April 2024 arXiv papers — page 102

Ambroise Baril, Miguel Couceiro, Victor Lagerkvist

In this paper we are interested in the fine-grained complexity of deciding whether there is a homomorphism from an input graph $G$ to a fixed graph $H$ (the $H$-Coloring problem). The starting point is that these problems can be viewed as constraint satisfaction problems (CSPs), and that (partial) polymorphisms of binary relations are of paramount importance

Bozhi Luan, Hao Feng, Hong Chen, Yonghui Wang

The advent of Large Multimodal Models (LMMs) has sparked a surge in research aimed at harnessing their remarkable reasoning abilities. However, for understanding text-rich images, challenges persist in fully leveraging the potential of LMMs, and existing methods struggle with effectively processing high-resolution images. In this work, we propose TextCoT, a

José M. Gaspar

We study the impact of economic integration on agglomeration in a model where all consumers are inter-regionally mobile and have heterogeneous preferences regarding their residential location choices. This heterogeneity is the unique dispersion force in the model. We show that, under reasonable values for the elasticity of substitution among varieties of con

Predrag Jovanović, Vesna Borka Jovanović, Duško Borka, Alexander F. Zakharov

In this paper we use a modification of the Newtonian gravitational potential with a non-linear Yukawa-like correction, as it was proposed by C. Will earlier to obtain new bounds on graviton mass from the observed orbits of S-stars around the Galactic Center (GC). This phenomenological potential differs from the gravitational potential obtained in the weak fi

W. Dörfler, M. Elasmi, T. Laufer

This work addresses the scattering problem of an incident wave at a junction connecting two semi-infinite waveguides, which we intend to solve using Physics-Informed Neural Networks (PINNs). As with other deep learning-based approaches, PINNs are known to suffer from a spectral bias and from the hyperbolic nature of the Helmholtz equation. This makes the tra

Ammar Ahmed Pallikonda Latheef, Alberto Santamaria-Pang, Craig K Jones, Haris I Sair

Brain networks display a hierarchical organization, a complexity that poses a challenge for existing deep learning models, often structured as flat classifiers, leading to difficulties in interpretability and the 'black box' issue. To bridge this gap, we propose a novel architecture: a symbolic autoencoder informed by weak supervision and an Emergent Languag

J. X. Liu, L. T. Yang, Q. Yue, K. J. Kang

We present the first results of the search for sub-MeV fermionic dark matter absorbed by electron targets of germanium using the 205.4~kg$\cdot$day data collected by the CDEX-10 experiment, with the analysis threshold of 160~eVee. No significant dark matter (DM) signals over the background are observed. Results are presented as limits on the cross section of

Xinze Li

This note is based on Professor Vitali Kapovitch's comparison geometry course at the University of Toronto. It delves into various comparison theorems, including those by Rauch and Toponogov, focusing on their applications, such as Bishop-Gromov volume comparison, critical point theory of distance functions, diameter sphere theorem, and negative and nonnegat

Pierrick Verwilghen, Eric Emsellem, Florent Renaud, Milena Valentini

Past studies have long emphasised the key role played by galactic stellar bars in the context of disc secular evolution, via the redistribution of gas and stars, the triggering of star formation, and the formation of prominent structures such as rings and central mass concentrations. However, the exact physical processes acting on those structures, as well a

Jesus M. Gonzalez-Barahona

Let's imagine that in a few years generative AI has changed software development dramatically, taking charge of most of the programming tasks. Let's also assume that extended reality devices became ubiquitous, being the preferred interface for interacting with computers. This paper proposes how this situation would impact IDEs, by exploring how the developme

Krzysztof Kacprzyk, Mihaela van der Schaar

Symbolic regression has excelled in uncovering equations from physics, chemistry, biology, and related disciplines. However, its effectiveness becomes less certain when applied to experimental data lacking inherent closed-form expressions. Empirically derived relationships, such as entire stress-strain curves, may defy concise closed-form representation, com

Alessandra Sabatti, Jost Kellner, Fabian Kaufmann, Robert J. Chapman

Intensity modulators are fundamental components for integrated photonics. From near infrared to visible spectral ranges, they find applications in optical communication and quantum technologies. In particular, they are required for the control and manipulation of atomic systems such as atomic clocks and quantum computers. Typical integrated electro-optic mod

Johannes van Randenborgh, Moritz Schulze Darup

Aquifer thermal energy storages (ATES) are used to temporally store thermal energy in groundwater saturated aquifers. Typically, two storages are combined, one for heat and one for cold, to support heating and cooling of buildings. This way, the use of classical fossil fuel-based heating, ventilation, and air conditioning can be significantly reduced. Exploi

