March 2024 arXiv papers — page 106

I. -M. Vladisavlevici, C. Vlachos, J. -L. Dubois, A. Huerta

We present a novel scheme for rapid quantitative analysis of debris generated during experiments with solid targets following relativistic laser-plasma interaction at high-power laser facilities. Experimental data indicates that predictions by available modelling for non-mass-limited targets are reasonable, with debris on the order of hundreds ug-per-shot. W

Kirti Joshi

This is a continuation of my work on Arithmetic Teichmuller Spaces developed in the present series of papers. In this paper, I show that the Theory of Arithmetic Teichmuller Spaces leads, using Shinichi Mochizuki's rubric, to a proof of the $abc$-conjecture (as asserted by Mochizuki).

J. M. Aldaz, A. Bravo, H. Render

We consider inequalities of Bombieri type for polynomials that need not be homogeneous, using the apolar inner product.

David Martínez-Rubio, Christophe Roux, Sebastian Pokutta

In this work, we analyze two of the most fundamental algorithms in geodesically convex optimization: Riemannian gradient descent and (possibly inexact) Riemannian proximal point. We quantify their rates of convergence and produce different variants with several trade-offs. Crucially, we show the iterates naturally stay in a ball around an optimizer, of radiu

Peter Leer, Jesper Jensen, Zheng-Hua Tan, Jan Østergaard

Advanced auditory models are useful in designing signal-processing algorithms for hearing-loss compensation or speech enhancement. Such auditory models provide rich and detailed descriptions of the auditory pathway, and might allow for individualization of signal-processing strategies, based on physiological measurements. However, these auditory models are o

Hiba Dahmani, Moussab Bennehar, Nathan Piasco, Luis Roldao

Implicit neural representation methods have shown impressive advancements in learning 3D scenes from unstructured in-the-wild photo collections but are still limited by the large computational cost of volumetric rendering. More recently, 3D Gaussian Splatting emerged as a much faster alternative with superior rendering quality and training efficiency, especi

Emanuele De Bono, Manuel Collet, Morvan Ouisse

Sound attenuation along a waveguide is intensively studied for applications ranging from heating and air-conditioning ventilation systems, to aircraft turbofan engines. In particular, the new generation of Ultra-High-By-Pass-Ratio turbofan requires higher attenuation at low frequencies, in less space for liner treatment. This demands to go beyond the classic

Zhiyong Zhang, Huaizu Jiang, Hanumant Singh

Real-time high-accuracy optical flow estimation is a crucial component in various applications, including localization and mapping in robotics, object tracking, and activity recognition in computer vision. While recent learning-based optical flow methods have achieved high accuracy, they often come with heavy computation costs. In this paper, we propose a hi

Scott Cheng-Hsin Yang, Baxter Eaves, Michael Schmidt, Ken Swanson

Tabular data is common yet typically incomplete, small in volume, and access-restricted due to privacy concerns. Synthetic data generation offers potential solutions. Many metrics exist for evaluating the quality of synthetic tabular data; however, we lack an objective, coherent interpretation of the many metrics. To address this issue, we propose an evaluat

Yanan Bo, Yongqiang Wang

Distributed nonconvex optimization underpins key functionalities of numerous distributed systems, ranging from power systems, smart buildings, cooperative robots, vehicle networks to sensor networks. Recently, it has also merged as a promising solution to handle the enormous growth in data and model sizes in deep learning. A fundamental problem in distribute

Florian Kischel, Stefan Wessel

We derive an exact formula for the corner free-energy contribution of weakly-anisotropic two-dimensional critical systems in the Ising universality class on rectangular domains, expressed in terms of quantities that specify the anisotropic fluctuations. The resulting expression agrees with numerical exact calculations that we perform for the anisotropic tria

Joshua Townsend, Ravi Kashikar, S. Lisenkov, I. Ponomareva

First-principles-based atomistic simulations are used to reveal ferroelectric phases and phase transitions induced in a semiconductor ferroelectric, CsGeBr$_3$, by external loads: hydrostatic pressure, uniaxial and biaxial stresses, and misfit strain. Hydrostatic pressure was found to suppress the Curie point at the rate -0.45$T_C(0)$ K/GPa, where $T_C(0)$ i

Peter Leer, Jesper Jensen, Laurel H. Carney, Zheng-Hua Tan

This article investigates the use of deep neural networks (DNNs) for hearing-loss compensation. Hearing loss is a prevalent issue affecting millions of people worldwide, and conventional hearing aids have limitations in providing satisfactory compensation. DNNs have shown remarkable performance in various auditory tasks, including speech recognition, speaker

