March 2023 arXiv papers — page 48

Elena Moltchanova, Daniel Gerhard, Rory Ellis

Growth curves are commonly used in modeling aimed at crop yield prediction. Fitting such curves often depends on availability of detailed observations, such as individual grape bunch weight or individual apple weight. However, in practice, aggregated weights (such as a bucket of grape bunches or apples) are available instead. While treating such bucket avera

Adriano Torres, Sebastian Baltes, Christoph Treude, Markus Wagner

Although information theory has found success in disciplines, the literature on its applications to software evolution is limit. We are still missing artifacts that leverage the data and tooling available to measure how the information content of a project can be a proxy for its complexity. In this work, we explore two definitions of entropy, one structural

Akio Kawauchi

Kervaire conjecture that the weight of the free product of every non-trivial group and the infinite cyclic group is not one is confirmed by confirming Conjecture Z of a knot exterior.

Ervin Moore, Ahmed Imteaj, Shabnam Rezapour, M. Hadi Amini

Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm

Jaehyun Shim, Carlos Mastalli, Thomas Corbères, Steve Tonneau

State-of-the-art approaches to footstep planning assume reduced-order dynamics when solving the combinatorial problem of selecting contact surfaces in real time. However, in exchange for computational efficiency, these approaches ignore joint torque limits and limb dynamics. In this work, we address these limitations by presenting a topology-based approach t

Yoshiyasu Ozeki

Let $K$ and $k$ be $p$-adic fields. Let $L$ be the composite field of $K$ and a certain Lubin-Tate extension over $k$ (including the case where $L=K(\mu_{p^{\infty}})$). In this paper, we show that there exists an explicitly described constant $C$, depending only on $K,k$ and an integer $g \ge 1$, which satisfies the following property: If $A_{/K}$ is a $g$-

Weide Liu, Zhonghua Wu, Yang Zhao, Yuming Fang

Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes. To overcome this limitation, the task of generalized few-shot semantic segmentation (GFSSeg) has been introduced, aiming to predict segmentation masks for both base and novel classes. However, t

Christophe Vuillot, Alessandro Ciani, Barbara M. Terhal

We formally define homological quantum rotor codes which use multiple quantum rotors to encode logical information. These codes generalize homological or CSS quantum codes for qubits or qudits, as well as linear oscillator codes which encode logical oscillators. Unlike for qubits or oscillators, homological quantum rotor codes allow one to encode both logica

Shan Chen, Marco Guevara, Nicolas Ramirez, Arpi Murray

Radiotherapy (RT) toxicities can impair survival and quality-of-life, yet remain under-studied. Real-world evidence holds potential to improve our understanding of toxicities, but toxicity information is often only in clinical notes. We developed natural language processing (NLP) models to identify the presence and severity of esophagitis from notes of patie

Yung Kim, Hee Chul Park, Minwook Kyung, Kyungmin Lee

Line excitations in topological phases are a subject of particular interest because their mutual linking structures encode robust topological information of matter. It has been recently shown that the linking and winding of complex eigenenergy strings can classify one-dimensional non-Hermitian topological matter. However, in higher dimensions, bundles of lin

Hui Duan, Giray Ökten

We introduce a new Shapley value approach for global sensitivity analysis and machine learning explainability. The method is based on the first-order partial derivatives of the underlying function. The computational complexity of the method is linear in dimension (number of features), as opposed to the exponential complexity of other Shapley value approaches

Yuli Wang

Multiple application-specific integrated circuits (ASIC) are required for the detectors if their readout channels are larger than that of ASIC channels. For a system with such a readout scheme, there is a need to configure channels among ASICs to achieve the lowest electronics noise and highest count rate. In this work, experiments were performed to investig

S. Y. Haffert, J. R. Males, K. Ahn, K. Van Gorkom

Direct imaging of Earth-like planets is one of the main science cases for the next generation of extremely large telescopes. This is very challenging due to the star-planet contrast that must be overcome. Most current high-contrast imaging instruments are limited in sensitivity at small angular separations due to non-common path aberrations (NCPA). The NCPA

Benjamin M. Coughenour, John A. Tomsick, Guglielmo Mastroserio, James M. Steiner

The transient black hole X-ray binary MAXI J1803-298 was discovered on 2021 May 1, as it went into outburst from a quiescent state. As the source rose in flux it showed periodic absorption dips and fit the timing and spectral characteristics of a hard state accreting black hole. We report on the results of a Target-of-Opportunity observation with NuSTAR obta

