May 2024 arXiv papers — page 113

Ashlin Iser, Christoph Jabs

This paper presents Global Benchmark Database (GBD), a comprehensive suite of tools for provisioning and sustainably maintaining benchmark instances and their metadata. The availability of benchmark metadata is essential for many tasks in empirical research, e.g., for the data-driven compilation of benchmarks, the domain-specific analysis of runtime experime

Pham Huy Nguyen

There are various desired capabilities to create aerial forest-traversing robots capable of monitoring both biological and abiotic data. The features range from multi-functionality, robustness, and adaptability. These robots have to weather turbulent winds and various obstacles such as forest flora and wildlife thus amplifying the complexity of operating in

Mohammed M. H. Qazzaz, Syed A. R. Zaidi, Desmond C. McLernon, Abdelaziz Salama

Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a reinforcement learning multi Q-learning algorithm to optimize UAV connectivity in such scenarios. We introduce

Weizhen Liu, Ao Li, Ze Wu, Yue Li

Hierarchical leaf vein segmentation is a crucial but under-explored task in agricultural sciences, where analysis of the hierarchical structure of plant leaf venation can contribute to plant breeding. While current segmentation techniques rely on data-driven models, there is no publicly available dataset specifically designed for hierarchical leaf vein segme

Arne Binder, Leonhard Hennig, Christoph Alt

The objective of Information Extraction (IE) is to derive structured representations from unstructured or semi-structured documents. However, developing IE models is complex due to the need of integrating several subtasks. Additionally, representation of data among varied tasks and transforming datasets into task-specific model inputs presents further challe

Abhishek Divekar, Greg Durrett

It is often desirable to distill the capabilities of large language models (LLMs) into smaller student models due to compute and memory constraints. One way to do this for classification tasks is via dataset synthesis, which can be accomplished by generating examples of each label from the LLM. Prior approaches to synthesis use few-shot prompting, which reli

Hossein Darban, Raimondo Luciano, Michał Basista

Studying the dynamics of small-scale beams with attached particles is crucial for sensing applications in various fields, such as bioscience, material science, energy storage devices, and environmental monitoring. Here, a stress-driven nonlocal model is presented for the free transverse vibration of small-scale beams carrying multiple masses taking into acco

C. Pallis

We review a number of unimodular no-scale supergravity models with F-term SUSY breaking which support technically natural de Sitter vacua. A variant of these models develops a stage of inflection-point inflation which can be realized for subplanckian field values consistently with the observational data. For central value of the spectral index ns, the necess

Kirill A. Buzaverov, Aleksandr S. Baburin, Evgeny V. Sergeev, Sergey S. Avdeev

Recently, silicon nitride (Si3N4) photonic integrated circuits (PICs) are of a great interest due to their extremely low waveguides losses. The number of Si3N4 integrated photonics platform applications is constantly growing including the Internet of Things (IoT), artificial intelligence (AI), light detection and ranging (LiDAR) devices, hybrid neuromorphic

Zhilin Huang, Quanmin Liang, Yijie Yu, Chujun Qin

Event Stream Super-Resolution (ESR) aims to address the challenge of insufficient spatial resolution in event streams, which holds great significance for the application of event cameras in complex scenarios. Previous works for ESR often process positive and negative events in a mixed paradigm. This paradigm limits their ability to effectively model the uniq

Felix Held

Unsupervised integrative analysis of multiple data sources has become common place and scalable algorithms are necessary to accommodate ever increasing availability of data. Only few currently methods have estimation speed as their focus, and those that do are only applicable to restricted data layouts such as different data types measured on the same observ

Alessio Perinelli, Leonardo Ricci

The analysis of electrophysiological recordings of the human brain in resting state is a key experimental technique in neuroscience. Resting state is indeed the default condition to characterize brain dynamics. Its successful implementation relies both on the capacity of subjects to comply with the requirement of staying awake while not performing any cognit

Mohammad Ghuneim, Raditya Weda Bomantara

In this work, we theoretically study a modified Su-Schrieffer-Heeger (SSH) model in which each unit cell consists of three sites. Unlike existing extensions of the SSH model which are made by enlarging the periodicity of the (nearest-neighbor) hopping amplitudes, our modification is obtained by replacing the Pauli matrices in the system's Hamiltonian by thei