J. M. Aldaz, H. Render

The existence of decompositions of the form $f=P\cdot q+r$ with $P_k^{\ast}\left( D\right) r=0$, where $f$ is entire, $P$ a polynomial and $P^{\ast}_k$ the principal part of $P$ with its coefficients conjugated, was achieved in \cite{AlRe23} under certain restrictions on the order of $f$. Here we prove uniqueness, thereby obtaining Fischer decompositions, un

Quy Ong, Xufeng Xu, Francesco Stellacci

In concentrated macromolecular dispersions, far-from-ideal intermolecular interactions determine the dispersion behaviors including phase transition, crystallization, and liquid-liquid phase separation. Here, we present a novel versatile capillary-cell design for analytical ultracentrifugation-sedimentation equilibrium (AUC-SE), ideal for studying samples at

Boxun Liu, Shijian Gao, Zonghui Yang, Xiang Cheng

Integrated Sensing and Communication (ISAC) emerges as a promising technology for B5G/6G, particularly in the millimeter-wave (mmWave) band. However, the widespread adoption of hybrid architecture in mmWave systems compromises multiplexing gain due to limited radio-frequency chains, resulting in mediocre performance when embedding sensing functionality. To a

Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia

We study Gaussian sparse estimation tasks in Huber's contamination model with a focus on mean estimation, PCA, and linear regression. For each of these tasks, we give the first sample and computationally efficient robust estimators with optimal error guarantees, within constant factors. All prior efficient algorithms for these tasks incur quantitatively subo

Yongjie Wang, Tong Zhang, Xu Guo, Zhiqi Shen

The surge in black-box AI models has prompted the need to explain the internal mechanism and justify their reliability, especially in high-stakes applications, such as healthcare and autonomous driving. Due to the lack of a rigorous definition of explainable AI (XAI), a plethora of research related to explainability, interpretability, and transparency has be

Shahidul Asif, Hang Chen, Johannes Cremer, Shantam Ravan

The nitrogen vacancy (NV) center in diamond is an increasingly popular quantum sensor for microscopy of electrical current, magnetization, and spins. However, efficient NV-sample integration with a robust, high-quality interface remains an outstanding challenge to realize scalable, high-throughput microscopy. In this work, we characterize a diamond micro-chi

Hongyuan Yu, Cheng Wan, Xiyang Dai, Mengchen Liu

Real-time image segmentation demands architectures that preserve fine spatial detail while capturing global context under tight latency and memory budgets. Image segmentation is one of the most fundamental problems in computer vision and has drawn a lot of attention due to its vast applications in image understanding and autonomous driving. However, designin

Majid H. Khoshafa, Omar Maraqa, Jules M. Moualeu, Sylvester Aboagye

Physical layer security (PLS) has received a growing interest from the research community for its ability to safeguard data confidentiality without relying on key distribution or encryption/decryption. However, the evolution towards the 5G technology and beyond poses new security challenges that must be addressed in order to fulfill the unprecedented perform

Débora Princepe, Marcus A. M. de Aguiar

Mitochondrial function relies on the coordinated expression of mitochondrial and nuclear genes, exhibiting remarkable resilience regardless the susceptibility of mitochondrial DNA (mtDNA) to accumulate harmful mutations. A suggested mechanism for preserving this mito-nuclear compatibility is the nuclear compensation, where deleterious mitochondrial alleles d

Katharina Kaiser, Michelangelo Romeo, Fabrice Scheurer, Guillaume Schull

Tip-enhanced photoluminescence (TEPL) measurements are performed with sub-nanometer spatial resolution on individual molecules decoupled from a metallic substrate by a thin NaCl layer. TEPL spectra reveal progressive fluorescence quenching with decreasing tip-molecule distance when electrons tunneling from the tip of a scanning tunneling microscope are injec

Naoki Ogino, Makoto Arimoto, Tatsuya Sawano, Daisuke Yonetoku

We developed an FPGA-based high-speed readout system for a complementary metal-oxide-semiconductor (CMOS) image sensor to observe soft X-ray transients in future satellite missions, such as HiZ-GUNDAM. Our previous research revealed that the CMOS image sensor has low-energy X-ray detection capability (0.4-4 keV) and strong radiation tolerance, which satisfie

Vidminas Vizgirda, Rui Zhao, Naman Goel

We present SocialGenPod, a decentralised and privacy-friendly way of deploying generative AI Web applications. Unlike centralised Web and data architectures that keep user data tied to application and service providers, we show how one can use Solid -- a decentralised Web specification -- to decouple user data from generative AI applications. We demonstrate