Katsuki Aoki, Shinji Tsujikawa

We study vector-tensor theories in which a 4-dimensional vector field $A_{\mu}$ is coupled to a vector quantity ${\cal J}^{\mu}$, which is expressed in terms of $A_{\mu}$ and a metric tensor $g_{\mu \nu}$. The divergence of ${\cal J}^{\mu}$ is equivalent to a Gauss-Bonnet (GB) term. We show that an interacting Lagrangian of the form $f(X)A_{\mu}{\cal J}^{\mu

Zhengxuan Wu, Christopher D. Manning, Christopher Potts

Compositional generalization benchmarks for semantic parsing seek to assess whether models can accurately compute meanings for novel sentences, but operationalize this in terms of logical form (LF) prediction. This raises the concern that semantically irrelevant details of the chosen LFs could shape model performance. We argue that this concern is realized f

Chen Wang, Dasong Gao, Kuan Xu, Junyi Geng

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and reliance on manual parametric t

Diego Catalano Ferraioli, Tarcísio Castro Silva

Third order equations, which describe spherical surfaces (ss) or pseudospherical surfaces (pss), of the form \[ \nu\,z_{t}-\lambda\,z_{xxt}=A(z,z_{x},z_{xx})\,z_{xxx}+B(z,z_{x},z_{xx}) \] with $\nu$, $\lambda$ $\in$ $\mathbb{R}$, $\nu^2+\lambda^2\neq 0$, are considered. These equations are equivalent to the structure equations of a metric with Gaussian curva

Troy Luhman, Eric Luhman

We present fast, realistic image generation on high-resolution, multimodal datasets using hierarchical variational autoencoders (VAEs) trained on a deterministic autoencoder's latent space. In this two-stage setup, the autoencoder compresses the image into its semantic features, which are then modeled with a deep VAE. With this method, the VAE avoids modelin

Huajie Chen, Tianqing Zhu, Yuan Zhao, Bo Liu

Image deep steganography (IDS) is a technique that utilizes deep learning to embed a secret image invisibly into a cover image to generate a container image. However, the container images generated by convolutional neural networks (CNNs) are vulnerable to attacks that distort their high-frequency components. To address this problem, we propose a novel method

Ruqing Xu, Sarah Dean

Algorithms are used to aid human decision makers by making predictions and recommending decisions. Currently, these algorithms are trained to optimize prediction accuracy. What if they were optimized to control final decisions? In this paper, we study a decision-aid algorithm that learns about the human decision maker and provides ''personalized recommendati

Andrés Argandoña

In this thesis we explore general aspects of the entanglement entropy (EE) for Conformal Field Theories (CFTs) dual to Cubic Curvature Gravity. We derived a covariant expression for the EE by using a scheme inherited from the bulk renormalization method through extrinsic counterterms. We evaluate this functional in different entangling regions to calculate C

Duff Neill, Aditya Pathak, Iain Stewart

We derive a factorization theorem that allows for resummation of small-$x$ logarithms by exploiting Glauber operators in the soft collinear effective field theory. Our analysis is carried out for the hadronic tensor $W^{\mu\nu}$ in deep inelastic scattering, and leads to the definition of a new gauge invariant soft function $S^{\mu\nu}$ that describes quark

Peter Borg

Given a set $\mathcal{F}$ of graphs, we call a copy of a graph in $\mathcal{F}$ an $\mathcal{F}$-graph. The $\mathcal{F}$-isolation number of a graph $G$, denoted by $\iota(G,\mathcal{F})$, is the size of a smallest set $D$ of vertices of $G$ such that the closed neighbourhood of $D$ intersects the vertex sets of the $\mathcal{F}$-graphs contained by $G$ (eq

C. Kvande, C. Feng, F. Hébert, G. G. Batrouni

The Holstein Hamiltonian describes itinerant electrons whose site density couples to local phonon degrees of freedom. In the single site limit, at half-filling, the electron-phonon coupling results in a double well structure for the lattice displacement, favoring empty or doubly occupied sites. In two dimensions, and on a bipartite lattice in $d \geq 2$, an

