December 2024 arXiv papers — page 103

Naoki Sato, Koshiro Izumi, Hideaki Iiduka

A scaled conjugate gradient method that accelerates existing adaptive methods utilizing stochastic gradients is proposed for solving nonconvex optimization problems with deep neural networks. It is shown theoretically that, whether with constant or diminishing learning rates, the proposed method can obtain a stationary point of the problem. Additionally, its

Edward Kim, Manil Shrestha, Richard Foty, Tom DeLay

Creation and curation of knowledge graphs can accelerate disease discovery and analysis in real-world data. While disease ontologies aid in biological data annotation, codified categories (SNOMED-CT, ICD10, CPT) may not capture patient condition nuances or rare diseases. Multiple disease definitions across data sources complicate ontology mapping and disease

Xiaochong Dong, Jun Dan, Yingyun Sun, Yang Liu

Driven by global climate change and the ongoing energy transition, the coupling between power supply capabilities and meteorological factors has become increasingly significant. Over the long term, accurately quantifying the power generation of renewable energy under the influence of climate change is essential for the development of sustainable power system

Dong Chen, Bingxu Wang, Shunning Li, Wentao Zhang

Lithium superionic conductors (LSICs) are crucial for next-generation solid-state batteries, offering exceptional ionic conductivity and enhanced safety for renewable energy and electric vehicles. However, their discovery is extremely challenging due to the vast chemical space, limited labeled data, and the understanding of complex structure-function relatio

Naoki Kitazawa

We characterize $3$-dimensional manifolds represented as connected sums of Lens spaces, copies of $S^2 \times S^1$, and torus bundles over the circle by certain Morse-Bott functions. This adds to our previous result around 2024, classifying Morse functions whose preimages containing no singular points are disjoint unions of spheres and tori on $3$-dimensiona

Antonio Carlos Rivera, Anthony Moore, Steven Robinson

Object-aware reasoning in vision-language tasks poses significant challenges for current models, particularly in handling unseen objects, reducing hallucinations, and capturing fine-grained relationships in complex visual scenes. To address these limitations, we propose the Vision-Aware Retrieval-Augmented Prompting (VRAP) framework, a generative approach th

Junkai Fan, Kun Wang, Zhiqiang Yan, Xiang Chen

In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos. These tasks are inherently complementary: enhanced depth estimation improves dehazing via the atmospheric scattering model (ASM), while superior dehazing contributes to more accurate depth estimation through the brightness con

Francis Tang, Ying Ding

Time series data plays a critical role across diverse domains such as healthcare, energy, and finance, where tasks like classification, anomaly detection, and forecasting are essential for informed decision-making. Recently, large language models (LLMs) have gained prominence for their ability to handle complex data and extract meaningful insights. This stud

K. Xia, W. Duch, Y. Sun, K. Xu

A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, etc. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial

Xiaochong Dong, Xuemin Zhang, Ming Yang, Shengwei Mei

Leveraging spatio-temporal correlations among wind farms can significantly enhance the accuracy of ultra-short-term wind power forecasting. However, the complex and dynamic nature of these correlations presents significant modeling challenges. To address this, we propose a spatio-temporal dynamic hypergraph learning (STDHL) model. This model uses a hypergrap

Jan Büthe, Jean-Marc Valin

Reducing the bandwidth of speech is common practice in resource constrained environments like low-bandwidth speech transmission or low-complexity vocoding. We propose a lightweight and robust method for extending the bandwidth of wideband speech signals that is inspired by classical methods developed in the speech coding context. The resulting model has just

Rafael Souza, Jia-Hao Lim, Alexander Davis

Temporal reasoning is a critical challenge in video-language understanding, as it requires models to align semantic concepts consistently across time. While existing large vision-language models (LVLMs) and large language models (LLMs) excel at static tasks, they struggle to capture dynamic interactions and temporal dependencies in video sequences. In this w

Xiaoqing Chen, Tianwang Jia, Yunlu Tu, Dongrui Wu

An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, such systems face at least three major challenges in real-world applications: limited decoding accuracy, poor robustness, and privacy risks. Although prior studies have addressed one or two of these issues, methods

Maokai Hu, Yiping Ao, Yi Yang, Lei Hu

The short-lived ionized emission lines in early spectroscopy of the nearby type II supernova SN 2024ggi signify the presence of dense circumstellar matter (CSM) close to its progenitor star. We proposed the Atacama Large Millimeter/submillimeter Array (ALMA) observations by its Director's Discretionary Time program to catch the potential synchrotron radiatio