Hervé Déjean, Stéphane Clinchant, Thibault Formal

We present a comparative study between cross-encoder and LLMs rerankers in the context of re-ranking effective SPLADE retrievers. We conduct a large evaluation on TREC Deep Learning datasets and out-of-domain datasets such as BEIR and LoTTE. In the first set of experiments, we show how cross-encoder rerankers are hard to distinguish when it comes to re-reran

Yixiao Li, Xiaoyuan Yang, Jun Fu, Guanghui Yue

There has emerged a growing interest in exploring efficient quality assessment algorithms for image super-resolution (SR). However, employing deep learning techniques, especially dual-branch algorithms, to automatically evaluate the visual quality of SR images remains challenging. Existing SR image quality assessment (IQA) metrics based on two-stream network

Peng Zhang, Ting Gao, Jin Guo, Jinqiao Duan

Critical transitions and tipping phenomena between two meta-stable states in stochastic dynamical systems are a significant scientific issue. In this work, we expand the methodology of identifying the most probable transition pathway between two meta-stable states with Onsager-Machlup action functional, to investigate the evolutionary transition dynamics bet

Tom F. Hansen, Georg H. Erharter, Zhongqiang Liu, Jim Torresen

Current rock engineering design in drill and blast tunnelling primarily relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected during tunnel excavation, is underutilised, mainly serving for geological visualisation. This study aims to automate the translation of MWD data into actionable m

Marc Lafon, Clément Rambour, Nicolas Thome

In this work, we study the out-of-distribution (OOD) detection problem through the use of the feature space of a pre-trained deep classifier. We show that learning the density of in-distribution (ID) features with an energy-based models (EBM) leads to competitive detection results. However, we found that the non-mixing of MCMC sampling during the EBM's train

Jeffrey W. Herrmann, Hongjie Liu, Donald K. Milton

Mathematical and simulation models are often used to predict the spread of a disease and estimate the impact of public health interventions, and many such models have been developed and used during the COVID-19 pandemic. This paper describes a study that systematically compared models for a university community, which has a much smaller but more connected po

Alison Bartsch, Arvind Car, Charlotte Avra, Amir Barati Farimani

Manipulating deformable objects remains a challenge within robotics due to the difficulties of state estimation, long-horizon planning, and predicting how the object will deform given an interaction. These challenges are the most pronounced with 3D deformable objects. We propose SculptDiff, a goal-conditioned diffusion-based imitation learning framework that

J. M. Aldaz, H. Render

We continue the study initiated by H. S. Shapiro on Fischer decompositions of entire functions, showing that such decomposition exist in a weak sense (we do not prove uniqueness) under hypotheses regarding the order of the entire function $f$ to be expressed as $f= P\cdot q+r$, the polynomial $P$, and bounds on the apolar norm of homogeneous polynomials of d

Juan Ignacio Ibañez, Lena Klaaßen, Ulrich Gallersdörfer, Christian Stoll

This document is written as an academic exercise, with the goal of exploring the feasibility of writing a white paper in accordance with Regulation (EU) 2023/1114 (MiCA). It is meant as a Proof of Concept (PoC) illustrating a concrete application of the requirements of MiCA. Like the MiCA white papers PoC shared by ESMA, this document is solely for the purpo

Zifan Wang, Yi Shen, Michael M. Zavlanos, Karl H. Johansson

This paper considers risk-averse learning in convex games involving multiple agents that aim to minimize their individual risk of incurring significantly high costs. Specifically, the agents adopt the conditional value at risk (CVaR) as a risk measure with possibly different risk levels. To solve this problem, we propose a first-order risk-averse leaning alg

A. Reguitti, G. Pignata, A. Pastorello, R. Dastidar

We conducted a search for luminous outbursts prior to the explosion of Type IIn Supernovae (SNe IIn). We built a sample of 27 objects spectroscopically classified as SNe IIn, all located at $z<0.015$. Using deep archival SN fields images taken up to nearly 20 years prior from transient surveys (PTF, ZTF, DES, CHASE) and major astronomical observatories (ESO

Xueliang Cheng, Ognjen Marjanovic, Barry Lennox, Keir Groves

Accurate positioning of underwater robots in confined environments is crucial for inspection and mapping tasks and is also a prerequisite for autonomous operations. Presently, there are no positioning systems available that are suited for real-world use in confined underwater environments, unconstrained by environmental lighting and water turbidity levels, a

