic $p$-Laplacian $L_{\Delta_p}$, which emerges from the formal derivative of the fractional $p$-Laplacian $(-\Delta_p)^s$ at $s=0$. This operator is nonlocal, has logarithmic order, and is the nonlinear version of the newly developed logarithmic Laplacian operator. We present a variational framework to study the Dirichlet

Benjamin M. Peter, Mert Korkali

Reinforcement learning (RL) agents are powerful tools for managing power grids. They use large amounts of data to inform their actions and receive rewards or penalties as feedback to learn favorable responses for the system. Once trained, these agents can efficiently make decisions that would be too computationally complex for a human operator. This ability

J. Lu

With the growing popularity of ACG (Anime, Comics, and Games) culture, generating high-quality anime character images has become an important research topic. This paper introduces a novel Generative Adversarial Network model, USE-CMHSA-GAN, designed to produce high-quality anime character images. The model builds upon the traditional DCGAN framework, incorpo

A. B. Alpat, A. Bozkurt, G. Bartolini, R. Bayram

The Moon is directly exposed to various space radiation types: Solar Wind (ions between 0.5 to 10 keV and lower energy electrons), Solar Energetic Particles (SEPs, ranging from 10 keV to several hundred MeV ions and electrons), Galactic Cosmic Rays (GCRs) and Anomalous Cosmic Rays (ACRs, ranging from 1 to 100 MeV particles). Monitoring SEPs and GCRs is criti

Amin Aboubrahim, Pran Nath

One of the important issues both in particle physics and cosmology relates to whether dark energy is a cosmological constant $\Lambda$, or is dynamical in nature such as quintessence. In this work, we discuss a model of quintessence interacting with dark matter and analyze the resulting phenomenology of the dark energy equation of state. We identify two regi

Maximilian Fleissner, Gautham Govind Anil, Debarghya Ghoshdastidar

The NTK is a widely used tool in the theoretical analysis of deep learning, allowing us to look at supervised deep neural networks through the lenses of kernel regression. Recently, several works have investigated kernel models for self-supervised learning, hypothesizing that these also shed light on the behavior of wide neural networks by virtue of the NTK.

Jaykumar H. Patel, Brenden T. Kadota, Calder D. Sheagren, Mark Chiew

Cardiovascular diseases (CVDs) remain the leading cause of mortality and morbidity worldwide. Both diagnosis and prognosis of these diseases benefit from high-quality imaging, which cardiac magnetic resonance imaging provides. CMR imaging requires lengthy acquisition times and multiple breath-holds for a complete exam, which can lead to patient discomfort an

Gautam Chandrasekaran, Adam Klivans

We consider the fundamental problem of learning the parameters of an undirected graphical model or Markov Random Field (MRF) in the setting where the edge weights are chosen at random. For Ising models, we show that a multiplicative-weight update algorithm due to Klivans and Meka learns the parameters in polynomial time for any inverse temperature $\beta \le

Ali Behcet Alpat, Giovanni Bartolini, Talifujiang Wusimanjiang, Haider Raheem

Matter-RADiation interaction SIMulation (MRADSIM) is an innovative modular software toolkit developed to simulate the effects of radiation on electronic components, human beings and various materials. It incorporates innovative features aimed at enhancing parametric precision, reducing computational time, and introducing supplementary functions for tailored

James Woodfield

This paper extends deterministic notions of Strong Stability Preservation (SSP) to the stochastic setting, enabling nonlinearly stable numerical solutions to stochastic differential equations (SDEs) and stochastic partial differential equations (SPDEs) with pathwise solutions that remain unconditionally bounded. This approach may offer modelling advantages i

Jan Pfister, Julia Wunderle, Andreas Hotho

We create two German-only decoder models, LL\"aMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to use. The model training involved several key steps, including extensive data preprocessing, the creation of a custom German tokenizer, the training itself, as well as the evalua

Kathrin Krieger, Yuri De Pra, Helge Ritter, Alexandra Moringen

Humans seem to have a bias to overshoot when rotating a rotary knob blindfolded around a specified target angle (i.e. during haptic rotation). Whereas some influence factors that strengthen or weaken such an effect are already known, the underlying reasons for the overshoot are still unknown. This work approaches the topic of haptic rotations by analyzing a

Alexander Anferov, Fanghui Wan, Shannon P. Harvey, Jonathan Simon

Manipulating the electromagnetic spectrum at the single-photon level is fundamental for quantum experiments. In the visible and infrared range, this can be accomplished with atomic quantum emitters, and with superconducting qubits such control is extended to the microwave range (below 10 GHz). Meanwhile, the region between these two energy ranges presents an