Wai H. Oo, Mehmet Z. Baykara, Hongyu Gao

We present a computational study of sliding between gold clusters and a highly oriented pyrolytic graphite substrate, a material system that exhibits ultra-low friction due to structural lubricity. By means of molecular dynamics, it is found that clusters may undergo spontaneous rotations during manipulation as a result of elastic instability, leading to att

Guo-yuan Huang, Manfred Lindner, Nele Volmer

We infer the ultrahigh energy neutrino source by using the Glashow resonance candidate event recently identified by the IceCube Observatory. For the calculation of the cross section for the Glashow resonance, we incorporate both the atomic Doppler broadening effect and initial state radiation $\overline{\nu}^{}_{e} e^- \to W^- \gamma$, which correct the orig

Masud Mansuripur, Ewan M. Wright

A lossless beam-splitter has certain (complex-valued) probability amplitudes for sending an incoming photon into one of two possible directions. We use elementary laws of classical and quantum optics to obtain general relations among the magnitudes and phases of these probability amplitudes. Proceeding to examine a pair of (nearly) single-mode wavepackets in

Shuo Wang, Ze-Nan Wu, Jing-Zhi Fang, Zhongming Wei

The recently discovered kagome superconductors offer a promising platform for investigating intertwined orders and novel states, including topology, superconductivity, charge density waves, and more. The interplay among these orders can spontaneously break rotational symmetry, giving rise to exotic phenomena such as nematicity or even nematic superconductivi

Bram Wallace, Akash Gokul, Stefano Ermon, Nikhil Naik

Classifier guidance -- using the gradients of an image classifier to steer the generations of a diffusion model -- has the potential to dramatically expand the creative control over image generation and editing. However, currently classifier guidance requires either training new noise-aware models to obtain accurate gradients or using a one-step denoising ap

Seungjun Ahn, Somnath Datta

A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to t

Tomohiro Fukaya, Takumi Matsuka

The first author and Oguni introduced a wide class of metric spaces, called coarsely convex spaces. It includes Gromov hyperbolic metric spaces, CAT(0) spaces, systolic complexes, proper injective metric spaces. We introduce the notion of free products of metric spaces and show that free products of symmetric geodesic coarsely convex spaces are also symmetri

Lisa Y. W. Tang

Ground-glass opacity is a hallmark of numerous lung diseases, including patients with COVID19 and pneumonia, pulmonary fibrosis, and tuberculosis. This brief note presents experimental results of a proof-of-concept framework that got implemented and tested over three days as driven by the third challenge entitled "COVID-19 Competition", hosted at the AI-Enab

Yihong Xu, Chad W. Farris, Stephan W. Anderson, Xin Zhang

A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations such as parallel imaging, compressed sensing, and deep learning-based reconstruction. Here, we propose and demonstrate a Bayesian method to build statistical libraries of magnetic res

Srijan Bharati Das, Samarth G. Kashyap, Deniz Oktay, Shravan M. Hanasoge

The observed solar oscillation spectrum is influenced by internal perturbations such as flows and structural asphericities. These features induce splitting of characteristic frequencies and distort the resonant-mode eigenfunctions. Global axisymmertric flow -- differential rotation -- is a very prominent perturbation. Tightly constrained rotation profiles as

Jennifer Bergner, Darryl Seligman

In 2017, 1I/`Oumuamua was identified as the first known interstellar object in the Solar System. Although typical cometary activity tracers were not detected, `Oumuamua exhibited a significant non-gravitational acceleration. To date there is no explanation that can reconcile these constraints. Due to energetic considerations, outgassing of hyper-volatile mol

Haoze Wu, Min Wu, Dorsa Sadigh, Clark Barrett

Piecewise-affine (PWA) systems are widely used for modeling and control of robotics problems including modeling contact dynamics. A common approach is to encode the control problem of the PWA system as a Mixed-Integer Convex Program (MICP), which can be solved by general-purpose off-the-shelf MICP solvers. To mitigate the scalability challenge of solving the

Muhammad Asad, Helena Williams, Indrajeet Mandal, Sarim Ather

Existing interactive segmentation methods leverage automatic segmentation and user interactions for label refinement, significantly reducing the annotation workload compared to manual annotation. However, these methods lack quick adaptability to ambiguous and noisy data, which is a challenge in CT volumes containing lung lesions from COVID-19 patients. In th

Christian Gaetz, Yibo Gao

For $w$ in the symmetric group, we provide an exact formula for the smallest positive power $q^{h(w)}$ appearing in the Kazhdan-Lusztig polynomial $P_{e,w}(q)$. We also provide a tight upper bound on $h(w)$ in simply-laced types, resolving a conjecture of Billey-Postnikov from 2002.

