March 2023 arXiv papers — page 76

Hanjia Gao, Runmin Wang, Xiaofeng Shao

Change point testing for high-dimensional data has attracted a lot of attention in statistics and machine learning owing to the emergence of high-dimensional data with structural breaks from many fields. In practice, when the dimension is less than the sample size but is not small, it is often unclear whether a method that is tailored to high-dimensional dat

Hiroki Nemoto, Yasutaka Shimizu

Estimating parameters of drift and diffusion coefficients for multidimensional stochastic delay equations with small noise are considered. The delay structure is written as an integral form with respect to a delay measure. Our contrast function is based on a local-Gauss approximation to the transition probability density of the process. We show consistency a

San-Dong Guo, Xiao-Shu Guo, Shuo-Ning Si, Kai Cheng

Coexistence of ferromagnetism, piezoelectricity and valley in two-dimensional (2D) materials is crucial to advance multifunctional electronic technologies. Here, Janus ScXY (X$\neq$Y=Cl, Br and I) monolayers are predicted to be in-plane piezoelectric ferromagnetic (FM) semiconductors with dynamical, mechanical and thermal stabilities. The predicted piezoelec

Lin Lan, He Gao, An Li, Shuo Xiao

The gamma-ray burst GRB 221009A, known as the ``brightest-of-all-time" (BOAT), is the closest energetic burst detected so far, with an energy of $E_{\gamma,\rm iso} \sim 10^{55}$ ergs. This study aims to assess its compatibility with known GRB energy and luminosity distributions. Our analysis indicates that the energy/luminosity function of GRBs is consisten

Kayue Daniel Wong, Hongfeng Zhang

In this manuscript, we give a classification of all irreducible, unitary representations of complex spin groups.

Arpit Garg, Cuong Nguyen, Rafael Felix, Thanh-Toan Do

The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with noisy-label (LNL) techniques that focus on separating noisy- and clean-label samples to apply different learning strategies to each group of samples. Current methodologies often rely

Robert N. Wolf, Joseph H. Pham, Julian Y. Z. Jee, Alexander Rischka

Resolving the locations and discriminating the spin states of individual trapped ions with high fidelity is critical for a large class of applications in quantum computing, simulation, and sensing. We report on a method for high-fidelity state discrimination in large two-dimensional (2D) crystals with over 100 trapped ions in a single trapping region, combin

Xiangpeng Li, Yuqin Jiang, Ali Mostafavi

ChatGPT has been emerging as a novel information source, and it is likely that the public might seek information from ChatGPT while taking protective actions when facing climate hazards such as floods and hurricanes. The objective of this study is to evaluate the accuracy and completeness of responses generated by ChatGPT when individuals seek information ab

Nathan Inkawhich

In real-world scenarios, it may not always be possible to collect hundreds of labeled samples per class for training deep learning-based SAR Automatic Target Recognition (ATR) models. This work specifically tackles the few-shot SAR ATR problem, where only a handful of labeled samples may be available to support the task of interest. Our approach is composed

Bhagyashree Prabhune, Krishnan Suresh

An important requirement in the standard finite element method (FEM) is that all elements in the underlying mesh must be tangle-free i.e., the Jacobian must be positive throughout each element. To relax this requirement, an isoparametric tangled finite element method (i-TFEM) was recently proposed for linear elasticity problems. It was demonstrated that i-TF

David Smith, Joseph Samuel Myers, Craig S. Kaplan, Chaim Goodman-Strauss

A longstanding open problem asks for an aperiodic monotile, also known as an "einstein": a shape that admits tilings of the plane, but never periodic tilings. We answer this problem for topological disk tiles by exhibiting a continuum of combinatorially equivalent aperiodic polygons. We first show that a representative example, the "hat" polykite, can form c

Xin-Yu Wang, Chung-Han Hsieh

In this paper, we extend the existing double linear policy by incorporating time-varying weights instead of constant weights and study a certain robustness property, called robust positive expectation (RPE), in a discrete-time setting. We prove that the RPE property holds by employing a novel elementary symmetric polynomials characterization approach and der

Janghoon Ock, Tian Tian, John Kitchin, Zachary Ulissi

The practical applications of determining the relative difference in adsorption energies are extensive, such as identifying optimal catalysts, calculating reaction energies, and determining the lowest adsorption energy on a catalytic surface. Although Density Functional Theory (DFT) can effectively calculate relative values through systematic error cancellat