Aum Kendapadi, Kerem Zaman, Rakesh R. Menon, Shashank Srivastava

Large language models (LLMs) excel at answering questions but remain passive learners-absorbing static data without the ability to question and refine knowledge. This paper explores how LLMs can transition to interactive, question-driven learning through student-teacher dialogues. We introduce INTERACT (INTERactive learning for Adaptive Concept Transfer), a

Abdulrahman Althobaiti, Angel Ayala, JingYing Gao, Ali Almutairi

Large Language Models (LLMs) are transforming the robotics domain by enabling robots to comprehend and execute natural language instructions. The cornerstone benefits of LLM include processing textual data from technical manuals, instructions, academic papers, and user queries based on the knowledge provided. However, deploying LLM-generated code in robotic

Dong Chen, Chun-Long Chen, Guo-Wei Wei

Metal-organic frameworks (MOFs) are porous, crystalline materials with high surface area, adjustable porosity, and structural tunability, making them ideal for diverse applications. However, traditional experimental and computational methods have limited scalability and interpretability, hindering effective exploration of MOF structure-property relationships

Tom S. Juzek, Zina B. Ward

Scientific English is currently undergoing rapid change, with words like "delve," "intricate," and "underscore" appearing far more frequently than just a few years ago. It is widely assumed that scientists' use of large language models (LLMs) is responsible for such trends. We develop a formal, transferable method to characterize these linguistic changes. Ap

Syed Quiser Ahmed, Bharathi Vokkaliga Ganesh, Sathyanarayana Sampath Kumar, Prakhar Mishra

This research provides a comprehensive overview of adversarial attacks on AI and ML models, exploring various attack types, techniques, and their potential harms. We also delve into the business implications, mitigation strategies, and future research directions. To gain practical insights, we employ the Adversarial Robustness Toolbox (ART) [1] library to si

Mingxin Guo, Zuo Quan Xu

In this paper, we explore a new class of stochastic control problems characterized by specific control constraints. Specifically, the admissible controls are subject to the ratcheting constraint, meaning they must be non-decreasing over time and are thus self-path-dependent. This type of problems is common in various practical applications, such as optimal c

Cheng Huang, Nikolaos Parthenios, Maksim Ulybyshev, Xu Zhang

The fascinating quantum many-body states in twisted bilayber graphene (TBG) at magic angle, due to the interplay of Coulomb interactions and the quantum metrics of flat bands, have been well understood both experimentally and theoretically. However, the phase diagram and excitations as functions of twist angle and permittivity are still largely unknown. Here

Anika Tabassum, Amirkoushyar Ziabari

Industrial X-ray computed tomography (XCT) is a powerful tool for non-destructive characterization of materials and manufactured components. XCT commonly accompanied by advanced image analysis and computer vision algorithms to extract relevant information from the images. Traditional computer vision models often struggle due to noise, resolution variability,

Yingping Liang, Ying Fu

Data-free knowledge distillation transfers knowledge by recovering training data from a pre-trained model. Despite the recent success of seeking global data diversity, the diversity within each class and the similarity among different classes are largely overlooked, resulting in data homogeneity and limited performance. In this paper, we introduce a novel Re

Chuqin Zhou, Guo Lu, Jiangchuan Li, Xiangyu Chen

Neural image compression often faces a challenging trade-off among rate, distortion and perception. While most existing methods typically focus on either achieving high pixel-level fidelity or optimizing for perceptual metrics, we propose a novel approach that simultaneously addresses both aspects for a fixed neural image codec. Specifically, we introduce a

Weijin Xu

Epilepsy and epileptiform discharges are common harmful brain activities, and electroencephalogram (EEG) signals are widely used to monitor the onset status of patients. However, due to the lack of unified EEG signal acquisition standards, there are many obstacles in practical applications, especially the difficulty in transferring and using models trained o

Dannong Wang, Daniel Kim, Bo Jin, Xingjian Zhao

Finetuned large language models (LLMs) have shown remarkable performance in financial tasks, such as sentiment analysis and information retrieval. Due to privacy concerns, finetuning and deploying Financial LLMs (FinLLMs) locally are crucial for institutions. However, finetuning FinLLMs poses challenges including GPU memory constraints and long input sequenc

