July 2022 arXiv papers — page 16

Omead Pooladzandi, David Davini, Baharan Mirzasoleiman

Training machine learning models on massive datasets incurs substantial computational costs. To alleviate such costs, there has been a sustained effort to develop data-efficient training methods that can carefully select subsets of the training examples that generalize on par with the full training data. However, existing methods are limited in providing the

Carlos Antonio Guevara-Valadez, Rahul Marathe, Juan Ruben Gomez-Solano

We investigate a model for a Stirling-like engine consisting of a passive Brownian particle confined by a harmonic potential and interacting with a suspension of active Brownian particles that self-propel in a viscous solvent, which cyclically operates under isothermal conditions by means of temporal variations of the trap stiffness and the self-propulsion s

Oscar Ocampo

The virtual singular braid group arises as a natural common generalization of classical singular braid groups and virtual braid groups. In this paper, we study several algebraic properties of the virtual singular braid group $VSG_n$. We introduce numerical invariants for virtual singular braids arising from exponent sums of words in $VSG_n$, and describe exp

Shavbo Salehi, Jahan Hassan, Ayub Bokani

Next-generation wireless networks will deploy UAVs dynamically as aerial base stations (UAV-BSs) to boost the wireless network coverage in the out of reach areas. To provide an efficient service in stochastic environments, the optimal number of UAV-BSs, their locations, and trajectories must be specified appropriately for different scenarios. Such deployment

Kun Yang, Weiming Feng, Luyuan Xu, Xiaodong Liu

Lunar dust particles are generated by hypervelocity impacts of interplanetary micron-meteoroids onto the surface of the Moon, which seriously threatens the security of explorations. Studying the lunar dust dynamics helps to understand the origin and migration mechanism of lunar dust, and to provide the theoretical guidelines for the orbital design of lunar s

Yan Hu, Zhongxi Qiu, Dan Zeng, Li Jiang

Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases. Ophthalmologists often require high-resolution segmentation results for analysis, which leads to super-computational load by most existing methods. If based on low-resolution input, they easily ignore tiny vessels or cau

Rong Han, Zihuai Lin

This paper introduces likelihood-based and feature-based modulation recognition methods. In the feature-based modulation simulation part, instantaneous feature, cyclic spectrum, high-order cumulants, and wavelet transform features are used as the entry point, and six digital signals including 2ASK, 4ASK, BPSK, QPSK, 2FSK and 4FSK are simulated, showing the d

Abdul W. Khanday, Sudhaker Upadhyay, Prince A. Ganai

We discuss the galaxy clustering based on thermodynamics and statistical mechanics in the expanding universe in a modified theory of gravity. The modified general relativity (MGR) is developed using the regular line element field to construct a symmetric tensor that represents the energy momentum of the gravitational field. This in turn provides a modified g

Rees Chang, Yu-Xiong Wang, Elif Ertekin

While machine learning has emerged in recent years as a useful tool for rapid prediction of materials properties, generating sufficient data to reliably train models without overfitting is still impractical for many applications. Towards overcoming this limitation, we present a general framework for leveraging complementary information across different model

Fabian C. Weigend, Edward Gray, Oliver Obst, Jason Siegler

Purpose: Performance models are important tools for coaches and athletes to optimise competition outcomes or training schedules. A recently published hydraulic performance model has been reported to outperform established work-balance models in predicting recovery during intermittent exercise. The new hydraulic model was optimised to predict exercise recover

Yuanfan Zhang, Gen Li, Lei Sun

Since convolutional neural networks perform well in learning generalizable image priors from large-scale data, these models have been widely used in image denoising tasks. However, the computational complexity increases dramatically as well on complex model. In this paper, We propose a novel lightweight Complementary Attention Module, which includes a densit

Taiga Hiroka, Tomoyuki Morimae, Ryo Nishimaki, Takashi Yamakawa

Computational security in cryptography has a risk that computational assumptions underlying the security are broken in the future. One solution is to construct information-theoretically-secure protocols, but many cryptographic primitives are known to be impossible (or unlikely) to have information-theoretical security even in the quantum world. A nice compro

W. A. Zúñiga-Galindo

In this work we initiate the study of the correspondence between p-adic statistical field theories (SFTs) and neural networks (NNs). In general quantum field theories over a p-adic spacetime can be formulated in a rigorous way. Nowadays these theories are considered just mathematical toy models for understanding the problems of the true theories. In this wor