Abdullah Nazib, Riad Hassan, Zahidul Islam, Clinton Fookes

Organ at risk (OAR) segmentation in computed tomography (CT) imagery is a difficult task for automated segmentation methods and can be crucial for downstream radiation treatment planning. U-net has become a de-facto standard for medical image segmentation and is frequently used as a common baseline in medical image segmentation tasks. In this paper, we propo

Vaibhav Garg, Hui Guo, Nirav Ajmeri, Saikath Bhattacharya

Problem: We address the challenge in responsible computing where an exploitable mobile app is misused by one app user (an abuser) against another user or bystander (victim). We introduce the idea of a misuse audit of apps as a way of determining if they are exploitable without access to their implementation. Method: We leverage app reviews to identify exploi

Shenghan Zhang, Haoxuan Li, Ruixiang Tang, Sirui Ding

Detailed phenotype information is fundamental to accurate diagnosis and risk estimation of diseases. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from the heterogeneous EHR data remains a challenge. In this work,

Vincent E. Ledvina, Erika Palmerio, Christina Kay, Nada Al-Haddad

Context: Coronal mass ejections (CMEs) are eruptions of plasma from the Sun that travel through interplanetary space and may encounter Earth. CMEs often enclose a magnetic flux rope (MFR), the orientation of which largely determines the CME's geoeffectiveness. Current operational CME models do not model MFRs, but a number of research ones do, including the O

Audrey E Hendricks

Undergraduate research experiences hold many potential benefits. Students can learn about new areas opening up previously unknown paths in academia and industry. The hands-on experience often provides a deeper understanding of what science, research, and data analysis is and, importantly, is not. While numerous studies have provided information about the ben

Stephan Radonic, Jürgen Besserer, Jessica Kneubühl, Valeria Meier

In radiation therapy tumor size, and thus also volume, has a significant impact on the local control of tumors. Moreover, tumor volume is a significant prognostic factor for modelling and predicting therapeutic outcomes in cancer treatment. In research, the distribution of tumor volumes in patient populations has so far remained widely unexplored. In this wo

Gábor Czédli

Let Sp($k$) denote the number of the $\lfloor k/2\rfloor$-element subsets of a finite $k$-element set. We prove that the least size of a generating subset of the Boolean lattice with $n$ atoms (or, equivalently, the powerset lattice of an $n$-element set) is the least number $k$ such that $n\leq$ Sp($k$). Based on this fact and our 2021 protocol based on equ

Yaozhi Lu, Shahab Aslani, An Zhao, Ahmed Shahin

In this study, we present a hybrid CNN-RNN approach to investigate long-term survival of subjects in a lung cancer screening study. Subjects who died of cardiovascular and respiratory causes were identified whereby the CNN model was used to capture imaging features in the CT scans and the RNN model was used to investigate time series and thus global informat

Kaitlin N. Smith, Michael A. Perlin, Pranav Gokhale, Paige Frederick

Quantum computing has potential to provide exponential speedups over classical computing for many important applications. However, today's quantum computers are in their early stages, and hardware quality issues hinder the scale of program execution. Benchmarking and simulation of quantum circuits on classical computers is therefore essential to advance the

Liu He, Yijuan Lu, John Corring, Dinei Florencio

We develop a diffusion-based approach for various document layout sequence generation. Layout sequences specify the contents of a document design in an explicit format. Our novel diffusion-based approach works in the sequence domain rather than the image domain in order to permit more complex and realistic layouts. We also introduce a new metric, Document Ea

Kaan Gokcesu, Hakan Gokcesu

Our study focuses on determining the best weight windows for a weighted moving average smoother under squared loss. We show that there exists an optimal weight window that is symmetrical around its center. We study the class of tapered weight windows, which decrease in weight as they move away from the center. We formulate the corresponding least squares pro

Daniele Alessandrini, Sara Maloni, Nicolas Tholozan, Anna Wienhard

Anosov representations $\rho$ of a hyperbolic group $\Gamma$ into a semisimple Lie group $G$ are known to admit cocompact domains of discontinuity in flag varieties $G/Q$, endowing the compact quotient manifolds $M_\rho$ with a $(G,G/Q)$-structure. In general the topology of $M_\rho$ can be quite complicated. In this article, we consider the case when $\Gamm

Hemanth Chandravamsi, Amareshwara Sainadh Chamarthi, Natan Hoffmann, Steven H. Frankel

