December 2024 arXiv papers — page 108

Harshvivek Kashid, Pushpak Bhattacharyya

Optical Character Recognition (OCR) technology has revolutionized the digitization of printed text, enabling efficient data extraction and analysis across various domains. Just like Machine Translation systems, OCR systems are prone to errors. In this work, we address the challenge of data generation and post-OCR error correction, specifically for low-resour

Simeon Kolev

Bertsimas and Lo's seminal work laid the groundwork for addressing the implementation shortfall dilemma in institutional investing, emphasizing the significance of market microstructure and price dynamics in minimizing execution costs. However, the ability to derive a theoretical Optimum market order policy is an unrealistic assumption for many investors. Th

Simeon Kolev

Bertsimas and Lo's seminal work established a foundational framework for addressing the implementation shortfall dilemma faced by large institutional investors. Their models emphasized the critical role of accurate knowledge of market microstructure and price/information dynamics in optimizing trades to minimize execution costs. However, this paper recognize

Berke Doga Basaran, Paul M. Matthews, Wenjia Bai

Assessing lesions and tracking their progression over time in brain magnetic resonance (MR) images is essential for diagnosing and monitoring multiple sclerosis (MS). Machine learning models have shown promise in automating the segmentation of MS lesions. However, training these models typically requires large, well-annotated datasets. Unfortunately, MS imag

M. Giselle Fernández-Godino, Wai Tong Chung, Akshay A. Gowardhan, Matthias Ihme

High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes them impractical for applications requiring rapid responses or iterative processes, such as optimization, uncertainty quantification, or inverse modeling. To address this challenge, t

Honglian Wang, Sijing Tu, Aristides Gionis

Diversification is a useful tool for exploring large collections of information items. It has been used to reduce redundancy and cover multiple perspectives in information-search settings. Diversification finds applications in many different domains, including presenting search results of information-retrieval systems and selecting suggestions for recommende

Zhangjun Zhou, Yiping Li, Chunlin Zhong, Jianuo Huang

While the human visual system employs distinct mechanisms to perceive salient and camouflaged objects, existing models struggle to disentangle these tasks. Specifically, salient object detection (SOD) models frequently misclassify camouflaged objects as salient, while camouflaged object detection (COD) models conversely misinterpret salient objects as camouf

Miquel Miró-Nicolau, Antoni Jaume-i-Capó, Gabriel Moyà-Alcover

The use of eXplainable Artificial Intelligence (XAI) systems has introduced a set of challenges that need resolution. The XAI robustness, or stability, has been one of the goals of the community from its beginning. Multiple authors have proposed evaluating this feature using objective evaluation measures. Nonetheless, many questions remain. With this work, w

Hong-Wei Wu, Wei-Yao Wang, Kuang-Da Wang, Wen-Chih Peng

Tabular data are fundamental in common machine learning applications, ranging from finance to genomics and healthcare. This paper focuses on tabular regression tasks, a field where deep learning (DL) methods are not consistently superior to machine learning (ML) models due to the challenges posed by irregular target functions inherent in tabular data, causin

Fabio Nicola, Federico Riccardi, Paolo Tilli

Lieb and Solovej proved that, for the symmetric $SU(N)$ representations, the corresponding Wehrl-type entropy is minimized by symmetric coherent states. However, the uniqueness of the minimizers remained an open problem when $N\geq 3$. In this note, we complete the proof of the Wehrl entropy conjecture for such representations by showing that symmetric coher

Piyapath T Spencer

Human-Centric NLP often claims to prioritise human needs and values, yet many implementations reveal an underlying AI-centric focus. Through an analysis of case studies in language modelling, behavioural testing, and multi-modal alignment, this study identifies a significant gap between the ideas of human-centricity and actual practices. Key issues include m

Antonio Cáceres, Alberto Lastra, Sławomir Michalik, Maria Suwińska

Integral representations of two $q$-difference operators are provided in terms of special functions arising in the theory of asymptotic solutions to $q$-difference equations in the complex domain. Both representations are unified through the so-called $(p,q)$-differential operator, for which a kernel-like function is provided, generating the sequence of $(p,

Jason M. Gray, Rowan J. Barker-Clarke, Jacob G. Scott, Michael Hinczewski

An organism that is newly introduced into an existing population has a survival probability that is dependent on both the population density of its environment and the competition it experiences with the members of that population. Expanding populations naturally form regions of high and low density, and simultaneously experience ecological interactions both