Akihiro Mizutani, Toyohiro Tsurumaru

The performance of quantum key distribution (QKD) protocols is evaluated based on the ease of implementation and key generation rate. Among major protocols, the differential-phase-shift (DPS) protocol has the advantage of simple implementation using a train of coherent pulses and a passive detection unit. Unfortunately, however, its key rate is known to be a

Abdallah Ammar, Arnaud Leclerc, Lorenzo Ugo Ancarani

We study two classes of radial integrals involving a product of bound and continuum one-electron states. Using a representation of the continuum part with an expansion on complex Gaussian Type Orbitals, such integrals can be performed analytically. We investigate the reliability of this scheme for low-energy physical parameters. This study serves as a premis

Yao Rong, David Scheerer, Enkelejda Kasneci

In recent years, model explanation methods have been designed to interpret model decisions faithfully and intuitively so that users can easily understand them. In this paper, we propose a framework, Faithful Attention Explainer (FAE), capable of generating faithful textual explanations regarding the attended-to features. Towards this goal, we deploy an atten

Zu-Cheng Chen, Lang Liu

The detection of gravitational waves (GWs) has opened a new window to test the fundamental nature of gravity. We present constraints on the nonstandard propagation of GWs using the spectral siren method applied to binary black hole (BBH) mergers from the third Gravitational-Wave Transient Catalog (GWTC-3). The spectral siren method exploits the redshift dist

Huiling Zhou, Xianhao Wu, Hongming Chen, Xiang Chen

Remote sensing image dehazing (RSID) aims to remove nonuniform and physically irregular haze factors for high-quality image restoration. The emergence of CNNs and Transformers has taken extraordinary strides in the RSID arena. However, these methods often struggle to demonstrate the balance of adequate long-range dependency modeling and maintaining computati

Ziyu Gong, Chengcheng Mai, Yihua Huang

The image-text retrieval task aims to retrieve relevant information from a given image or text. The main challenge is to unify multimodal representation and distinguish fine-grained differences across modalities, thereby finding similar contents and filtering irrelevant contents. However, existing methods mainly focus on unified semantic representation and c

Andrzej Wolski, Diego Botelho, David Dunning, Amelia E. Pollard

We describe a technique for reconstruction of the four-dimensional transverse phase space of a beam in an accelerator beamline, taking into account the presence of unknown errors on the strengths of magnets used in the data collection. Use of machine learning allows rapid reconstruction of the phase-space distribution while at the same time providing estimat

Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour

We revisit the classical problem of multiclass classification with bandit feedback (Kakade, Shalev-Shwartz and Tewari, 2008), where each input classifies to one of $K$ possible labels and feedback is restricted to whether the predicted label is correct or not. Our primary inquiry is with regard to the dependency on the number of labels $K$, and whether $T$-s

Margarita Moreno-Betancur, Rushani Wijesuriya, John B. Carlin

Causal inference is the goal of randomized trials and many observational studies. The first step in a formal causal inference framework is to define the causal estimand, and in both types of study this can be intuitively defined as the effect in an ideal trial: a hypothetical perfect randomized experiment (with representative sample, perfect adherence, etc.)

Yuchen Hu, Chen Chen, Chengwei Qin, Qiushi Zhu

Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR), which aims to predict the ground-truth transcription from the decoded N-best hypotheses. Thanks to the strong language generation ability of LLMs and rich information in the N-best list, GER shows great effectiveness in enha

Olivier Jeunen, Aleksei Ustimenko

The off-policy paradigm casts recommendation as a counterfactual decision-making task, allowing practitioners to unbiasedly estimate online metrics using offline data. This leads to effective evaluation metrics, as well as learning procedures that directly optimise online success. Nevertheless, the high variance that comes with unbiasedness is typically the

Łukasz Baran, Dariusz Tarasewicz, Daniel M. Kamiński, Andrzej Patrykiejew

Achieving the formation of target open crystalline lattices from colloidal particles is of paramount importance for their potential application in photonics. Examples of such desired structures are the diamond, tetrastack, and pyrochlore lattices. Here, we demonstrate that the self-assembly of tetravalent patchy particles results in the selective formation o