Tomáš Nagy, Ahmad Amine, Truong X. Nghiem, Ugo Rosolia

Driving under varying road conditions is challenging, especially for autonomous vehicles that must adapt in real-time to changes in the environment, e.g., rain, snow, etc. It is difficult to apply offline learning-based methods in these time-varying settings, as the controller should be trained on datasets representing all conditions it might encounter in th

Martin Costabel

For a singular integral equation on an interval of the real line, we study the behavior of the error of a delta-delta discretization. We show that the convergence is non-uniform, between order $O(h^{2})$ in the interior of the interval and a boundary layer where the consistency error does not tend to zero.

Anupam Bhardwaj, Earl P. Bellinger, Shashi M. Kanbur, Marcella Marconi

We present a machine learning method to estimate the physical parameters of classical pulsating stars such as RR Lyrae and Cepheid variables based on an automated comparison of their theoretical and observed light curve parameters at multiple wavelengths. We train artificial neural networks (ANNs) on theoretical pulsation models to predict the fundamental pa

E. Paxon Frady, Spencer Kent, Quinn Tran, Pentti Kanerva

Complex visual scenes that are composed of multiple objects, each with attributes, such as object name, location, pose, color, etc., are challenging to describe in order to train neural networks. Usually,deep learning networks are trained supervised by categorical scene descriptions. The common categorical description of a scene contains the names of individ

María del Pilar García-Chitiva, Juan C. Correa

Is it possible to measure how critical soft skills like leadership or teamwork are from the viewpoint of graduate studies offerings? This paper provides a conceptual and methodological framework that introduces the concept of a bipartite network as a practical way to estimate the importance of soft skills as socio-emotional abilities trained in graduate stud

Christopher S. Campbell, Mirza Akhter, Samuel Clark, Kamel Fezzaa

The first-known observation of plasma-induced cavitation bubbles and expanding shockwaves in liquid during plasma initiation timescales reveals deviation from expected Rankine-Hugoniot shock behavior due to coupled shock-cavitation dynamics, imaged using megahertz-framerate picosecond X-ray imaging. The imaging target features an inexpensive benchtop-scale p

Nick Indriolo

Supernova remnants act as particle accelerators, providing the cosmic-ray protons that permeate the interstellar medium and initiate the ion-molecule reactions that drive interstellar chemistry. Enhanced fluxes of cosmic-ray protons in close proximity to supernova remnants have been inferred from observations tracing particle interactions with nearby molecul

Mahsa Sanei, Solomon Atcitty, Fernando Moreu

Sensors have the capability of collecting engineering data and quantifying environmental changes, activities, or phenomena. Civil engineers lack of knowledge in sensor technology. Therefore, the vision of smart cities equipped with sensors informing decisions has not been realized to date. The cost associated with data acquisition systems, laboratories, and

Lars Winther Christensen, Orin Gotchey, Alexis Hardesty

Ideals in the ring of power series in three variables can be classified based on algebra structures on their minimal free resolutions. The classification is incomplete in the sense that it remains open which algebra structures actually occur; this realizability question was formally raised by Avramov in 2012. We discuss the outcomes of an experiment performe

Dun Yuan, Yujin Nam, Amal Feriani, Abhisek Konar

Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G systems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulatio

Khandokar Md. Nayem, Donald S. Williamson

Perceptually-inspired objective functions such as the perceptual evaluation of speech quality (PESQ), signal-to-distortion ratio (SDR), and short-time objective intelligibility (STOI), have recently been used to optimize performance of deep-learning-based speech enhancement algorithms. These objective functions, however, do not always strongly correlate with

Karoline Brehm, Yan Shvartzshnaider, David Goedicke

The multicontextual nature of immersive VR makes it difficult to ensure contextual integrity of VR-generated information flows using existing privacy design and policy mechanisms. In this position paper, we call on the HCI community to do away with lengthy disclosures and permissions models and move towards embracing privacy mechanisms rooted in Contextual I