Alex Reda, Tristan Pinsonneault-Marotte, Meiling Deng, Mandana Amiri

The Canadian Hydrogen Intensity Mapping Experiment (CHIME) will measure the 21 cm emission of astrophysical neutral hydrogen to probe large scale structure at redshifts z=0.8-2.5. However, detecting the 21 cm signal beneath substantially brighter foregrounds remains a key challenge. Due to the high dynamic range between 21 cm and foreground emission, an exqu

James David Nixon

In this report we construct a family of holomorphic functions $\beta_{\lambda,\mu} (s)$ which behave asymptotically like iterated exponentials as $|s| \to \infty$ in the right half plane. Each $\beta_{\lambda,\mu}$ satisfies a convenient functional relationship with nested exponentials; and has a series expansion that converges in a half-plane. They provide

E. J. Robles-Raygoza, V. G. Ibarra-Sierra, J. C. Sandoval-Santana, R. Carrillo-Bastos

Graphene nanoribbons (GNRs) are natural waveguides for electrons in graphene. Nevertheless, unlike micron-sized samples, conductance is nearly suppressed in these narrow graphene stripes, mainly due to scattering with edge disorder generated during synthesis or cut. A possible way to circumvent this effect is to define an internal waveguide that isolates spe

Qiao-Long Huang, Xiao-Shan Gao

An input- and output-sensitive GCD algorithm for multi-variate polynomials over finite fields is proposed by combining the modular method with the Ben-Or/Tiwari sparse interpolation. The bit complexity of the algorithm is given and is sensitive to the sparse representation, while for previous sparse GCD algorithms, the complexities were given only in some sp

Brett T. Lopez, Jean-Jacques Slotine

This work applies universal adaptive control to control barrier functions to achieve forward invariance of a safe set despite the presence of unmatched parametric uncertainties. The approach combines two ideas. The first is to construct a family of control barrier functions that ensures the system is safe for all possible models. The second is to use online

Thomas Woodruff, Iman Askari, Guanghui Wang, Huazhen Fang

Control of nonlinear uncertain systems is a common challenge in the robotics field. Nonlinear latent force models, which incorporate latent uncertainty characterized as Gaussian processes, carry the promise of representing such systems effectively, and we focus on the control design for them in this work. To enable the design, we adopt the state-space repres

Qingyang Tan, Yi Zhou, Tuanfeng Wang, Duygu Ceylan

Despite recent success, deep learning-based methods for predicting 3D garment deformation under body motion suffer from interpenetration problems between the garment and the body. To address this problem, we propose a novel collision handling neural network layer called Repulsive Force Unit (ReFU). Based on the signed distance function (SDF) of the underlyin

Shunsuke Kanda, Koichi Akabe, Yusuke Oda

Multiple pattern matching in strings is a fundamental problem in text processing applications such as regular expressions or tokenization. This paper studies efficient implementations of double-array Aho-Corasick automata (DAACs), data structures for quickly performing the multiple pattern matching. The practical performance of DAACs is improved by carefully

Saulo Mendes, Jérôme Kasparian

The shoaling of surface gravity waves has been acknowledged as a mechanism of rogue wave formation. This problem is generally reduced to water waves passing over a step, but non-equilibrium physics allows finite slopes to be considered. Using non-homogeneous spectral analysis of a spatially varying energy density ratio we describe the dependence of the ampli

Qinghua Huang, Lizhi Jia, Guanqing Ren, Xiaoyi Wang

Ultrasound imaging plays an important role in the diagnosis of vascular lesions. Accurate segmentation of the vascular wall is important for the prevention, diagnosis and treatment of vascular diseases. However, existing methods have inaccurate localization of the vascular wall boundary. Segmentation errors occur in discontinuous vascular wall boundaries and

Ping Wei, Sheng Li, Xinpeng Zhang, Ge Luo

Steganography usually modifies cover media to embed secret data. A new steganographic approach called generative steganography (GS) has emerged recently, in which stego images (images containing secret data) are generated from secret data directly without cover media. However, existing GS schemes are often criticized for their poor performances. In this pape

Tiffany J. Callahan, Adrianne L. Stefanski, Jin-Dong Kim, William A. Baumgartner

Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differenti

Zhiqi Zhang, Wen Lu, Jinshan Cao, Guangqi Xie

Land cover classification is a multi-class segmentation task to classify each pixel into a certain natural or man-made category of the earth surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by do