Xingyu Chen, Hanwen Bi, Wei-Ting Lai, Fei Ma

Monaural Speech enhancement on drones is challenging because the ego-noise from the rotating motors and propellers leads to extremely low signal-to-noise ratios at onboard microphones. Although recent masking-based deep neural network methods excel in monaural speech enhancement, they struggle in the challenging drone noise scenario. Furthermore, existing dr

Naoya Hiramae, Yuta Kozakai

Demonet, Iyama and Jasso introduced a new class of finite dimensional algebras, $\tau$-tilting finite algebras. It was shown by Eisele, Janssens and Raedschelders that tame blocks of group algebras of finite groups are always $\tau$-tilting finite. Given the classical result that the representation type (representation finite, tame or wild) of blocks is dete

Albert Yu, Adeline Foote, Raymond Mooney, Roberto Martín-Martín

The main challenge in learning image-conditioned robotic policies is acquiring a visual representation conducive to low-level control. Due to the high dimensionality of the image space, learning a good visual representation requires a considerable amount of visual data. However, when learning in the real world, data is expensive. Sim2Real is a promising para

Robert Monjo, Indranil Banik

General relativity (GR) is the most successful theory of gravity, with great observational support on local scales. However, to keep GR valid over cosmic scales, some phenomena (such as flat galaxy rotation curves and the cosmic expansion history) require the assumption of exotic dark matter. The radial acceleration relation (RAR) indicates a tight correlati

Florian Schmid, Paul Primus, Toni Heittola, Annamaria Mesaros

This article describes the Data-Efficient Low-Complexity Acoustic Scene Classification Task in the DCASE 2024 Challenge and the corresponding baseline system. The task setup is a continuation of previous editions (2022 and 2023), which focused on recording device mismatches and low-complexity constraints. This year's edition introduces an additional real-wor

Yutian Wang, Jiang Xiao

The linewidth of antiferromagnetic resonance (AFMR) is found to be significantly broader than that of ferromagnetic resonance (FMR), even when the intrinsic Gilbert damping parameter is the same for both systems. We investigate the origin of this enhanced damping rate in AFMR by studying a bipartite magnet model. Through analytical calculations and numerical

Anton Ilderton, William Lindved

Double copy relates scattering amplitudes in a web of gravitational and gauge theories. Although it has seen great success when applied to amplitudes in vacuum, far less is known about double copy in arbitrary gravitational and gauge backgrounds. Focussing on the simplest pair production amplitudes of scalar QCD in a background gauge field, we construct, at

Shu Hamanaka, Tsuneya Yoshida, Kohei Kawabata

Non-Hermiticity gives rise to distinctive topological phenomena absent in Hermitian systems. However, connection between such intrinsic non-Hermitian topology and Hermitian topology has remained largely elusive. Here, considering the bulk and boundary as an environment and system, respectively, we demonstrate that anomalous boundary states in Hermitian topol

Xingjian Wang, Li Chai, Jiming Chen

The performance of single image super-resolution depends heavily on how to generate and complement high-frequency details to low-resolution images. Recently, diffusion-based DDPM models exhibit great potential in generating high-quality details for super-resolution tasks. They tend to directly predict high-frequency information of wide bandwidth by solely ut

Hyeong-Chan Kim, Sung-Won Kim, Bum-Hoon Lee, Wonwoo Lee

We present and investigate charged wormhole solutions of the Einstein-Maxwell equations supported by anisotropic matter fields, with the purpose of establishing their physical plausibility as traversable wormholes. To this end, we examine the flare-out condition and evaluate tidal forces to confirm their traversability. We also analyze light deflection aroun

Ruilei Wang, Andrey A. Sukhanov, Yue He, Aidar Mambetov

Herein we studied the excited state dynamics of two bis-amino substituted anthraquinone (AQ) derivatives. Femtosecond transient absorption spectra show that intersystem crossing (ISC) takes place in 190-320 ps, and nanosecond transient absorption spectra demonstrated unusually short triplet state lifetime (2.1-5.4 us) for the two AQ derivatives at room tempe

David Schönheit, Ivan Marjanović

Flow-based market coupling is substantially altering the computation of cross-zonal capacities for the trade of electricity in the vast majority of European markets. The main benefit of the flow-based method is improved accuracy by better representing the impact of cross-zonal trade on the power flows in transmission grids. Some borders, adjacent to flow-bas