Yibo Yang, Justus C. Will, Stephan Mandt

Diffusion probabilistic models have achieved mainstream success in many generative modeling tasks, from image generation to inverse problem solving. A distinct feature of these models is that they correspond to deep hierarchical latent variable models optimizing a variational evidence lower bound (ELBO) on the data likelihood. Drawing on a basic connection b

Ilia Khomchenko, Andrei Fionov, Artem Alekseev, Daniil Volkov

Trapped ions are among the leading platforms for quantum technologies, particularly in the field of quantum computing. Detecting states of trapped ions is essential for ensuring high-fidelity readouts of quantum states. In this work, we develop and benchmark a set of methods for ion quantum state detection using images obtained by a highly sensitive camera.

Xiaobin Shen, Daniel Lee, Sumit Ranjan, Sai Sree Harsha

Enterprise conversational AI systems are becoming increasingly popular to assist users in completing daily tasks such as those in marketing and customer management. However, new users often struggle to ask effective questions, especially in emerging systems with unfamiliar or evolving capabilities. This paper proposes a framework to enhance question suggesti

Luis F. Rodriguez, Susana Lizano, Jorge Canto, Ricardo F. Gonzalez

We analyze the radio emission from the $\beta$ Cep star V2187 Cyg using archive data from the Jansky Very Large Array. The observations were made in ten epochs at 1.39 and 4.96 GHz in the highest angular resolution A configuration. We determine a spectral index of of $\alpha = 0.6\pm0.2$ ($S_{\nu} \propto \nu^\alpha$), consistent with an ionized wind or a pa

Haoran Sun, Upendra Harbola, Shaul Mukamel, Michael Galperin

Two-dimensional spectroscopy is discussed for open quantum systems with multiple simultaneously measurable fluxes. In particular, we discuss a junction where optical measurements of photon flux are complemented with simultaneous transport measurements of electron currents. Theory of two-dimensional spectroscopy in both fluxes is developed employing non-self-

Vitalii Vertogradov, Ali Övgün

We develop a compact and transparent framework for photon dynamics and shadow formation in slowly evolving, spherically symmetric spacetimes. Starting from the Eddington-Finkelstein action, we derive a force-decomposed radial equation in which the radial acceleration splits into an induced term sourced by mass variation, a centrifugal term, and a purely gene

Haoran Sun, Upendra Harbola, Shaul Mukamel, Michael Galperin

Development of experimental techniques at nanoscale resulted in ability to perform spectroscopic measurements on single-molecule current carrying junctions. These experiments are natural meeting point for research fields of optical spectroscopy and molecular electronics. We present a pedagogical comparison between perturbation theory expansion of standard no

Desmond J. Quinn, Diptabrata Paul, Frank Cichos

Non-equilibrium assembly, driven by fluxes controllable by continuous external energy inputs, enables dynamic and reconfigurable structures. Such controlled 3D assembly is desired for the design of adaptive materials that exploit structure-function relationships, but has remained challenging. We present a non-equilibrium assembly of colloidal particles media

Mukhtiar Ahmad, Faaiq Bilal, Mutahar Ali, Muhammad Ali Nawazish

Edge computing is considered a key paradigm for supporting real-time applications over 5G networks, as hosting applications at the network edge can substantially reduce delays. A significant fraction of real-time applications over 5G are expected to be highly mobile applications. However, one challenge with hosting mobile applications on the network edge is

Jake Berg, Perryn Chang, Claire Kaneshiro, Kirsti Kuenzel

A set of edges $F$ in a graph $G$ is an edge dominating set if every edge in $G$ is either in $F$ or shares a vertex with an edge in $F$. $G$ is said to be well-edge-dominated if all of its minimal edge dominating sets have the same cardinality. Recently it was shown that any triangle-free well-edge-dominated graph is either bipartite or in the set $\{C_5, C

Katrina Drozdov, Ravid Shwartz-Ziv, Yann LeCun

Video representation learning is an increasingly important topic in machine learning research. We present Video JEPA with Variance-Covariance Regularization (VJ-VCR): a joint-embedding predictive architecture for self-supervised video representation learning that employs variance and covariance regularization to avoid representation collapse. We show that hi

Binita Maity, Shrutimoy Das, Anirban Dasgupta

Individual fairness guarantees are often desirable properties to have, but they become hard to formalize when the dataset contains outliers. Here, we investigate the problem of developing an individually fair $k$-means clustering algorithm for datasets that contain outliers. That is, given $n$ points and $k$ centers, we want that for each point which is not