David Nadeau, Mike Kroutikov, Karen McNeil, Simon Baribeau

This paper introduces fourteen novel datasets for the evaluation of Large Language Models' safety in the context of enterprise tasks. A method was devised to evaluate a model's safety, as determined by its ability to follow instructions and output factual, unbiased, grounded, and appropriate content. In this research, we used OpenAI GPT as point of compariso

Hiroki Yamazaki, Gabor Csire, Nora Kucska, Nic Shannon

We have measured the critical current density, superconducting coherence length, and superconducting transition temperature of single-domain, epitaxially-grown Nb(110)/Au(111)/Nb(110) trilayers, all of which show a non-monotonic dependence on the thickness of the Au layer. These results are compared with the predictions of a relativistic, ab-initio theory, w

Ryszard Rudnicki

The paper below is a written version of the 17th Andrzej Lasota Lecture presented on January 12th, 2024 in Katowice. During the lecture we tried to show the impact of Andrzej Lasota's results on the author's research concerning various fields of mathematics, including chaos and ergodicity of dynamical systems, Markov operators and semigroups and partial diff

Nicolò Vallarano, Claudio J. Tessone

ChainScience 2024, the second edition of the interdisciplinary conference, brought together academics, practitioners, and industry experts to explore novel developments in the realm of distributed ledger technologies. The conference aimed to bridge diverse fields such as informatics, business, economics, finance, regulation, law, mathematics, physics, and co

André Gomes, Wladimir Neves

This paper proposes a new approach to solving the Buckley-Leverett System, which is to consider a compressible approximation model characterized by a stiff pressure law. Passing to the incompressible limit, the compressible model gives rise to a Hele-Shaw type free boundary limit of Buckley-Leverett System, and it is shown the existence of a weak solution of

Wojciech Czaja, Jeremiah Emidih, Brandon Kolstoe, Richard G. Spencer

Hyperspectral imagery (HSI) is an established technique with an array of applications, but its use is limited due to both practical and technical issues associated with spectral devices. The goal of the ICASSP 2024 'Hyper-Skin' Challenge is to extract skin HSI from matching RGB images and an infrared band. To address this problem we propose a model using fea

Takashi Takahashi

Under-bagging (UB), which combines under-sampling and bagging, is a popular ensemble learning method for training classifiers on an imbalanced data. Using bagging to reduce the increased variance caused by the reduction in sample size due to under-sampling is a natural approach. However, it has recently been pointed out that in generalized linear models, nai

Victor de Boer, Lise Stork

In this paper, we explore the synergies between Digital Humanities (DH) as a discipline and Hybrid Intelligence (HI) as a research paradigm. In DH research, the use of digital methods and specifically that of Artificial Intelligence is subject to a set of requirements and constraints. We argue that these are well-supported by the capabilities and goals of HI

Yaohui Li, Qifeng Zhou, Haoxing Chen, Jianbing Zhang

Contrastive Language-Image Pre-training (CLIP) has shown powerful zero-shot learning performance. Few-shot learning aims to further enhance the transfer capability of CLIP by giving few images in each class, aka 'few shots'. Most existing methods either implicitly learn from the few shots by incorporating learnable prompts or adapters, or explicitly embed th

Yao Dong, Zhicong Lin, Qiongqiong Pan

In 1974, Carlitz and Scoville introduced the Stirling-Eulerian polynomial $A_n(x,y|\alpha,\beta)$ as the enumerator of permutations by descents, ascents, left-to-right maxima and right-to-left maxima. Recently, Ji considered a refinement of $A_n(x,y|\alpha,\beta)$, denoted $P_n(u_1,u_2,u_3,u_4|\alpha,\beta)$, which is the enumerator of permutations by valley

Yu-Hong Dai, Kangkang Deng, Hui Zhang

The linearized Bregman iterations (LBreI) and its variants are powerful tools for finding sparse or low-rank solutions to underdetermined linear systems. In this study, we propose a cut-and-project perspective for the linearized Bregman method via a bilevel optimization formulation, along with a new unified algorithmic framework. The new perspective not only

Julien Dular, Fredrik Magnus, Erik Schnaubelt, Arjan Verweij

In this paper, we propose the Coupled Axial and Transverse currents (I) (CATI) method, as an efficient and accurate finite element approach for modelling the electric and magnetic behavior of periodic composite superconducting conductors. The method consists of a pair of two-dimensional models coupled via circuit equations to account for the conductor geomet