David Kramar, David Krejcirik

We consider Dirac operators on the half-line, subject to generalised infinite-mass boundary conditions. We derive sufficient conditions which guarantee the stability of the spectrum against possibly non-self-adjoint potential perturbations and study the optimality of the obtained results. Finally, we establish a non-relativistic limit which makes a relations

Michail Mamalakis, Antonios Mamalakis, Ingrid Agartz, Lynn Egeland Mørch-Johnsen

The accelerated progress of artificial intelligence (AI) has popularized deep learning models across various domains, yet their inherent opacity poses challenges, particularly in critical fields like healthcare, medicine, and the geosciences. Explainable AI (XAI) has emerged to shed light on these 'black box' models, aiding in deciphering their decision-maki

Nikolai Dokuchaev

The paper establishes an analog Whittaker-Shannon-Kotelnikov sampling theorem with fast decreasing coefficient, as well as a new modification of the corresponding interpolation formula applicable for general type non-vanishing bounded continuous signals.

Jonathan Ethier, Mathieu Chateauvert

Propagation modeling is a crucial tool for successful wireless deployments and spectrum planning with the demand for high modeling accuracy continuing to grow. Recognizing that detailed knowledge of the physical environment (terrain and clutter) is essential, we propose a novel approach that uses environmental information for predictions. Instead of relying

Ankush Mandal, Dipanjan Mukherjee, Christoph Federrath, Geoffrey V. Bicknell

The impact of winds and jet-inflated bubbles driven by active galactic nuclei (AGN) are believed to significantly affect the host galaxy's interstellar medium (ISM) and regulate star formation. To explore this scenario, we perform a suite of hydrodynamic simulations to model the interaction between turbulent star-forming clouds and highly pressurised AGN-dri

Johannes Rückert, Louise Bloch, Raphael Brüngel, Ahmad Idrissi-Yaghir

Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Ope

Henri Guenancia, Mihai Păun

In this article we are mainly concerned with three dimensional compact K\"ahler spaces with log terminal singularities. We establish the orbifold version of the Bogomolov-Gieseker inequality for stable $\mathbb Q$-sheaves.

Hoai-Minh Nguyen

We propose a method to establish the rapid stabilization of the bilinear Schr\"odinger control system and its linearized system, and the finite time stabilization of the linearized system using the Grammian operators. The analysis of the rapid stabilization involves a new quantity (variable) which is inspired by the adjoint state in the optimal control theor

Hoang-Son Nguyen, Hoi-To Wai

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signa

Kaïs Ammari, Makrem Salhi, Farhat Shel

In this paper, we consider the associated semigroups to some abstract thermoelastic systems (in particular the {\alpha}-\b{eta} system), with a partial delay on the coupled system. We will prove that the corresponding semigroups (in appropriate Hilbert spaces) are not differentiable.

Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton

We show that discounted methods for solving continuing reinforcement learning problems can perform significantly better if they center their rewards by subtracting out the rewards' empirical average. The improvement is substantial at commonly used discount factors and increases further as the discount factor approaches one. In addition, we show that if a pro

Calista Bernard, Jeremy Miller, Robin J. Sroka

Let $R$ be a unital ring satisfying the invariant basis number property, that every stably free $R$-module is free, and that the complex of partial bases of every finite rank free module is Cohen--Macaulay. This class of rings includes every ring of stable rank $1$ (e.g. any local, semi-local or Artinian ring), every Euclidean domain, and every Dedekind doma

Adam Gaier, James Stoddart, Lorenzo Villaggi, Shyam Sudhakaran

Two fundamental challenges face generative models in engineering applications: the acquisition of high-performing, diverse datasets, and the adherence to precise constraints in generated designs. We propose a novel approach combining optimization, constraint satisfaction, and language models to tackle these challenges in architectural design. Our method uses

Junkai Fan, Jiangwei Weng, Kun Wang, Yijun Yang

Real driving-video dehazing poses a significant challenge due to the inherent difficulty in acquiring precisely aligned hazy/clear video pairs for effective model training, especially in dynamic driving scenarios with unpredictable weather conditions. In this paper, we propose a pioneering approach that addresses this challenge through a nonaligned regulariz