Kevin Li, Lin Ling, Jinqiu Yang, Lili Wei

Mobile apps are predominantly integrated with cloud services to benefit from enhanced functionalities. Adopting authentication using secrets such as API keys is crucial to ensure secure mobile-cloud interactions. However, developers often overlook the proper storage of such secrets, opting to put them directly into their projects. These secrets are checked i

Haofan Dong, Ozgur B. Akan

This paper presents the Mars Dust Storm Detector (MDSD), a system that leverages the THz Opportunistic Integrated Sensing and Communications (OISAC) signals between Mars surface assets (rovers and landers) to extract environmental information, particularly dust storm properties. The MDSD system utilizes the multi-parameter sensitivity of THz signal attenuati

Rafal Baranowski, Yining Chen, Piotr Fryzlewicz

We propose a multiscale approach to time series autoregression, in which linear regressors for the process in question include features of its own path that live on multiple timescales. We take these multiscale features to be the recent averages of the process over multiple timescales, whose number or spans are not known to the analyst and are estimated from

Abhiram Raju, Praneeth Vepakomma

Hemodialysis patients who are on donor lists for kidney transplant may get misidentified, delaying their wait time. Thus, predicting their survival time is crucial for optimizing waiting lists and personalizing treatment plans. Predicting survival times for patients often requires large quantities of high quality but sensitive data. This data is siloed and s

Murat Gunay, Bunyamin Keles, Raife Hizlan

The rise of chronic diseases and pandemics like COVID-19 has emphasized the need for effective patient data processing while ensuring privacy through anonymization and de-identification of protected health information (PHI). Anonymized data facilitates research without compromising patient confidentiality. This paper introduces expert small AI models develop

Minjae Kwon, Ingy ElSayed-Aly, Lu Feng

There is a surge of interest in using formal languages such as Linear Temporal Logic (LTL) to precisely and succinctly specify complex tasks and derive reward functions for Reinforcement Learning (RL). However, existing methods often assign sparse rewards (e.g., giving a reward of 1 only if a task is completed and 0 otherwise). By providing feedback solely u

Toshiyuki Oshima, Junya Yamauchi, Tatsuya Ibuki, Michio Seto

This paper addresses distributed learning of a complex object for multiple networked robots based on distributed optimization and kernel-based support vector machine. In order to overcome a fundamental limitation of polynomial kernels assumed in our antecessor, we employ Gaussian kernel as a kernel function for classification. The Gaussian kernel prohibits t

Chenxi Yang, Divyanshu Saxena, Rohit Dwivedula, Kshiteej Mahajan

Learning-based congestion controllers offer better adaptability compared to traditional heuristics. However, the unreliability of learning techniques can cause learning-based controllers to behave poorly, creating a need for formal guarantees. While methods for formally verifying learned congestion controllers exist, these methods offer binary feedback that

Lisa Rohde, Desmond J. Quinn, Diptabrata Paul, Frank Cichos

Regulation to a well-defined target state is a fundamental requirement for achieving reliable functionality in living systems and maintaining specific non-equilibrium states. The control of certain properties and functionalities of systems on the microscale presents a particular challenge since thermal fluctuations and environmental perturbations dominate. W

Akram Shafie, Jinhong Yuan, Nan Yang, Hai Lin

In this work, we study the time-frequency (TF) localization characteristics of the prototype pulse of orthogonal delay-Doppler (DD) division multiplexing modulation, namely, the DD plane orthogonal pulse (DDOP). The TF localization characteristics examine how concentrated or spread out the energy of a pulse is in the joint TF domain, the time domain (TD), an

Alessio Guerra, Marcello Lepre, Oktay Karakus

This study investigates the dynamics of extreme opinions on social media during the 2023 Israeli-Palestinian conflict, utilising a comprehensive dataset of over 450,000 posts from four Reddit subreddits (r/Palestine, r/Judaism, r/IsraelPalestine, and r/worldnews). A lexicon-based, unsupervised methodology was developed to measure "extreme opinions" by consid

Zhenyu Lei, Yushun Dong, Jundong Li, Chen Chen

Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningful insights from this data. However, in real-world applications, most nodes may not possess any available temporal data during training. For example, the pandemic dynamics of most

Ahmad Ali, Haya Monawwar, Hantao Cui

The partitioned approach for the numerical integration of power system differential algebraic equations faces inherent numerical stability challenges due to delays between the computation of state and algebraic variables. Such delays can compromise solution accuracy and computational efficiency, particularly in large-scale system simulations. We present an $