Accurate high-speed flow simulations of practical interest require numerical methods with high-resolution properties. In this paper, we present an extension and demonstration of the high-accuracy Gradient-based reconstruction and $\alpha$-damping schemes introduced by Chamarthi (2022) [1] for simulating high-speed flows in generalized curvilinear and dynamic

Julio Candanedo

Matrices and more generally multidimensional arrays, form the backbone of computational studies. In this paper we demonstrate increases in computational efficiency by performing partial-tracing/tensor-contractions on sparse-arrays. It was shown that sparse-arrays are really 3 dense-arrays (dense-shape, index-array, and data-array). Dense-array manipulations

Xiaoyu Feng, Huangxin Chen, Bo Yu, Shuyu Sun

The solidification and macro-segregation problem involving unsteady multi-physics and multi-phase fields is typically a complex process with mass, momentum, heat, and species transfers among solid, mushy, and liquid phase regions. The quantitative prediction of phase change, chemical heterogeneities, and multi-phase and multi-component flows plays critical r

Abhilash Pal, Stephan Huber, Cyrine Chaabani, Alessandro Manzotti

Sign language detection, identifying if someone is signing or not, is becoming crucially important for its applications in remote conferencing software and for selecting useful sign data for training sign language recognition or translation tasks. We argue that the current benchmark data sets for sign language detection estimate overly positive results that

Emily Adlam

Entropy bounds have played an important role in the development of holography as an approach to quantum gravity, so in this article we seek to gain a better understanding of the covariant entropy bound. We observe that there is a possible way of thinking about the covariant entropy bound which would suggest that it encodes an epistemic limitation rather than

Kai Malcolm, Josue Casco-Rodriguez

Biological neural networks continue to inspire breakthroughs in neural network performance. And yet, one key area of neural computation that has been under-appreciated and under-investigated is biologically plausible, energy-efficient spiking neural networks, whose potential is especially attractive for low-power, mobile, or otherwise hardware-constrained se

Sylvain Mezil, Irène Wang, Emmanuel Bossy

The existence of a shift-shift memory effect, whereby any translation of the input field induces translations in the output field in four symmetrical directions, has been observed in square waveguides by correlation measurements. Here we demonstrate that this memory effect is also observed in real space and can be put to use for imaging purposes. First, a fo

Ming Xu, Sourav Garg, Michael Milford, Stephen Gould

This paper addresses learning end-to-end models for time series data that include a temporal alignment step via dynamic time warping (DTW). Existing approaches to differentiable DTW either differentiate through a fixed warping path or apply a differentiable relaxation to the min operator found in the recursive steps used to solve the DTW problem. We instead

Alejandro Martinez-Calvo, Matthew D Biviano, Anneline Christensen, Eleni Katifori

Fluid flow networks are ubiquitous and can be found in a broad range of contexts, from human-made systems such as water supply networks to living systems like animal and plant vasculature. In many cases, the elements forming these networks exhibit a highly non-linear pressure-flow relationship. Although we understand how these elements work individually, the

Nilay Kushawaha, Yulia Furletova, Ankhi Roy, Dmitry Romanov

Machine learning (ML) is no new concept in the high-energy physics community, in fact, many ML techniques have been employed since the early 80s to deal with a broad spectrum of physics problems. In this paper, we present a novel technique to separate electrons from pions in the Gas Electron Multiplier Transition Radiation Detector (GEM TRD) using deep learn

Alejandro Garcia-Uribe, Todd N. Erpelding, Haixin Ke, Kavya Narayana Reddy

In many clinical conditions, such as head trauma, stroke, and low cardiac output states, the brain is at risk for hypoxic-ischemic injury. The metabolic rate and oxygen consumption of the brain are reflected in internal jugular venous oxygen saturation (sijvO2). The current gold standard for monitoring brain oxygenation is invasive; it requires jugular vein

Matthew L. Olson, Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan

Generative Adversarial Networks (GANs) are notoriously difficult to train especially for complex distributions and with limited data. This has driven the need for tools to audit trained networks in human intelligible format, for example, to identify biases or ensure fairness. Existing GAN audit tools are restricted to coarse-grained, model-data comparisons b

Peter Doyle, Matthew Ellison, Zili Wang

Define the tet-volume of a triangulation of the 2-sphere to be the minimum number of tetrahedra in a 3-complex of which it is the boundary, and let $d(v)$ be the maximum tet-volume for $v$-vertex triangulations. In 1986 Sleator, Tarjan, and Thurston (STT) proved that $d(v) = 2v-10$ holds for large $v$, and conjectured that it holds for all $v \geq 13$. Their