Shijia Zhou, Euijoon Ahn, Hao Wang, Ann Quinton

The measurement of fetal thalamus diameter (FTD) and fetal head circumference (FHC) are crucial in identifying abnormal fetal thalamus development as it may lead to certain neuropsychiatric disorders in later life. However, manual measurements from 2D-US images are laborious, prone to high inter-observer variability, and complicated by the high signal-to-noi

Chengsen Wang, Qi Qi, Jingyu Wang, Haifeng Sun

Human experts typically integrate numerical and textual multimodal information to analyze time series. However, most traditional deep learning predictors rely solely on unimodal numerical data, using a fixed-length window for training and prediction on a single dataset, and cannot adapt to different scenarios. The powered pre-trained large language model has

Yayuan Li, Jintao Guo, Lei Qi, Wenbin Li

Contrastive Language-Image Pretraining (CLIP) has been widely used in vision tasks. Notably, CLIP has demonstrated promising performance in few-shot learning (FSL). However, existing CLIP-based methods in training-free FSL (i.e., without the requirement of additional training) mainly learn different modalities independently, leading to two essential issues:

Che-Yung Shen, Paolo Batoni, Xilin Yang, Jingxi Li

We present a broadband and polarization-insensitive unidirectional imager that operates at the visible part of the spectrum, where image formation occurs in one direction while in the opposite direction, it is blocked. This approach is enabled by deep learning-driven diffractive optical design with wafer-scale nano-fabrication using high-purity fused silica

Matthew Stephenson, Matthew Sidji, Benoît Ronval

In this paper, we propose the use of the popular word-based board game Codenames as a suitable benchmark for evaluating the reasoning capabilities of Large Language Models (LLMs). Codenames presents a highly interesting challenge for achieving successful AI performance, requiring both a sophisticated understanding of language, theory of mind, and epistemic r

Xiao-Xu Fang, Hao-Yang Du, Xiuquan Zhang, Lei Wang

Thin-film lithium niobate on insulator~(LNOI) emerges as a promising platform for integrated quantum photon source, enabling scalable on-chip quantum information processing. The most popular technique to overcome the phase mismatching between interacting waves in waveguide is periodic poling, which is intrinsically sensitive to poling uniformity. Here, we re

Zhu-Qi Tao, Xiao-Xu Fang, He Lu

Lithium niobate~(LN) is a birefringent material, where the strong birefringence thermo-optic effect is promising for the generation of quantum photon source with widely tunable wavelength. Here, we demonstrate birefringent phase-matching in a 20-mm-long waveguide fabricated on 5~$\mu$m-thick x-cut lithium niobate on insulator. The waveguide is deviated from

Si-Qi Chen, He Lu

Shortcuts to adiabaticity~(STA) enables fast and robust coherent control of quantum system, which has been well placed in quantum technologies. In particular, inverse engineering STA provides much more freedom for the optimization of shortcut, which alleviates the complexity for experimental realization. Here, we implement a STA technique, known as invariant

Ka Wai Wu

Graph neural networks (GNNs) have seen extensive application in domains such as social networks, bioinformatics, and recommendation systems. However, the irregularity and sparsity of graph data challenge traditional computing methods, which are insufficient to meet the performance demands of GNNs. Recent research has explored parallel acceleration using CUDA

Quan Yuan, Zhikun Zhang, Linkang Du, Min Chen

Streaming graphs are ubiquitous in daily life, such as evolving social networks and dynamic communication systems. Due to the sensitive information contained in the graph, directly sharing the streaming graphs poses significant privacy risks. Differential privacy, offering strict theoretical guarantees, has emerged as a standard approach for private graph da

Li Li

Deep learning (DL) has significantly transformed cybersecurity, enabling advancements in malware detection, botnet identification, intrusion detection, user authentication, and encrypted traffic analysis. However, the rise of adversarial examples (AE) poses a critical challenge to the robustness and reliability of DL-based systems. These subtle, crafted pert

Ilya D. Shkredov

Let $A$ be a subset of a finite abelian group such that $A$ has a small difference set $A-A$ and the density of $A$ is small. We prove that, counter--intuitively, the smallness (in terms of $|A-A|$) of the Fourier coefficients of $A$ guarantees that $A$ is correlated with a large Bohr set. Our bounds on the size and the dimension of the resulting Bohr set ar

Yijun Wang, Tao Wang, Ke Xu, Junjie Mao

We report a rare case where an elliptical radio-loud quasar host, 3C 59, rejuvenates star formation activity through minor mergers with its nearby satellite galaxies. The inferred star formation history of 3C 59 shows significant star formation rejuvenation within the past 500 Myr, before which remains rather quiescent for most of the cosmic time. Three near