Haimin Zhang, Min Xu

Studies continually find that message-passing graph convolutional networks suffer from the over-smoothing issue. Basically, the issue of over-smoothing refers to the phenomenon that the learned embeddings for all nodes can become very similar to one another and therefore are uninformative after repeatedly applying message passing iterations. Intuitively, we

Samuel Poincloux, Kazumasa A. Takeuchi

A wide range of disordered materials, from biological to geological assemblies, feature discrete elements undergoing large shape changes. How significant geometrical variations at the microscopic scale affect the response of the assembly, in particular rigidity transitions, is an ongoing challenge in soft matter physics. However, the lack of a model granular

Hemaditya Malla, Brian M. Hare, Steven Cummer, Yihao Guo

We study radio emissions from positive streamers in air using 3D simulations, from which the radiated electric field is computed by solving Jefimenko's equations. The simulations are performed at 0.5 bar using two photoionization methods: the Helmholtz approximation for a photon density and a Monte Carlo method using discrete photons, with the latter being t

Akanksha Agrawal, Sergio Cabello, Michael Kaufmann, Saket Saurabh

Drawing a graph in the plane with as few crossings as possible is one of the central problems in graph drawing and computational geometry. Another option is to remove the smallest number of vertices or edges such that the remaining graph can be drawn without crossings. We study both problems in a book-embedding setting for ordered graphs, that is, graphs wit

Henry S. Thompson

Common Crawl is a multi-petabyte longitudinal dataset containing over 100 billion web pages which is widely used as a source of language data for sequence model training and in web science research. Each of its constituent archives is on the order of 75TB in size. Using it for research, particularly longitudinal studies, which necessarily involve multiple ar

Bart M. P. Jansen, Ruben F. A. Verhaegh

For an optimization problem $\Pi$ on graphs whose solutions are vertex sets, a vertex $v$ is called $c$-essential for $\Pi$ if all solutions of size at most $c \cdot OPT$ contain $v$. Recent work showed that polynomial-time algorithms to detect $c$-essential vertices can be used to reduce the search space of fixed-parameter tractable algorithms solving such

Ivica Obadic, Alex Levering, Lars Pennig, Dario Oliveira

Predicting socioeconomic indicators from satellite imagery with deep learning has become an increasingly popular research direction. Post-hoc concept-based explanations can be an important step towards broader adoption of these models in policy-making as they enable the interpretation of socioeconomic outcomes based on visual concepts that are intuitive to h

Sadaf Madni, Sumit, Lata Thakur, Najmul Haque

We investigate the electrical conductivity of the quark-gluon plasma (QGP) using a non-perturbative resummation scheme incorporating the Gribov-modified gluon propagator. The electrical conductivity is evaluated by solving the relativistic Boltzmann transport equation within the relaxation-time approximation, where the relaxation times are obtained from micr

Andrzej Derdzinski, Ivo Terek

In the paper [3] the value of the invariant denoted by d and often called "rank" was misstated for a narrow class of examples of ECS manifolds: they were identified as having d = 1 instead of the correct value d = 2. The error was repeated, by citing [3], in [1], [2] and [4] -- [7]. We briefly describe the class in question, explain why it has d = 2, and lis

Ashish Devadas Nair, Julien Kindle, Plamen Levchev, Davide Scaramuzza

Simultaneous Localization and Mapping systems are a key enabler for positioning in both handheld and robotic applications. The Hilti SLAM Challenges organized over the past years have been successful at benchmarking some of the world's best SLAM Systems with high accuracy. However, more capabilities of these systems are yet to be explored, such as platform a

Paramita Das, Isaac Johnson, Diego Saez-Trumper, Pablo Aragón

Wikipedia is the largest web repository of free knowledge. Volunteer editors devote time and effort to creating and expanding articles in more than 300 language editions. As content quality varies from article to article, editors also spend substantial time rating articles with specific criteria. However, keeping these assessments complete and up-to-date is

Avinash Anand, Mohit Gupta, Kritarth Prasad, Ujjwal Goel

Citation Text Generation (CTG) is a task in natural language processing (NLP) that aims to produce text that accurately cites or references a cited document within a source document. In CTG, the generated text draws upon contextual cues from both the source document and the cited paper, ensuring accurate and relevant citation information is provided. Previou