Xi Leng, Xiaoying Tang, Yatao Bian

Machine learning algorithms minimizing the average training loss usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training data, which are not stable under distributional shifts. It inspires various works for domain generalization (DG), where a series of methods, such as Causal Matching and FISH, wo

Takayoshi Oba, Toshifumi Shimizu, Yukio Katsukawa, Masahito Kubo

We developed a scan mirror mechanism (SMM) that enable a slit-based spectrometer or spectropolarimeter to precisely and quickly map an astronomical object. The SMM, designed to be installed in the optical path preceding the entrance slit, tilts a folding mirror and then moves the reflected image laterally on the slit plane, thereby feeding a different one-di

Zixiang Ren, Ling Qiu, Jie Xu, Derrick Wing Kwan Ng

This paper studies a downlink secure integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) transmits confidential messages to a single-antenna communication user (CU) while performing sensing on targets that may act as suspicious eavesdroppers. To ensure the quality of target sensing while preventing their potential e

Sadataka Furui, Serge Dos Santos

We propose application of Machine Learning (ML) and Neural Network (NN) technique for the analysis of ultrasonic Time Reversal based Nonlinear Elastic Wave Spectroscopy (TR-NEWS). In order to acquire topological features, we adopt the $(2+1)D$ lattice simulation with fixed point (FP) actions. We consider 7 A type loops which sit on $2D$ spacial plane spanned

Wenzhi Gao, Dongdong Ge, Yinyu Ye

HDSDP is a numerical software solving the semidefinite programming problems. The main framework of HDSDP resembles the dual-scaling interior point solver DSDP [BY2008] and several new features, including a dual method based on the simplified homogeneous self-dual embedding, have been implemented. The embedding technique enhances stability of the dual method

Hao Li, Zhijing Yang, Xiaobin Hong, Ziying Zhao

Real-world image denoising is a practical image restoration problem that aims to obtain clean images from in-the-wild noisy inputs. Recently, the Vision Transformer (ViT) has exhibited a strong ability to capture long-range dependencies, and many researchers have attempted to apply the ViT to image denoising tasks. However, a real-world image is an isolated

Themiya Nanayakkara, Karl Glazebrook, Colin Jacobs, Andrea Bonchi

We use the GLASS-JWST Early Release Science NIRCam parallel observations to provide a first view of the UV continuum properties of NIRCam/F444W selected galaxies at 4<z<7. By combining multiwavelength NIRCam observations, we constrain the UV continuum slope for a sample of 401 galaxies with stringent quality controls. We find that >99% of the galaxies are bl

Xuewei Zhang, Yuan Ren, Tiejun Lv, Lajos Hanzo

By pre-fetching popular videos into the local caches of edge nodes, wireless edge caching provides an effective means of reducing repeated content deliveries. To meet the various viewing quality requirements of multimedia users, scalable video coding (SVC) is integrated with edge caching, where the constituent layers of scalable videos are flexibly cached an

Kazuya Yonekura

We study the structure of anomalies in general heterotic string theories by considering general 2-dimensional $\mathcal{N}=(0,1)$ supersymmetric quantum field theories (SQFTs), without assuming conformal invariance nor the correct central charges. First we generalize the precise notion of the $B$-field introduced by Witten. Then we express the target space a

Ekaterina Nikonova, Cheng Xue, Vimukthini Pinto, Chathura Gamage

Current AI systems are designed to solve close-world problems with the assumption that the underlying world is remaining more or less the same. However, when dealing with real-world problems such assumptions can be invalid as sudden and unexpected changes can occur. To effectively deploy AI-powered systems in the real world, AI systems should be able to deal

Renzhen Wang, Xixi Jia, Quanziang Wang, Yichen Wu

Pseudo-labeling has proven to be a promising semi-supervised learning (SSL) paradigm. Existing pseudo-labeling methods commonly assume that the class distributions of training data are balanced. However, such an assumption is far from realistic scenarios and thus severely limits the performance of current pseudo-labeling methods under the context of class-im

Ta Sheng Tan, Wen Chean Teh

The burning number of a graph can be used to measure the spreading speed of contagion in a network. The burning number conjecture is arguably the main unresolved conjecture related to this graph parameter, which can be settled by showing that every tree of order $m^2$ has burning number at most $m$. This is known to hold for many classes of trees, including

