November 2022 arXiv papers — page 89

Hikaru Tamura, Cheng-An Chen, Chen-Lung Hung

Unveiling nonequilibrium dynamics of solitonic and topological defect structures in a multidimensional nonlinear medium is a current frontier across diverse fields. One of the quintessential objects is a ring dark soliton (RDS), whose dynamics are expected to display remarkable interplay between symmetry and self-patterned topological defect formation from a

Shangshuai Li, Changzheng Qu, Da-jun Zhang

In this paper we aim to derive solutions for the SU($\mathcal{N}$) self-dual Yang-Mills (SDYM) equation with arbitrary $\mathcal{N}$. A set of noncommutative relations are introduced to construct a matrix equation that can be reduced to the SDYM equation. It is shown that these relations can be generated from two different Sylvester equations, which correspo

Jia Li, Xiang Li, Xiaowei Jia, Michael Steinbach

Causal DAGs(Directed Acyclic Graphs) are usually considered in a 2D plane. Edges indicate causal effects' directions and imply their corresponding time-passings. Due to the natural restriction of statistical models, effect estimation is usually approximated by averaging the individuals' correlations, i.e., observational changes over a specific time. However,

Alexia Atsidakou, Sumeet Katariya, Sujay Sanghavi, Branislav Kveton

Fixed-budget best-arm identification (BAI) is a bandit problem where the agent maximizes the probability of identifying the optimal arm within a fixed budget of observations. In this work, we study this problem in the Bayesian setting. We propose a Bayesian elimination algorithm and derive an upper bound on its probability of misidentifying the optimal arm.

Fan Ge

Assume the Riemann Hypothesis and a hypothesis on small gaps between zeta zeros, we prove a conjecture of Bailey, Bettin, Blower, Conrey, Prokhorov, Rubinstein and Snaith, which states that for any positive integer $K$ and real number $a>0$, \begin{align*} \lim_{a \to 0^+}\lim_{T \to \infty} \frac{(2a)^{2K-1}}{T (\log T)^{2K}} \int_{T}^{2T} \left|\frac{\zeta

Jaeyoung Kang, Weihong Xu, Wout Bittremieux, Tajana Rosing

Mass spectrometry, commonly used for protein identification, generates a massive number of spectra that need to be matched against a large database. In reality, most of them remain unidentified or mismatched due to unexpected post-translational modifications. Open modification search (OMS) has been proposed as a strategy to improve the identification rate by

Mohammadreza Naderi, Nader Karimi, Ali Emami, Shahram Shirani

Learning to translate images from a source to a target domain with applications such as converting simple line drawing to oil painting has attracted significant attention. The quality of translated images is directly related to two crucial issues. First, the consistency of the output distribution with that of the target is essential. Second, the generated ou

Libby Farrell, Gregg Musiker, Kayla Wright

This is a sequel to the second and third author's Mixed Dimer Configuration Model in Type $D$ Cluster Algebras where we extend our model to work for quivers that contain oriented cycles. Namely, we extend a combinatorial model for $F$-polynomials for type $D_n$ using dimer and double dimer configurations. In particular, we give a graph theoretic recipe that

Linhao Luo, Reza Haffari, Shirui Pan

Link prediction on dynamic graphs is an important task in graph mining. Existing approaches based on dynamic graph neural networks (DGNNs) typically require a significant amount of historical data (interactions over time), which is not always available in practice. The missing links over time, which is a common phenomenon in graph data, further aggravates th

Adam Burgess, Marian Florescu

Quantum reservoir computing has emerged as a promising paradigm within the field of quantum machine learning, harnessing the inherent properties of quantum systems to optimise and enhance information processing capabilities. Here, we explore the potential of quantum-inspired machine learning methodologies by leveraging the complex dynamics of quantum reservo

Woo Seok Kang, Eunchan Kim, Wookjae Heo

An understanding of how Social Overhead Capital (SOC) is associated with the land value of the local community is important for effective urban planning. However, even within a district, there are multiple sections used for different purposes; the term for this is spatial heterogeneity. The spatial heterogeneity issue has to be considered when attempting to

Tharindu Fernando, Clinton Fookes, Sridha Sridharan, Dana Michalski

Person re-identification (re-id) is a pivotal task within an intelligent surveillance pipeline and there exist numerous re-id frameworks that achieve satisfactory performance in challenging benchmarks. However, these systems struggle to generate acceptable results when there are significant differences between the camera views, illumination conditions, or oc