Matteo Castagnola, Marcus T. Lexander, Enrico Ronca, Henrik Koch

We analyze the real-time electron-photon dynamics in long-range polariton-mediated energy transfer using a real-time quantum electrodynamics coupled cluster (RT-QED-CC) model, which allows for spatial and temporal visualization of transport processes. We compute the time evolution of photonic and molecular observables, such as the dipole moment and the photo

Monika Górka, Daniel Jaworek, Marek Wodzinski

Cancer is one of the leading causes of death globally, and early diagnosis is crucial for patient survival. Deep learning algorithms have great potential for automatic cancer analysis. Artificial intelligence has achieved high performance in recognizing and segmenting single lesions. However, diagnosing multiple lesions remains a challenge. This study examin

Xianghua Zeng, Hao Peng, Dingli Su, Angsheng Li

Hierarchical Reinforcement Learning (HRL) is a promising approach for managing task complexity across multiple levels of abstraction and accelerating long-horizon agent exploration. However, the effectiveness of hierarchical policies heavily depends on prior knowledge and manual assumptions about skill definitions and task decomposition. In this paper, we pr

Alejandro Hnilo, Mónica Agüero, Marcelo Kovalsky, Myriam Nonaka

The conflict between Quantum Mechanics (QM) and Local Realism is most noticeable in the correlations observed between distant regions of a spatially spread entangled state. It has been hypothesized that transient deviations (from the values predicted by QM) may be observed if the correlations are measured in a time shorter than L/c, where L is the spatial sp

Thomas Deliot, Eric Heitz, Laurent Belcour

We show how to transform a non-differentiable rasterizer into a differentiable one with minimal engineering efforts and no external dependencies (no Pytorch/Tensorflow). We rely on Stochastic Gradient Estimation, a technique that consists of rasterizing after randomly perturbing the scene's parameters such that their gradient can be stochastically estimated

Li-Hua Zhang, Bang Liu, Zong-Kai Liu, Zheng-Yuan Zhang

Detecting microwave signals over a wide frequency range has numerous advantages as it enables simultaneous transmission of a large amount of information and access to more spectrum resources. This capability is crucial for applications such as microwave communication, remote sensing, and radar. However, conventional microwave receiving systems are limited by

K. -Y. Huang, D. Abbink, S. Viti, S. García-Burillo

The outflowing molecular gas in the circumnuclear disk (CND) of the nearby (D=14 Mpc) AGN-starburst composite galaxy NGC 1068 is considered as a manifestation of ongoing AGN feedback. The large spread of velocities from the outflowing gas is likely driving various kinds of shock chemistry across the CND. We performed a multiline molecular study using CH3OH w

Emiel Slootman, Igor Poltavsky, Ravindra Shinde, Jacopo Cocomello

Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC forces for the fluxional ethanol molecule at room temperature by using either multi-determinant Jastrow-Slater wave functions in variational Monte Carlo or just a single determinant in dif

Bin Wang, Chengwei Wei, Zhengyuan Liu, Geyu Lin

As the rapidly advancing domain of natural language processing (NLP), large language models (LLMs) have emerged as powerful tools for interpreting human commands and generating text across various tasks. Nonetheless, the resilience of LLMs to handle text containing inherent errors, stemming from human interactions and collaborative systems, has not been thor

Nicolas Wagner, Dongyang Fan, Martin Jaggi

We explore on-device self-supervised collaborative fine-tuning of large language models with limited local data availability. Taking inspiration from the collaborative learning community, we introduce three distinct trust-weighted gradient aggregation schemes: weight similarity-based, prediction similarity-based and validation performance-based. To minimize

Shruthi Gowda, Elahe Arani, Bahram Zonooz

Self-supervised learning (SSL) has emerged as a promising solution for addressing the challenge of limited labeled data in deep neural networks (DNNs), offering scalability potential. However, the impact of design dependencies within the SSL framework remains insufficiently investigated. In this study, we comprehensively explore SSL behavior across a spectru

Grégoire Barrué, Tony Quertier, Orlane Zang

Continuing our analysis of quantum machine learning applied to our use-case of malware detection, we investigate the potential of quantum convolutional neural networks. More precisely, we propose a new architecture where data is uploaded all along the quantum circuit. This allows us to use more features from the data, hence giving to the algorithm more infor

Cordian Riener, Robin Schabert, Thi Xuan Vu

Semi-algebraic set is a subset of the real space defined by polynomial equations and inequalities. In this paper, we consider the problem of deciding whether two given points in a semi-algebraic set are connected. We restrict to the case when all equations and inequalities are invariant under the action of the symmetric group and their degrees at most $d<n$,