Zihe Zhao, Chunyue Wang

With the advancement of Artificial Intelligence (AI) technology, next-generation wireless communication network is facing unprecedented challenge. Semantic communication has become a novel solution to address such challenges, with enhancing the efficiency of bandwidth utilization by transmitting meaningful information and filtering out superfluous data. Unfo

R Lalnuntluanga, R K Pradhan, A Giri

The neutrino experiments utilize heavy nuclear targets to achieve high statistics neutrino-nucleus interaction event rate, which leads to systematic uncertainties in the oscillation parameters due to the nuclear effects and uncertainties in the cross-section. Understanding the interaction of neutrinos with the nucleus becomes crucial in determining the oscil

Sergei Gukov, Rak-Kyeong Seong

We explore statistical properties of BPS q-series for 3d N=2 strongly coupled supersymmetric theories that correspond to a particular family of 3-manifolds Y. We discover that gaps between exponents in the q-series are statistically more significant at the beginning of the q-series compared to gaps that appear in higher powers of q. Our observations are obta

Katharina Schuh, Peter A. Whalley

We study three kinetic Langevin samplers including the Euler discretization, the BU and the UBU splitting scheme. We provide contraction results in $L^1$-Wasserstein distance for non-convex potentials. These results are based on a carefully tailored distance function and an appropriate coupling construction. Additionally, the error in the $L^1$-Wasserstein d

Ákos K. Matszangosz, Ferenc Szöllősi

It is shown that a normalized complex Hadamard matrix of order $6$ having three distinct columns, each containing at least one $-1$ entry necessarily belongs to the transposed Fourier family, or to the family of $2$-circulant complex Hadamard matrices. The proofs rely on solving polynomial system of equations by Gr\"obner basis techniques, and make use of a

Jack Breen, Katie Allen, Kieran Zucker, Lucy Godson

Large pretrained transformers are increasingly being developed as generalised foundation models which can underpin powerful task-specific artificial intelligence models. Histopathology foundation models show great promise across many tasks, but analyses have typically been limited by arbitrary hyperparameters that were not tuned to the specific task. We repo

Arron Gosnell, Evangelos Evangelou

With the proliferation of screening tools for chemical testing, it is now possible to create vast databases of chemicals easily. However, rigorous statistical methodologies employed to analyse these databases are in their infancy, and further development to facilitate chemical discovery is imperative. In this paper, we address the challenge of predicting an

Marta Pita-Vidal, Jaap J. Wesdorp, Christian Kraglund Andersen

Andreev (or superconducting) spin qubits (ASQs) have recently emerged as a promising qubit platform that combines superconducting circuits with semiconductor spin degrees of freedom. While recent experiments have successfully coupled two ASQs, how to realize a scalable architecture for extending this coupling to multiple distant qubits remains an open questi

Igor Pesando

We compute the irreps and their multiplicities of bosonic string spectrum up to level 10 and we give explicitly the on shell lightcone states which make the irreps. For scalars and vectors we compute the multiplicity up to level 22 and 19 respectively. The first scalar at odd level appears at level 11. For the bosonic string in non critical dimensions we arg

Ling Ma, Vincent Andrieu, Daniele Astolfi, Mathieu Bajodek

In this article, we present a stabilization feedback law with integral action for conservative abstract linear systems subjected to actuator nonlinearity. Based on the designed control law, we first prove the well-posedness and global asymptotic stability of the origin of the closed-loop system by constructing a weak Lyapunov functional. Secondly, as an illu

Binghui Chen, Chongyang Zhong, Wangmeng Xiang, Yifeng Geng

Due to the significant advances in large-scale text-to-image generation by diffusion model (DM), controllable human image generation has been attracting much attention recently. Existing works, such as Controlnet [36], T2I-adapter [20] and HumanSD [10] have demonstrated good abilities in generating human images based on pose conditions, they still fail to me

N. S. Gonchar

To implement the previously formulated principles of sustainable economic development, all non-negative solutions of the linear system of equations and inequalities, which are satisfied by the vector of real consumption, are completely described. It is established that the vector of real consumption with the minimum level of excess supply is determined by th

Federico Moiraghi, Matteo Palmonari, Davide Allavena, Federico Morando

Classifying public tenders is a useful task for both companies that are invited to participate and for inspecting fraudulent activities. To facilitate the task for both participants and public administrations, the European Union presented a common taxonomy (Common Procurement Vocabulary, CPV) which is mandatory for tenders of certain importance; however, the