Marco Feder, Luca Heltai, Martin Kronbichler, Peter Munch

Traditionally, the geometric multigrid method is used with nested levels. However, the construction of a suitable hierarchy for very fine and unstructured grids is, in general, highly non-trivial. In this scenario, the non-nested multigrid method could be exploited in order to handle the burden of hierarchy generation, allowing some flexibility on the choice

Omkar Sridhar, Soham Bhattacharyya, Kaushik Paul, Chandra Kant Mishra

Compact binary sources that emit gravitational waves (GW) are expected to be both spinning and on eccentric orbits. No closed-form expression for the phasing of GWs are available to date that contain information from both spin and eccentricity. The introduction of eccentricity can slow waveform generation, often requiring slower numerical methods governing i

Sagi Eppel

Large vision language models (LVLM) are the leading A.I approach for achieving a general visual understanding of the world. Models such as GPT, Claude, Gemini, and LLama can use images to understand and analyze complex visual scenes. 3D objects and shapes are the basic building blocks of the world, recognizing them is a fundamental part of human perception.

Antonino D'Aì, Dimitrios K. Maniadakis, Carlo Ferrigno, Elena Ambrosi

We aim to investigate the energy-resolved pulse profile changes of the accreting X-ray pulsar V 0332+53, focusing in the cyclotron line energy range, using the full set of available NuSTAR observations. We applied a tailored pipeline to study the energy dependence of the pulse profiles and to build the pulsed fraction spectra (PFS) for the different observat

Qilong Wu, Xiaoneng Xiang, Hejia Huang, Xuan Wang

The rapid growth of the financial sector and the rising focus on Environmental, Social, and Governance (ESG) considerations highlight the need for advanced NLP tools. However, open-source LLMs proficient in both finance and ESG domains remain scarce. To address this gap, we introduce SusGen-30K, a category-balanced dataset comprising seven financial NLP task

Matteo Novaga, Emanuele Paolini, Eugene Stepanov

We prove that if we fill without gaps a bag with infinitely many potatoes, in such a way that they touch each other in few points, then the total surface area of the potatoes must be infinite. In this context potatoes are measurable subsets of the Euclidean space, the bag is any open set of the same space. As we show, this result also holds in the general co

Alex Wollman, John Hastings

Literature reviews are a critical component of formulating and justifying new research, but are a manual and often time-consuming process. This research introduces a novel, generalizable approach to literature analysis called CEKER which uses a three-step process to streamline the collection of literature, the extraction of key insights, and the summarized a

Ernestine Großmann, Kenneth Langedal, Christian Schulz

The Maximum Weight Independent Set problem is a fundamental NP-hard problem in combinatorial optimization with several real-world applications. Given an undirected vertex-weighted graph, the problem is to find a subset of the vertices with the highest possible weight under the constraint that no two vertices in the set can share an edge. An important part of

Leonard H. Soicher

We describe the methods and results of a classification of the non-synchronizing primitive permutation groups of degree up to 624. We make use of theory and computation to determine the primitive groups of degree up to 624 that are non-separating, which is a necessary, and very often sufficient, condition for a primitive group to be non-synchronizing, and de

Jiaze Tang, Angzehua Feng, Vladimir Korkhov, Yuxi Pu

Effective road crack detection is crucial for road safety, infrastructure preservation, and extending road lifespan, offering significant economic benefits. However, existing methods struggle with varied target scales, complex backgrounds, and low adaptability to different environments. This paper presents the BsS-YOLO model, which optimizes multi-scale feat

Marija Maksimović, Sanja Rukavina

In this paper we present a method for constructing directed strongly regular graphs with assumed action of an automorphism group. The application of this method leads to first examples of directed strongly regular graphs with parameters $(22,9,6,3,4)$. We have shown that an automorphism of prime order acting on such a graph can only be of order two or three.