Jiadi Cui, Junming Cao, Fuqiang Zhao, Zhipeng He

Large garages are ubiquitous yet intricate scenes that present unique challenges due to their monotonous colors, repetitive patterns, reflective surfaces, and transparent vehicle glass. Conventional Structure from Motion (SfM) methods for camera pose estimation and 3D reconstruction often fail in these environments due to poor correspondence construction. To

N. M. Beaver, N. Voce, P. Meisenheimer, R. Ramesh

Vector magnetometry is an essential tool in characterizing the distribution of currents and magnetization in a broad range of systems. Point defect sensors, like the nitrogen vacancy (NV) center in diamond, have demonstrated impressive sensitivity and spatial resolution for detecting these fields. Measuring the vector field at a single point in space using s

Hiroshi Hirai, Keiya Sakabe

In this paper, we study the asymptotic behavior of continuous- and discrete-time gradient flows of a ``lower-unbounded" convex function $f$ on a Hadamard manifold $M$, particularly, their convergence properties to the boundary $M^{\infty}$ at infinity of $M$. We establish a duality theorem that the infimum of the gradient-norm $\|\nabla f(x)\|$ of $f$ over $

Dongryul M. Kim, Hee Oh

For a geometrically finite Kleinian group $\Gamma$, the Bowen-Margulis-Sullivan measure is finite and is the unique measure of maximal entropy for the geodesic flow, as shown by Sullivan and Otal-Peign\'e respectively. Moreover, it is strongly mixing by a result of Babillot. We obtain a higher-rank analogue of this theorem. Given a relatively Anosov subgroup

Allen H Boozer

Tokamak disruptions are associated with breaking magnetic surfaces, which makes magnetic field lines chaotic in large regions of the plasma. The enforcement of quasi-neutrality in a region of chaotic field lines requires an electric potential that has both short and long correlation distances across the magnetic field lines. The short correlation distances p

Abdelilah Karara, Mohamed Rossafi

In this paper, we will introduce the new concepts of continuous bi-g-frames and continuous K-bi-g-frame for Hilbert spaces. Then, we examine some characterizations properties with the help of a biframe operator. Finally, we investigate several results about the stability of continuous bi-g-Bessel sequence and K-bi-g-frame are produced via the use of frame th

H. Shen, C. Y. Zhao

The research on sensing the sensitivity of the light field in the whispering gallery mode (WGM) to the micro-cavity environment has already appeared, which uses the frequency shift of the light field in the WGM or the sensitivity of the resonance peak frequency shift. Multi-mode comb teeth of optical frequency comb(OFC) generated by nonlinear micro-cavity ha

Christian Fröhlich, Robert C. Williamson

Motivated by recently emerging problems in machine learning and statistics, we propose data models which relax the familiar i.i.d. assumption. In essence, we seek to understand what it means for data to come from a set of probability measures. We show that our frequentist data models, parameterized by such sets, manifest two aspects of imprecision. We charac

Yutaka Hirano, Tomohiro Itogawa, Keisuke Fujii

Magic state distillation plays an important role in universal fault-tolerant quantum computing, and its overhead is one of the major obstacles to realizing fault-tolerant quantum computers. Hence, many studies have been conducted to reduce this overhead. Among these, Litinski has provided a concrete assessment of resource-efficient distillation protocol impl

M. Majewski, S. Qiu, O. Ronsin, L. Lüer

Perovskite solar cells (PSC) are promising potential competitors to established photovoltaic technologies due to their superior efficiency and low-cost solution processability. However, the limited understanding of the crystallization behaviour hinders the technological transition from lab-scale cells to modules. In this work, we perform Phase Field (PF) sim

Kewei Li, Yuqian Wu, Yinheng Li, Yutong Guo

Since the mechanism of action of drug molecules in the human body is difficult to reproduce in the in vitro environment, it becomes difficult to reveal the causes of the activity cliff phenomenon of drug molecules. We found out the AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in peptides

Daniil Merkulov, Daria Cherniuk, Alexander Rudikov, Ivan Oseledets

In this paper, we introduce an algorithm for data quantization based on the principles of Kashin representation. This approach hinges on decomposing any given vector, matrix, or tensor into two factors. The first factor maintains a small infinity norm, while the second exhibits a similarly constrained norm when multiplied by an orthogonal matrix. Surprisingl