Victor Molnö, Henrik Sandberg

With the advent of integrated sensor technology (smart flow meters and pressure sensors), various new numerical algorithms for leak localization (a core element of water distribution system operation) have been developed. However, there is a lack of theory regarding the limitations of leak localization. In this work, we contribute to the development of such

Pengkun Liu, Yikai Wang, Fuchun Sun, Jiafang Li

Encouraged by the growing availability of pre-trained 2D diffusion models, image-to-3D generation by leveraging Score Distillation Sampling (SDS) is making remarkable progress. Most existing methods combine novel-view lifting from 2D diffusion models which usually take the reference image as a condition while applying hard L2 image supervision at the referen

Robert Arnold, Anton J. Prassl, Aurel Neic, Franz Thaler

Background and Objective: Data from electro-anatomical mapping (EAM) systems are playing an increasingly important role in computational modeling studies for the patient-specific calibration of digital twin models. However, data exported from commercial EAM systems are challenging to access and parse. Converting to data formats that are easily amenable to be

Marco Rampazzo

Given a vector bundle $\mathcal E$ on a smooth projective variety $B$, the flag bundle $\mathcal F l(1,2,\mathcal E)$ admits two projective bundle structures over the Grassmann bundles $\mathcal G r(1, \mathcal E)$ and $G r(2, \mathcal E)$. The data of a general section of a suitably defined line bundle on $\mathcal F l(1,2,\mathcal E)$ defines two varieties

Kartika Sangal, A. K. Srivastava, P. Kayshap, Ding Yuan

In the current analysis, we use spectroscopic observations of the quiet-Sun made by IRIS instrument, and investigate wave propagation. We analyze various spectral lines formed in different atmospheric layers such as the photosphere, chromosphere, and transition region. We examine Doppler velocity time-series at various locations in the quiet-Sun to determine

Hyuck Lee, Heeyoung Kim

Pseudo-label-based semi-supervised learning (SSL) algorithms trained on a class-imbalanced set face two cascading challenges: 1) Classifiers tend to be biased towards majority classes, and 2) Biased pseudo-labels are used for training. It is difficult to appropriately re-balance the classifiers in SSL because the class distribution of an unlabeled set is oft

Alexander Hepburn, Raul Santos-Rodriguez, Javier Portilla

The Two Alternative Forced Choice (2AFC) paradigm offers advantages over the Mean Opinion Score (MOS) paradigm in psychophysics (PF), such as simplicity and robustness. However, when evaluating perceptual distance models, MOS enables direct correlation between model predictions and PF data. In contrast, 2AFC only allows pairwise comparisons to be converted i

Björn Braun, Daniel McDuff, Christian Holz

Remote camera measurement of the blood volume pulse via photoplethysmography (rPPG) is a compelling technology for scalable, low-cost, and accessible assessment of cardiovascular information. Neural networks currently provide the state-of-the-art for this task and supervised training or fine-tuning is an important step in creating these models. However, most

Li Ge

In this work we first show a simple approach to constructing non-Hermitian Hamiltonians with a real spectrum, which are \textit{not} obtained by a non-unitary transformation such as the imaginary gauge transformation. They are given, instead, by the product of a Hermitian Hamiltonian $H_0$ and a positive semi-definite matrix $A$. Depending on whether $A$ has

Gabriel O'Ryan, Joaquín Medina Dueñas, Diego Guzmán-Silva, Luis E. F. Foa Torres

Advancements in photonics technologies have significantly enhanced their capability to facilitate experiments involving quantum light, even at room temperature. Nevertheless, fully integrating photonic chips that include quantum light sources, effective manipulation and transport of light minimizing losses, and appropriate detection systems remains an ongoin

Naoki Ogino, Daisuke Yonetoku, Makoto Arimoto, Tatsuya Sawano

Some short gamma-ray bursts (SGRBs) exhibit a short duration and spectral hard emission (referred to as a "hard spike") followed by a slightly longer soft emission (known as a "soft tail"). We identified nine SGRBs with known redshift in the \textit{Swift}/BAT gamma-ray burst catalog by specifically searching for the soft tail. We found that spectra of these

O. V. Pshyk, J. Patidar, S. Siol

The crystalline quality and degree of c-axis orientation of hexagonal AlN thin films correlate directly with their functional properties. Therefore, achieving AlN thin films of high crystalline quality and texture is of extraordinary importance for many applications, but in particular in electronic devices. Here, we present a systematic study revealing that