Yuzhou Jiang, Erman Ayday

Reproducibility in genome-wide association studies (GWAS) is crucial for ensuring reliable genomic research outcomes. However, limited access to original genomic datasets (mainly due to privacy concerns) prevents researchers from reproducing experiments to validate results. In this paper, we propose a novel method for GWAS reproducibility validation that det

Rupert L. Frank, Bernard Helffer, Ari Laptev

We show that the $j$-th Dirichlet eigenvalue of the sub-Laplacian on an open set of a Carnot group is greater than the $(j+1)$-st Neumann eigenvalue. This extends earlier results in the Euclidean and Heisenberg case and has a remarkably simple proof.

Esther Liu, Pei Xi Lin, Qianqi Wang, Karina Chen Feng

This project is based on the dataset "exposome_NA.RData", which contains a subcohort of 1301 mother-child pairs who were enrolled into the HELIX study during pregnancy. Several health outcomes were measured on the child at birth or at age 6-11 years, taking environmental exposures of interest and other covariates into account. This report outlines the proces

C. Estelle Smith, Kylee Shiekh, Hayden Cooreman, Sharfi Rahman

Because of the rapid development and increasing public availability of Generative Artificial Intelligence (GenAI) models and tools, educational institutions and educators must immediately reckon with the impact of students using GenAI. There is limited prior research on computing students' use and perceptions of GenAI. In anticipation of future advances and

Weiqing He, Bojian Hou, Tianqi Shang, Davoud Ataee Tarzanagh

The widespread adoption of large language models (LLMs) has created an urgent need for robust tools to detect LLM-generated text, especially in light of \textit{paraphrasing} techniques that often evade existing detection methods. To address this challenge, we present a novel semantic-enhanced framework for detecting LLM-generated text (SEFD) that leverages

David Barnhill, John Cobb, Matthew Faust

This note introduces the $\texttt{LikelihoodGeometry}$ package for the computer algebra system $\textit{Macaulay2}$. This package gives tools to construct the likelihood correspondence of a discrete algebraic statistical model, a variety that that ties together data and their maximum likelihood estimators. This includes methods for constructing and combining

Xuefei Guo, Jin Chen, Farzaneh Hoveyda-Marashi, Simon L. Bettler

The strange metal is a peculiar phase of matter in which the electron scattering rate, $\tau^{-1} \sim k_B T/\hbar$, which determines the electrical resistance, is universal across a wide family of materials and determined only by fundamental constants. In 1989, theorists hypothesized that this universality would manifest as scale-invariant behavior in the d

Yiwen Liu, Ambikesh Gupta, Youngjoon Choi, Yaar Vituri

ABC-stacked rhombohedral graphene multilayers exhibit a wide variety of electronic ground states characterized by broken isospin symmetry and superconductivity. Recently, indirect evidence of inter-valley coherent (IVC) order has been reported in rhombohedral trilayer graphene (RTG), with possible implications for the origin of superconductivity. Here, we re

Jiawei Zhang

This paper builds upon our previous work on the Reconciled Polynomial Network (RPN). The original RPN model was designed under the assumption of input data independence, presuming the independence among both individual instances within data batches and attributes in each data instance. However, this assumption often proves invalid for function learning tasks

Eric Yang, Pengfei Hu, Xiaoxue Han, Yue Ning

The adoption of digital systems in healthcare has resulted in the accumulation of vast electronic health records (EHRs), offering valuable data for machine learning methods to predict patient health outcomes. However, single-visit records of patients are often neglected in the training process due to the lack of annotations of next-visit information, thereby

Daniela Caraeni, Yahya Modarres-Sadeghi

We study the collective behavior of clusters of cylinders placed in the wake of a fixed cylinder and free to move in a direction perpendicular to that of the incoming flow, with no structural damping or stiffness. We keep the Reynolds number, defined based on the cylinder diameter, at 100 and consider five different configurations for the initial positions o

Kürşat Tekbıyık, Güneş Karabulut Kurt, Antoine Lesage-Landry

The increasing demand for data usage in wireless communications requires using wider bands in the spectrum, especially for backhaul links. Yet, allocations in the spectrum for non-communication systems inhibit merging bands to achieve wider bandwidth. To overcome this issue, spectrum-sharing or opportunistic spectrum utilization by secondary users stands out