Andre Rochow, Max Schwarz, Sven Behnke

The task of face reenactment is to transfer the head motion and facial expressions from a driving video to the appearance of a source image, which may be of a different person (cross-reenactment). Most existing methods are CNN-based and estimate optical flow from the source image to the current driving frame, which is then inpainted and refined to produce th

Wenyi Lian, Wenjing Lian, Ziwei Luo

Image restoration, which aims to recover high-quality images from their corrupted counterparts, often faces the challenge of being an ill-posed problem that allows multiple solutions for a single input. However, most deep learning based works simply employ l1 loss to train their network in a deterministic way, resulting in over-smoothed predictions with infe

Biqian Feng, Yongpeng Wu, Xiang-Gen Xia, Chengshan Xiao

This letter investigates the weighted sum rate maximization problem in movable antenna (MA)-enhanced systems. To reduce the computational complexity, we transform it into a more tractable weighted minimum mean square error (WMMSE) problem well-suited for MA. We then adopt the WMMSE algorithm and majorization-minimization algorithm to optimize the beamforming

Shuyu Ou, Ariya Sangwongwanich, Subham Sahoo, Frede Blaabjerg

The health status of power semiconductor devices in power converters is important but difficult to monitor. This paper analyzes the relationship between harmonics in inverter control variables and a health precursor (the on-state voltage Von of power semiconductor devices). Based on the analysis, harmonics can estimate Von without adding extra sensing circui

Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund

Though diffusion models have been successfully applied to various image restoration (IR) tasks, their performance is sensitive to the choice of training datasets. Typically, diffusion models trained in specific datasets fail to recover images that have out-of-distribution degradations. To address this problem, this work leverages a capable vision-language mo

Zhenwei Huang, Wen Huang, Pratik Jawanpuria, Bamdev Mishra

In recent years, federated learning (FL) has emerged as a prominent paradigm in distributed machine learning. Despite the partial safeguarding of agents' information within FL systems, a malicious adversary can potentially infer sensitive information through various means. In this paper, we propose a generic private FL framework defined on Riemannian manifol

Kari J. Eskola, Vadim Guzey, Ilkka Helenius, Petja Paakkinen

We present next-to-leading order perturbative QCD predictions for inclusive dijet photoproduction in ultra-peripheral nucleus-nucleus collisions (UPCs) within the impact-parameter dependent equivalent photon approximation. Taking into account the finite size of both the photon-emitting and the target nucleus, we show that this process is sensitive to the tra

Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin

Score-based generative models have emerged as a powerful approach for sampling high-dimensional probability distributions. Despite their effectiveness, their theoretical underpinnings remain relatively underdeveloped. In this work, we study the convergence properties of deterministic samplers based on probability flow ODEs from both theoretical and numerical

Shuaicong Hu, Yanan Wang, Jian Liu, Jingyu Lin

Considering the variability of amplitude and phase patterns in electrocardiogram (ECG) signals due to cardiac activity and individual differences, existing entropy-based studies have not fully utilized these two patterns and lack integration. To address this gap, this paper proposes a novel fusion entropy metric, morphological ECG entropy (MEE) for the first

Michal H. Kolář, Hugo Mc Grath, Felipe C. Nepomuceno, Michaela Černeková

All proteins in living organisms are produced in ribosomes that facilitate the translation of genetic information into a sequence of amino acid residues. During translation, the ribosome undergoes initiation, elongation, termination, and recycling. In fact, peptide bonds are formed only during the elongation phase, which comprises periodic association of tra

K. Altenmueller, . Antolin, D. Calvet, F. R. Candon

Gas time projection chambers (TPCs) with Micromegas pixelated readouts are being used in dark matter searches and other rare event searches, due to their potential in terms of low background levels, energy and spatial resolution, gain, and operational stability. Moreover, these detectors can provide precious features,such as topological information, allowing

Michael Eden, Adrian Muntean

We consider the mathematical analysis and homogenization of a moving boundary problem posed for a highly heterogeneous, periodically perforated domain. More specifically, we are looking at a one-phase thermo-elasticity system with phase transformations where small inclusions, initially periodically distributed, are growing or shrinking based on a kinetic und

Céline Duval, Taher Jalal, Ester Mariucci

We consider the problem of estimating the density of the process associated with the small jumps of a pure jump L\'evy process, possibly of infinite variation, from discrete observations of one trajectory. The interest of such a question lies on the observation that even when the L\'evy measure is known, the density of the increments of the small jumps of th