Pengfei Gu, Yejia Zhang, Chaoli Wang, Danny Z. Chen

Convolutional neural network (CNN) based methods have achieved great successes in medical image segmentation, but their capability to learn global representations is still limited due to using small effective receptive fields of convolution operations. Transformer based methods are capable of modelling long-range dependencies of information for capturing glo

Or Zamir

Let $\mathcal{A}$ be an algorithm with expected running time $e^X$, conditioned on the value of some random variable $X$. We construct an algorithm $\mathcal{A'}$ with expected running time $O(e^{E[X]})$, that fully executes $\mathcal{A}$. In particular, an algorithm whose running time is a random variable $T$ can be converted to one with expected running ti

Adam Burgess, Marian Florescu

In this article we explore the dynamics of many-body atomic systems symmetrically coupled to a single Lorentzian photonic cavity. Our study reveals interesting dynamical characteristics including non-zero steady states, superradiant decay, enhanced energy transfer and the ability to modulate oscillations in the atomic system by tuning environmental degrees o

M. Ablikim, M. N. Achasov, P. Adlarson, M. Albrecht

Cross sections for the process $e^{+} e^{-} \to K_{S}^{0} K_{S}^{0} J/\psi$ at center-of-mass energies from $4.128$ to $4.950$ GeV are measured using data samples with a total integrated luminosity of 21.2 fb$^{-1}$ collected by the BESIII detector operating at the BEPCII storage ring. The $Y(4230)$ state is observed in the energy dependence of the $e^{+} e^

Pontus Svensson, Thomas Campbell, Frank Graziani, Zhandos Moldabekov

An extension to the wave packet description of quantum plasmas is presented, where the wave packet can be elongated in arbitrary directions. A generalised Ewald summation is constructed for the wave packet models accounting for long-range Coulomb interactions and fermionic effects are approximated by purpose-built Pauli potentials, self-consistent with the w

Saba Dadsetan, Mohsen Hejrati, Shandong Wu, Somaye Hashemifar

Developing successful artificial intelligence systems in practice depends on both robust deep learning models and large, high-quality data. However, acquiring and labeling data can be prohibitively expensive and time-consuming in many real-world applications, such as clinical disease models. Self-supervised learning has demonstrated great potential in increa

Arnab Chattopadhyay, Swarnendu Banerjee, Amit Samadder, Sabyasachi Bhattacharya

The study of infectious disease has been of interest to ecologists since long. The initiation of epidemic and the long term disease dynamics are largely influenced by the nature of the underlying consumer (host)-resource dynamics. Ecological traits of such systems may be often modulated by toxins released in the environment due to ongoing anthropogenic activ

Yejia Zhang, Xinrong Hu, Nishchal Sapkota, Yiyu Shi

Self-supervised instance discrimination is an effective contrastive pretext task to learn feature representations and address limited medical image annotations. The idea is to make features of transformed versions of the same images similar while forcing all other augmented images' representations to contrast. However, this instance-based contrastive learnin

Sushil Bhunia, Gangotryi Sorcar

In this paper we study free mappings of the plane, that is orientation preserving fixed point free homeomorphisms of $\mathbb{R}^2$. We provide a necessary and sufficient condition under which two free mappings of the plane that are embedded in flows are conjugate to one another using Haefliger-Reeb theory of plane foliations.

Arnaud Brothier, Dilshan Wijesena

Given any linear isometry from a Hilbert space to its square one can explicitly construct a so-called Pythagorean unitary representation of Richard Thompson's group F. We introduce a condition on the isometry implying that the associated representation does not contain any induced representations by finite-dimensional ones. This provides the first result of

Ravi Teja Addanki Tirumala, Sundaram Bhardwaj Ramakrishnan, Marimuthu Andiappan

Increasing anthropogenic carbon dioxide (CO$_2$) emissions have led to rising global temperatures and climate change. Using earth-abundant metal-oxide catalysts such as Cu$_2$O for reducing CO$_2$ through RWGS reaction seems lucrative. In this work, we have used Cu$_2$O nanostructures and identified its activity, stability, and selectivity for reducing CO$_2

Simon Rouard, Francisco Massa, Alexandre Défossez

A natural question arising in Music Source Separation (MSS) is whether long range contextual information is useful, or whether local acoustic features are sufficient. In other fields, attention based Transformers have shown their ability to integrate information over long sequences. In this work, we introduce Hybrid Transformer Demucs (HT Demucs), an hybrid