Zeyu Wei, Yen-Chi Chen

We introduce a new regression framework designed to deal with large-scale, complex data that lies around a low-dimensional manifold with noises. Our approach first constructs a graph representation, referred to as the skeleton, to capture the underlying geometric structure. We then define metrics on the skeleton graph and apply nonparametric regression techn

Xuqian Ren, Shaopeng Yang, Saihui Hou, Chunshui Cao

Previous gait recognition methods primarily trained on labeled datasets, which require painful labeling effort. However, using a pre-trained model on a new dataset without fine-tuning can lead to significant performance degradation. So to make the pre-trained gait recognition model able to be fine-tuned on unlabeled datasets, we propose a new task: Unsupervi

Anthony Nouy, Alexandre Pasco

We consider the problem of state estimation from a few linear measurements, where the state to recover is an element of the manifold $\mathcal{M}$ of solutions of a parameter-dependent equation. The state is estimated using prior knowledge on $\mathcal{M}$ coming from model order reduction. Variational approaches based on linear approximation of $\mathcal{M}

Sangmin Yoo, Eric Yeu-Jer Lee, Ziyu Wang, Xinxin Wang

Event-based cameras are inspired by the sparse and asynchronous spike representation of the biological visual system. However, processing the event data requires either using expensive feature descriptors to transform spikes into frames, or using spiking neural networks that are expensive to train. In this work, we propose a neural network architecture, Rese

Adam Dor-On, Matthieu Dussaule, Ilya Gekhtman

We study boundaries arising from limits of ratios of transition probabilities for random walks on relatively hyperbolic groups. We extend, as well as determine significant limitations of, a strategy employed by Woess for computing ratio-limit boundaries for the class of hyperbolic groups. On the one hand we employ results of the second and third authors to a

Gargi Ghosh, Włodzimierz Zwonek

Motivated by the way two special domains, namely the symmetrized bidisc and the tetrablock, could be defined as the images of $2$-proper holomorphic images of classical Cartan domains, we present a general approach to study $2$-proper holomorphic images of bounded symmetric domains. We show some special properties of $2$-proper holomorphic maps (such as the

Jyoti Shakya, Min-A Kang, Jian Li, Armin VahidMohammadi

In the past two decades another transistor based on conducting polymers, called the organic electrochemical transistor (ECT) was shown and largely studied. The main difference between organic ECTs and FETs is the mode and extent of channel doping: while in FETs the channel only has surface doping through dipoles, the mixed ionic-electronic conductivity of th

Samar Elaraby, Sherif M. Abuelenin

Graph theory is a promising approach in handling the problem of estimating the connectivity probability of vehicular ad-hoc networks (VANETs). With a communication network represented as graph, graph connectivity indicators become valid for connectivity analysis of communication networks as well. In this article, we discuss two different graph-based methods

M. Falxa, S. Babak, P. T. Baker, B. Bécsy

The International Pulsar Timing Array 2nd data release is the combination of datasets from worldwide collaborations. In this study, we search for continuous waves: gravitational wave signals produced by individual supermassive black hole binaries in the local universe. We consider binaries on circular orbits and neglect the evolution of orbital frequency ove

Ali Abedi, Hossein Karshenas, Peyman Adibi

Deep neural networks have achieved promising results in automatic image captioning due to their effective representation learning and context-based content generation capabilities. As a prominent type of deep features used in many of the recent image captioning methods, the well-known bottomup features provide a detailed representation of different objects o

Tolga Yilmaz, Özgür Ulusoy

Misinformation propagation in online social networks has become an increasingly challenging problem. Although many studies exist to solve the problem computationally, a permanent and robust solution is yet to be discovered. In this study, we propose and demonstrate the effectiveness of a blockchain-machine learning hybrid approach for addressing the issue of

Anna Abasheva, Rodion Déev

We find new examples of complex surfaces with countably many non-isomorphic algebraic structures. Here is one such example: take an elliptic curve $E$ in $\mathbb P^2$ and blow up nine general points on $E$. Then the complement $M$ of the strict transform of $E$ in the blow-up has countably many algebraic structures. Moreover, each algebraic structure comes

Evgeniy Slobodkin, Alexander Sadovnikov

In this paper, we describe and present the first dataset of source code plagiarism specifically aimed at contest plagiarism. The dataset contains 251 pairs of plagiarized solutions of competitive programming tasks in Java, as well as 660 non-plagiarized ones, however, the described approach can be used to extend the dataset in the future. Importantly, each p

