October 2022 arXiv papers — page 57

Sumedha Singla, Nihal Murali, Forough Arabshahi, Sofia Triantafyllou

A highly accurate but overconfident model is ill-suited for deployment in critical applications such as healthcare and autonomous driving. The classification outcome should reflect a high uncertainty on ambiguous in-distribution samples that lie close to the decision boundary. The model should also refrain from making overconfident decisions on samples that

Giorgio Giannone, Serhii Havrylov, Jordan Massiah, Emine Yilmaz

Advances in deep learning theory have revealed how average generalization relies on superficial patterns in data. The consequences are brittle models with poor performance with shift in group distribution at test time. When group annotation is available, we can use robust optimization tools to tackle the problem. However, identification and annotation are ti

Andreas Søgaard, Rasmus F. Ørsøe, Leon Bozianu, Morten Holm

GraphNeT is an open-source python framework aimed at providing high quality, user friendly, end-to-end functionality to perform reconstruction tasks at neutrino telescopes using graph neural networks (GNNs). GraphNeT makes it fast and easy to train complex models that can provide event reconstruction with state-of-the-art performance, for arbitrary detector

Jan Hohenheim, Zhaoyu Devon Liu, Tommaso Stecconi, Pietro Palopoli

We present two designs for an analog circuit that can learn to detect a temporal sequence of two inputs. The training phase is done by feeding the circuit with the desired sequence and, after the training is completed, each time the trained sequence is encountered again the circuit will emit a signal of correct recognition. Sequences are in the order of tens

Max W. Shen, Ehsan Hajiramezanali, Gabriele Scalia, Alex Tseng

How much explicit guidance is necessary for conditional diffusion? We consider the problem of conditional sampling using an unconditional diffusion model and limited explicit guidance (e.g., a noised classifier, or a conditional diffusion model) that is restricted to a small number of time steps. We explore a model predictive control (MPC)-like approach to a

Rui Huang, Vincent W. S. Wong, Robert Schober

The growing demand for virtual reality (VR) applications requires wireless systems to provide a high transmission rate to support 360-degree video streaming to multiple users simultaneously. In this paper, we propose an intelligent reflecting surface (IRS)-aided rate-splitting (RS) VR streaming system. In the proposed system, RS facilitates the exploitation

Christina Karafyllia

Let $D$ be a domain in the complex plane $\mathbb{C}$. The Hardy number of $D$, which first introduced by Hansen, is the maximal number $h(D)$ in $[0,+\infty]$ such that $f$ belongs to the classical Hardy space $H^p (\mathbb{D})$ whenever $0<p<h(D)$ and $f$ is holomorphic on the unit disk $\mathbb{D}$ with values in $D$. As an analogue notion to the Hardy nu

A. Weiland, F. B. Santos, J. D. Thompson, E. D. Bauer

$\require{mediawiki-texvc}$The locally non-centrosymmetric superconductor SrPtAs is proposed to host a topological chiral $d$-wave state, but experimental reports have been limited to polycrystalline samples. Here we report the synthesis of single crystalline SrPtAs grown from Pb flux. SrPtAs crystallizes in the hexagonal space group $P6_{3}$/$mmc$ with latt

Atul C. Thakur, Richard C. Remsing

The Saturnian moon Titan has a thick, organic-rich atmosphere, and condensed phases of small organic molecules are anticipated to be stable on its surface. Of particular importance are crystalline phases of organics, known as cryominerals, which can play important roles in surface chemistry and geological processes on Titan. Many of these cryominerals could

Suhas Arehalli, Brian Dillon, Tal Linzen

Humans exhibit garden path effects: When reading sentences that are temporarily structurally ambiguous, they slow down when the structure is disambiguated in favor of the less preferred alternative. Surprisal theory (Hale, 2001; Levy, 2008), a prominent explanation of this finding, proposes that these slowdowns are due to the unpredictability of each of the

Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai

Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors. These systems have achieved promising performance as measured by widely used NLG metrics such as BLEU and CIDEr. However, the current systems face important

Jason Stock, Chuck Anderson

Multi-channel satellite imagery, from stacked spectral bands or spatiotemporal data, have meaningful representations for various atmospheric properties. Combining these features in an effective manner to create a performant and trustworthy model is of utmost importance to forecasters. Neural networks show promise, yet suffer from unintuitive computations, fu

Oyebade K. Oyedotun, Konstantinos Papadopoulos, Djamila Aouada

Deep neural networks (DNNs) are typically optimized using various forms of mini-batch gradient descent algorithm. A major motivation for mini-batch gradient descent is that with a suitably chosen batch size, available computing resources can be optimally utilized (including parallelization) for fast model training. However, many works report the progressive

Atul Kumar Shriwastva, R. S. Selvaraj

Given $[n]=\{1,2,\ldots,n\}$, a partial order $\preceq$ on $[n]$, a label map $\pi : [n] \rightarrow \mathbb{N}$ defined by $\pi(i) = k_i$ with $\sum_{i=1}^{n}\pi (i) = N$, the direct sum $ \mathbb{F}_{q}^{k_1} \oplus \mathbb{F}_{q}^{k_2}\oplus \ldots \oplus \mathbb{F}_{q}^{k_n} $ of $ \mathbb{F}_q^N $, and a weight function $w$ on $ \mathbb{F}_q $, we defin

Irene Cavallari, Giuseppe Pucacco

We propose a methodology to study the bifurcation sequences of frozen orbits when the 2nd-order fundamental model of the satellite problem is augmented with the contribution of octupolar terms and relativistic corrections. The method is based on the analysis of twice-reduced closed normal forms expressed in terms of suitable combinations of the invariants of

Weizi Li

The future of the human race is urban. The world's population is projected to grow an additional 2.5 billion by 2050, with all expected to live in urban areas. This will increase the percentage of urban population from 55% today to 70% within three decades and further strengthen the role of cities as the hub for information, transportation, and overall socio

Gabriela P. Ovando, Mauro Subils

The aim of this work is the study of left-invariant magnetic fields on 2-step nilpotent Lie groups. While the existence of closed 2-forms for which the center is either nondegenerate or in the kernel of the 2-form, is always guaranteed, the existence of closed 2-forms for which the center is isotropic but not in the kernel of the 2-form, is a special situati

Ren Pang, Changjiang Li, Zhaohan Xi, Shouling Ji

This paper asks the intriguing question: is it possible to exploit neural architecture search (NAS) as a new attack vector to launch previously improbable attacks? Specifically, we present EVAS, a new attack that leverages NAS to find neural architectures with inherent backdoors and exploits such vulnerability using input-aware triggers. Compared with existi

Zuxuan Ou, Yucheng Wang, Linhu Li

Spectral winding of complex eigenenergies represents a topological aspect unique in non-Hermitian systems, which vanishes in one-dimensional (1D) systems under the open boundary conditions (OBC). In this work, we discover a boundary spectral winding in two-dimensional non-Hermitian systems under the OBC, originating from the interplay between Hermitian bound

A. Mavi, A. C. Bekar, E. Haghighat, E. Madenci

This study presents a novel unsupervised convolutional Neural Network (NN) architecture with nonlocal interactions for solving Partial Differential Equations (PDEs). The nonlocal Peridynamic Differential Operator (PDDO) is employed as a convolutional filter for evaluating derivatives the field variable. The NN captures the time-dynamics in smaller latent spa

Lars Aalsma, Sergio E. Aguilar-Gutierrez, Watse Sybesma

Entanglement islands play a crucial role in our understanding of how Hawking radiation encodes information in a black hole, but their relevance in cosmological spacetimes is less clear. In this paper, we continue our investigation of information recovery in de Sitter space and construct a two-dimensional model of gravity containing a domain wall that interpo