Zachery A. Enderson, Harshavardhan Murali, Raghunath R. Dasari, Qingqing Dai

Relative to conventional wet-chemical synthesis techniques, on-surface synthesis of organic networks in ultrahigh vacuum has few control parameters. The molecular deposition rate and substrate temperature are typically the only synthesis variables to be adjusted dynamically. Here we demonstrate that reducing conditions in the vacuum environment can be create

Julian Karl Bauer, Matti Schneider, Thomas Böhlke

Fiber-orientation tensors describe the relevant features of the fiber-orientation distribution compactly and are thus ubiquitous in injection-molding simulations and subsequent mechanical analyses. In engineering applications to date, the second-order fiber-orientation tensor is the basic quantity of interest, and the fourth-order fiber-orientation tensor is

Francesco Genovese

We prove a uniqueness result of dg-lifts for the derived pushforward and pullback functors of a flat morphism between separated Noetherian schemes, between the unbounded or bounded below derived categories of quasi-coherent sheaves. The technique is purely algebraic-categorical and involves reconstructing dg-lifts uniquely from their restrictions to the subc

Maria Cummings, Michael Filaseta, Ognian Trifonov

Based on work of P. Balister, B. Bollob\'as, R. Morris, J. Sahasrabudhe and M. Tiba, we show that if a covering system has distinct squarefree moduli, then the minimum modulus is at most 118. We also show that in general the $k^{\rm th}$ smallest modulus in a covering system with distinct moduli (provided it is required for the covering) is bounded by an abs

Henry Tang, Ameet Deshpande, Karthik Narasimhan

Multilingual pre-trained models exhibit zero-shot cross-lingual transfer, where a model fine-tuned on a source language achieves surprisingly good performance on a target language. While studies have attempted to understand transfer, they focus only on MLM, and the large number of differences between natural languages makes it hard to disentangle the importa

Q. Barthélemy, É. Lefrançois, J. Baglo, P. Bourgeois-Hope

We report thermal conductivity measurements on single crystals of herbertsmithite, over a wide range of temperatures (0.05-120 K) in magnetic fields up to 15 T. We also report measurements of the thermal Hall effect, found to be vanishingly small. At high temperatures, in the paramagnetic regime, the thermal conductivity has a negligible field dependence. Up

Shuaichen Chang, David Palzer, Jialin Li, Eric Fosler-Lussier

Choropleth maps are a common visual representation for region-specific tabular data and are used in a number of different venues (newspapers, articles, etc). These maps are human-readable but are often challenging to deal with when trying to extract data for screen readers, analyses, or other related tasks. Recent research into Visual-Question Answering (VQA

Stefaan G. Verhulst

We live in an era of datafication, one in which life is increasingly quantified and transformed into intelligence for private or public benefit. When used responsibly, this offers new opportunities for public good. However, three key forms of asymmetry currently limit this potential, especially for already vulnerable and marginalized groups: data asymmetries

Zakhar Kabluchko, Alexander Marynych

We prove limit theorems for random walks with $n$ steps in the $d$-dimensional Euclidean space as both $n$ and $d$ tend to infinity. One of our results states that the path of such a random walk, viewed as a compact subset of the infinite-dimensional Hilbert space $\ell^2$, converges in probability in the Hausdorff distance up to isometry and also in the Gro

Kota Katsumi, Alexandr Alekhin, Sofia-Michaela Souliou, Michael Merz

Ta$_2$NiSe$_5$ is an excitonic insulator candidate showing the semiconductor/semimetal-to-insulator (SI) transition below $T_{\text{c}}$ = 326 K. However, since a structural transition accompanies the SI transition, deciphering the role of electronic and lattice degrees of freedom in driving the SI transition has remained controversial. Here, we investigate

Anwesha Bhattacharyya, Joel Vaughan, Vijayan N. Nair

Hyper-parameters (HPs) are an important part of machine learning (ML) model development and can greatly influence performance. This paper studies their behavior for three algorithms: Extreme Gradient Boosting (XGB), Random Forest (RF), and Feedforward Neural Network (FFNN) with structured data. Our empirical investigation examines the qualitative behavior of

Yujin Cho, Dipti Jasrasaria, Keith G. Ray, Daniel M. Tennant

Material defects fundamentally limit the coherence times of superconducting qubits, and manufacturing completely defect-free devices is not yet possible. Therefore, understanding the interactions between defects and a qubit in a real quantum processor design is essential. We build a model that incorporates the standard tunneling model, the electric field dis