Sergey Sinitsa, Ohad Fried

The generation of high-quality images has become widely accessible and is a rapidly evolving process. As a result, anyone can generate images that are indistinguishable from real ones. This leads to a wide range of applications, including malicious usage with deceptive intentions. Despite advances in detection techniques for generated images, a robust detect

Xiaodan Xing, Giorgos Papanastasiou, Simon Walsh, Guang Yang

As a pragmatic data augmentation tool, data synthesis has generally returned dividends in performance for deep learning based medical image analysis. However, generating corresponding segmentation masks for synthetic medical images is laborious and subjective. To obtain paired synthetic medical images and segmentations, conditional generative models that use

Ruslan Vasilev, Alexander D'yakonov

Neural networks solving real-world problems are often required not only to make accurate predictions but also to provide a confidence level in the forecast. The calibration of a model indicates how close the estimated confidence is to the true probability. This paper presents a survey of confidence calibration problems in the context of neural networks and p

Lukas Koch

We prove a geometric linearisation result for minimisers of optimal transport problems where the cost-function is strongly p-convex and of p-growth. Initial and target measures are allowed to be rough, but are assumed to be close to Lebesgue measure.

Siiri Kivimaki, Boban Velickovic

The logic $\mathcal L^1_\kappa$ was introduced by Shelah in [3]. In [4], he proved that for a strongly compact cardinal $\kappa$, it admits the following algebraic characterization: two structures are $\mathcal L^1_\kappa$-equivalent if and only if they have isomorphic iterated ultrapowers via $\kappa$-complete ultrafilters. We give a presentation of the log

Peiyuan Zhang, Jiaye Teng, Jingzhao Zhang

This work studies the generalization error of gradient methods. More specifically, we focus on how training steps $T$ and step-size $\eta$ might affect generalization in smooth stochastic convex optimization (SCO) problems. We first provide tight excess risk lower bounds for Gradient Descent (GD) and Stochastic Gradient Descent (SGD) under the general non-re

Wentao Zhu, Mohamed Omar

Audio event has a hierarchical architecture in both time and frequency and can be grouped together to construct more abstract semantic audio classes. In this work, we develop a multiscale audio spectrogram Transformer (MAST) that employs hierarchical representation learning for efficient audio classification. Specifically, MAST employs one-dimensional (and t

Aaron Friedrich Kurz, Timotheus Kampik, Luise Pufahl, Ingo Weber

In order to better facilitate the need for continuous business process improvement, the application of DevOps principles has been proposed. In particular, the AB-BPM methodology applies AB testing and reinforcement learning to increase the speed and quality of improvement efforts. In this paper, we provide an industry perspective on this approach, assessing

Zhu Zhang, Sanli Faez

Optical methods for monitoring the electrochemical reaction at the interface are advantageous because of their table-top setup and ease of integration into reactors. Here we apply EDL-modulation microscopy to one of the main components of amperometric measurement devices: a microelectrode. We present experimental measurements of the EDL-modulation contrast f

Wojciech Dybalski, Alexander Stottmeister, Yoh Tanimoto

The exponential decay of lattice Green functions is one of the main technical ingredients of the Ba{\l}aban's approach to renormalization. We give here a self-contained proof, whose various ingredients were scattered in the literature. The main sources of exponential decay are the Combes-Thomas method and the analyticity of the Fourier transforms. They are c

Rui Luo, Vikram Krishnamurthy

This paper proposes a method to detect change points in dynamic social networks using Fr\'echet statistics. We address two main questions: (1) what metric can quantify the distances between graph Laplacians in a dynamic network and enable efficient computation, and (2) how can the Fr\'echet statistics be extended to detect multiple change points while mainta

Haozhe Si, Bin Zhao, Dong Wang, Yunpeng Gao

Depth-from-defocus (DFD), modeling the relationship between depth and defocus pattern in images, has demonstrated promising performance in depth estimation. Recently, several self-supervised works try to overcome the difficulties in acquiring accurate depth ground-truth. However, they depend on the all-in-focus (AIF) images, which cannot be captured in real-

Myriam Bontonou, Anaïs Haget, Maria Boulougouri, Jean-Michel Arbona

Understanding the molecular processes that drive cellular life is a fundamental question in biological research. Ambitious programs have gathered a number of molecular datasets on large populations. To decipher the complex cellular interactions, recent work has turned to supervised machine learning methods. The scientific questions are formulated as classica

Andrea Di Lorenzo, Giovanni Inchiostro

We give a valuative criterion for when a smooth algebraic stack with a separated good moduli space is the quotient of a separated Deligne-Mumford stack by a torus. For doing so, we introduce a new class of morphisms, the so-called effective morphisms, which are a generalization of separated morphisms.