Pooja Kathail, Ayesha Bajwa, Nilah M. Ioannidis

Characterizing non-coding variant function remains an important challenge in human genetics. Genomic deep learning models have emerged as a promising approach to enable in silico prediction of variant effects. These include supervised sequence-to-activity models, which predict molecular phenotypes such as genome-wide chromatin states or gene expression level

Tyler Schmaltz, Yue Hu, Alex Lazarian

Understanding the role of turbulence in shaping the interstellar medium (ISM) is crucial for studying star formation, molecular cloud evolution, and cosmic ray propagation. Central to this is the measurement of the sonic Mach number ($M_s$), which quantifies the ratio of turbulent velocity to the sound speed. In this work, we introduce a convolutional neural

Dmitry V. Chichinadze, Naiyuan James Zhang, Jiang-Xiazi Lin, Erin Morissette

In a system of two-dimensional electrons, a combination of broken symmetry, interactions, and nontrivial topology can conspire to give rise to a nonlinear transport regime, where electric current density scales as the square of electric field. This regime has become a venue for exciting discoveries such as the nonlinear Hall effect and diode-like nonreciproc

Kiryl Piasotski, Aleksandr Svetogorov, Wolfgang Belzig, Mikhail Pletyukhov

In this paper, we introduce a concise theoretical framework for the equilibrium three-terminal Josephson effect in spin-orbit-interacting systems, inspired by recent experiments on an InAs/Al heterostructure [Phys. Rev. X 14, 031024 (2024)]. We develop an analytical model to capture the essential low-energy physics of the system and examine its potential as

Nishant Tiwari, Chinmayee Chowde Gowda, Subhendu Mishra, Prafull Pandey

Transition metal telluride compositions are explored extensively for their unique magnetic behavior. Since chromium telluride (Cr2Te3) exhibits a near-room-temperature phase transition, the material can be effectively used in applications such as magnetic refrigeration. Compared to existing magnetocaloric materials, Heusler alloys, and rare-earth-based alloy

Xavier Viader-Godoy, Maria Manosas, Felix Ritort

Base stacking is crucial in nucleic acid stabilization, from DNA duplex hybridization to single-stranded DNA (ssDNA) protein binding. While stacking energies are tiny in ssDNA, they are inextricably mixed with hydrogen bonding in DNA base pairing, making their measurement challenging. We conduct unzipping experiments with optical tweezers of short poly-purin

Sudipta Mondal, Pritam Halder, Saptarshi Roy, Aditi Sen De

Connecting incompatibility in measurements with the violation of local realism is one of the fundamental avenues of research. For two qubits, any incompatible pair of projective measurements can violate Clauser-Horne-Shimony-Holt (CHSH) inequality for some states, and there is a monotonic relationship between the level of measurement incompatibility (project

Svetlana Seliunina, Artem Otelepko, Raphael Memmesheimer, Sven Behnke

Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes where persons perform three different ac

Raihan Kabir, Naznin Haque, Md Saiful Islam, Marium-E-Jannat

Visual question answering (VQA) refers to the problem where, given an image and a natural language question about the image, a correct natural language answer has to be generated. A VQA model has to demonstrate both the visual understanding of the image and the semantic understanding of the question, demonstrating reasoning capability. Since the inception of

Konstantinos Bougiatiotis, Georgios Paliouras

Multi-relational networks capture intricate relationships in data and have diverse applications across fields such as biomedical, financial, and social sciences. As networks derived from increasingly large datasets become more common, identifying efficient methods for representing and analyzing them becomes crucial. This work extends the Prime Adjacency Matr

Suiyao Chen, Jing Wu, Yunxiao Wang, Cheng Ji

Representation learning is a fundamental aspect of modern artificial intelligence, driving substantial improvements across diverse applications. While selfsupervised contrastive learning has led to significant advancements in fields like computer vision and natural language processing, its adaptation to tabular data presents unique challenges. Traditional ap

Gilles Tarjus, Matthieu Tissier, Ivan Balog

We discuss the breakdown of the Parisi-Sourlas supersymmetry (SUSY) and of the dimensional-reduction (DR) property in the random field Ising and O($N$) models as a function of space dimension $d$ and/or number of components $N$. The functional renormalization group (FRG) predicts that this takes place below a critical line $d_{\rm DR}(N)$. We revisit the per