Pietro Lualdi, Ralf Sturm, Tjark Siefkes

Studying complex phenomena in detail by performing real experiments is often an unfeasible task. Virtual experiments using simulations are usually used to support the development process. However, numerical simulations are limited by their computational cost. Metamodeling techniques are commonly used to mimic the behavior of unknown solver functions, especia

Yejia Zhang, Pengfei Gu, Nishchal Sapkota, Hao Zheng

High annotation costs and limited labels for dense 3D medical imaging tasks have recently motivated an assortment of 3D self-supervised pretraining methods that improve transfer learning performance. However, these methods commonly lack spatial awareness despite its centrality in enabling effective 3D image analysis. More specifically, position, scale, and o

Sandro Costa Magalhães, António Paulo Moreira, Paulo Costa

A robot simulation system is a basic need for any robotics application. With it, developers' teams of robots can test their algorithms and make initial calibrations without risk of damage to the real robots, assuring safety. However, building these simulation environments is usually time-consuming work, and when considering robot fleets, the simulation revea

Antoine Demiquel, Vassos Achilleos, Georgios Theocharis, Vincent Tournat

In this paper, we study modulation instabilities (MI) in a one-dimensional chain configuration of a flexible mechanical metamaterial (flexMM). Using the lumped element approach, flexMMs can be modeled by a coupled system of discrete equations for the longitudinal displacements and rotations of the rigid mass units. In the long wavelength regime, and applying

Mansi Girdhar, Junho Hong, Yongsik You, Tai-jin Song

Safe and secure electric vehicle charging stations (EVCSs) are important in smart transportation infrastructure. The prevalence of EVCSs has rapidly increased over time in response to the rising demand for EV charging. However, developments in information and communication technologies (ICT) have made the cyber-physical system (CPS) of EVCSs susceptible to c

M. Matushko, A. Zotov

We propose and prove a set of identities for ${\rm GL}_M$ elliptic $R$-matrix (in the fundamental representation). In the scalar case ($M=1$) these are elliptic function identities derived by S.N.M. Ruijsenaars as necessary and sufficient conditions for his kernel identity underlying construction of integral solutions to quantum spinless Ruijsenaars-Schneide

Ermerson Araujo, Alex Zamudio Espinosa

We construct a version of kneading theory for families of monotonous functions on the real line. The generality of the setup covers two classical results from Milnor-Thurston's kneading theory: the first one is to dynamically characterise an $l$-modal map by its kneading sequence, the second one is to define the concept of kneading determinant, relate it to

Robin Gutzen, Giulia De Bonis, Chiara De Luca, Elena Pastorelli

Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be

Yuanchao Li, Catherine Lai, Divesh Lala, Koji Inoue

As the aging of society continues to accelerate, Alzheimer's Disease (AD) has received more and more attention from not only medical but also other fields, such as computer science, over the past decade. Since speech is considered one of the effective ways to diagnose cognitive decline, AD detection from speech has emerged as a hot topic. Nevertheless, such

Joseph Gallego-Mejia, Oscar Bustos-Brinez, Fabio A. González

This paper presents an anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the representation-learning ability of deep-learning models. The method combines an autoencoder, for learning a low-dimensional representation of the data, with a density-estimation model based on random Fo

Todd Bichoupan

A convex geometry is finite zero-closed closure system that satisfies the anti-exchange property. Complexity results are given for two open problems related to representations of convex geometries using implication bases. In particular, the problem of optimizing an implication basis for a convex geometry is shown to be NP-hard by establishing a reduction fro

Shamil Asgarli, Dragos Ghioca, Chi Hoi Yip

A plane curve $C\subset\mathbb{P}^2$ of degree $d$ is called \emph{blocking} if every $\mathbb{F}_q$-line in the plane meets $C$ at some $\mathbb{F}_q$-point. We prove that the proportion of blocking curves among those of degree $d$ is $o(1)$ when $d\geq 2q-1$ and $q \to \infty$. We also show that the same conclusion holds for smooth curves under the somewha

Leo Pio-Lopez, Johanna Bischof, Jennifer V. LaPalme, Michael Levin

All cognitive agents are composite beings. Specifically, complex living agents consist of cells, which are themselves competent sub-agents navigating physiological and metabolic spaces. Behavior science, evolutionary developmental biology, and the field of machine intelligence all seek an answer to the scaling of biological cognition: what evolutionary dynam