Tianyi Sun

This project contains two chapters. Chapter 2 has two sections. First, we define the well-formed formulas of the Language of Sentential Logic using Construction Sequences. Second, we prove the Truth Assignments using the Language of Sentential Logic. Chapter 3 has two sections. First, we define the Recursion Theorem. Second, we prove the Truth Assignments us

Dmitry Gurevich, Pavel Saponov, Vladimir Sokolov

In this note we are dealing with a particular class of quadratic algebras -- the so-called quantum matrix algebras. The well-known examples are the algebras of quantized functions on classical Lie groups (the RTT algebras). We consider the problem of constructing some projectors on homogenous components of such algebras, which are analogs of the usual symmet

Jun Yan

An edge colouring of $K_n$ with $k$ colours is a Gallai $k$-colouring if it does not contain any rainbow triangle. Gy\'arf\'as, P\'alv\"olgyi, Patk\'os and Wales proved that there exists a number $g(k)$ such that $n\geq g(k)$ if and only if for any colour distribution sequence $(e_1,\cdots,e_k)$ with $\sum_{i=1}^ke_i=\binom{n}{2}$, there exist a Gallai $k$-c

Daniel B. Araya, Neal P. Bitter, Bradley M. Wheaton, Omar Kamal

Boundary-layer instabilities for a finned cone at Mach=6, $Re=8.4 \times 10^6$ [m$^{-1}$], and zero incidence angle are examined using linear stability methods of varying fidelity and maturity, following earlier analysis presented in [doi.org/10.2514/6.2022-3247]. The geometry and laminar flow conditions correspond to experiments conducted at the Boeing Air

Dominic Hayward, Purushottam S Dubey, Marie-Sousai Appavou, Olaf Holderer

In this manuscript we describe the investigation of the SARS-CoV2 membrane fusion timescale by means of small-angle neutron scattering (SANS) using hydrogen/deuterium contrast variation. After the successful production of virus-like vesicles and human-host-cell-like vesicles we were able to follow the fusion of the respective vesicles in real-time. This was

P. Kalamvokas, V. G. Papageorgiou, A. S. Fokas, L. -Y. Sung

We investigate the Cauchy problem on the cylinder, namely the semi-periodic problem where there is periodicity in the $x$-direction and decay in the $y$-direction, for the Kadomtsev-Petviashvili II equation by the inverse spectral transform method. For initial data with small $L^1$ and $L^2$ norms, assuming the zero mass constraint, this initial-value proble

Bui Xuan Hai, Huynh Viet Khanh

In this paper, we prove that the multiplicative group of a unital non-commutative Leavitt path algebra $L_K(E)$ and Cohn path algebra $C_K(E)$ contain a non-cyclic free subgroup, provided $K$ is a non-absolute field. We also provide a description of the generators of free subgroups in term of the graph $E$. Finally, we determine multiplicative groups of Leav

Vanja Nikolić

Motivated by numerical modeling of ultrasound waves, we investigate robust conforming finite element discretizations of quasilinear and possibly nonlocal equations of Westervelt type. These wave equations involve either a strong dissipation or damping of fractional-derivative type and we unify them into one class by introducing a memory kernel that satisfies

Manoj Kumar, Aniruddh Murali, Arvin Gopal Subramaniam, Rajesh Singh

The field of synthetic active matter has, thus far, been led by efforts to create point-like, isolated (yet interacting) self-propelled objects (\emph{e.g.} colloids, droplets, microrobots) and understanding their collective dynamics. The design of flexible, freely jointed active assemblies from autonomously powered components remains a challenge. Here, we r

Yang Qian, Ali Kargarandehkordi, Onur Cezmi Mutlu, Saimourya Surabhi

Emotions play an essential role in human communication. Developing computer vision models for automatic recognition of emotion expression can aid in a variety of domains, including robotics, digital behavioral healthcare, and media analytics. There are three types of emotional representations which are traditionally modeled in affective computing research: A