Jaehan Im, Yue Yu, David Fridovich-Keil, Ufuk Topcu

Coordination in multiplayer games enables players to avoid the lose-lose outcome that often arises at Nash equilibria. However, designing a coordination mechanism typically requires the consideration of the joint actions of all players, which becomes intractable in large-scale games. We develop a novel coordination mechanism, termed reduced rank correlated e

Atul Anurag, Roy H. Goodman, Ellison K. O'Grady

We introduce a new reduction of the motion of three point vortices in a two-dimensional ideal fluid. This proceeds in two stages: a change of variables to Jacobi coordinates and then a Nambu reduction. The new coordinates demonstrate that the dynamics evolve on a two-dimensional manifold whose topology depends on the sign of a parameter $\kappa_2$ that arise

Dylan Jones, Marcin Mucha-Kruczynski, Adelina Ilie, Lucian Covaci

The Lieb lattice is one of the simplest lattices that exhibits both linear Dirac-like and flat topological electronic bands. We propose to further tailor its electronic properties through periodic 1D electrostatic superlattices (SLs), which, in the long wavelength limit, were predicted to give rise to novel transport signatures, such as the omnidirectional s

Benjamin Heinzerling, Kentaro Inui

Language models (LMs) can express factual knowledge involving numeric properties such as Karl Popper was born in 1902. However, how this information is encoded in the model's internal representations is not understood well. Here, we introduce a simple method for finding and editing representations of numeric properties such as an entity's birth year. Empiric

Lukas Rauch, Raphael Schwinger, Moritz Wirth, René Heinrich

Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domains. While AudioSet is a pivotal step to bridge this gap as a universal-domain dataset, its restricted accessibility and limited range of evaluation use cases challenge its role as

Johannes Kirschner, Seyed Alireza Bakhtiari, Kushagra Chandak, Volodymyr Tkachuk

A long line of works characterizes the sample complexity of regret minimization in sequential decision-making by min-max programs. In the corresponding saddle-point game, the min-player optimizes the sampling distribution against an adversarial max-player that chooses confusing models leading to large regret. The most recent instantiation of this idea is the

Rocktim Jyoti Das, Simeon Emilov Hristov, Haonan Li, Dimitar Iliyanov Dimitrov

We introduce EXAMS-V, a new challenging multi-discipline multimodal multilingual exam benchmark for evaluating vision language models. It consists of 20,932 multiple-choice questions across 20 school disciplines covering natural science, social science, and other miscellaneous studies, e.g., religion, fine arts, business, etc. EXAMS-V includes a variety of m

Waleska P. F. de Medeiros, Matheus J. Lazo, Daniel Müller, Dinalva A. Sales

In this work, tilted source solutions in both Einstein-Hilbert General Relativity (GR) and Quadratic Gravity (QG) for the anisotropic Bianchi V model are addressed. Since the excellent CMBR match of Starobinsky's inflation with Planck's team measurements data, QG has acquired a prominent status in the effective sense, for sufficiently strong gravity fields.

Denis Levchenko, Efstratios Rappos, Shabnam Ataee, Biagio Nigro

Neural architecture search (NAS) emerged as a way to automatically optimize neural networks for a specific task and dataset. Despite an abundance of research on NAS for images and natural language applications, similar studies for time series data are lacking. Among NAS search spaces, chain-structured are the simplest and most applicable to small datasets li

Xiaoning Liu, Ao Li, Zongwei Wu, Yapeng Du

Leveraging Transformer attention has led to great advancements in HDR deghosting. However, the intricate nature of self-attention introduces practical challenges, as existing state-of-the-art methods often demand high-end GPUs or exhibit slow inference speeds, especially for high-resolution images like 2K. Striking an optimal balance between performance and

Yuheng Wu, Xuejie Liu, Yue Tan, Hongxia Huang

Inspired by the recent Altas and CMS experiments on the invariant mass spectrum of $J/\psi J/\psi$, we systematically study the $c\bar{c}c\bar{c}$ system of $J^{P}=0^{+}$. In the framework of chiral quark model, we have carried out bound-state calculation and resonance-state calculation respectively by using Real-scaling method. The results of bound-state ca

Edward P. Chandler, Shirin Shoushtari, Jiaming Liu, M. Salman Asif

Plug-and-Play Priors (PnP) is a well-known class of methods for solving inverse problems in computational imaging. PnP methods combine physical forward models with learned prior models specified as image denoisers. A common issue with the learned models is that of a performance drop when there is a distribution shift between the training and testing data. Te