Yongchun Qin, Pengfei Fang, Hui Xue

Class-incremental learning (CIL) aims to continuously introduce novel categories into a classification system without forgetting previously learned ones, thus adapting to evolving data distributions. Researchers are currently focusing on leveraging the rich semantic information of pre-trained models (PTMs) in CIL tasks. Prompt learning has been adopted in CI

Wilsun Xu, Jing Yong, Horacio J. Marquez, Chun Li

Power system oscillations are a significant concern for system operators, a problem that has grown due to the interconnection of inverter-based resources. To address this issue, various methods have been proposed to locate the sources of oscillations, which is essential for effective mitigation actions. A common characteristic of these methods is that they r

Hu Qiye, Zhou Hao, Yu RuoXi

The phenomenon of grokking in over-parameterized neural networks has garnered significant interest. It involves the neural network initially memorizing the training set with zero training error and near-random test error. Subsequent prolonged training leads to a sharp transition from no generalization to perfect generalization. Our study comprises extensive

Junliang Lyu, Yixuan Zhang, Xiaoling Lu, Feng Zhou

This work addresses a key limitation in current federated learning approaches, which predominantly focus on homogeneous tasks, neglecting the task diversity on local devices. We propose a principled integration of multi-task learning using multi-output Gaussian processes (MOGP) at the local level and federated learning at the global level. MOGP handles corre

Fouad Trad, Ryan Yammine, Jana Charafeddine, Marlene Chakhtoura

Systematic reviews (SRs) are essential for evidence-based guidelines but are often limited by the time-consuming nature of literature screening. We propose and evaluate an in-house system based on Large Language Models (LLMs) for automating both title/abstract and full-text screening, addressing a critical gap in the literature. Using a completed SR on Vitam

Noël Hallemans, Nicola E. Courtier, Colin P. Please, Brady Planden

Non-invasive parametrisation of physics-based battery models can be performed by fitting the model to electrochemical impedance spectroscopy (EIS) data containing features related to the different physical processes. However, this requires an impedance model to be derived, which may be complex to obtain analytically. We have developed the open-source softwar

Claudio Moroni, Claudio Borile, Carolina Mattsson, Michele Starnini

Link Prediction is a foundational task in Graph Representation Learning, supporting applications like link recommendation, knowledge graph completion and graph generation. Graph Neural Networks have shown the most promising results in this domain and are currently the de facto standard approach to learning from graph data. However, a key distinction exists b

Irina G. Lodkina, Yuri I. Yermolaev, Alexander A. Khokhlachev

The response of the magnetosphere to interplanetary drivers depends on their type. The reliability of their identification affects the conclusions of the analysis of connections between the solar wind and the magnetosphere. In this work, we analyze the list of moderate and strong geomagnetic storms and their interplanetary sources for the period 2009 - 2019,

Anton Alexandrov, Veselin Raychev, Dimitar I. Dimitrov, Ce Zhang

We present BgGPT-Gemma-2-27B-Instruct and BgGPT-Gemma-2-9B-Instruct: continually pretrained and fine-tuned versions of Google's Gemma-2 models, specifically optimized for Bulgarian language understanding and generation. Leveraging Gemma-2's multilingual capabilities and over 100 billion tokens of Bulgarian and English text data, our models demonstrate strong

Haocheng Duan, Hao Wu, Sean Qian

This research aims to know traffic anomalies as early as possible. A traffic anomaly refers to a generic incident on the road that influences traffic flow and calls for urgent traffic management measures. `Knowing'' the occurrence of a traffic anomaly is twofold: the ability to detect this anomaly before it is reported anywhere, or it may be such that an ano

Lichen Bai, Shitong Shao, Zikai Zhou, Zipeng Qi

Diffusion models, the most popular generative paradigm so far, can inject conditional information into the generation path to guide the latent towards desired directions. However, existing text-to-image diffusion models often fail to maintain high image quality and high prompt-image alignment for those challenging prompts. To mitigate this issue and enhance

Terence Tao

We obtain bounds on the distribution of normalized gaps of eigenvalues of $N \times N$ GUE matrix in the bulk, that do not lose logarithmic factors of $N$ in the limit $N \to \infty$. As an application, we obtain fixed index universality results for the GUE minor process, which in turn are useful for establishing limiting results for random hives with GUE bo

Yifan Liu, Zehan Chen, Qiming Shao

Realizing novel topological states in magnonic systems unlocks robust, low-power spin-wave devices. In this letter, we show that incorporating left-handed spin waves (antimagnons) fundamentally reorganizes band topology, and enables tunable spin-wave coupling and chirality. We proposed a two-dimensional Su-Schrieffer-Heeger like model, the 2D-SSH4 chain, whe

Wei Jiang, Chunmei Su, Ganghui Zhang, Lian Zhang

We present a novel parametric finite element approach for simulating the surface diffusion of curves and surfaces. Our core strategy incorporates a predictor-corrector time-stepping method, which enhances the classical first-order temporal accuracy to achieve second-order accuracy. Notably, our new method eliminates the necessity for mesh regularization tech

Seyed Ebrahim Akrami

Inspired by quantum mechanics, we introduce a weak form of solutions for differential equations and differential identities like Stokes theorem and Euler-Lagrange equation. We show that Schr\"{o}dinger equation is a weak from of the classical Euler-Lagrange equation.