Rodrigo Moreira, Flávio de Oliveira Silva

Networks should connect communicating peers, supporting vertical services requirements. The network evolution towards 6G requires native network slicing techniques. Some literature approaches claim network slice realization, but they do not convincingly address the deployment across multiple Autonomous Systems. This work investigates the current 6G network s

Wonyong Seo, Jihyong Oh, Munchurl Kim

Existing Video Frame interpolation (VFI) models tend to suffer from time-to-location ambiguity when trained with video of non-uniform motions, such as accelerating, decelerating, and changing directions, which often yield blurred interpolated frames. In this paper, we propose (i) a novel motion description map, Bidirectional Motion field (BiM), to effectivel

Xiannan Huang, Yixin Chen, Quan Yuan, Chao Yang

Predicting future bus trip chains for an existing user is of great significance for operators of public transit systems. Existing methods always treat this task as a time-series prediction problem, but the 1-dimensional time series structure cannot express the complex relationship between trips. To better capture the inherent patterns in bus travel behavior,

Nádia Aparecida de Oliveira Silva, Rodrigo Moreira, Larissa Ferreira Rodrigues, Rafael Marinho e Silva

People with visual impairments struggle with urban mobility and independent travel, opening up opportunities for technological advances to improve their quality of life. The Internet of Things (IoT) plays an essential role in bringing improvements and accessibility for visually impaired people. Although alternatives aimed to use IoT in urban mobility, those

Qiang Hu, Houqiang Zhong, Zihan Zheng, Xiaoyun Zhang

Neural Radiance Field (NeRF)-based volumetric video has revolutionized visual media by delivering photorealistic Free-Viewpoint Video (FVV) experiences that provide audiences with unprecedented immersion and interactivity. However, the substantial data volumes pose significant challenges for storage and transmission. Existing solutions typically optimize NeR

Takao Tsuneda, Tetsuya Taketsugu

This study offers a physics-based framework for understanding chemical reactions, unveiling the pivotal role of the occupied reactive orbital (ORO), the most stabilized occupied molecular orbital during a reaction, in driving atomic nuclei along the reaction pathway via electrostatic forces. We show that these electrostatic forces are governed by the negativ

Ehsan Asali, Prashant Doshi

We present a novel method for collaborative robots (cobots) to learn manipulation tasks and perform them in a human-like manner. Our method falls under the learn-from-observation (LfO) paradigm, where robots learn to perform tasks by observing human actions, which facilitates quicker integration into industrial settings compared to programming from scratch.

Ivan Arraut

The cosmological constant is normally introduced as an additional term entering the Einstein-Hilbert (EH) action. In this letter we demonstrate that instead, it appears naturally from the standard EH action as an invariant term emerging from spacetime symmetries. We then demonstrate that the same constraint emerging from this invariant, suppresses the short

Si-Tong Jin, Shi-Wen He, Zi-Long Yang, Xuanxuan Xin

Magnon blockade is a fundamental quantum phenomenon for generating single-magnon state, which gradually becomes one of the candidates for quantum information processing. In this paper, we propose a theoretical scheme to generate the magnon blockade in a hybrid system consisting of a YIG micromagnet and a skyrmion. Considering weak probing of the magnon and d

Catherine Falvey, Heewon Hah, William Sheppard, Brian Sittinger

Although a good portion of elementary linear algebra concerns itself with matrices over a field such as $\mathbb{R}$ or $\mathbb{C}$, many combinatorial problems naturally surface when we instead work with matrices over a finite field. As some recent work has been done in these areas, we turn our attention to the problem of enumerating the square matrices wi

Zepu Kou, Yuquan Zhou, Zonghuiyi Jiang, Alexandre Tkatchenko

Van der Waals torque determines the relative rotational motion between anisotropic objects, being of relevance to low-dimensional systems. Here we demonstrate a substantial torque between anisotropic two-dimensional materials that arises from the interaction between many-body charge density fluctuations, exceeding by twenty-fold the torque computed with atom

Yuqing Li, Jinglei Cheng, Xulong Tang, Youtao Zhang

Quantum machine learning is considered one of the flagship applications of quantum computers, where variational quantum circuits could be the leading paradigm both in the near-term quantum devices and the early fault-tolerant quantum computers. However, it is not clear how to identify the regime of quantum advantages from these circuits, and there is no expl