Andrea Apicella, Salvatore Giugliano, Francesco Isgrò, Roberto Prevete

Modern Artificial Intelligence (AI) systems, especially Deep Learning (DL) models, poses challenges in understanding their inner workings by AI researchers. eXplainable Artificial Intelligence (XAI) inspects internal mechanisms of AI models providing explanations about their decisions. While current XAI research predominantly concentrates on explaining AI sy

Yogesh Kumar, P. R. Mishra, Susanta Samanta, Kishan Chand Gupta

In this paper, we propose two algorithms for a hybrid construction of all $n\times n$ MDS and involutory MDS matrices over a finite field $\mathbb{F}_{p^m}$, respectively. The proposed algorithms effectively narrow down the search space to identify $(n-1) \times (n-1)$ MDS matrices, facilitating the generation of all $n \times n$ MDS and involutory MDS matri

Yousef AlShehri, Lakshmish Ramaswamy

Machine Learning (ML) is becoming increasingly important for IoT-based applications. However, the dynamic and ad-hoc nature of many IoT ecosystems poses unique challenges to the efficacy of ML algorithms. One such challenge is data incompleteness, which is manifested as missing sensor readings. Many factors, including sensor failures and/or network disruptio

Kevin Schäfers, Jacob Finkenrath, Michael Günther, Francesco Knechtli

We propose a new framework of Hessian-free force-gradient integrators that do not require the analytical expression of the force-gradient term based on the Hessian of the potential. Due to that the new class of decomposition algorithms for separable Hamiltonian systems with quadratic kinetic energy may be particularly useful when applied to Hamiltonian syste

Sarah Antiles, Sachin S. Talathi

We present the Open Stamped Parts Dataset (OSPD), featuring synthetic and real images of stamped metal sheets for auto manufacturing. The real part images, captured from 7 cameras, consist of 7,980 unlabeled images and 1,680 labeled images. In addition, we have compiled a defect dataset by overlaying synthetically generated masks on 10\% of the holes. The sy

Alberto Carlevaro, Teodoro Alamo Cantarero, Fabrizio Dabbene, Maurizio Mongelli

Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the concept of scalable classifier was introduced to generalize

Anna Kuznetsova, Vadim Kimmelman

Advances in Deep Learning have made possible reliable landmark tracking of human bodies and faces that can be used for a variety of tasks. We test a recent Computer Vision solution, MediaPipe Holistic (MPH), to find out if its tracking of the facial features is reliable enough for a linguistic analysis of data from sign languages, and compare it to an older

Jürgen Fuchs, Tobias Grøsfjeld

We consider algebras and Frobenius algebras, internal to a monoidal category, that are graded over a finite abelian group. For the case that A is a twisted group algebra in a linear abelian monoidal category we obtain a graded generalization of the Schur Lemma for the category of induced A-modules. We further show that if the monoidal category is braided and

Ron Mosenzon, Ali Vakilian

In this paper, we study the individual preference (IP) stability, which is an notion capturing individual fairness and stability in clustering. Within this setting, a clustering is $\alpha$-IP stable when each data point's average distance to its cluster is no more than $\alpha$ times its average distance to any other cluster. In this paper, we study the nat

Eugene Levin

In this paper we show that the sum of enhanced BFKL Pomeron loop diagrams generates the scattering amplitude, which turns out to be much smaller, than in the case of deep inelastic scattering. We use the simplified BFKL kernel in the leading twist approximation, which reproduces the main features of the scattering amplitude in the deep inelastic scattering(D

Yingjie Zhu, Shadia R. Habbal, Adalbert Ding, Bryan Yamashiro

The spectroscopic observations presented here were acquired during the 2017 August 21 total solar eclipse with a three-channel partially multiplexed imaging spectrometer (3PAMIS) operating at extremely high orders ($>$ 50). The 4 $R_\odot$ extent of the slit in the North-South direction scanned the corona starting from the central meridian out to approximate

Qiang Zhu, Jinhua Hao, Yukang Ding, Yu Liu

Recently, numerous approaches have achieved notable success in compressed video quality enhancement (VQE). However, these methods usually ignore the utilization of valuable coding priors inherently embedded in compressed videos, such as motion vectors and residual frames, which carry abundant temporal and spatial information. To remedy this problem, we propo

Yuanzheng Niu, Xiaoqi Li, Hongli Peng, Wenkai Li

As emerging digital assets, NFTs are susceptible to anomalous trading behaviors due to the lack of stringent regulatory mechanisms, potentially causing economic losses. In this paper, we conduct the first systematic analysis of four non-fungible tokens (NFT) markets. Specifically, we analyze more than 25 million transactions within these markets, to explore

Ali Taghavi

In this note we present a survey on some classical and modern approaches on Pythagorean triples. Some questions are also posed in direction of some materials under review. In particular some non commutative and operator theoretical approaches of Pythagorean triples are discussed

Volodymyr Riabov, László Erdős

We prove the Eigenstate Thermalization Hypothesis for general Wigner-type matrices in the bulk of the self-consistent spectrum, with optimal control on the fluctuations for observables of arbitrary rank. As the main technical ingredient, we prove rank-uniform optimal local laws for one and two resolvents of a Wigner-type matrix with regular observables.