Kareem Eltouny, Seyedomid Sajedi, Xiao Liang

Visual inspection is the predominant technique for evaluating the condition of civil infrastructure. The recent advances in unmanned aerial vehicles (UAVs) and artificial intelligence have made the visual inspections faster, safer, and more reliable. Camera-equipped UAVs are becoming the new standard in the industry by collecting massive amounts of visual da

Isabela S. Matos, Emilio Bellini, Maurício O. Calvão, Martin Kunz

Gravitational waves (GWs) emitted by binary sources are interesting signals for testing gravity on cosmological scales since they allow measurements of the luminosity distance. When followed by electromagnetic counterparts, in particular, they enable a reconstruction of the GW-distance-redshift relation. In the context of several modified gravity (MG) theori

Seyedomid Sajedi, Xiao Liang

Bridges are indispensable elements in resilient communities as essential parts of the lifeline transportation systems. Knowledge about the functionality of bridge structures is crucial, especially after a major earthquake event. In this study, we propose signal processing approaches for automated AI-equipped damage detection of bridges. Mel-scaled filter ban

Ankit Beniwal

In this proceeding, we present results from a global fit of Dirac fermion dark matter (DM) effective field theory (EFT) based on arXiv:2106.02056 using the GAMBIT framework. Here we show results only for the dimension-6 operators that describe the interactions between a gauge-singlet Dirac fermion and Standard Model quarks. Our global fit combines the latest

Ankush G. Kumar, P. Vishal, V. Meenakshi, R. Aravinda Narayanan

We performed two-dimensional numerical simulations of passive stirring of two liquids which generated two spatially distinguishable paradigmatic velocity flows, viz. few large extended vortices and rapid oscillations. Using the Okubo- Weiss criterion, we mapped regions of stretching and rotation in the flow field. We find that an elementary oscillatory seque

Li Lucy, Divya Tadimeti, David Bamman

A common paradigm for identifying semantic differences across social and temporal contexts is the use of static word embeddings and their distances. In particular, past work has compared embeddings against "semantic axes" that represent two opposing concepts. We extend this paradigm to BERT embeddings, and construct contextualized axes that mitigate the pitf

Abdulrahman Aloraini, Sameer Pradhan, Massimo Poesio

Most existing proposals about anaphoric zero pronoun (AZP) resolution regard full mention coreference and AZP resolution as two independent tasks, even though the two tasks are clearly related. The main issues that need tackling to develop a joint model for zero and non-zero mentions are the difference between the two types of arguments (zero pronouns, being

Jean-Francois Pommaret

When ${\cal{D}}: E \rightarrow F$ is a linear differential operator of order $q$ between the sections of vector bundles over a manifold $X$ of dimension $n$, it is defined by a bundle map $\Phi: J_q(E) \rightarrow F=F_0$ that may depend, explicitly or implicitly, on constant parameters $a, b, c, ...$. A "direct problem " is to find the generating compatibili

R. A. J. Eyles-Ferris, R. L. C. Starling, P. T. O'Brien, C. J. Nixon

We present simulated optical light curves of super-Eddington tidal disruption events (TDEs) using the zero-Bernoulli accretion (ZEBRA) flow model, which proposes that during the super-Eddington phase, the disc is quasi-spherical, radiation-pressure dominated, and accompanied by the production of strong jets. We construct light curves for both on- and off-axi

Aaron Z. Goldberg, Aephraim M. Steinberg, Khabat Heshami

Semiclassically, laser pulses can be used to implement arbitrary transformations on atomic systems; quantum mechanically, residual atom-field entanglement spoils this promise. Transcoherent states are field states that fix this problem in the fully quantized regime by generating perfect coherence in an atom initially in its ground or excited state. We extend

Tom Peham, Lukas Burgholzer, Robert Wille

Variational quantum algorithms have been introduced as a promising class of quantum-classical hybrid algorithms that can already be used with the noisy quantum computing hardware available today by employing parameterized quantum circuits. Considering the non-trivial nature of quantum circuit compilation and the subtleties of quantum computing, it is essenti