Insang Song, Kyle P. Messier

An increasing volume of studies utilize geocomputation methods in large spatial data. There is a bottleneck in scalable computation for general scientific use as the existing solutions require high-performance computing domain knowledge and are tailored for specific use cases. This study presents an R package `chopin` to reduce the technical burden for paral

Artem Malko

Following the classical results of Stong, we introduce a cohomological analogue of a core of a finite sheaved topological space and propose an algorithm for simplification in this category. In particular we generalize the notion of beat vertices and show that if a vertex of a sheaved space has topologically acyclic downset (with trivial coefficients), then i

Huyen Thanh Phan, Shun Takahashi, Satoshi Iwamoto, Katsunori Wakabayashi

We numerically study the first and the second order topological states of electromagnetic (EM) wave in the three-dimensional (3D) woodpile photonic crystal (PhC). The recent studies on 3D PhCs have mainly focused on the observation of the topological states. Here, we not only focus on finding the topological states but also propose a numerical calculation me

Colin D. Reid

Let $G$ be a totally disconnected, locally compact (t.d.l.c.) group. The scale $s_G(g)$ of $g \in G$ in the sense of Willis is given by the minimum value of the index $|gUg^{-1}:U \cap gUg^{-1}|$ as $U$ ranges over the compact open subgroups; the theory associated to the scale has been very successful in describing general dynamical features of automorphisms

Yue Xiu, Yang Zhao, Ran Yang, Huimin Tang

Device-to-device (D2D)-assisted mobile edge computing (MEC) is one of the critical technologies of future sixth generation (6G) networks. The core of D2D-assisted MEC is to reduce system latency for network edge UEs by supporting cloud computing services, thereby achieving high-speed transmission. Due to the sensitivity of communication signals to obstacles,

Hang Jin, Xin He, Lingyun Wang, Yujun Zhu

Poor sitting posture can lead to various work-related musculoskeletal disorders (WMSDs). Office employees spend approximately 81.8% of their working time seated, and sedentary behavior can result in chronic diseases such as cervical spondylosis and cardiovascular diseases. To address these health concerns, we present SitPose, a sitting posture and sedentary

Weibin Chen, Azhir Mahmood, Michel Tsamados, So Takao

The rapid growth of earth observation systems calls for a scalable approach to interpolate remote-sensing observations. These methods in principle, should acquire more information about the observed field as data grows. Gaussian processes (GPs) are candidate model choices for interpolation. However, due to their poor scalability, they usually rely on inducin

Amadeu Delshams, Ke Zhang

We show that given a general uncoupled a priori unstable Hamiltonian \[ \frac12 p^2 + V(q) + G(I) + \epsilon h(p, q, I, \varphi, t), \] where $h$ is a generic Ma\~n\'e analytic function and $\epsilon$ is small enough, there is an orbit for which the momentum $I$ changes by any arbitrarily prescribed value. We call this phenomenon as global diffusion since th

Shoumi Sarkar, Anish Mukherjee, Jeremy T. Gaskins, Steven Levy

Motivated by the Iowa Fluoride Study (IFS), which tracked fluoride intake and dental outcomes from childhood to young adulthood (ages 9, 13, 17, and 23), we analyze dental fluorosis - a condition caused by excessive fluoride exposure during enamel formation. In this context, fluorosis scores across tooth surfaces present as zero-inflated, clustered, and long

T. Dial, A. T. Deller, P. A. Uttarkar, M. E. Lower

There has been a rapid increase in the known fast radio burst (FRB) population, yet the progenitor(s) of these events have remained an enigma. A small number of FRBs have displayed some level of quasi-periodicity in their burst profile, which can be used to constrain their plausible progenitors. However, these studies suffer from the lack of polarisation dat

Jude Ann Vishnu, Torsten Gereon Linder, Sebastian Seiffert, Friederike Schmid

Letting free polymers diffuse from solution into a crosslinked polymer gel is often a crucial processing step in the synthesis of multiphase polymer-based gels, e.g., core-shell microgels. Here we use coarse-grained molecular dynamics simulations to obtain molecular insights into this process. We consider idealized situations where the gel is modeled as a re

A. Ahmad, S. Dai, S. Lazarević, M. D. Filipović

Detecting a pulsar associated with a supernova remnant (SNR) and/or pulsar wind nebula (PWN) is crucial for unraveling its formation history and pulsar wind dynamics, yet the association with a radio pulsar is observed only in a small fraction of known SNRs and PWNe. In this paper, we report the discovery of a young pulsar J1631$-$4722, associated with the G

O. Kounchev, H. Render, Ts. Tsachev

The main result of the paper is an interesting relation between the solution of the truncated Exponential Moment problem and truncated Classical Moment problem, considered on the half-line or on a compact interval.