Andrus Giraldo, Bernd Krauskopf, Hinke M. Osinga

We study a homoclinic flip bifurcation of case~\textbf{C}, where a homoclinic orbit to a saddle equilibrium with real eigenvalues changes from being orientable to nonorientable. This bifurcation is of codimension two, and it is the lowest codimension for a homoclinic bifurcation of a real saddle to generate chaotic behavior in the form of (suspended) Smale h

Youngjae Min, Namhoon Cho, Navid Azizan

While large machine learning models have shown remarkable performance in various domains, their training typically requires iterating for many passes over the training data. However, due to computational and memory constraints and potential privacy concerns, storing and accessing all the data is impractical in many real-world scenarios where the data arrives

Yongbo Deng, Weihong Zhang, Jihong Zhu, Yingjie Xu

This paper presents a topology optimization approach for the surface flows on variable design domains. Via this approach, the matching between the pattern of a surface flow and the 2-manifold used to define the pattern can be optimized, where the 2-manifold is implicitly defined on another fixed 2-manifold named as the base manifold. The fiber bundle topolog

Zakariah Crane

In this paper we will demonstrate the use of Feynman Diagrams for one dimensional scattering in quantum mechanics. We will evaluate the S-Matrix explicitly for the Dirac delta and finite wall potentials by summing the full series of Feynman diagrams, illustrating the spirit of perturbation theory. This technique may be useful in introductory quantum mechanic

Kai-Siang Chen, Gelo Noel M. Tabia, Chellasamy Jebarathinam, Shiladitya Mal

In device-independent quantum information, correlations between local measurement outcomes observed by spatially separated parties in a Bell test play a fundamental role. Even though it is long-known that the set of correlations allowed in quantum theory lies strictly between the Bell-local set and the no-signaling set, many questions concerning the geometry

Xiaohan Xu, Peng Zhang, Yongquan He, Chengpeng Chao

Inductive link prediction for knowledge graph aims at predicting missing links between unseen entities, those not shown in training stage. Most previous works learn entity-specific embeddings of entities, which cannot handle unseen entities. Recent several methods utilize enclosing subgraph to obtain inductive ability. However, all these works only consider

Jacob R. Pierce, John P. Palastro, Fei Li, Bernardo Malaca

Spatiotemporal control refers to a class of optical techniques for structuring a laser pulse with coupled space-time dependent properties, including moving focal points, dynamic spot sizes, and evolving orbital angular momenta. Here we introduce the concept of arbitrarily structured laser (ASTRL) pulses which generalizes these techniques. The ASTRL formalism

Samuel G. G. Johnston

In a previous article, we develop a continuous version of Kasteleyn theory to study the bead model on the torus. These are the point processes on the semi-discrete torus $\mathbb{T}_n := [0,1) \times \{0,1,\ldots,n-1\}$ (thought of as $n$ unit length strings wrapped around a doughnut) with the property that between every two consecutive points on same string

Zhaoyang Du, Yijin Guan, Tianchan Guan, Dimin Niu

Sparse general matrix multiplication (SpGEMM) is a fundamental building block in numerous scientific applications. One critical task of SpGEMM is to compute or predict the structure of the output matrix (i.e., the number of nonzero elements per output row) for efficient memory allocation and load balance, which impact the overall performance of SpGEMM. Exist

Yuka Oshima, Satoru Takano, Ching Pin Ooi, Yuta Michimura

Torsion-Bar Antenna (TOBA) is a ground-based gravitational wave detector using torsion pendulums. TOBA can detect intermediate-mass black hole binary mergers, gravitational wave stochastic background, and Newtonian noise, and is useful for earthquake early warning. A prototype detector Phase-III TOBA with 35 cm-scale pendulums is under development to demonst

Andrus Giraldo, Bernd Krauskopf, Hinke M. Osinga

When a real saddle equilibrium in a three-dimensional vector field undergoes a homoclinic bifurcation, the associated two-dimensional invariant manifold of the equilibrium closes on itself in an orientable or non-orientable way. We are interested in the interaction between global invariant manifolds of saddle equilibria and saddle periodic orbits for a vecto

Adolfo G. Ramirez-Aristizabal, Chris Kello

Information retrieval from brain responses to auditory and visual stimuli has shown success through classification of song names and image classes presented to participants while recording EEG signals. Information retrieval in the form of reconstructing auditory stimuli has also shown some success, but here we improve on previous methods by reconstructing mu