Kyle Finner, Scott W. Randall, M. James Jee, Elizabeth L. Blanton

A dissociative merger is formed by the interplay of ram pressure and gravitational forces, which can lead to a spatial displacement of the dark matter and baryonic components of the recently collided subclusters. CIZA J0107.7+5408 is a nearby (z=0.105) dissociative merger that hosts two X-ray brightness peaks and a bimodal galaxy distribution. Analyzing MMT/

Ankita Bera, Raghunath Ghara, Atrideb Chatterjee, Kanan K. Datta

In this review article, we briefly outline our current understanding of the physics associated with the HI 21-cm signal from cosmic dawn. We discuss different phases of cosmic dawn as the ambient gas and the background radiations evolve with the redshift. We address the consequences of several possible heating sources and radiation background on the global 2

Clifford Cheung, Grant N. Remmen

String theory offers an elegant and concrete realization of how to consistently couple states of arbitrarily high spin. But how unique is this construction? In this paper we derive a novel, multi-parameter family of four-point scattering amplitudes exhibiting i) polynomially bounded high-energy behavior and ii) exchange of an infinite tower of high-spin mode

Yuh Tsunetoe, Shin Mineshige, Tomohisa Kawashima, Ken Ohsuga

Here, we demonstrate that polarization properties show a wide diversity depending on viewing angles. To simulate images of a supermassive black hole and surrounding plasma, we performed a full-polarimetric general relativistic radiative transfer based on three-dimensional general relativistic magnetohydrodynamics models with moderate magnetic strengths. Unde

Brandon Amos

This paper focuses on computing the convex conjugate (also known as the Legendre-Fenchel conjugate or c-transform) that appears in Euclidean Wasserstein-2 optimal transport. This conjugation is considered difficult to compute and in practice, methods are limited by not being able to exactly conjugate the dual potentials in continuous space. To overcome this,

Matthew Ho, Aditya Sharma, Justin Chang, Michael Saxon

As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging. Recent question answering (QA) benchmarks that attempt to assess reasoning are often limited by a narrow scope of covered situations and subject matters. We introduce WikiWhy, a QA dataset built around a nov

Kevin Slagle

Although tensor networks are powerful tools for simulating low-dimensional quantum physics, tensor network algorithms are very computationally costly in higher spatial dimensions. We introduce quantum gauge networks: a different kind of tensor network ansatz for which the computation cost of simulations does not explicitly increase for larger spatial dimensi

Maxwell P. Bobbin, Samiha Sharlin, Parivash Feyzishendi, An Hong Dang

Chemical theory can be made more rigorous using the Lean theorem prover, an interactive theorem prover for complex mathematics. We formalize the Langmuir and BET theories of adsorption, making each scientific premise clear and every step of the derivations explicit. Lean's math library, mathlib, provides formally verified theorems for infinite geometries ser

Nicusor Minculete, Diana Savin

In this article we find some properties of certain types of entropies of a natural number. Also, regarding the entropy H of a natural number, introduced by Minculete and Pozna, we generalize this notion for ideals and we find some of its properties. In the last section we find some inequalities, involving the entropy H of an exponential divisor of a positive

Laurynas Karazija, Subhabrata Choudhury, Iro Laina, Christian Rupprecht

We propose a new approach to learn to segment multiple image objects without manual supervision. The method can extract objects form still images, but uses videos for supervision. While prior works have considered motion for segmentation, a key insight is that, while motion can be used to identify objects, not all objects are necessarily in motion: the absen

Henry Maxfield, Zahra Zahraee

We study tools of the conformal bootstrap in simplifying limits, primarily a limit of large operator dimensions and small cross-ratios corresponding to non-relativistic physics in AdS. We show that T-channel conformal blocks give the classical limit of correlation functions to linear order in an interaction potential. We use the Lorentzian inversion formula