Xiaohui Zhang, Zhiming Li, Shenglong Chen, Jikai Yang

Stochastic models are widely used to investigate the spread of epidemics in a complex environment. This paper extends a deterministic SAIRS epidemic model to a stochastic case with limited patient capacity and exposure. We first study the dynamical properties of the model under certain conditions, including persistence, extinction, and ergodic. Then, we intr

Chen Chen, Ruizhe Li, Yuchen Hu, Yuanyuan Chen

Intelligent task-oriented dialogue systems (ToDs) are expected to continuously acquire new knowledge, also known as Continual Learning (CL), which is crucial to fit ever-changing user needs. However, catastrophic forgetting dramatically degrades the model performance in face of a long streamed curriculum. In this paper, we aim to overcome the forgetting prob

Kuofeng Gao, Yang Bai, Jiawang Bai, Yong Yang

Multi-modal Large Language Models (MLLMs) have recently achieved enhanced performance across various vision-language tasks including visual grounding capabilities. However, the adversarial robustness of visual grounding remains unexplored in MLLMs. To fill this gap, we use referring expression comprehension (REC) as an example task in visual grounding and pr

Jian Chen, Peilin Zhou, Yining Hua, Yingxin Loh

Accurate evaluation of financial question answering (QA) systems necessitates a comprehensive dataset encompassing diverse question types and contexts. However, current financial QA datasets lack scope diversity and question complexity. This work introduces FinTextQA, a novel dataset for long-form question answering (LFQA) in finance. FinTextQA comprises 1,2

Pei Yuhang, Yu Min, Yu Yan

The proposed method introduces a parameter determination approach based on the minimum Fractal box dimension (FBD) of Variational Mode Decomposition (VMD) components, aiming to address the issue of manual determination of VMD decomposition layers in advance. Initially, VMD is applied to the original power signal, and the layer number for VMD decomposition is

Jeremie Pantin, Christophe Marsala

Anomaly detection (AD) is a fast growing and popular domain among established applications like vision and time series. We observe a rich literature for these applications, but anomaly detection in text is only starting to blossom. Recently, self-supervised methods with self-attention mechanism have been the most popular choice. While recent works have propo

Amir Zablotsky, Marcelo N Kuperman, Sebastián Bouzat

We analyze the dynamics of room evacuation for mixed populations that include both competitive and cooperative individuals through numerical simulations using the social force model. Cooperative agents represent well-trained individuals who know how to behave in order to reduce risks within high-density crowds. We consider that competitive agents can imitate

Shiran Even-Haim, Asaf A. Diringer, Ron Ruimy, Gefen Baranes

Conditional displacement with a qubit ancilla is a critical component in continuous-variable error correction protocols. We present the generalized conditional displacement operator, conditioned on a qudit ancilla, and explore potential implementations. We show how this operator can be used to enhance error correction with Gottesman-Kitaev-Preskill (GKP) cod

Giordano Cicchetti, Eleonora Grassucci, Jihong Park, Jinho Choi

In the new paradigm of semantic communication (SC), the focus is on delivering meanings behind bits by extracting semantic information from raw data. Recent advances in data-to-text models facilitate language-oriented SC, particularly for text-transformed image communication via image-to-text (I2T) encoding and text-to-image (T2I) decoding. However, although

Yannic Maus, Magnús M. Halldórsson

We consider the problem of coloring graphs of maximum degree $\Delta$ with $\Delta$ colors in the distributed setting with limited bandwidth. Specifically, we give a $\mathsf{poly}\log\log n$-round randomized algorithm in the CONGEST model. This is close to the lower bound of $\Omega(\log \log n)$ rounds from [Brandt et al., STOC '16], which holds also in th

Luigi Corona

The Belle and Belle II experiments have collected a 1.4 ab$^{-1}$ sample of $e^+e^-$ collision data at centre-of-mass energies near the $\Upsilon (nS)$ resonances, of which 424 fb$^{-1}$ were collected at Belle II in Run1 (2019--2022). We present a measurement of the lepton-flavour universality between electrons and muons, the search for the lepton-flavour v