Melanie McGrath, Harrison Bailey, Necva Bölücü, Xiang Dai

Information extraction from the scientific literature is one of the main techniques to transform unstructured knowledge hidden in the text into structured data which can then be used for decision-making in down-stream tasks. One such area is Trust in AI, where factors contributing to human trust in artificial intelligence applications are studied. The relati

Leon Gorißen, Jan-Niklas Schneider, Mohamed Behery, Philipp Brauner

The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building

Yamin Sepehri, Pedram Pad, Pascal Frossard, L. Andrea Dunbar

The training phase of deep neural networks requires substantial resources and as such is often performed on cloud servers. However, this raises privacy concerns when the training dataset contains sensitive content, e.g., facial or medical images. In this work, we propose a method to perform the training phase of a deep learning model on both an edge device a

Anna Poggialini, Pablo Villegas, Miguel A. Muñoz, Andrea Gabrielli

Scale invariance profoundly influences the dynamics and structure of complex systems, spanning from critical phenomena to network architecture. Here, we propose a precise definition of scale-invariant networks by leveraging the concept of a constant entropy-loss rate across scales in a renormalization-group coarse-graining setting. This framework enables us

Vladimir Dotsenko

We prove a theorem that computes, for any augmented operad $\mathcal{O}$, the stable homology of the Lie algebra of derivations of the free algebra $\mathcal{O}(V)$ with twisted bivariant coefficients (here stabilization occurs as $\dim(V)\to\infty$) out of the homology of the wheeled bar construction of $\mathcal{O}$; this can further be used to prove unifo

Byung-Kwan Ko, Jun-Young Kim, Seo-Hyun Lee

This study examines the effectiveness of traditional machine learning classifiers versus deep learning models for detecting the imagined speech using electroencephalogram data. Specifically, we evaluated conventional machine learning techniques such as CSP-SVM and LDA-SVM classifiers alongside deep learning architectures such as EEGNet, ShallowConvNet, and D

Ibon Gracia, Luca Laurenti, Manuel Mazo, Alessandro Abate

In this paper, we present a novel framework to synthesize robust strategies for discrete-time nonlinear systems with random disturbances that are unknown, against temporal logic specifications. The proposed framework is data-driven and abstraction-based: leveraging observations of the system, our approach learns a high-confidence abstraction of the system in

Zhiheng Wang, Jiarui Liu

Font design poses unique challenges for logographic languages like Chinese, Japanese, and Korean (CJK), where thousands of unique characters must be individually crafted. This paper introduces a novel Vision Transformer (ViT)-based model for multi-language font generation, effectively addressing the complexities of both logographic and alphabetic scripts. By

Joseph Sartini, Xinkai Zhou, Liz Selvin, Scott Zeger

Functional Principal Components Analysis (FPCA) is a widely used analytic tool for dimension reduction of functional data. Traditional implementations of FPCA estimate the principal components from the data, then treat these estimates as fixed in subsequent analyses. To account for the uncertainty of PC estimates, we propose FAST, a fully-Bayesian FPCA with

Zsolt Gulacsi

Recently a Jordan-Wigner transformation was constructed for spinful fermions at S=1/2 spins in one dimension connecting the spin-1/2 operators to genuine spinful canonical Fermi operators. In the presented paper this exact transformation is generalized to two dimensions.

Hayley Mills, George Janes, Anthony Bishopp, Natasha Savage

Arabidopsis root hair patterning is controlled by a complex transcription factor network containing positive and negative feedback loops, epidermal cell-cell signalling, and positional signalling from underlying tissue. Recently, several long accepted regulatory interactions within the network have been revised, and while there are extensive data regarding i

Fabio Zamio

Liver cancer is a leading cause of cancer-related mortality worldwide, with its high genetic heterogeneity complicating diagnosis and treatment. This study introduces DLSOM, a deep learning framework utilizing stacked autoencoders to analyze the complete somatic mutation landscape of 1,139 liver cancer samples, covering 20,356 protein-coding genes. By transf