Joshua Hope-Collins, Luca di Mare

Liou-Steffen splitting (AUSM) schemes are popular for low Mach number simulations, however, like many numerical schemes for compressible flow they require careful modification to accurately resolve convective features in this regime. Previous analyses of these schemes usually focus only on a single discrete scheme at the convective limit, only considering fl

Jamie S. Luskin, Ekkehart Schmidt, Boris Korzh, Andrew D. Beyer

Superconducting nanowire single photon detectors (SNSPDs) are the highest-performing technology for time-resolved single-photon counting from the UV to the near-infrared. The recent discovery of single-photon sensitivity in micrometer-scale superconducting wires is a promising pathway to explore for large active area devices with application to dark matter s

Igor Kenzo Ishikawa Oshiro Nakashima, Giovanna Vendramini, Helio Pedrini

Early and accurate diagnosis of COVID-19 is essential to control the rapid spread of the pandemic and mitigate sequelae in the population. Current diagnostic methods, such as RT-PCR, are effective but require time to provide results and can quickly overwhelm clinics, requiring individual laboratory analysis. Automatic detection methods have the potential to

Jacob Mostovoy

We study the space $Q_n$ of all configurations of $n$ ordered points on the circle such that no three points coincide, and in which one of the points (say, the last one) is fixed. We compute its fundamental group for $n<6$ and describe its homology for $n=6,7$. For arbitrary $n$, we compute its first homology and its Euler characteristic. We use three geomet

Lucas C. F. Ferreira, Daniel P. A. Lima

We consider a chemotaxis-fluid system in the whole plane $\mathbb{R}^{2}$ which describes the motion of bacteria suspended in a Navier-Stokes fluid and attracted by a chemical (oxygen). Employing the vorticity formulation for the fluid equations, we obtain local and global solutions with large (Radon) measures as initial data for the bacterial density and vo

Cora Brown, Sarah Milstein, Tianyi Sun, Cooper Zhao

When COVID-19 first started spreading and quarantine was implemented, the Society for Industrial and Applied Mathematics (SIAM) Student Chapter at the University of Minnesota-Twin Cities began a collaboration with Ecolab to use our skills as data scientists and mathematicians to extract useful insights from relevant data relating to the pandemic. This collab

Aryan Mikaeili, Or Perel, Mehdi Safaee, Daniel Cohen-Or

Text-to-image diffusion models are gradually introduced into computer graphics, recently enabling the development of Text-to-3D pipelines in an open domain. However, for interactive editing purposes, local manipulations of content through a simplistic textual interface can be arduous. Incorporating user guided sketches with Text-to-image pipelines offers use

Carline Biesdorf, Debora P. Menezes, Luiz L. Lopes

In this paper the QCD phase diagram is obtained by the crossing of two effective models: the MIT bag based models are used to describe quark matter and QHD type models to describe hadronic matter, being the use of the former something new to this kind of approach and the latter used with improved parameterizations as compared with previous calculations. We u

Yat Long Lo, Christian Schroeder de Witt, Samuel Sokota, Jakob Nicolaus Foerster

By enabling agents to communicate, recent cooperative multi-agent reinforcement learning (MARL) methods have demonstrated better task performance and more coordinated behavior. Most existing approaches facilitate inter-agent communication by allowing agents to send messages to each other through free communication channels, i.e., cheap talk channels. Current

Juan S. Angarita-Zapata, Antonio D. Masegosa, Isaac Triguero

Intelligent Transportation Systems are producing tons of hardly manageable traffic data, which motivates the use of Machine Learning (ML) for data-driven applications, such as Traffic Forecasting (TF). TF is gaining relevance due to its ability to mitigate traffic congestion by forecasting future traffic states. However, TF poses one big challenge to the ML

Vinayak M. Kulkarni, N. S. Vidhyadhiraja

We show that exceptional points (EPs) and non-Hermitian behavior can emerge dynamically in impurity models with Hermitian microscopic origins. Using perturbative renormalization group (RG) analysis, Fock-space diagonalization, and spin-spin relaxation time calculations, we demonstrate that nonlinear (NL) dispersion and anisotropic pseudochiral (PC) interacti

Bonaventure F. P. Dossou, Yenoukoume S. K. Gbenou, Miglanche Ghomsi Nono

Cancer is increasingly a global health issue. Seconding cardiovascular diseases, cancers are the second biggest cause of death in the world with millions of people succumbing to the disease every year. According to the World Health Organization (WHO) report, by the end of 2020, more than 7.8 million women have been diagnosed with breast cancer, making it the