Sachin Chauhan

The Gukov-Pei-Putrov-Vafa (GPPV) conjecture is a relationship between two three-manifold invariants: the Witten-Reshetikhin-Turaev (WRT) invariant and the \(\widehat{Z}\) (``Z-hat'') invariant. In fact, WRT invariant is defined at roots of unity, $\mathbbm{q}\left(\exp\left(\frac{2\pi i}{k+2}\right),~k\in\mathbb{Z}_+,~\text{for}~SU(2)\right)$, and is general

Hebert Pérez-Rosés, José Miguel Serradilla-Merinero, Maria Bras-Amorós

Motivated by the change-making problem, we extend the notion of greediness to sets of positive integers not containing the element $1$, and from there to numerical semigroups. We provide an algorithm to determine if a given set (not necessarily containing the number $1$) is greedy. We also give specific conditions for sets of cardinality three, and we prove

Charles Emmett Maher, Salvatore Torquato

Jammed (mechanically rigid) polydisperse circular-disk packings in two dimensions (2D) are popular models for structural glass formers. Maximally random jammed (MRJ) states, which are the most disordered packings subject to strict jamming, have been shown to be hyperuniform. The characterization of the hyperuniformity of MRJ circular-disk packings has covere

Nouar AlDahoul, Myles Joshua Toledo Tan, Raghava Reddy Tera, Hezerul Abdul Karim

License plate recognition (LPR) involves automated systems that utilize cameras and computer vision to read vehicle license plates. Such plates collected through LPR can then be compared against databases to identify stolen vehicles, uninsured drivers, crime suspects, and more. The LPR system plays a significant role in saving time for institutions such as t

Canberk Ekmekci, Tekin Bicer, Zichao Wendy Di, Junjing Deng

Ptychography is a scanning coherent diffractive imaging technique that enables imaging nanometer-scale features in extended samples. One main challenge is that widely used iterative image reconstruction methods often require significant amount of overlap between adjacent scan locations, leading to large data volumes and prolonged acquisition times. To addres

Ben Bals, Michelle Döring, Nicolas Klodt, George Skretas

Researchers, policy makers, and engineers need to make sense of data from spreading processes as diverse as rumor spreading in social networks, viral infections, and water contamination. Classical questions include predicting infection behavior in a given network or deducing the network structure from infection data. Most of the research on network infection

Mark B. Villarino, Joseph C. Varilly

The Dutch scientist Christiaan Huygens refined Archimedes' celebrated geometrical computation of $\pi$ to its highest point. Yet the rich content of his beautiful treatise \emph{De circuli magnitudine inventa} (1654) has apparently never been presented in modern form. Here we offer a detailed and contemporary development of several of his most striking resul

Weinan Lin, Guozhen Wang, Zhouli Xu

We prove that the element $h_6^2$ is a permanent cycle in the Adams spectral sequence. As a result, we establish the existence of smooth framed manifolds with Kervaire invariant one in dimension 126, thereby resolving the final case of the Kervaire invariant problem. Combining this result with the theorems of Browder, Mahowald--Tangora, Barratt--Jones--Mahow

Afsaneh Mahmoudi, Emil Björnson

Federated learning (FL) is a distributed learning framework where users train a global model by exchanging local model updates with a server instead of raw datasets, preserving data privacy and reducing communication overhead. However, the latency grows with the number of users and the model size, impeding the successful FL over traditional wireless networks

Weinan Lin, Guozhen Wang, Zhouli Xu

In this document, we describe the process of obtaining numerous Adams differentials and extensions using computational methods, as well as how to interpret the dataset uploaded to Zenodo. Detailed proofs of the machine-generated results are also provided. The dataset includes information on 49 CW spectra, 180 maps, and 61 cofiber sequences. Leveraging these

Donatella Donatelli, Lorenzo Pescatore, Stefano Spirito

In this paper we prove the existence of global weak dissipative martingale solutions for a one-dimensional compressible fluid model with capillarity and density dependent viscosity, driven by random initial data and a stochastic forcing term. These solutions are weak in both PDEs and Probability sense and may have vacuum regions. The proof relies on the cons