Janghoon Ock, Parisa Mollaei, Amir Barati Farimani

The exploration of molecular systems' potential energy surface is important for comprehending their complex behaviors, particularly through identifying various metastable states. However, the transition between these states is often hindered by substantial energy barriers, demanding prolonged molecular simulations that consume considerable computational effo

Odin Zhang, Yufei Huang, Shichen Cheng, Mengyao Yu

Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets. These methods, while effective in designing tightly bound ligands, often overlook other essential properties such as synthesizability. The fragment-wise generation paradigm of

Marco Pesavento, Yuanlu Xu, Nikolaos Sarafianos, Robert Maier

Recent progress in human shape learning, shows that neural implicit models are effective in generating 3D human surfaces from limited number of views, and even from a single RGB image. However, existing monocular approaches still struggle to recover fine geometric details such as face, hands or cloth wrinkles. They are also easily prone to depth ambiguities

Manjeet Yadav, Nilesh Kumar Sahu

Stress is a dynamic process that reflects the responses of the brain. Traditional methods for measuring stress are often time-consuming and susceptible to recall bias. To address this, we investigated changes in heart rate (HR) during the Trier Social Stress Test (TSST). Our study incorporated varying levels of complexity in mental arithmetic problems. Parti

William J. Hughes, Joseph F. Goodwin, Peter Horak

We develop methods to find the limits to finite-time single photon extraction from emitter-cavity systems. We first establish analytic upper and lower bounds on the maximum extraction probability from a canonical $\Lambda$-system before developing a numeric method to optimise generic output probabilities from $\Lambda$-systems generalised to multiple ground

Francis Watson, Daniel Andre, William Robert Breckon Lionheart

Through-wall synthetic aperture radar (SAR) imaging is of significant interest for security purposes, in particular when using multi-static SAR systems consisting of multiple distributed radar transmitters and receivers to improve resolution and the ability to recognise objects. Yet there is a significant challenge in forming focused, useful images due to mu

Yingqi Tang, Zhaotie Meng, Guoliang Chen, Erkang Cheng

The field of autonomous driving has attracted considerable interest in approaches that directly infer 3D objects in the Bird's Eye View (BEV) from multiple cameras. Some attempts have also explored utilizing 2D detectors from single images to enhance the performance of 3D detection. However, these approaches rely on a two-stage process with separate detector

Cui Rui, Li Yuhao

This paper introduces a novel goodness-of-fit test technique for parametric conditional distributions. The proposed tests are based on a residual marked empirical process, for which we develop a conditional Principal Component Analysis. The obtained components provide a basis for various types of new tests in addition to the omnibus one. Component tests that

Pengcheng Jiang, Cao Xiao, Zifeng Wang, Parminder Bhatia

The advent of large language models (LLMs) has significantly advanced natural language processing tasks like text summarization. However, their large size and computational demands, coupled with privacy concerns in data transmission, limit their use in resource-constrained and privacy-centric settings. To overcome this, we introduce TriSum, a framework for d

Aleksandar Aksentijević, Suzana Aleksić, Stevan Pilipović

We connect through the Fourier transform shift-invariant Sobolev type spaces $V_s\subset H^s$, $s\in\mathbb R,$ and the spaces of periodic distributions and analyze the properties of elements in such spaces with respect to the product. If the series expansions of two periodic distributions have compatible coefficient estimates, then their product is a period

Qijian Zhang, Junhui Hou, Ying He

Surface parameterization is a fundamental geometry processing problem with rich downstream applications. Traditional approaches are designed to operate on well-behaved mesh models with high-quality triangulations that are laboriously produced by specialized 3D modelers, and thus unable to meet the processing demand for the current explosion of ordinary 3D da

Jin-Young Kim, Hyojun Go, Soonwoo Kwon, Hyun-Gyoon Kim

Diffusion-based generative models have emerged as powerful tools in the realm of generative modeling. Despite extensive research on denoising across various timesteps and noise levels, a conflict persists regarding the relative difficulties of the denoising tasks. While various studies argue that lower timesteps present more challenging tasks, others contend