Devesh Khandelwal, Sean Campos, Shwetha Nagaraj, Fred Nugen

In this paper, we demonstrate a unique recipe to enhance the effectiveness of audio machine learning approaches by fusing pre-processing techniques into a deep learning model. Our solution accelerates training and inference performance by optimizing hyper-parameters through training instead of costly random searches to build a reliable mosquito detector from

Yanhua Xu, Dominik Wojtczak

Influenza viruses mutate rapidly and can pose a threat to public health, especially to those in vulnerable groups. Throughout history, influenza A viruses have caused pandemics between different species. It is important to identify the origin of a virus in order to prevent the spread of an outbreak. Recently, there has been increasing interest in using machi

Amanda Bianco, Raphael Zonis, Anne-Marie Lauzon, James Richard Forbes

Objective: This paper presents a force control scheme for brief isotonic holds in an isometrically contracted muscle tissue, with minimal overshoot and settling time to measure its shortening velocity, a key parameter of muscle function. Methods: A two-degree-of-freedom control configuration, formed by a feedback controller and a feedforward controller, is e

William J. Keith

In this note a bijection is constructed between the set of partitions of n simultaneously s-regular and t-distinct, and those simultaneously t-regular and s-distinct. Some implications of the map are discussed. As a generalized version of Glaisher's bijection, the map may be widely useful in other partition combinatorics. A previous conjecture concerning ite

Hang Du, Rebecca Pillai Riddell, Xiaogang Wang

In this article, we present a new EEG signal classification framework by integrating the complex-valued and real-valued Convolutional Neural Network(CNN) with discrete Fourier transform (DFT). The proposed neural network architecture consists of one complex-valued convolutional layer, two real-valued convolutional layers, and three fully connected layers. Ou

Lei Xue

In 1967, Gr\"unmbaum conjectured that any $d$-dimensional polytope with $d+s\leq 2d$ vertices has at least \[\phi_k(d+s,d) = {d+1 \choose k+1 }+{d \choose k+1 }-{d+1-s \choose k+1 } \] $k$-faces. This conjecture along with the characterization of equality cases was recently proved by the author. In this paper, several extensions of this result are establishe

Mark R. Krumholz, Roland M. Crocker, Matt L. Sampson

We present criptic, the Cosmic Ray Interstellar Propagation Tool using It\^o Calculus, a new open-source software package to simulate the propagation of cosmic rays through the interstellar medium and to calculate the resulting observable non-thermal emission. Criptic solves the Fokker-Planck equation describing transport of cosmic rays on scales larger than

Seung Yeon Shin, Soochahn Lee, Kyoung Jin Noh, Il Dong Yun

We present a method to extract coronary vessels from fluoroscopic x-ray sequences. Given the vessel structure for the source frame, vessel correspondence candidates in the subsequent frame are generated by a novel hierarchical search scheme to overcome the aperture problem. Optimal correspondences are determined within a Markov random field optimization fram

Xing Zhang, Garnet Kin-Lic Chan

We introduce an extension to the PySCF package which makes it automatically differentiable. The implementation strategy is discussed, and example applications are presented to demonstrate the automatic differentiation framework for quantum chemistry methodology development. These include orbital optimization, properties, excited-state energies, and derivativ

Nathaniel G. Luttmer, Takara E. Truong, Alicia M. Boynton, Andrew S. Merryweather

Wearable robotic devices have potential to assist and protect their users. Toward design of a Smart Helmet, this article examines the effectiveness of audio and visual warnings to help participants brace for impacts. A user study examines different warnings and impacts applied to users while running. Perturbation forces scaled to user mass are applied from d

Kenneth Holstein, Maria De-Arteaga, Lakshmi Tumati, Yanghuidi Cheng

In many real world contexts, successful human-AI collaboration requires humans to productively integrate complementary sources of information into AI-informed decisions. However, in practice human decision-makers often lack understanding of what information an AI model has access to in relation to themselves. There are few available guidelines regarding how

Jacob L. Shelton, Kathryn E. Knowles

Polaron formation following optical absorption is a key process that defines the photophysical properties of many semiconducting transition metal oxides, which comprise an important class of materials with potential optoelectronic and photocatalytic applications. In this work, we use hematite ($α-Fe_{2}O_{3}$) as a model transition metal oxide semiconductor