Rafael C. Ito, Fernando J. Von Zuben

Once-for-All (OFA) is a Neural Architecture Search (NAS) framework designed to address the problem of searching efficient architectures for devices with different resources constraints by decoupling the training and the searching stages. The computationally expensive process of training the OFA neural network is done only once, and then it is possible to per

Mateusz Borkowski, Lukas Reichsöllner, Premjith Thekkeppatt, Vincent Barbé

Feshbach association of ultracold molecules using narrow resonances requires exquisite control of the applied magnetic field. Here we present a magnetic field control system to deliver magnetic fields of over 1000 G with ppm-level precision integrated into an ultracold-atom experimental setup. We combine a battery-powered current-stabilized power supply with

Gary Lvov, Mark Zolotas, Nathaniel Hanson, Austin Allison

Motion capture through tracking retroreflectors obtains highly accurate pose estimation, which is frequently used in robotics. Unlike commercial motion capture systems, fiducial marker-based tracking methods, such as AprilTags, can perform relative localization without requiring a static camera setup. However, popular pose estimation methods based on fiducia

Howard S. Cohl, Roberto S. Costas-Santos

We study special values for the continuous $q$-Jacobi polynomials and present applications of these special values which arise from bilinear generating functions, and in particular the Poisson kernel for these polynomials.

Mengxin Zheng, Qian Lou, Lei Jiang

It is increasingly important to enable privacy-preserving inference for cloud services based on Transformers. Post-quantum cryptographic techniques, e.g., fully homomorphic encryption (FHE), and multi-party computation (MPC), are popular methods to support private Transformer inference. However, existing works still suffer from prohibitively computational an

Andrew Wilkinson, Marc Pradas, Michael Wilkinson

If a flat, horizontal, plate settles onto a flat surface, it is known that the gap $h$ decreases with time $t$ as a power-law: $h\sim t^{-1/2}$. We consider what happens if the plate is not initially horizontal, and/or the centre of mass is not symmetrically positioned: does one edge contact the surface in finite time, or does the plate approach the horizont

Joseph Cho, Dami Lee, Wonjoo Lee, Seong-Deog Yang

We give a detailed description of the geometry of isotropic space, in parallel to those of Euclidean space within the realm of Laguerre geometry. After developing basic surface theory in isotropic space, we define spin transformations, directly leading to the spinor representation of conformal surfaces in isotropic space. As an application, we obtain the Wei

I. Pessa, E. Schinnerer, P. Sanchez-Blazquez, F. Belfiore

Analyzing resolved stellar populations across the disk of a galaxy can provide unique insights into how that galaxy assembled its stellar mass over its lifetime. Previous work at ~1 kpc resolution has already revealed common features in the mass buildup (e.g., inside-out growth of galaxies). However, even at approximate kpc scales, the stellar populations ar

Andrew Halterman

Mordecai3 is a new end-to-end text geoparser and event geolocation system. The system performs toponym resolution using a new neural ranking model to resolve a place name extracted from a document to its entry in the Geonames gazetteer. It also performs event geocoding, the process of linking events reported in text with the place names where they are report

Daniel Turyansky, Oded Ovdat, Roie Dann, Ziv Aqua

We present rapid and robust protocols for STIRAP and quantum logic gates. Our gates are based on geometric phases acquired by instantaneous eigenstates of a slowly accelerating inertial Hamiltonian. To begin, we establish the criteria for inertial evolution and subsequently engineer pulse shapes that fulfill these conditions. These tailored pulses are then u

Nasrin Altafi

We discuss whether the Jordan degree type encodes \break more information about graded artinian Gorenstein algebras than the Jordan type for linear forms. We show that in codimension two, the Jordan type determines the Jordan degree type. We provide examples showing that this is no longer the case in higher codimensions.