Shiva Heidari, Reza Asgari

The strong correlation between the non-trivial band topology and the magnetic texture makes magnetic Weyl semimetals excellent candidates for the manipulation and detection of magnetization dynamics. The parity violation together with the Pauli blocking cause only one Weyl node to contribute to the photocurrent response, which in turn affects the magnetic te

Emmanuel Gianuzzi, Cristian A. Giuppone, Nicolás Cuello

Most of the planetary systems discovered around binary stars are located at approximately three semi-major axes from the barycentre of their system, curiously close to low-order mean-motion resonances (MMRs). The formation mechanism of these circumbinary planets is not yet fully understood. In situ formation is extremely challenging because of the strong int

Manuel F. Ferrer-Garcia, Kyrylo Snizhko, Alessio D'Errico, Alessandro Romito

Distinct from the dynamical phase, in a cyclic evolution, a system's state may acquire an additional component, a.k.a. geometric phase. The latter is a manifestation of a closed path in state space. Geometric phases underlie various physical phenomena, notably the emergence of topological invariants of many-body states. Recently it has been demonstrated that

Alex Nguyen-Le, Victor M. Preciado

Linear time series modelling is dominated by the use of purely autoregressive models even though incorporating moving average components can greatly improve parsimony. We present a convex formulation for vector-ARMA system identification which respects this fundamental property, thus granting access to the nice properties afforded by convex programming. The

Arash Mahyari

Software vulnerabilities, caused by unintentional flaws in source codes, are the main root cause of cyberattacks. Source code static analysis has been used extensively to detect the unintentional defects, i.e. vulnerabilities, introduced into the source codes by software developers. In this paper, we propose a deep learning approach to detect vulnerabilities

Ting Hu, Gabriela Ochoa, Wolfgang Banzhaf

Genotype-to-phenotype mappings translate genotypic variations such as mutations into phenotypic changes. Neutrality is the observation that some mutations do not lead to phenotypic changes. Studying the search trajectories in genotypic and phenotypic spaces, especially through neutral mutations, helps us to better understand the progression of evolution and

John Ebert, David J. Grynkiewicz

Let $G=(\mathbb Z/n\mathbb Z) \oplus (\mathbb Z/n\mathbb Z)$. Let $\mathsf {s}_{\leq k}(G)$ be the smallest integer $\ell$ such that every sequence of $\ell$ terms from $G$, with repetition allowed, has a nonempty zero-sum subsequence with length at most $k$. It is known that $\mathsf {s}_{\leq 2n-1-k}(G)=2n-1+k$ for $k\in [0,n-1]$, with the structure of ext

Carla Silva Oliveira, Fausto Marques Pinheiro Junior, Jose Andre de Moura Brito

The operability of a network concerns its ability to remain operational, despite possible failures in its links or equipment. One may model the network through a graph to evaluate and increase this operability. Its vertices and edges correspond to the users equipment and their connections, respectively. In this article, the problem addressed is identifying t

Corentin van den Broek d'Obrenan, Frédéric Galliano, Jeremy Minton, Viktor Botev

The explosion of scientific publications overloads researchers with information. This is even more dramatic for interdisciplinary studies, where several fields need to be explored. A tool to help researchers overcome this is Natural Language Processing (NLP): a machine-learning (ML) technique that allows scientists to automatically synthesize information fro

Eva Höck, Tim-Oliver Buchholz, Anselm Brachmann, Florian Jug

In recent years, neural network based image denoising approaches have revolutionized the analysis of biomedical microscopy data. Self-supervised methods, such as Noise2Void (N2V), are applicable to virtually all noisy datasets, even without dedicated training data being available. Arguably, this facilitated the fast and widespread adoption of N2V throughout

Joseph H. Silverman

Charles, Goren, and Lauter [J. Cryptology 22(1), 2009] explained how one can construct hash functions using expander graphs in which it is hard to find paths between specified vertices. The set of solutions to the classical Markoff equation $X^2+Y^2+Z^2=XYZ$ in a finite field $\mathbb{F}_q$ has a natural structure as a tri-partite graph using three non-commu

Boris Feigin, Alexei Kanel-Belov, Anton Khoroshkin

We show that the tensor product of modules of tensor fields is a noetherian module as a module over any graded Lie subalgebra of finite codimension in the Lie algebra of polynomial vector fields on $\mathbb{R}^n$. As a corollary, we prove the conjecture of I.M.Gelfand announced at ICM'1970 at Nice on finite dimensionality of continuous cohomology of graded L