Felipe Arbulú, Fabien Durand

We provide an explicit S-adic representation of rank one subshifts with bounded spacers and call the subshifts obtained in this way &#39;&#39;Ferenczi subshifts&#39;&#39;. We aim to show that this approach is very convenient to study the dynamical behavior of rank one systems. For instance, we compute their topological rank, the strong and the weak orbit equ

Max Goldberg, Konstantin Batygin, Alessandro Morbidelli

Uncovering the formation process that reproduces the distinct properties of compact super-Earth exoplanet systems is a major goal of planet formation theory. The most successful model argues that non-resonant systems begin as resonant chains of planets that later experience a dynamical instability. However, both the boundary of stability in resonant chains a

Sangwon Hwang, Hoon Lee, Juseong Park, Inkyu Lee

Limited computing resources of internet-of-things (IoT) nodes incur prohibitive latency in processing input data. This triggers new research opportunities toward task offloading systems where edge servers handle intensive computations of IoT devices. Deploying the computing servers at existing base stations may not be sufficient to support IoT nodes operatin

Jun Ohkubo

The numerical evaluation of statistics plays a crucial role in statistical physics and its applied fields. It is possible to evaluate the statistics for a stochastic differential equation with Gaussian white noise via the corresponding backward Kolmogorov equation. The important notice is that there is no need to obtain the solution of the backward Kolmogoro

Elias Munoz, Pierre Baudot, Van-Khoa Le, Charles Voyton

Pathologies systematically induce morphological changes, thus providing a major but yet insufficiently quantified source of observables for diagnosis. The study develops a predictive model of the pathological states based on morphological features (3D-morphomics) on Computed Tomography (CT) volumes. A complete workflow for mesh extraction and simplification

Shalini Chakraborty, Grischa Liebel

Background: Despite the potential benefits of software modelling, developers have shown a considerable reluctance towards its application. There is substantial existing research studying industrial use and technical challenges of modelling. However, there is a lack of detailed empirical work investigating how students perceive modelling. Aim: We investigate

William Anderson, Mohammad Farazmand

We develop fast and scalable methods for computing reduced-order nonlinear solutions (RONS). RONS was recently proposed as a framework for reduced-order modeling of time-dependent partial differential equations (PDEs), where the modes depend nonlinearly on a set of time-varying parameters. RONS uses a set of ordinary differential equations (ODEs) for the par

Haoxian Chen, Gerald Whitters, Mohammad Javad Amiri, Yuepeng Wang

This paper presents DeCon, a declarative programming language for implementing smart contracts and specifying contract-level properties. Driven by the observation that smart contract operations and contract-level properties can be naturally expressed as relational constraints, DeCon models each smart contract as a set of relational tables that store transact

Edward W. G. Skevington, Robert M. Dorrell

Turbidity currents, seafloor flows driven by the excess density of suspended particles, are key conveyors of sediment, nutrient, and pollutant from the continental margins to deep ocean, and pose critical submarine geohazard risks. Due to their vast scale and extreme aspect ratio, extant models are constrained to highly simplified depth-averaged theory and f

Andrew J Lohn, Krystal Alex Jackson

We aim to demonstrate the value of mathematical models for policy debates about technological progress in cybersecurity by considering phishing, vulnerability discovery, and the dynamics between patching and exploitation. We then adjust the inputs to those mathematical models to match some possible advances in their underlying technology. We find that AI's i

David B. Dahl, Devin J. Johnson, R. Jacob Andros

Feature allocation models postulate a sampling distribution whose parameters are derived from shared features. Bayesian models place a prior distribution on the feature allocation, and Markov chain Monte Carlo is typically used for model fitting, which results in thousands of feature allocations sampled from the posterior distribution. Based on these samples

Sandhya Susarla, Mit H. Naik, Daria D. Blach, Jonas Zipfel

Electronic and optical excitations in two-dimensional moir\'e systems are uniquely sensitive to local atomic registries, leading to materials- and twist-angle specific correlated electronic ground states with varied degree of localization. However, there has been no direct experimental correlation between the sub-nanometer structure and emergent excitonic tr

Bogeun Gwak

We investigate the weak cosmic censorship conjecture in Myers-Perry black holes with arbitrary rotations in general dimensions based on the scattering of a massless scalar field. From the fluxes of the scalar field flowing into the black hole, the changes in mass and angular momenta of the black hole are obtained. However, the extremal and near-extremal blac