Peini Yi, Wenchi Cheng, Zhanyu Ju, Jingqing Wang

With the increasing complexity of Wi-Fi networks and the iterative evolution of 802.11 protocols, the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol faces significant challenges in achieving fair channel access and efficient resource allocation between legacy and modern Wi-Fi devices. To address these challenges, we propose an AI-d

Ting Ling, Zhang Li, Minyu Feng, Attila Szolnoki

It is a challenging task to reach global cooperation among self-interested agents, which often requires sophisticated design or usage of incentives. For example, we may apply supervisors or referees who are able to detect and punish selfishness. As a response, defectors may offer bribes for corrupt referees to remain hidden, hence generating a new conflict a

Ming Xu, Hongtai Wang, Jiahao Liu, Xinfeng Li

Efficient defense against dynamically evolving advanced persistent threats (APT) requires the structured threat intelligence feeds, such as techniques used. However, existing threat-intelligence extraction techniques predominantly focuses on individual pieces of intelligence-such as isolated techniques or atomic indicators-resulting in fragmented and incompl

Zhi Zhou, Kun-Yang Yu, Lan-Zhe Guo, Yu-Feng Li

Tabular data plays a vital role in various real-world scenarios and finds extensive applications. Although recent deep tabular models have shown remarkable success, they still struggle to handle data distribution shifts, leading to performance degradation when testing distributions change. To remedy this, a robust tabular model must adapt to generalize to un

Yaqiong Qiao, Guojun Huang

Timely detection and geolocation of events based on social data can provide critical information for applications such as crisis response and resource allocation. However, most existing methods are greatly affected by event detection errors, leading to insufficient geolocation accuracy. To this end, this paper proposes a novel end-to-end event geolocation me

Marcin Pietroń, Kamil Faber, Dominik Żurek, Roberto Corizzo

Continual Learning (CL) is a highly relevant setting gaining traction in recent machine learning research. Among CL works, architectural and hybrid strategies are particularly effective due to their potential to adapt the model architecture as new tasks are presented. However, many existing solutions do not efficiently exploit model sparsity, and are prone t

Qingtao Chen, Shengmao Zhu

In this article, we obtain an asymptotic expansion formula for the relative Reshetikhin-Turaev invariant in the case that the ambient 3-manifold is gained by doing rational surgery along one component of Whitehead link. In addition, we obtain an asymptotic expansion formula for the Turaev-Viro invariant of the cusped 3-manifold which is gained by doing ratio

Peini Yi, Wenchi Cheng, Jingqing Wang, Wei Zhang

In recent years, reconfigurable intelligent surfaces (RIS) have garnered significant attention for their ability to control the phase shifts in reflected signals. By intelligently adjusting these phases, RIS can establish seamless direct paths between communication devices obstructed by obstacles, eliminating the need for forwarding and significantly reducin

P. Sawyer

In this paper, we provide a reduction formula for the Dunkl kernel for the root systems of type $A$. The Dunkl kernel for the root system $A_n$ is expressed as an integral involving the Dunkl kernel for the root system $A_{n-1}$. The corresponding reduction formula for the intertwining operator $V_k$ is given.

Andrzej Opala, Krzysztof Tyszka, Mateusz Kędziora, Magdalena Furman

Limitations of electronics have stimulated the search for novel unconventional computing platforms that enable energy-efficient and ultra-fast information processing. Among various systems, exciton-polaritons stand out as promising candidates for the realization of optical neuromorphic devices. This is due to their unique hybrid light-matter properties, resu

Zeno Capatti, Mao Zeng

We present a systematic diagrammatic investigation of the classical limit of observables computed from scattering amplitudes in quantum field theory through the Kosower-Maybee-O'Connell (KMOC) formalism, motivated by the study of gravitational waves from black hole binaries. We achieve the manifest cancellation of divergences in the $\hbar \to 0$ limit at th

Jouni Järvinen, Sándor Radeleczki

For several types of information relations, the induced rough sets system RS does not form a lattice but only a partially ordered set. However, by studying its Dedekind-MacNeille completion DM(RS), one may reveal new important properties of rough set structures. Building upon D. Umadevi's work on describing joins and meets in DM(RS), we previously investigat

Daniel V. Mathews, Varsha

This article is an exposition and elaboration of recent work of the first author on spinors and horospheres. It presents the main results in detail, and includes numerous subsidiary observations and calculations. It is intended to be accessible to graduate and advanced undergraduate students with some background in hyperbolic geometry. The main result is the