Roberto Menta, Anna Grassellino, Roni Harnik, Asher Berlin

A short review which focuses on the employment of a Superconducting Radio Frequency cavity for detection of Gravitational Waves. This first part is inspired by the works of Berlin et al.. In the second part, we analyze also the idea to use a cavity-qubit system for the GWs detection and the Dark Photons detection. In this case, the starting point is the work

Hayder Al-Hraishawi, Junaid ur Rehman, Mohsen Razavi, Symeon Chatzinotas

Quantum technologies are increasingly recognized as groundbreaking advancements set to redefine the landscape of computing, communications, and sensing by leveraging quantum phenomena, like entanglement and teleportation. Quantum technologies offer an interesting set of advantages such as unconditional security, large communications capacity, unparalleled co

Tsai-Hsuan Chung, Vahid Rostami, Hamsa Bastani, Osbert Bastani

We study the problem of allocating limited supply of medical resources in developing countries, in particular, Sierra Leone. We address this problem by combining machine learning (to predict demand) with optimization (to optimize allocations). A key challenge is the need to align the loss function used to train the machine learning model with the decision lo

Shehtab Zaman, Ethan Ferguson, Cecile Pereira, Denis Akhiyarov

From AlexNet to Inception, autoencoders to diffusion models, the development of novel and powerful deep learning models and learning algorithms has proceeded at breakneck speeds. In part, we believe that rapid iteration of model architecture and learning techniques by a large community of researchers over a common representation of the underlying entities ha

Salih Atici, Hongyi Pan, Mohammed H. Elnagar, Veerasathpurush Allareddy

We present a novel deep learning method for fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. The deep convolutional neural network consists of three parallel networks (TriPodNet) independently trained with different initialization parameters. They also have a built-in set of novel directional filters that highli

Sibendu Paul, Kunal Rao, Giuseppe Coviello, Murugan Sankaradas

Cameras are increasingly being deployed in cities, enterprises and roads world-wide to enable many applications in public safety, intelligent transportation, retail, healthcare and manufacturing. Often, after initial deployment of the cameras, the environmental conditions and the scenes around these cameras change, and our experiments show that these changes

Erin Ellefsen, Nancy Rodriguez

Animals use various processes to inform themselves about their environment and make decisions about how to move and form their territory. In some cases, populations inform themselves of competing groups through observations at distances, scent markings, or memories of locations where an individual has encountered competing populations. As the process of gath

Mingjian Tuo, Xingpeng Li

Conventional synchronous generators are gradually being replaced by inverter-based resources, such transition introduces more complicated operation conditions. And the reduction in system inertia imposes challenges for system operators on maintaining system rate-of-change-of-frequency (RoCoF) security. This paper presents a selectively linearized neural netw

Ben Abramowitz, Nicholas Mattei

Agents care not only about the outcomes of collective decisions but also about how decisions are made. In many cases, both the outcome and the procedure affect whether agents see a decision as legitimate, justifiable, or acceptable. We propose a novel model for collective decisions that takes into account both the preferences of the agents and their higher o

Younggil Song, Fatima L. Mota, Damien Tourret, Kaihua Ji

Spatially extended cellular and dendritic array structures forming during solidification processes such as casting, welding, or additive manufacturing are generally polycrystalline. Both the array structure within each grain and the larger scale grain structure determine the performance of many structural alloys. How those two structures coevolve during soli

Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth

Continuous-time event sequences, i.e., sequences consisting of continuous time stamps and associated event types ("marks"), are an important type of sequential data with many applications, e.g., in clinical medicine or user behavior modeling. Since these data are typically modeled autoregressively (e.g., using neural Hawkes processes or their classical count

Aleksis Koski, Jani Onninen

We give a full characterization of circle homeomorphisms which admit a homeomorphic extension to the unit disk with finite bi-Sobolev norm. As a special case, a bi-conformal variant of the famous Beurling-Ahlfors extension theorem is obtained. Furthermore we show that the existing extension techniques such as applying either the harmonic or the Beurling-Ahlf

Sherry Lin, Winthrop F. Gillis, Caleb Weinreb, Ayman Zeine

Spontaneous mouse behavior is composed from repeatedly-used modules of movement (e.g., rearing, running, grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the impact of drugs, genes, context, sensory stimuli and neur

Javier Lopez-Piqueres, Romain Vasseur

We analyze the onset of diffusive hydrodynamics in the one-dimensional hard-rod gas subject to stochastic backscattering. While this perturbation breaks integrability and leads to a crossover from ballistic to diffusive transport, it preserves infinitely many conserved quantities corresponding to even moments of the velocity distribution of the gas. In the l