Connor N. Mallon, Aaron W. Thornton, Matthew R. Hill, Santiago Badia

To facilitate widespread adoption of automated engineering design techniques, existing methods must become more efficient and generalizable. In the field of topology optimization, this requires the coupling of modern optimization methods with solvers capable of handling arbitrary problems. In this work, a topology optimization method for general multiphysics

Mathijs Janssen, Taras Verkholyak, Andrij Kuzmak, Svyatoslav Kondrat

Innovative ways of harnessing sustainable energy are needed to meet the world's ever-increasing energy demands. Supercapacitors may contribute, as they can convert waste heat to electricity through cyclic charging and discharging at different temperatures. Herein, we use an analytically-solvable model of a cylindrical pore filled with a single file of ions t

Luca Bastioni, Giacomo Micheli

Let $m$ be a positive integer and $q$ be a prime power. For large finite base fields $\mathbb F_q$, we show that any curve can be used to produce a complete $m$-arc as long as some generic explicit geometric conditions on the curve are verified. To show the effectiveness of our theory, we derive complete $m$-arcs from hyperelliptic curves and from Artin-Schr

Kate Schneider, Jessica Fanzo, Lawrence Haddad, Mario Herrero

Transforming food systems is essential to bring about a healthier, equitable, sustainable, and resilient future, including achieving global development and sustainability goals. To date, no comprehensive framework exists to track food systems transformation and their contributions to global goals. In 2021, the Food Systems Countdown to 2030 Initiative (FSCI)

K. Deja, V. Martinez-Fernandez, B. Pire, P. Sznajder

We revisit the phenomenology of the deep exclusive electroproduction of a lepton pair, i.e. double deeply virtual Compton scattering (DDVCS), in view of new experiments planned in the near future. The importance of DDVCS in the reconstruction of generalized parton distributions (GPDs) in their full kinematic domain is emphasized. Using Kleiss-Stirling spinor

Sandro Dias Pinto Vitenti, Eduardo J. Barroso

This paper proposes a novel approach to generate samples from target distributions that are difficult to sample from using Markov Chain Monte Carlo (MCMC) methods. Traditional MCMC algorithms often face slow convergence due to the difficulty in finding proposals that suit the problem at hand. To address this issue, the paper introduces the Approximate Poster

Víctor Jaramillo, Daniel Martínez-Carbajal, Juan Carlos Degollado, Darío Núñez

We study the Einstein-Klein-Gordon system coupled to the Born-Infeld electrodynamics. We explore the solution space of a static spherically symmetric, complex scalar field minimally coupled to both gravitational and electromagnetic fields. The resulting asymptotically flat solutions resemble the known charged boson stars in Maxwell electrodynamics. The behav

Zahra Moslehi, Abdolreza Mirzaei, Mehran Safayani

Creating low dimensional representations of a high dimensional data set is an important component in many machine learning applications. How to cluster data using their low dimensional embedded space is still a challenging problem in machine learning. In this article, we focus on proposing a joint formulation for both clustering and dimensionality reduction.

Anna Kukleva, Moritz Böhle, Bernt Schiele, Hilde Kuehne

Most approaches for self-supervised learning (SSL) are optimised on curated balanced datasets, e.g. ImageNet, despite the fact that natural data usually exhibits long-tail distributions. In this paper, we analyse the behaviour of one of the most popular variants of SSL, i.e. contrastive methods, on long-tail data. In particular, we investigate the role of th

The IceCube Collaboration, R. Abbasi, M. Ackermann, J. Adams

Dark Matter particles in the Galactic Center and halo can annihilate or decay into a pair of neutrinos producing a monochromatic flux of neutrinos. The spectral feature of this signal is unique and it is not expected from any astrophysical production mechanism. Its observation would constitute a dark matter smoking gun signal. We performed the first dedicate

Yiyou Sun, Yaojie Liu, Xiaoming Liu, Yixuan Li

This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations. Most prior works regard domain-specific signals as a negative impact, and apply metric learning or adversarial losses to remove them from feature representation. Though learning a domain-invariant feature s

Jonas R. F. Lima, Guido Burkard

The performance and scalability of silicon spin qubits depend directly on the value of the conduction band valley splitting. In this work, we investigate the influence of electromagnetic fields and the interface width on the valley splitting of a quantum dot in a Si/SiGe heterostructure. We propose a new three-dimensional theoretical model within the effecti

David Radke, Alexi Orchard

This paper draws correlations between several challenges and opportunities within the area of team sports analytics and key research areas within multiagent systems (MAS). We specifically consider invasion games, defined as sports where players invade the opposing team's territory and can interact anywhere on a playing surface such as ice hockey, soccer, and