Freddie Grabovski, Lior Yasur, Yaniv Hacmon, Lior Nisimov

This study explores the generation of synthesized fingerprint images using Denoising Diffusion Probabilistic Models (DDPMs). The significant obstacles in collecting real biometric data, such as privacy concerns and the demand for diverse datasets, underscore the imperative for synthetic biometric alternatives that are both realistic and varied. Despite the s

Andreas Haller, Sebastián A. Díaz, Wolfgang Belzig, Thomas L. Schmidt

We propose a variational wave function to represent quantum skyrmions as bosonic operators. The operator faithfully reproduces two fundamental features of quantum skyrmions: their classical magnetic order and a "quantum cloud" of local spin-flip excitations. Using exact numerical simulations of the ground states of a 2D chiral magnetic model, we find two reg

Manuel de Buenaga, Francisco Javier Bueno

The GPT (Generative Pre-trained Transformer) language models are an artificial intelligence and natural language processing technology that enables automatic text generation. There is a growing interest in applying GPT language models to university teaching in various dimensions. From the perspective of innovation in student and teacher activities, they can

George Yiasemis, Jan-Jakob Sonke, Jonas Teuwen

$\textbf{Background:}$ Accelerating dynamic MRI is vital for advancing clinical applications and improving patient comfort. Commonly, deep learning (DL) methods for accelerated dynamic MRI reconstruction typically rely on uniformly applying non-adaptive predetermined or random subsampling patterns across all temporal frames of the dynamic acquisition. This a

Wojciech Domitrz, Marcin Zubilewicz

This paper focuses on local curvature invariants associated with bi-Lagrangian structures. We establish several geometric conditions that determine when the canonical connection is flat, building on our previous findings regarding divergence-free webs. Addressing questions raised by Tabachnikov, we provide complete solutions to two problems: the existence of

Hala Djeghim, Nathan Piasco, Moussab Bennehar, Luis Roldão

Neural implicit surface representation methods have recently shown impressive 3D reconstruction results. However, existing solutions struggle to reconstruct driving scenes due to their large size, highly complex nature and their limited visual observation overlap. Hence, to achieve accurate reconstructions, additional supervision data such as LiDAR, strong g

Sebastian Kokott, Florian Merz, Yi Yao, Christian Carbogno

Hybrid density functional approximations (DFAs) offer compelling accuracy for ab initio electronic-structure simulations of molecules, nanosystems, and bulk materials, addressing some deficiencies of computationally cheaper, frequently used semilocal DFAs. However, the computational bottleneck of hybrid DFAs is the evaluation of the non-local exact exchange

Sayed Amir Hoseini, Faycal Bouhafs, Neda Aboutorab, Parastoo Sadeghi

Wireless data communications are always facing the risk of eavesdropping and interception. Conventional protection solutions which are based on encryption may not always be practical as is the case for wireless IoT networks or may soon become ineffective against quantum computers. In this regard, Physical Layer Security (PLS) presents a promising approach to

CMS Collaboration

A search is presented for the decay of the 125 GeV Higgs boson (H) to a pair of new light pseudoscalar bosons (a), followed by the prompt decay of each a boson to a bottom quark-antiquark pair, H $\to$ aa $\to$ $\mathrm{b\bar{b}b\bar{b}}$. The analysis is performed using a data sample of proton-proton collisions collected with the CMS detector at a center-of

Tianxiang Ye, Qi Wu, Junyuan Deng, Guoqing Liu

In recent years, Neural Radiance Fields (NeRFs) have demonstrated significant potential in encoding highly-detailed 3D geometry and environmental appearance, positioning themselves as a promising alternative to traditional explicit representation for 3D scene reconstruction. However, the predominant reliance on RGB imaging presupposes ideal lighting conditio

Rui Zhang, Dawei Cheng, Xin Liu, Jie Yang

Graph-based anomaly detection is currently an important research topic in the field of graph neural networks (GNNs). We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than those in homophilic and heterophilic graphs. For the first time, we introduce a new metric called Class Ho

Priyanka Sukumaran, Conor Houghton, Nina Kazanina

Humans can learn a new word and infer its grammatical properties from very few examples. They have an abstract notion of linguistic properties like grammatical gender and agreement rules that can be applied to novel syntactic contexts and words. Drawing inspiration from psycholinguistics, we conduct a noun learning experiment to assess whether an LSTM and a