Paul Escapil-Inchauspé, Gonzalo A. Ruz

We consider the computation of statistical moments to operator equations with stochastic data. We remark that application of PINNs -- referred to as TPINNs -- allows to solve the induced tensor operator equations under minimal changes of existing PINNs code, and enabling handling of non-linear and time-dependent operators. We propose two types of architectur

Alberto Soria

In this article spacelike hypersurfaces immersed in twisted product spacetimes $I\times_f F$ with complete fiber are studied. Several conditions ensuring global hyperbolicity are presented, as well as a relation that needs to hold on each spacelike hypersurface in $I\times_f F$ for it to be a simple warped product. When the fiber is assumed to be closed (com

Ben Abramowitz, Omer Lev, Nicholas Mattei

We consider the problem of determining a binary ground truth using advice from a group of independent reviewers (experts) who express their guess about a ground truth correctly with some independent probability (competence). In this setting, when all reviewers are competent (competence greater than one-half), the Condorcet Jury Theorem tells us that adding m

Roland Bacon, Jarle Brinchmann, Simon Conseil, Michael Maseda

We present the second data release of the MUSE Hubble UDF surveys, which includes the deepest spectroscopic survey ever performed. The MUSE data, with their 3D content, amazing depth, wide spectral range, and excellent spatial and medium spectral resolution, are rich in information. This update of the first release incorporates a new 141-hour adaptive-optics

A. Das, C. Ott, D. Pechimuthu, R. Moosavi

Metallic glasses are known to have a remarkably robust yield strength, admitting Weibull moduli as high as for crystalline engineering alloys. However, their post-yielding behavior is strongly varying, with large scatter in both flow stress levels and strains at failure. Using x-ray tomography we reveal for the first time how a strain-dependent internal evol

Hongyi Pan, Xin Zhu, Zhilu Ye, Pai-Yen Chen

In this paper, we present a wireless ECG-derived Respiration Rate (RR) estimation using an autoencoder with a DCT Layer. The wireless wearable system records the ECG data of the subject and the respiration rate is determined from the variations in the baseline level of the ECG data. A straightforward Fourier analysis of the ECG data obtained using the wirele

Christian Magliano, Giovanni Covone, Richa Dobal, Luca Cacciapuoti

We statistically validated a sample of hot Neptune candidates applying a two-step vetting technique using DAVE and TRICERATOPS. We performed a systematic validation of 250 transit-like events in the Transiting Exoplanet Survey Satellite (TESS) archive in the parameter region defined by $P\leq 4$ d and $3R_\oplus\leq R\leq 5R_\oplus$. Through our analysis, we

Anna Chrysostomou, Alan Cornell, Aldo Deandrea, Étienne Ligout

We investigate whether quasinormal modes (QNMs) can be used in the search for signatures of extra dimensions. To address a gap in the Beyond the Standard Model (BSM) literature, we focus here on higher dimensions characterised by negative Ricci curvature. As a first step, we consider a product space comprised of a four-dimensional Schwarzschild black hole sp

Daizhong Liu, N. M. Förster Schreiber, R. Genzel, D. Lutz

We present a high-resolution kinematic study of the massive main-sequence star-forming galaxy (SFG) SDSS J090122.37+181432.3 (J0901) at z=2.259, using 0.36 arcsec ALMA CO(3-2) and 0.1-0.5 arcsec SINFONI/VLT H-alpha observations. J0901 is a rare, strongly-lensed but otherwise normal massive (log(M_star/M_sun)~11) main sequence SFG, offering a unique opportuni

Francesco Belfiore, Adam K. Leroy, Jiayi Sun, Ashley T. Barnes

Mapping star-formation rates (SFR) within galaxies is key to unveiling their assembly and evolution. Calibrations exist for computing SFR from a combination of ultraviolet and infrared bands for galaxies as integrated systems, but their applicability to sub-galactic (kpc) scales remains largely untested. Here we use integral field spectroscopy of 19 nearby (

Chuqin Geng, Xiaojie Xu, Haolin Ye, Xujie Si

Just like weights, bias terms are the learnable parameters of many popular machine learning models, including neural networks. Biases are thought to enhance the representational power of neural networks, enabling them to solve a variety of tasks in computer vision. However, we argue that biases can be disregarded for some image-related tasks such as image cl