Qingyu Xu, Longguang Wang, Weidong Sheng, Yingqian Wang

Tracking multiple tiny objects is highly challenging due to their weak appearance and limited features. Existing multi-object tracking algorithms generally focus on single-modality scenes, and overlook the complementary characteristics of tiny objects captured by multiple remote sensors. To enhance tracking performance by integrating complementary informatio

Xiangfei Qiu, Xingjian Wu, Yan Lin, Chenjuan Guo

Multivariate time series forecasting is crucial for various applications, such as financial investment, energy management, weather forecasting, and traffic optimization. However, accurate forecasting is challenging due to two main factors. First, real-world time series often show heterogeneous temporal patterns caused by distribution shifts over time. Second

Yaqiong Qiao, Shixuan Peng

Chinese Named Entity Recognition (NER) is an important task in information extraction, which has a significant impact on downstream applications. Due to the lack of natural separators in Chinese, previous NER methods mostly relied on external dictionaries to enrich the semantic and boundary information of Chinese words. However, such methods may introduce no

Ali Nasr-Esfahani, Mehdi Bekrani, Roozbeh Rajabi

Artificial intelligence (AI) has significantly advanced speech recognition applications. However, many existing neural network-based methods struggle with noise, reducing accuracy in real-world environments. This study addresses isolated spoken Persian digit recognition (zero to nine) under noisy conditions, particularly for phonetically similar numbers. A h

Wonkyo Choe, Yangfeng Ji, Felix Xiaozhu Lin

To deploy LLMs on resource-contained platforms such as mobile robots and smartphones, non-transformers LLMs have achieved major breakthroughs. Recently, a novel RNN-based LLM family, Repentance Weighted Key Value (RWKV) has shown strong computational efficiency; nevertheless, RWKV models still have high parameter counts which limited their deployment. In thi

Haoran Ding, Noémie Jaquier, Jan Peters, Leonel Rozo

Diffusion-based visuomotor policies excel at learning complex robotic tasks by effectively combining visual data with high-dimensional, multi-modal action distributions. However, diffusion models often suffer from slow inference due to costly denoising processes or require complex sequential training arising from recent distilling approaches. This paper intr

Haoming Luo, Xiaotian Yu, Shengxuming Zhang, Jiabin Xia

Pathology images are considered the ``gold standard" for cancer diagnosis and treatment, with gigapixel images providing extensive tissue and cellular information. Existing methods fail to simultaneously extract global structural and local detail features for comprehensive pathology image analysis efficiently. To address these limitations, we propose a self-

R. Kumar, R. P. Malik

We discuss the discrete as well as the continuous symmetry transformations for a three $(2+1)$-dimensional $(3D)$ combined system of the free Abelian 1-form and 2-form gauge theories within the framework of Becchi-Rouet-Stora-Tyutin (BRST) formalism and establish their relevance in the context of the algebraic structures that are obeyed by the de Rham cohomo

Cristian Cortes-Aguirre, Yi-An Chen, Avik Ghosh, Jan Kleissl

Monthly demand charges form a significant portion of the electric bill for microgrids with variable renewable energy generation. A battery energy storage system (BESS) is commonly used to manage these demand charges. Economic model predictive control (EMPC) with a reference trajectory can be used to dispatch the BESS to optimize the microgrid operating cost.

Yongyu Wang

User based collaborative filtering (CF) relies on a user and user similarity graph, making it vulnerable to profile injection (shilling) attacks that manipulate neighborhood relations to promote (push) or demote (nuke) target items. In this work, we propose an adversarial robustness based edge reweighting defense for CF. We first assign each user and user ed

Peter G. Brodeur, Thomas A. Buckley, Zahir Kanjee, Ethan Goh

A seminal paper published by Ledley and Lusted in 1959 introduced complex clinical diagnostic reasoning cases as the gold standard for the evaluation of expert medical computing systems, a standard that has held ever since. Here, we report the results of a physician evaluation of a large language model (LLM) on challenging clinical cases against a baseline o

Zeljko Kraljevic, Joshua Au Yeung, Daniel Bean, James Teo

Foresight 2 (FS2) is a large language model fine-tuned on hospital data for modelling patient timelines (GitHub 'removed for anon'). It can understand patients' clinical notes and predict SNOMED codes for a wide range of biomedical use cases, including diagnosis suggestions, risk forecasting, and procedure and medication recommendations. FS2 is trained on th