Naoki Wake, Atsushi Kanehira, Daichi Saito, Jun Takamatsu

Multi-step dexterous manipulation is a fundamental skill in household scenarios, yet remains an underexplored area in robotics. This paper proposes a modular approach, where each step of the manipulation process is addressed with dedicated policies based on effective modality input, rather than relying on a single end-to-end model. To demonstrate this, a dex

Anton Grishechkin, Abhirup Mukherjee, Omer Karin

The hierarchical organisation of cell identity is a fundamental feature of animal development with rich and well-characterized experimental phenomenology, yet the mechanisms driving its emergence remain unknown. The regulation of cell identity genes relies on a distinct mechanism involving higher-order interactions of transcription factors on distant regulat

Ruth Elisabeth Appel

There is strong agreement that generative AI should be regulated, but strong disagreement on how to approach regulation. While some argue that AI regulation should mostly rely on extensions of existing laws, others argue that entirely new laws and regulations are needed to ensure that generative AI benefits society. In this paper, I argue that the debates on

Hod Gilad, Andrey Ushkov, Denis Kolchanov, Andrey Machnev

The capability to tailor mutual interactions between colloidal nanoparticles strongly depends on the length scales involved. While electrostatic and optomechanically driven interactions can cover nano and micron-scale landscapes, controlling inter-particle dynamics at larger distances remains a challenge. Small physical and electromagnetic cross-sections of

Xiaochen Zhu, Georgi Karadzhov, Chenxi Whitehouse, Andreas Vlachos

Diffusion models have shown promise in text generation, but often struggle with generating long, coherent, and contextually accurate text. Token-level diffusion doesn't model word-order dependencies explicitly and operates on short, fixed output windows, while passage-level diffusion struggles with learning robust representations for long-form text. To addre

Jorge Flórez

We establish integrality and congruence properties for the Eisenstein-Kronecker cocycle of Bergeron, Charollois and Garc\'ia introduced in [arXiv:2107.01992v2 [math.NT]]. As a consequence, we recover the integrality of the critical values of Hecke $L$-functions over imaginary quadratic fields in the split case. Additionally, we construct a $p$-adic measure t

Amine M. Aboussalah, Xuanze Li, Cheng Chi, Raj Patel

We propose a Finance-Informed Neural Network (FINN) for option pricing and hedging that integrates financial theory directly into machine learning. Instead of training on observed option prices, FINN is learned through a self-supervised replication objective based on dynamic hedging, ensuring economic consistency by construction. We show theoretically that m

Christos G. Massouros, Gerasimos G. Massouros

The quotient hyperfield is a landmark on the borderline of fields and hyperfields. In this paper, which is the second part of our previously published paper, all the hyperfields of order 7 are constructed, enumerated and presented, in the course of which an important family of 7-element canonical hypergroups is revealed. The study of these hyperfields proved

Yun-Chen Lo, Gu-Yeon Wei, David Brooks

As cutting-edge large language models (LLMs) continue to transform various industries, their fast-growing model size and sequence length have led to memory traffic and capacity challenges. Recently, AMD, Arm, Intel, Meta, Microsoft, NVIDIA, and Qualcomm have proposed a Microscaling standard (Mx), which augments block floating-point with microexponents to ach

Corey Beard, Paul Robertson, Jack Lubin, Te Han

Stellar activity contamination of radial velocity (RV) data is one of the top challenges plaguing the field of extreme precision RV (EPRV) science. Previous work has shown that photometry can be very effective at removing such signals from RV data, especially stellar activity caused by rotating star spots and plage.The exact utility of photometry for removin

Kristian Kolthoff, Felix Kretzer, Lennart Fiebig, Christian Bartelt

Graphical user interface (GUI) prototyping represents an essential activity in the development of interactive systems, which are omnipresent today. GUI prototypes facilitate elicitation of requirements and help to test, evaluate, and validate ideas with users and the development team. However, creating GUI prototypes is a time-consuming process and often req

Bogdan Malaescu

A measurement of additional radiation in $e^+e^- \to \mu^+\mu^- \gamma$ and $e^+e^- \to \pi^+\pi^- \gamma$ initial-state-radiation events is presented using the full $BaBar$ data sample. For the first time results are presented at next-to- and next-to-next-to-leading order, with one and two additional photons, respectively, for radiation from the initial and

Portia Cooper, Harshita Narnoli, Mihai Surdeanu

Text-to-image models are vulnerable to the stepwise "Divide-and-Conquer Attack" (DACA) that utilize a large language model to obfuscate inappropriate content in prompts by wrapping sensitive text in a benign narrative. To mitigate stepwise DACA attacks, we propose a two-layer method involving text summarization followed by binary classification. We assembled