Sihang Jiang, Johanna Loomba, Suchetha Sharma, Donald Brown

It is shown that various symptoms could remain in the stage of post-acute sequelae of SARS-CoV-2 infection (PASC), otherwise known as Long COVID. A number of COVID patients suffer from heterogeneous symptoms, which severely impact recovery from the pandemic. While scientists are trying to give an unambiguous definition of Long COVID, efforts in prediction of

Manuel Mundt

Military drones are taking an increasingly prominent role in armed conflict, and the use of multiple drones in a swarm can be useful. Who the drivers of the research are and what sub-domains exist is analyzed and visually presented in this research using NLP techniques based on 946 studies. Most research is conducted in the Western world, led by the United S

I. V. Vovcenko, A. A. Zyablovsky, A. A. Pukhov, E. S. Andrianov

Recently, strong coupling between non-Hermitian physical systems of different nature is widely investigated due to it endows them with new properties. In this work, we investigate the energy transport between strongly coupled systems. We use a partial-secular approach for the description of an open quantum system to investigate the system dynamics during the

Alexis Poignant, Nathanael Jarrasse, Guillaume Morel

Robotic sensorimotor extensions (supernumerary limbs, prosthesis, handheld tools) are worn devices used to interact with the nearby environment, whether to assist the capabilities of impaired users or to enhance the dexterity of industrial operators. Despite numerous mechanical achievements, embedding these robotics devices remains critical due to their weig

Yu-Chuan Hsu, Markus J. Buehler

The dynamics of materials failure is one of the most critical phenomena in a range of scientific and engineering fields, from healthcare to structural materials to transportation. In this paper we propose a specially designed deep neural network, DyFraNet, which can predict dynamic fracture behaviors by identifying a complete history of fracture propagation

Peng Gao, Liangyi Zhao

We establish a subconvexity bound for a double Dirichlet series involving with the quadratic Hecke $L$-functions over the Gaussian field.

Elliot Glazer

We identify a choiceless variation of the box game paradox, in which players predict unknown real numbers with near-perfect accuracy despite lacking any useful information. We also verify that choice is necessary in the solution of the original problem and consider some further variations of the game.

Mikhail Kurenkov, Ivan Kalinov, Dzmitry Tsetserukou

Visual localization is an essential modern technology for robotics and computer vision. Popular approaches for solving this task are image-based methods. Nowadays, these methods have low accuracy and a long training time. The reasons are the lack of rigid-body and projective geometry awareness, landmark symmetry, and homogeneous error assumption. We propose

R. Austin McEver, Bowen Zhang, B. S. Manjunath

The quality and size of training sets often limit the performance of many state of the art object detectors. However, in many scenarios, it can be difficult to collect images for training, not to mention the costs associated with collecting annotations suitable for training these object detectors. For these reasons, on challenging video datasets such as the

Aayushya Agarwal, Carmel Fiscko, Soummya Kar, Larry Pileggi

We propose an adaptive optimization algorithm for solving unconstrained scaled gradient flow problems that achieves fast convergence by controlling the optimization trajectory shape and the discretization step sizes. Under a broad class of scaling functions, we establish convergence of the proposed approach to critical points of smooth objective functions, w

Abhishek Hegade K R, Justin L. Ripley, Nicolás Yunes

We present a systematic exploration of the loss of predictivity in Einstein-scalar-Gauss-Bonnet (ESGB) gravity. We first formulate a gauge covariant method of characterizing the breakdown of the hyperbolicity of the equations of motion in the theory. With this formalism, we show that strong geodesic focusing leads to the breakdown of hyperbolicity, and the l

CMS Collaboration

A search for supersymmetry is presented in events with a single charged lepton, electron or muon, and multiple hadronic jets. The data correspond to an integrated luminosity of 138 fb$^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV, recorded by the CMS experiment at the CERN LHC. The search targets gluino pair production, where the gl

Tanmay Vilas Samak, Chinmay Vilas Samak

This work presents AutoDRIVE, a comprehensive research and education platform for implementing and validating intelligent transportation algorithms pertaining to vehicular autonomy as well as smart city management. It is an openly accessible platform featuring a 1:14 scale car with realistic drive and steering actuators, redundant sensing modalities, high-pe

Muhammad Umar B. Niazi, Amr Alanwar, Michelle S. Chong, Karl Henrik Johansson

This paper considers the problem of set-based state estimation for linear time-invariant (LTI) systems under time-varying sensor attacks. Provided that the LTI system is stable and observable via every single sensor and that at least one sensor is uncompromised, we guarantee that the true state is always contained in the estimated set. We use zonotopes to re