Javier A. Diez, Alejandro G. González, Lou Kondic

We study the instability of a thin film composed of two miscible fluids (binary fluid) placed on a solid planar surface. We include the fact that both the free surface and wetting energies depend on the mixture concentration. By assuming a linear relationship between these energies and both the bulk and surface concentrations, we analyze their effect on the

Zifan Wang, Yulong Gao, Siyi Wang, Michael M. Zavlanos

Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard RL. At the same time, a main challenge in DRL is that policy evaluation in DRL typically relies on the representation of the retur

Hidenobu Matsuki, Keisuke Tateno, Michael Niemeyer, Federico Tombari

Neural field-based 3D representations have recently been adopted in many areas including SLAM systems. Current neural SLAM or online mapping systems lead to impressive results in the presence of simple captures, but they rely on a world-centric map representation as only a single neural field model is used. To define such a world-centric representation, accu

Monu Verma, Murari Mandal, Satish Kumar Reddy, Yashwanth Reddy Meedimale

Automated human emotion recognition from facial expressions is a well-studied problem and still remains a very challenging task. Some efficient or accurate deep learning models have been presented in the literature. However, it is quite difficult to design a model that is both efficient and accurate at the same time. Moreover, identifying the minute feature

Gyeongsik Moon

Despite recent achievements, existing 3D interacting hands recovery methods have shown results mainly on motion capture (MoCap) environments, not on in-the-wild (ITW) ones. This is because collecting 3D interacting hands data in the wild is extremely challenging, even for the 2D data. We present InterWild, which brings MoCap and ITW samples to shared domains

Daniel Hernangómez-Pérez, Amir Kleiner, Sivan Refaely-Abramson

Associating the presence of atomic vacancies to excited-state transport phenomena in two dimensional semiconductors is of emerging interest, and demands detailed understanding of the involved exciton transitions. Here we study the effect of such defects on the electronic and optical properties of WS$_2$-graphene and MoS$_2$-graphene van der Waals heterobilay

Bhargav Joshi, Taihui Li, Buyun Liang, Roger Rusack

The 75,848 lead tungstate crystals in CMS experiment at the CERN Large Hadron Collider are used to measure the energy of electrons and photons produced in the proton-proton collisions. The optical transparency of the crystals degrades slowly with radiation dose due to the beam-beam collisions. The transparency of each crystal is monitored with a laser monito

João Vitorino, Lourenço Rodrigues, Eva Maia, Isabel Praça

Drowsy driving is a major cause of road accidents, but drivers are dismissive of the impact that fatigue can have on their reaction times. To detect drowsiness before any impairment occurs, a promising strategy is using Machine Learning (ML) to monitor Heart Rate Variability (HRV) signals. This work presents multiple experiments with different HRV time windo

Balthazar Charles

In this paper we present an algorithm for efficiently counting fixed points in a finite monoid $M$ under a conjugacy-like action. We then prove a formula for the character table of $M$ in terms of fixed points and radical, which allows for the effective computation of the character table of $M$ over a field of null characteristic, as well as its Cartan matri

Desmond Coles, Netanel Friedenberg

Let $X$ be a variety over a complete nontrivially valued field $K$. We construct an algebraizable formal model for the analytification of $X$ in the case $X$ admits a closed embedding into a toric variety. By algebraizable we mean that the formal model is given by the completion along the special fiber of a locally finite type flat scheme over the valuation

Atanu Bhunia, Indranil Biswas, Indrani Chattopadhyay, Debasis Sarkar

Classical communication plays a crucial role to distinguish locally a class of quantum states. Despite considerable advances, we have very little knowledge about the number of measurement and communication rounds needed to implement a discrimination task by local quantum operations and classical communications (in short, LOCC). In this letter, we are able to

Alessandra De Luca, Raffaele Folino, Marta Strani

In this paper we deal with a reaction-diffusion equation in a bounded interval of the real line with a nonlinear diffusion of Perona-Malik's type and a balanced bistable reaction term. Under very general assumptions, we study the persistence of layered solutions, showing that it strongly depends on the behavior of the reaction term close to the stable equili