Yajie Yu, Xuehui Ma, Shiliang Zhang, Zhuzhu Wang

This paper presents an adaptive ensemble control for stochastic systems subject to asymmetric noises and outliers. Asymmetric noises skew system observations, and outliers with large amplitude deteriorate the observations even further. Such disturbances induce poor system estimation and degraded stochastic system control. In this work, we model the asymmetri

Tatiana Boura, Natalia Koliou, George Meramveliotakis, Stasinos Konstantopoulos

Utilizing solar energy to meet space heating and domestic hot water demand is very efficient (in terms of environmental footprint as well as cost), but in order to ensure that user demand is entirely covered throughout the year needs to be complemented with auxiliary heating systems, typically boilers and heat pumps. Naturally, the optimal control of such a

Ulf-G. Meißner, Bernard Ch. Metsch, Helen Meyer

We study the fine-structure constant dependence of the rates of some selected radiative capture reactions within the framework of so-called Halo Effective Field Theory in order to assess the adequacy of some assumptions made on the Coulomb penetrability. We find that this dependence deviates from that implied by a parameterization of the cross sections of th

Yiheng Wang, Yu Peng, Zhe Lin

In this paper, we use a new method to prove cut-elimination of weak intuitionistic tense logic. This method focuses on splitting the contraction rule and cut rules. Further general theories and applications of this method shall be developed in the future.

Camilo Angulo, Miquel Cueca

We construct a van Est map for strict Lie 2-groups from the Bott-Shulman-Stasheff double complex of the strict Lie 2-group to the Weil algebra of its associated strict Lie 2-algebra. We show that, under appropriate connectedness assumptions, this map induces isomorphisms in cohomology. As an application, we differentiate the Segal 2-form on the loop group.

Alan Maciel, Morgan Le Delliou, José P. Mimoso

We explore in detail the 2+2 and 1+1+2 formalism in spherically symmetric spacetimes, spanning from deducing the dynamical equations to relating them to the well-known generalised Painlev\'e-Gullstrand (GPG) coordinate system. The evolution equations are the Raychaudhuri equations for null rays, including those also known as cross-focusing equations whose de

Michal Kajan, Jiří Krtička, Jiří Kubát

Auroral emission lines result from the interaction between magnetic field and stellar wind, offering valuable insights into physical properties and processes occurring within magnetospheres of celestial bodies. While extensively studied in planetary and exoplanetary atmospheres, in ultra-cool dwarfs, and as radio emission from early-type stars, the presence

Neha Gupta, Aditya Maheshwari

Hawkes process (HP) is a point process with a conditionally dependent intensity function. This paper defines the tempered fractional Hawkes process (TFHP) by time-changing the HP with an inverse tempered stable subordinator. We obtained results that generalize the fractional Hawkes process defined in Hainaut (2020) to a tempered version which has \textit{sem

Tao Li, Chenhui Cui, Rubing Huang, Dave Towey

Testing web forms is an essential activity for ensuring the quality of web applications. It typically involves evaluating the interactions between users and forms. Automated test-case generation remains a challenge for web-form testing: Due to the complex, multi-level structure of web pages, it can be difficult to automatically capture their inherent context

Zhengxu Shi

Recent advancements in deep neural networks have improved depth estimation in clear, daytime driving scenarios. However, existing methods struggle with rainy conditions due to rain streaks and fog, which distort depth estimation. This paper introduces a novel dual-layer convolutional kernel prediction network for lane depth estimation in rainy environments.

Andrew Katumba, Sudi Murindanyi, John Trevor Kasule, Elvis Mugume

The advent of Internet of Things (IoT) technology has generated massive interest in voice-controlled smart homes. While many voice-controlled smart home systems are designed to understand and support widely spoken languages like English, speakers of low-resource languages like Luganda may need more support. This research project aimed to develop a Luganda sp

Luis Guijarro, Maurizio Naldi, Vicent Pla, Jose-Ramon Vidal

In Beyond5G and 6G networks, a common theme is that sensing will play a more significant role than ever before. Over this trend, Integrated Sensing and Communications (ISAC) is focused on unifying the sensing functionalities and the communications ones and to pursue direct tradeoffs between them as well as mutual performance gains. We frame the resource trad

Ryoki Hamano, Shota Saito, Masahiro Nomura, Kento Uchida

Black-box optimization problems often require simultaneously optimizing different types of variables, such as continuous, integer, and categorical variables. Unlike integer variables, categorical variables do not necessarily have a meaningful order, and the discretization approach of continuous variables does not work well. Although several Bayesian optimiza