Nicholas Charron, Huaiyuan Weng, Steven L. Waslander, Sriram Narasimhan

We present a novel target-based lidar-camera extrinsic calibration methodology that can be used for non-overlapping field of view (FOV) sensors. Contrary to previous work, our methodology overcomes the non-overlapping FOV challenge using a motion capture system (MCS) instead of traditional simultaneous localization and mapping approaches. Due to the high rel

Shaila Niazi, Navid Anjum Aadit, Masoud Mohseni, Shuvro Chowdhury

The slowing down of Moore's law has driven the development of unconventional computing paradigms, such as specialized Ising machines tailored to solve combinatorial optimization problems. In this paper, we show a new application domain for probabilistic bit (p-bit) based Ising machines by training deep generative AI models with them. Using sparse, asynchrono

Chaojian Li, Wenwan Chen, Jiayi Yuan, Yingyan Celine Lin

Social ambiance describes the context in which social interactions happen, and can be measured using speech audio by counting the number of concurrent speakers. This measurement has enabled various mental health tracking and human-centric IoT applications. While on-device Socal Ambiance Measure (SAM) is highly desirable to ensure user privacy and thus facili

Konrad Schlichtholz

The purpose of this comment is to show that a reinterpretation of the results from the Letter: "Physics and Metaphysics of Wigner's Friends: Even performed pre-measurements have no results" allows for reaching the conclusion "pre-measurements have no resul" [arXiv:2003.07464] based only on postulates of quantum mechanics without additional assumptions on irr

Yavdat Il'yasov

A variational method is presented for directly finding the bifurcation point of nonlinear equations as the saddle-node point of the extended nonlinear Rayleigh quotient. The method is applied for solving an open problem on the existence of a maximal saddle-node bifurcation point for set of positive solutions of system equations with convex-concave type nonli

Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes, Ronald Kemker

In supervised continual learning, a deep neural network (DNN) is updated with an ever-growing data stream. Unlike the offline setting where data is shuffled, we cannot make any distributional assumptions about the data stream. Ideally, only one pass through the dataset is needed for computational efficiency. However, existing methods are inadequate and make

J. Menezes

We study a three-species cyclic model whose organisms are vulnerable to contamination with an infectious disease which propagates person-to-person. We consider that individuals of one species perform an evolutionary self-preservation strategy by reducing the mobility rate to minimise infection risk whenever an epidemic outbreak reaches the neighbourhood. Run

Naoki Kitazawa

We present new real algebraic maps of non-positive codimensions with prescribed images whose boundaries consist of explicit non-singular real algebraic hypersurfaces satisfying so-called "transversality" as follows. Explicit information on important real polynomials is given. Preimages are one-point sets or products of spheres. They are locally like so-calle

Devendra K. Jangid, Neal R. Brodnik, McLean P. Echlin, Tresa M. Pollock

Gathering 3D material microstructural information is time-consuming, expensive, and energy-intensive. Acquisition of 3D data has been accelerated by developments in serial sectioning instrument capabilities; however, for crystallographic information, the electron backscatter diffraction (EBSD) imaging modality remains rate limiting. We propose a physics-base

Kathy Reid, Elizabeth T. Williams

Voice-enabled technology is quickly becoming ubiquitous, and is constituted from machine learning (ML)-enabled components such as speech recognition and voice activity detection. However, these systems don't yet work well for everyone. They exhibit bias - the systematic and unfair discrimination against individuals or cohorts of individuals in favour of othe

Junjiao Tian, Xiaoliang Dai, Chih-Yao Ma, Zecheng He

Recent studies on transfer learning have shown that selectively fine-tuning a subset of layers or customizing different learning rates for each layer can greatly improve robustness to out-of-distribution (OOD) data and retain generalization capability in the pre-trained models. However, most of these methods employ manually crafted heuristics or expensive hy

Yen Chin Ong

The generalized uncertainty principle (GUP) is a gravitational correction of Heisenberg's uncertainty principle, which allows us to probe some features of quantum gravity even without the full theory. We are used to working with metric tensors in general relativity; they are convenient to have available when we wish to calculate physical quantities like Hawk

Andrew Dalesandro

It is commonplace in helium cryogenic systems to utilize vacuum insulation to mitigate convective heat transfer to the low temperature fluids. While the insulating vacuum meaningfully improves cryogenic system thermal performance, failure of the insulating vacuum results in rapid heat transfer to the cryogenic helium often resulting in loss of fluids from ve