Xiaoyong Chu, Junji Hisano, Alejandro Ibarra, Jui-Lin Kuo

We consider electrically neutral complex vector particles $V$ below the GeV mass scale that, from a low energy perspective, couple to the photon via higher dimensional form factor interactions. We derive ensuing astrophysical constraints by considering the anomalous energy loss from the Sun, Horizontal Branch, and Red Giant stars as well as from SN1987A that

Andrew F. Magee, Andrew J. Holbrook, Jonathan E. Pekar, Itzue W. Caviedes-Solis

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dyn

Daniel Hickey, Matheus Schmitz, Daniel Fessler, Paul Smaldino

While online hate groups pose significant risks to the health of online platforms and safety of marginalized groups, little is known about what causes users to become active in hate groups and the effect of social interactions on furthering their engagement. We address this gap by first developing tools to find hate communities within Reddit, and then augmen

Ben Y. Israeli, Amitava Bhattacharjee, Hong Qin

Dusty plasmas are known to support a diverse range of instabilities, including both generalizations of standard plasma instabilities and ones caused by effects specific to dusty systems. It has been recently demonstrated that a novel broad class of streaming instabilities, termed resonant drag instabilities (RDIs), can be attributed to a particular resonance

Judith Zimmermann, Kathrin Bäumler, Michael Loecher, Tyler E. Cork

Understanding the complex interplay between morphologic and hemodynamic features in aortic dissection is critical for risk stratification and for the development of individualized therapy. This work evaluates the effects of entry and exit tear size on the hemodynamics in type B aortic dissection by comparing fluid-structure interaction (FSI) simulations with

Mikhail Soutchanski, Ryan Young

Planning as theorem proving in situation calculus was abandoned 50 years ago as an impossible project. But we have developed a Theorem Proving Lifted Heuristic (TPLH) planner that searches for a plan in a tree of situations using the A* search algorithm. It is controlled by a delete relaxation-based domain independent heuristic. We compare TPLH with Fast Dow

Yuntong Zhang, Jingye Xu, Mimi Xie, Dakai Zhu

Heart Rate Variability (HRV) measures the variation of the time between consecutive heartbeats and is a major indicator of physical and mental health. Recent research has demonstrated that photoplethysmography (PPG) sensors can be used to infer HRV. However, many prior studies had high errors because they only employed signal processing or machine learning (

Yuntong Zhang, Jingye Xu, Mimi Xie, Wei Wang

Recent studies showed that Photoplethysmography (PPG) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy. However, despite of prior research efforts, applying PPG sensor based HR estimation to embedded devices still faces challenges due to the energy-intensive high-frequency PPG sampling and the resource-intensive machine-le

Pengzi Miao, Annachiara Piubello

Given a metric $\gamma$ of nonnegative Gauss curvature and a positive function $H$ on a $2$-sphere $\Sigma$, we estimate the Bartnik quasi-local mass of $(\Sigma, \gamma, H)$ in terms of the area, the total mean curvature, and a quantity depending only on $\gamma$, measuring the roundness of the metric. If $\gamma$ has positive Gauss curvature, the roundness

Xin Wu, Tobias Kenter, Robert Schade, Thomas D. Kühne

The computation of electron repulsion integrals (ERIs) over Gaussian-type orbitals (GTOs) is a challenging problem in quantum-mechanics-based atomistic simulations. In practical simulations, several trillions of ERIs may have to be computed for every time step. In this work, we investigate FPGAs as accelerators for the ERI computation. We use template parame

Jeeseop Kim, Jaemin Lee, Aaron D. Ames

This paper presents a safety-critical approach to the coordinated control of cooperative robots locomoting in the presence of fixed (holonomic) constraints. To this end, we leverage control barrier functions (CBFs) to ensure the safe cooperation of the robots while maintaining a desired formation and avoiding obstacles. The top-level planner generates a set

Haoshuo Chen, Xiaonan Xu, Jesse E. Simsarian, Mijail Szczerban

We demonstrate the digital twin of a network, network elements, and operating environment using machine learning. We achieve network card failure localization and remote collaboration over 86 km of fiber using augmented reality.

Helge Kristian Jenssen

Helly's selection theorem provides a criterion for compactness of sets of single-variable functions with bounded pointwise variation. Fra{\v{n}}kov{\'a} has given a proper extension of Helly's theorem to the setting of single-variable regulated functions. We show how a similar approach yields extensions of the standard compactness criterion for multi-variabl