Ziyao Liu, Huanyi Ye, Yu Jiang, Jiyuan Shen

In recent years, Federated Unlearning (FU) has gained attention for addressing the removal of a client's influence from the global model in Federated Learning (FL) systems, thereby ensuring the ``right to be forgotten" (RTBF). State-of-the-art methods for unlearning use historical data from FL clients, such as gradients or locally trained models. However, st

Johann Ostmeyer, Pavel Buividovich

The hybrid Monte Carlo (HMC) algorithm is a ubiquitous method in computational physics with applications ranging from condensed matter to lattice QCD and beyond. However, HMC simulations often suffer from long autocorrelation times, severely reducing their efficiency. In this work two of the main sources of autocorrelations are identified and eliminated. The

Xun Yuan, Yang Yang, Prosanta Gope, Aryan Pasikhani

In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, good data is not a free lunch and is always hard to access due to privacy regulations like the General Data Protection Regulation (GDPR). A potential solution is to release a synthetic dataset with a similar

Philipp Glaum, Fabian Neumann, Tom Brown

The European North Sea has a vast renewable energy potential and can be a powerhouse for Europe's energy transition. However, currently there is uncertainty about how much offshore wind energy can be integrated, whether offshore grids should be meshed and to what extent offshore hydrogen should play a role. To address these questions, we use the open-source

Dominic Ryder

Stephen Hawking's derivation of Hawking radiation relied on one particular spacetime model, that of a star collapsing into a black hole which then remains in existence forever. He then argued that Hawking radiation implies this model should be thrown away in favour of a different model, that of an evaporating black hole. This aspect of Hawking's argument is

Mingyang Guo, Hongliang Lu

Let $n,k,s$ be three integers and $\beta$ be a sufficiently small positive number such that $k\geq 3$, $0<1/n\ll \beta\ll 1/k$ and $ks+k\leq n\leq (1+\beta)ks+k-2$. A $k$-graph is called non-trivial if it has no isolated vertex. In this paper, we determine the maximum number of edges in a non-trivial $k$-graph with $n$ vertices and matching number at most $s

Daomin Cao, Boquan Fan, Rui Li

In this paper, we study the radial symmetry properties of stationary and uniformly rotating solutions of the vortex-wave system introduced by Marchioro and Pulvirenti \cite{Mar1}. We show that every uniformly rotating patch $\left(D,x_1,x_2,..,x_k\right)$ with angular velocity $\Omega\leq 0$ must be radial with respect to the only point vortex $x_1$, implyin

Pierre-Louis Blayac, Richard Canary, Feng Zhu, Andrew Zimmer

We establish counting, mixing and equidistribution results for finite BMS measures on flow spaces associated to geometrically finite convergence group actions. We show that, in particular, these results apply to flow spaces associated to relatively Anosov groups.

Hyunsoo Cho

Many recent studies endeavor to improve open-source language models through imitation learning, and re-training on the synthetic instruction data from state-of-the-art proprietary models like ChatGPT and GPT-4. However, the innate nature of synthetic data inherently contains noisy data, giving rise to a substantial presence of low-quality data replete with e

Oscar Lado-Baleato, Carla Díaz-Louza, Francisco Gude, Marcos Matabuena

Establishing optimal cut-offs for clinical biomarkers is a fundamental statistical problem in epidemiology, clinical trials, and drug discovery. While there is extensive literature regarding the definition of optimal cut-offs for scalar biomarkers, methodologies for analyzing random statistical objects in the more complex spaces associated with random functi

Linjie Xu, Zichuan Liu, Alexander Dockhorn, Diego Perez-Liebana

One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency. Compared to single agent RL, the sample efficiency for Multi-Agent Reinforcement Learning (MARL) is more challenging because of its inherent partial observability, non-stationary training, and enormous strategy space. Although much effort has been devoted to developing new

Ben Elias, Edmund Heng

In recent work, the second author introduced the concept of Coxeter quivers, generalizing several previous notions of a quiver representation. Finite type Coxeter quivers were classified, and their indecomposable objects were shown to be in bijection with positive roots, generalizing a classical theorem of Gabriel. In this paper we define fusion quivers, a n

Pierre-Louis Blayac, Richard Canary, Feng Zhu, Andrew Zimmer

In this paper we develop a theory of Patterson--Sullivan measures associated to coarse cocycles of convergence groups. This framework includes Patterson-Sullivan measures associated to the Busemann cocycle on the geodesic boundary of a Gromov hyperbolic metric spaces and Patterson-Sullivan measures on flag manifolds associated to Anosov (or more general tran