Chien-Cheng Wu, Vasilis Friderikos, Cedomir Stefanovic

Network Slicing (NS) is crucial for efficiently enabling divergent network applications in next generation networks. Nonetheless, the complex Quality of Service (QoS) requirements and diverse heterogeneity in network services entails high computational time for Network Slice Provisioning (NSP) optimization. The legacy optimization methods are challenging to

Junhyeong Cho, Kim Youwang, Tae-Hyun Oh

Transformer encoder architectures have recently achieved state-of-the-art results on monocular 3D human mesh reconstruction, but they require a substantial number of parameters and expensive computations. Due to the large memory overhead and slow inference speed, it is difficult to deploy such models for practical use. In this paper, we propose a novel trans

Ben Heuer

We propose a new moduli-theoretic approach to the $p$-adic Simpson correspondence for a smooth proper rigid space $X$ over $\mathbb C_p$ with coefficients in any rigid analytic group $G$, in terms of a comparison of moduli stacks. For its formulation, we introduce the class of "smoothoid spaces" which are perfectoid families of smooth rigid spaces, well-suit

Dominik Walter Vogt

Terahertz (THz) technology is rapidly evolving, and the advancement of data and information processing devices is essential. Silicon THz microresonators provide perfect platforms to develop compact and integrated devices that could transform THz technology. Here we present a systematic study on the key figure of merit of silicon THz disc microresonators - th

Raouf Rajab

The present work deals with the characterization of parity vectors of Collatz sequences (of finite and infinite length). Such a characterization leads to the determination of several numbers (integers or non-integers) that we call the characteristic numbers of a given parity vector. Some characteristic numbers are linked together by equations that can be cal

M. Destefanis

Although the Standard Model has been firmly established, the search for physics beyond the SM is ongoing by investigating new experimental probes. Rare charm decays are a unique tool to access New Physics studies. The high luminosity achieved by the modern experiments and their high precision allow for rare charm decay studies in different scenarios. In this

Guillermo López Cafaggi

There is a lattice of torsion theories in simplicial groups such that the torsion/torsion-free categories are given by simplicial groups with truncated Moore complex below/above a certain degree. We study the restriction of these torsion theories to certain subcategories of simplicial groups. In particular, we prove that the categories of D.Conduch\'{e}'s 2-

Sohyeon Hwang, Emőke-Ágnes Horvát, Daniel M. Romero

The public interest in accurate scientific communication, underscored by recent public health crises, highlights how content often loses critical pieces of information as it spreads online. However, multi-platform analyses of this phenomenon remain limited due to challenges in data collection. Collecting mentions of research tracked by Altmetric LLC, we exam

Bailu Jin, Weisi Guo

Individuals modify their opinions towards a topic based on their social interactions. Opinion evolution models conceptualize the change of opinion as a uni-dimensional continuum, and the effect of influence is built by the group size, the network structures, or the relations among opinions within the group. However, how to model the personal opinion evolutio

C. Chace Ashcraft, Benjamin Stoler, Chigozie Ewulum, Susama Agarwala

Transfer learning is an increasingly common approach for developing performant RL agents. However, it is not well understood how to define the relationship between the source and target tasks, and how this relationship contributes to successful transfer. We present an algorithm called Structural Similarity for Two MDPS, or SS2, that calculates a state simila

Busson Valentin, Saiseau Raphaël, Durand Marc

Coordination of cytoplasmic flows on large scales in space and time are at the root of many cellular processes, including growth, migration or division. These flows are driven by organized contractions of the actomyosin cortex. In order to elucidate the basic mechanisms at work in the self-organization of contractile activity, we investigate the dynamic patt

Peng Cheng, Fernando E. Serna, Zhao-Qian Yao, Chen Chen

Octet baryon axial, induced pseudoscalar, and pseudoscalar form factors are computed using a symmetry-preserving treatment of a vector$\,\times\,$vector contact interaction (SCI), thereby unifying them with an array of other baryon properties and analogous treatments of semileptonic decays of pseudoscalar mesons. The baryons are treated as quark--plus--inter

Isma Javed, Muhammad Ashar Naveed, Muhammad Qasim Mehmood, Yehia Massoud

Many modern applications like entertainment displays, data encryption, security, and virtual reality (VR) technology require asymmetric light manipulation. Symmetric spin-orbit interactions (SOI) apply a limit in achieving an asymmetrical metahologram. However, different reported asymmetric SOI's based on propagation and geometric phase mergence techniques e