Ece Uykur, Oleg Janson, Victoria A. Ginga, Marcus Schmidt

Pressure evolution of RuO2 is studied using single-crystal x-ray diffraction in a diamond anvil cell, combined with \textit{ab initio} band-structure calculations. The tetragonal rutile structure transforms into the orthorhombic CaCl$_2$-type structure above 13 GPa under quasi-hydrostatic pressure conditions. This second-order transition is ferroelastic in n

Chen Chen

Documents serve as a crucial and indispensable medium for everyday workplace tasks. However, understanding, interacting and creating such documents on today's planar interfaces without any intelligent support are challenging due to our natural cognitive constraints on remembering, processing, understanding and interacting with these information. My doctorate

Jiyoon Pyo, Yao-Yi Chiang

Record linkage integrates diverse data sources by identifying records that refer to the same entity. In the context of mineral site records, accurate record linkage is crucial for identifying and mapping mineral deposits. Properly linking records that refer to the same mineral deposit helps define the spatial coverage of mineral areas, benefiting resource id

Depeng Chen, Xiao Liu, Jie Cui, Hong Zhong

Since machine learning model is often trained on a limited data set, the model is trained multiple times on the same data sample, which causes the model to memorize most of the training set data. Membership Inference Attacks (MIAs) exploit this feature to determine whether a data sample is used for training a machine learning model. However, in realistic sce

Olga Tapinova, Tal Finkelman, Tamar Reitich-Stolero, Rony Paz

Humans and other organisms make decisions choosing between different options, with the aim to maximize the reward and minimize the cost. The main theoretical framework for modeling the decision-making process has been based on the highly successful drift-diffusion model, which is a simple tool for explaining many aspects of this process. However, new observa

Dimitria Silveria, Kleber Cabral, Peter Jardine, Sidney Givigi

This work investigates the self-organization of multi-agent systems into closed trajectories, a common requirement in unmanned aerial vehicle (UAV) surveillance tasks. In such scenarios, smooth, unbiased control signals save energy and mitigate mechanical strain. We propose a decentralized control system architecture that produces a globally stable emergent

S. A. Myers, Haoyun Huang, Waseem Hussain, L. N. Pfeiffer

When interactions overcome disorder, integer quantum Hall plateaus support topological phases with different bulk insulators. In the center of the $\nu=1$ plateau the bulk is an Anderson-type insulator, while in the flanks of the plateau the bulk is the integer quantum Hall Wigner solid. We find that the activation energy along the $\nu=1$ plateau exhibits a

Aristides Kontogeorgis, Dimitrios Noulas

The Heisenberg curve is defined topologically as a cover of the Fermat curve and corresponds to an extension of the projective line minus three points by the non-abelian Heisenberg group modulo n. We compute its fundamental group and investigate an action from Artin's Braid group to the curve itself and its homology. We also provide a description of the homo

Dipanshu, Akashdeep Karan, Tarun Souradeep

In this work, we present a follow-up to our previous work where the concept of minimal Bipolar spherical harmonics (mBipoSH) functions was introduced as a natural basis for studying angular correlations in the non-statistical isotropic (nSI) Cosmic Microwave Background (CMB) sky. In this study, we extend the formalism of mBipoSH functions and apply it to ana

F. Hafiz, M. R. I. Rafi, M. Tasfia, M. M. Rahman

This paper extends a previous model for p-i-n GaAs quantum dot solar cells (QDSC) by revising the equation of photocarrier generation rate in quantum dots (QDs) inside the intrinsic region. In our model, we address a notable discrepancy that arose from the previous model where they did not consider the volume of QDs within the intrinsic region, leading to an

Michael Stoltz

This paper explores how automation and artificial intelligence (AI) are transforming U.S. cyber diplomacy. Leveraging these technologies helps the U.S. manage the complexity and urgency of cyber diplomacy, improving decision-making, efficiency, and security. As global inter connectivity grows, cyber diplomacy, managing national interests in the digital space

Annalena Aicher, Stefan Hillmann, Isabel Feustel, Thilo Michael

In the last decade, crowdsourcing has become a popular method for conducting quantitative empirical studies in human-machine interaction. The remote work on a given task in crowdworking settings suits the character of typical speech/language-based interactive systems for instance with regard to argumentative conversations and information retrieval. Thus, cro

Manal Mehdaoui, Amine Abouaomar

Deep Reinforcement Learning (DRL) is a powerful tool used for addressing complex challenges in mobile networks. This paper investigates the application of two DRL models, on-policy and off-policy, in the field of resource allocation for Open Radio Access Networks (O-RAN). The on-policy model is the Proximal Policy Optimization (PPO), and the off-policy model