Peter Danchev, Arash Javan, Omid Hasanzadeh, Ahmad Moussavi

We systematically study those rings whose non-units are a sum of an idempotent and a nilpotent. Some crucial characteristic properties are completely described as well as some structural results for this class of rings are obtained. This work somewhat continues two publications on the subject due to Diesl (J. Algebra, 2013) and Karimi-Mansoub et al. (Contemp

Abdullahi Isa Ahmed, Yaya Etiabi, Ali Waqar Azim, El Mehdi Amhoud

Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally. Real-time data collection, analysis, and decision-making facilitated by localization solutions form the foundation for location-based services, enabling them to support critical functions within diverse IoT ecosystems. However, most existi

Reuben Dorent, Erickson Torio, Nazim Haouchine, Colin Galvin

Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. However, its interpretation is challenging, even for expert neurosurgeons. In this work, we designed the first patient-specific framework that performs brain tumor segmentation in trackerless iUS. To disambiguate ultrasound imaging and adapt to the neuros

Jinbi Zhang, Junling Zheng

A new homological dimension, called the Igusa-Todorov distance, is introduced to measure how far an Artin algebra is from being an Igusa-Todorov algebra. An upper bound for the dimension is established in terms of the Loewy length, leading to the conclusion that every Artin algebra has a finite Igusa-Todorov distance.Using this dimension, we derive an upper

Jean-Claude Cuenin

We prove resolvent estimates in Schatten spaces for Laplace-Beltrami operators on compact manifolds at the critical exponent. Our proof only uses known bounds for the Hadamard parametrix.

Yuuki Kubo, Ryuhei Sato, Yuansheng Zhao, Takahiro Ishikawa

To determine crystal structures from an X-ray diffraction (XRD) pattern containing multiple unknown phases, a data-assimilated crystal growth (DACG) simulation method has been developed. The XRD penalty function selectively stabilizes the structures in the experimental data, promoting their grain growth during simulated annealing. Since the XRD pattern is ca

Usman A. Zahidi, Krystian Łukasik, Grzegorz Cielniak

The maturity classification of specialty crops such as strawberries and tomatoes is an essential agricultural downstream activity for selective harvesting and quality control (QC) at production and packaging sites. Recent advancements in Deep Learning (DL) have produced encouraging results in color images for maturity classification applications. However, hy

A. Hossain, A. Banerjee, Md. N. Akhtar

In this article, an iterated function system (IFS) is considered on the real projective line $\mathbb{RP}^1$ so that the attractor is a Cantor-like set. Hausdorff dimension of this attractor is estimated. The existence of a probability measure associated with this IFS on $\mathbb{RP}^1$ is also demonstrated. It is shown that the $n$-th quantization error of

Jessica A. F. Thompson, Hannah Sheahan, Tsvetomira Dumbalska, Julian Sandbrink

Deep neural networks have provided a computational framework for understanding object recognition, grounded in the neurophysiology of the primate ventral stream, but fail to account for how we process relational aspects of a scene. For example, deep neural networks fail at problems that involve enumerating the number of elements in an array, a problem that i

Gianluca Ceruti, Daniel Kressner, Dominik Sulz

Compactly representing and efficently applying linear operators are fundamental ingredients in tensor network methods for simulating quantum many-body problems and solving high-dimensional problems in scientific computing. In this work, we study such representations for tree tensor networks, the so called tree tensor network operators (TTNOs), paying particu

Wei Jiang, Hans Schotten

Intelligent Reflecting Surface (IRS) is envisioned to be a technical enabler for the sixth-generation (6G) wireless system. Its potential lies in delivering high performance while maintaining both power efficiency and cost-effectiveness. Previous studies have primarily focused on point-to-point IRS communications involving a single user. Nevertheless, a prac

François Delarue, Etienne Tanré, Raphaël Maillet

In this paper we consider stochastic Fokker-Planck Partial Differential Equations (PDEs), obtained as the mean-field limit of weakly interacting particle systems subjected to both independent (or idiosyncratic) and common Brownian noises. We provide sufficient conditions under which the deterministic counterpart of the Fokker-Planck equation, which correspon