K. J. Patten

Contact tracing is an essential tool in slowing and containing outbreaks of contagious diseases. Current contact tracing methods range from interviews with public health personnel to Bluetooth pings from smartphones. While all methods offer various benefits, it is difficult for different methods to integrate with one another. Additionally, for contact tracin

Xiaoxuan Liang, Wuyang Zhang, Hong Zhou, Zhaolong Wei

3D Human Mesh Reconstruction (HMR) from 2D RGB images faces challenges in environments with poor lighting, privacy concerns, or occlusions. These weaknesses of RGB imaging can be complemented by acoustic signals, which are widely available, easy to deploy, and capable of penetrating obstacles. However, no existing methods effectively combine acoustic signals

Florian Breuer, Mahefason Heriniaina Razafinjatovo

We investigate Drinfeld modular polynomials parametrizing $T$-isogenies between Drinfeld $\mathbb{F}_q[T]$-modules of rank $r\geq 2$. By providing an explicit classification of such isogenies, we derive explicit bounds on the $T$-degrees of the coefficients of the associated modular polynomials. In particular, we obtain exact expressions for the height (i.e.

Juraj Földes, David P. Herzog

An inductive procedure is developed to calculate the asymptotic behavior at time zero of a diffusion with polynomial drift and degenerate, additive noise. The procedure gives rise to two different rescalings of the process; namely, a functional law of the iterated logarithm rescaling and a distributional rescaling. The limiting behavior of these rescalings i

Juan Yang, Bao Quoc Tang

The global existence and boundedness of solutions to volume-surface reaction diffusion systems with a mass control condition are investigated. Such systems arise typically in e.g. cell biology, ecology or fluid mechanics, when some concentrations or densities are inside a domain and some others are on its boundary. Comparing to previous works, the difficulty

Sibasish Laha, Claudio Ricci, John C. Mather, Ehud Behar

Active galactic nuclei (AGN) are powerful sources of panchromatic radiation. All AGN emit in X-rays, contributing around $\sim 5-10\%$ of the AGN bolometric luminosity. The X-ray emitting region, popularly known as the corona, is geometrically and radiatively compact with a size typically $\lesssim 10 \, R_{\rm G}$ (gravitational radii). The rapid and extrem

Anton Pozharskiy, Armin Nurkanović, Moritz Diehl

Constrained dynamical systems are systems such that, by some means, the state stays within a given set. Two such systems are the (perturbed) Moreau sweeping process and the recently proposed extended Projected Dynamical System (ePDS). We show that under certain conditions solutions to the ePDS correspond to the solutions of a dynamic complementarity system,

Piotr Miska, Bartosz Sobolewski, Maciej Ulas

We introduce a new family of meta-Fibonacci sequences $(f(n))_{n\in\mathbb{N}}$, governed by the recurrence relation $$f(n)=af(n-u_{n}-1)+bf(n-u_{n}-2),$$ where $\mathbf{u}=(u_{n})_{n\in \mathbb{N}}$ is a sequence with values $0,1$. Our study focuses on the properties of the sequence of quotients $h(n) = f(n+1)/f(n)$ and its set of values $\mathcal{V}(f)=\{h

Gustavo Cilleruelo Calderón, Emily Allaway, Barry Haddow, Alexandra Birch

Generic sentences express generalisations about the world without explicit quantification. Although generics are central to everyday communication, building a precise semantic framework has proven difficult, in part because speakers use generics to generalise properties with widely different statistical prevalence. In this work, we study the implicit quantif

Andrei-Marius Avram, Mircea Timpuriu, Andreea Iuga, Vlad-Cristian Matei

Using supervised automatic summarisation methods requires sufficient corpora that include pairs of documents and their summaries. Similarly to many tasks in natural language processing, most of the datasets available for summarization are in English, posing challenges for developing summarization models in other languages. Thus, in this work, we introduce Ro

Marco Freibert

In this article, we provide a general set-up for arbitrary linear Lie groups $H\leq \mathrm{GL}(n,\mathbb{R})$ which allows to characterise the almost Abelian Lie algebras admitting a torsion-free $H$-structure. In more concrete terms, using that an $n$-dimensional almost Abelian Lie algebra $\mathfrak{g}=\mathfrak{g}_f$ is fully determined by an endomorphis