Mengyuan Hu, An Zhang, Yong Chen, Mingyang Gong

For a positive integer $k \ge 1$, a $k$-star ($k^+$-star, $k^-$-star, respectively) is a connected graph containing a degree-$\ell$ vertex and $\ell$ degree-$1$ vertices, where $\ell = k$ ($\ell \ge k$, $1 \le \ell \le k$, respectively). The $k^+$-star packing problem is to cover as many vertices of an input graph $G$ as possible using vertex-disjoint $k^+$-

Yan Zheng, Zhenxiao Liang, Xiaoyan Cong, Lanqing guo

We explore the oscillatory behavior observed in inversion methods applied to large-scale text-to-image diffusion models, with a focus on the "Flux" model. By employing a fixed-point-inspired iterative approach to invert real-world images, we observe that the solution does not achieve convergence, instead oscillating between distinct clusters. Through both to

P. Berczik, M. Ishchenko, O. Sobodar, M. Mardini

Aims. We identify the possible dynamical connection between individual r-process-enhanced stars and the ultra-faint dwarf galaxy Reticulum II based on the current phase-space information for these stars and the dynamical mass-loss model of Reticulum II during its orbital motion for 11.5 Gyr of lookback time. The dynamical orbital modelling together with the

Csaba Biró, André E. Kézdy, Jenő Lehel

The interval count problem, a classical question in the study of interval orders, was introduced by Ronald Graham in the 1980s. This problem asks: given an interval order $P$, what is the minimum number of distinct interval lengths required to construct an interval representation of $P$? Interval orders that can be represented with just one interval length a

Alisa Sheinkman, Sara Wade

Despite the dominant role of deep models in machine learning, limitations persist, including overconfident predictions, susceptibility to adversarial attacks, and underestimation of variability in predictions. The Bayesian paradigm provides a natural framework to overcome such issues and has become the gold standard for uncertainty estimation with deep model

Yue Wang, Xu Cao, Yaojun Hu, Haochao Ying

Electrocardiogram (ECG), a non-invasive and affordable tool for cardiac monitoring, is highly sensitive in detecting acute heart attacks. However, due to the lengthy nature of ECG recordings, numerous machine learning methods have been developed for automated heart disease detection to reduce human workload. Despite these efforts, performance remains subopti

Moshe Babaioff, Noam Manaker Morag

We consider the problem of allocating heterogeneous and indivisible goods among strategic agents, with preferences over subsets of goods, when there is no medium of exchange. This model captures the well studied problem of fair allocation of indivisible goods. Serial-quota mechanisms are allocation mechanisms where there is a predefined order over agents, an

Alessio Belfiglio, Orlando Luongo, Tommaso Mengoni

We explore cosmological particle production associated with inflationary fluctuations by comparing gravitational and geometric mechanisms, within a non-minimal Yukawa-like coupling between a inflaton and spacetime curvature. We show under which circumstances the number of geometric particles is comparable to the purely gravitational contribution by introduci

Philippe Humbert

For a number of applications like low-source reactor start-up or neutron coincidence counting it is necessary to take into account the stochastic nature of neutron transport and go beyond the average neutron density, which is solution of a linear Boltzmann equation. In this work, we are particularly interested in calculating the moments and probabilities of

Yann Cado, Christoph Englert, Tanmoy Modak, Mariano Quirós

We investigate the impact of preheating on baryogenesis in $R^2$-Higgs inflation. In this scenario, the inclusion of a dimension-six operator ${(R/ \Lambda^2)} B_{\mu\nu} \widetilde{B}^{\mu\nu} $ abundantly generates helical hypermagnetic fields during inflation, leading to a baryon asymmetric Universe at the electroweak crossover. Focusing on the $R^2$-like

Ben De Bondt, Alessandro Vignati

In a recent article by Farah and the authors, a strong lifting theorem was proved for a class of coordinate-respecting maps between reduced products of discrete structures, hereby working under mild Forcing Axioms. We generalise this lifting theorem to the metric setting.