Minglei Li, Abhishek Saha, Chao Sun, Chung K. Law

In this letter, the merging-vs-bouncing response of obliquely-oriented colliding jets under elevated and reduced gaseous environment pressures was experimentally examined. Experiments with water and n-tetradecane confirmed that the collision outcome transitions from merging to bouncing, and then to merging again, when the impact velocity was increased. This

Daniel F. V. James

We consider the kinematics of bi-partite quantum states as determined by observable quantities, in particular the Bloch vectors of the subsystems. In examining the simplest case of a pair of two-level systems, there is a remarkable connection between the presence of non-classical correlations and the chirality of the two bases generated by the singular value

Garvita Tiwari, Dimitrije Antic, Jan Eric Lenssen, Nikolaos Sarafianos

We present Pose-NDF, a continuous model for plausible human poses based on neural distance fields (NDFs). Pose or motion priors are important for generating realistic new poses and for reconstructing accurate poses from noisy or partial observations. Pose-NDF learns a manifold of plausible poses as the zero level set of a neural implicit function, extending

Otis Chodosh, Chao Li, Douglas Stryker

We give a new proof of a recent result of Munteanu--Wang relating scalar curvature to volume growth on a $3$-manifold with non-negative Ricci curvature. Our proof relies on the theory of $\mu$-bubbles introduced by Gromov as well as the almost splitting theorem due to Cheeger--Colding.

Alfonso Maiellaro, Francesco Romeo, Roberta Citro, Fabrizio Illuminati

Squashed entanglement and its universal upper bound, the quantum conditional mutual information, are faithful measures of bipartite quantum correlations defined in terms of multipartitions. As such, they are sensitive to the fine-grain structure of quantum systems. Building on this observation, we introduce the concept of quantum conditional mutual informati

Khalid S. Jassim, Ranya Mahmoud Mohammed

The longitudinal Coulomb C0, C2 and C4 form factors with core-polarization effects have been studied using shell model calculations for 3/2$^+_1$ state with excitation energy of 1.069 MeV, 5/2$^+_2$ state with excitation energy of 2.706 MeV and 7/2$^+_1$ state with excitation energy of 2.304 MeV state in $^{27}$Al nucleus and 1/2 state in $^{31}$P nucleus. T

Rim Rammal, Tudor-Bogdan Airimitoaie, Pierre Melchior, Franck Cazaurang

Fault detection and isolation on hydraulic systems are very important to ensure safety and avoid disasters. In this paper, a fault detection and isolation method, based on the flatness property of nonlinear systems, is experimentally applied on the three-tank system, which is considered as a popular prototype of hydraulic systems. Specifically, fault indicat

Hee Sun Hong

Quasi-Monte Carlo methods are a way of improving the efficiency of Monte Carlo methods. Digital nets and sequences are one of the low discrepancy point sets used in quasi-Monte Carlo methods. This thesis presents the three new results pertaining to digital nets and sequences: implementing randomized digital nets, finding the distribution of the discrepancy o

Nikola Lackovic, Claude Montacié, Gauthier Lalande, Marie-José Caraty

We present the corpus called HealthCall. This was recorded in real-life conditions in the call center of Malakoff Humanis. It includes two separate audio channels, the first one for the customer and the second one for the agent. Each conversation was anonymized respecting the General Data Protection Regulation. This corpus includes a transcription of the spo

Abdelhak Lemkhenter, Paolo Favaro

In this work we introduce a novel meta-learning method for sleep scoring based on self-supervised learning. Our approach aims at building models for sleep scoring that can generalize across different patients and recording facilities, but do not require a further adaptation step to the target data. Towards this goal, we build our method on top of the Model A

Andrew D. King, Jack Raymond, Trevor Lanting, Richard Harris

Experiments on disordered alloys suggest that spin glasses can be brought into low-energy states faster by annealing quantum fluctuations than by conventional thermal annealing. Due to the importance of spin glasses as a paradigmatic computational testbed, reproducing this phenomenon in a programmable system has remained a central challenge in quantum optimi

Ruben Interian, Ruslan G. Marzo, Isela Mendoza, Celso C. Ribeiro

Polarization arises when the underlying network connecting the members of a community or society becomes characterized by highly connected groups with weak inter-group connectivity. The increasing polarization, the strengthening of echo chambers, and the isolation caused by information filters in social networks are increasingly attracting the attention of r