Seungheon Shin, Masaaki Kimura, Bo Zhou, Qing Zhao

The 1/2$^-$ resonant states in $^{13}{\rm C}$ are investigated to search for the Hoyle-analog state. In order to treat the resonance states located around the 3$\alpha+n$ threshold, the analytic continuation in the coupling constant (ACCC) has been combined with the real-time evolution method (REM). The properties of the 1/2$^-$ resonance states such as the

Mathieu Mari, Michał Pawłowski, Runtian Ren, Piotr Sankowski

This paper presents a new research direction for online Multi-Level Aggregation (MLA) with delays. In this problem, we are given an edge-weighted rooted tree $T$, and we have to serve a sequence of requests arriving at its vertices in an online manner. Each request $r$ is characterized by two parameters: its arrival time $t(r)$ and location $l(r)$ (a vertex)

Lucas Slot, Manuel Wiedmer

Let $\mathbf{X} \subseteq \mathbb{R}^n$ be a closed set, and consider the problem of computing the minimum $f_{\min}$ of a polynomial $f$ on $\mathbf{X}$. Given a measure $\mu$ supported on $\mathbf{X}$, Lasserre (SIAM J. Optim. 21(3), 2011) proposes a decreasing sequence of upper bounds on $f_{\min}$, each of which may be computed by solving a semidefinite

Moyu Zhang, Yongxiang Tang, Jinxin Hu, Yu Zhang

Existing methods often adjust representations adaptively only after aggregating user behavior sequences. This coarse-grained approach to re-weighting the entire user sequence hampers the model's ability to accurately model the user interest migration across different scenarios. To enhance the model's capacity to capture user interests from historical behavio

Krzysztof Kowalczyk, Paweł Wachel, Cristian R. Rojas

This paper addresses a kernel-based learning problem for a network of agents locally observing a latent multidimensional, nonlinear phenomenon in a noisy environment. We propose a learning algorithm that requires only mild a priori knowledge about the phenomenon under investigation and delivers a model with corresponding non-asymptotic high probability error

Enzhi Zhang, Isaac Lyngaas, Peng Chen, Xiao Wang

Attention-based models are proliferating in the space of image analytics, including segmentation. The standard method of feeding images to transformer encoders is to divide the images into patches and then feed the patches to the model as a linear sequence of tokens. For high-resolution images, e.g. microscopic pathology images, the quadratic compute and mem

Amadeus M. Gebauer, Martin R. Pfaller, Jason M. Szafron, Wolfgang A. Wall

In the last decades, many computational models have been developed to predict soft tissue growth and remodeling (G&R). The constrained mixture theory describes fundamental mechanobiological processes in soft tissue G&R and has been widely adopted in cardiovascular models of G&R. However, even after two decades of work, large organ-scale models are rare, main

David Sobrín-Hidalgo, Miguel Ángel González-Santamarta, Ángel Manuel Guerrero-Higueras, Francisco Javier Rodríguez-Lera

This paper presents an improved system based on our prior work, designed to create explanations for autonomous robot actions during Human-Robot Interaction (HRI). Previously, we developed a system that used Large Language Models (LLMs) to interpret logs and produce natural language explanations. In this study, we expand our approach by incorporating Vision-L

Kilian Seibold, Orjan Ameye, Oded Zilberberg

Periodically-driven systems engender a rich competition between the time scales of the drives and those of the system, leading to a limited ability to describe the system in full. We present a framework for the description of interacting bosonic driven systems via a Floquet expansion on top of a quantization that "counts" the drive photons, and provide compe

Romain Egele, Julio C. S. Jacques Junior, Jan N. van Rijn, Isabelle Guyon

Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting and transforming data for practical use is intricate. Even in today's digital era, where substantial data is generated daily, it is uncommon for it to be readily usable; most ofte

Paola Cavaliere, Andrea Cianchi, Luboš Pick, Lenka Slavíková

Necessary and sufficient conditions are offered for Sobolev type spaces built on rearrangement-invariant spaces to be continuously embedded into (generalized) Campanato and Morrey spaces on open subsets of the $n$-dimensional Euclidean space. As a consequence, the optimal target and domain spaces in the relevant embeddings are identified. Our general criteri

G. Falcioni, F. Herzog, S. Moch, A. Pelloni

We present the even-N moments N =< 20 of the fourth-order (N^3LO) contribution P_{gq}^(3)(x) to the quark-to-gluon splitting function in perturbative QCD. These moments, obtained by analytically computing off-shell operator matrix elements for a general gauge group, agree with all known results, in particular with the moments N =< 10 derived before from stru