Edgar C. Merkle, Nikolay Petrov, Sophie Ma Zhu, Ezra Karger

Assessing forecasting performance is a time intensive activity, often requiring months or years before we know whether or not the reported forecasts were accurate. Cognitive tests can be quickly administered and are predictive of forecasting performance, but it is unclear which and how many tests are optimal. In this study, we develop adaptive cognitive test

Dan Crisan, Etienne Pardoux

Nonlinear filtering is a pivotal problem that has attracted significant attention from mathematicians, statisticians, engineers, and various other scientific disciplines. The solution to this problem is governed by the so-called filtering equations. In this paper, we investigate the uniqueness of solutions to these equations within measure spaces and introdu

Peter Bradshaw, Sergey Norin, Douglas B. West

An edge-coloring of a graph $G$ assigns a color to each edge of $G$. An edge-coloring is a parity edge-coloring if for each path $P$ in $G$, it uses some color on an odd number of edges in $P$. It is a strong parity edge-coloring if for every open walk $W$ in $G$, it uses some color an odd number of times along $W$. The minimum numbers of colors in parity an

Yu-Fei Shi, Yang Ai, Ye-Xin Lu, Hui-Peng Du

We participated in track 2 of the VoiceMOS Challenge 2024, which aimed to predict the mean opinion score (MOS) of singing samples. Our submission secured the first place among all participating teams, excluding the official baseline. In this paper, we further improve our submission and propose a novel Pitch-and-Spectrum-aware Singing Quality Assessment (PS-S

Ekaterina I. Marchenko, Maria G. Khrenova, Korolev V. V., Eugene A. Goodilin

Layered hybrid halide compounds offer promising functional properties, particularly tunable band gaps, conductivity, light harvesting thus making them prospective for applications in photovoltaics and optoelectronics. This study exemplifies an approach of predicting band gaps using machine learning models enhanced by invariant topological representations of

Ziteng Wang, Domenico Bongiovanni, Xiangdong Wang, Zhichan Hu

The discovery of topological phases of matter and topological boundary states had tremendous impact on condensed matter physics and photonics, where topological phases are defined via energy bands, giving rise to topological band theory. However, topological systems that cannot be described by band topology but still support non-trivial boundary states are l

Zikang Zhou, Hengjian Zhou, Haibo Hu, Zihao Wen

Anticipating the multimodality of future events lays the foundation for safe autonomous driving. However, multimodal motion prediction for traffic agents has been clouded by the lack of multimodal ground truth. Existing works predominantly adopt the winner-take-all training strategy to tackle this challenge, yet still suffer from limited trajectory diversity

S. Bondarenko, Ya. Dydyshka, L. Kalinovskaya, A. Kampf

This work is devoted to validating the results obtained using the Monte Carlo generator ReneSANCe. A comparison of differential cross sections, as well as single- and double-spin asymmetries, taking into account the polarization of initial states is presented.

Elinor Ginzburg-Ganz, Ittay Eyal, Ram Machlev, Dmitry Baimel

We propose an extended demand response program, based on ancillary service for supplying flexible electricity demand. In our proposed scheme, we suggest a broader management model to control the scheduling and power consumption of Bitcoin mining machines. The main aspect that we focus on is suppressing the power ramping and related transient effects. We exte

Kyosuke Tomonari

We investigate degrees of freedom in New General Relativity. This theory is the three-parameter extension of Teleparallel Equivalent to GR and classified into nine irreducible types according to the rotation symmetry $SO(3)$ on each leaf of ADM-foliation. In the previous work~[{\it Phys. Rev. D 112 (2025) 8, 084052}], we investigated the degrees of freedom i

Eveling C. Ribeiro, L. Formigari, Marcos R. Ribeiro, Elcio Abdalla

We investigate the stability of scalar perturbations around a magnetized stationary compact object in General Relativity. The considered object is one of the simplest exact solutions of Einstein electrovacuum equations corresponding to a spheroidal body endowed with a dipole magnetic moment. It is effectively constructed by imposing a perfect reflection (mir

Guoping Xu, Ximing Wu, Wentao Liao, Xinglong Wu

Accurately segmenting lesions in ultrasound images is challenging due to the difficulty in distinguishing boundaries between lesions and surrounding tissues. While deep learning has improved segmentation accuracy, there is limited focus on boundary quality and its relationship with body structures. To address this, we introduce UBBS-Net, a dual-branch deep n

Qingyi Zhan, Jinqiao Duan, Xiaofan Li, Lijin Wang

Stochastic contact Hamiltonian systems are a class of important mathematical models, which can describe the dissipative properties with odd dimensions in the stochastic environment. In this article, we investigate the numerical dynamics of the stochastic contact Hamiltonian systems via structure-preserving methods. The contact structure-preserving schemes ar