Patrick W Krantz, Venkat Chandrasekhar

Local and nonlocal differential resistance measurements on Hall bars defined in AlO$_x$/KTaO$_3$ heterostructures show anomalous behavior that depends on the crystal orientation and the applied back gate voltage. The local differential resistance is asymmetric in the dc bias current, with an antisymmetric component that grows with decreasing gate voltage. Mo

Yu-ang Fan, Yingcheng Li, Yuting Hu, Yishan Li

Topological quantum computation (TQC) is one of the most striking architectures that can realize fault-tolerant quantum computers. In TQC, the logical space and the quantum gates are topologically protected, i.e., robust against local disturbances. The topological protection, however, requires rather complicated lattice models and hard-to-manipulate dynamics

Alexis Nikolakopoulos, Raúl González-Jiménez, Natalie Jachowicz, José Manuel Udías

Pion production on nuclei constitutes a significant part of the total cross section in experiments involving few-GeV neutrinos. Combined analyses of data on deuterium and heavier nuclei points to tensions between the bubble chamber data and the data of the MINER$\nu$A experiment, which are often ascribed to unspecified nuclear effects. To understand the orig

Om Prakash Bhardwaj, Indranath Sengupta

Let $n_0, n_1, \ldots, n_p$ be a sequence of positive integers such that $n_0 < n_1 < \cdots < n_p$ and $\mathrm{gcd}(n_0,n_1, \ldots,n_p) = 1$. Let $S = \langle (0,n_p), (n_0,n_p-n_0),\ldots,(n_{p-1},n_p-n_{p-1}), (n_p,0) \rangle$ be an affine semigroup in $\mathbb{N}^2$. The semigroup ring $k[S]$ is the co-ordinate ring of the projective monomial curve in

Étienne Objois, Kaan Okumuş, Nicolas Bähler

Digital sensors can lead to noisy results under many circumstances. To be able to remove the undesired noise from images, proper noise modeling and an accurate noise parameter estimation is crucial. In this project, we use a Poisson-Gaussian noise model for the raw-images captured by the sensor, as it fits the physical characteristics of the sensor closely.

Jan Friedrich, Simone Göttlich, Alexander Keimer, Lukas Pflug

We consider conservation laws with nonlocal velocity and show for nonlocal weights of exponential type that the unique solutions converge in a weak or strong sense (dependent on the regularity of the velocity) to the entropy solution of the local conservation law when the nonlocal weight approaches a Dirac distribution. To this end, we establish first a unif

Minal Suresh Patil

We present the notion of explainability for decision-making processes in a pedagogically structured autonomous environment. Multi-agent systems that are structured pedagogically consist of pedagogical teachers and learners that operate in environments in which both are sometimes not fully aware of all the states in the environment and beliefs of other agents

Masaru Shibata, Dina Traykova

We study numerically the scalar wave emission by a non-spherical oscillation of neutron stars in a scalar-tensor theory of gravity with kinetic screening, considering both the monopole and quadrupole mode emission. In agreement with previous results in the literature, we find that the monopole is always suppressed by the screening effect, regardless of the s

Juha Leppäkangas, Nicolas Vogt, Keith R. Fratus, Kirsten Bark

In this paper we present a quantum algorithm that uses noise as a resource. The goal of our quantum algorithm is the calculation of operator averages of an open quantum system evolving in time. Selected low-noise system qubits and noisy bath qubits represent the system and the bath of the open quantum system. All incoherent qubit noise can be mapped to bath

Antoine Blanchard, Nishant Parashar, Boyko Dodov, Christian Lessig

Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year. Under climate change, their impact and intensity are expected to worsen significantly. Unfortunately, general circulation models (GCMs), which are currently the primary tool for climate projections, cannot characterize

M. R. Mumpower, T. M Sprouse, T. Kawano, M. W. Herman

Nuclear data is critical for many modern applications from stockpile stewardship to cutting edge scientific research. Central to these pursuits is a robust pipeline for nuclear modeling as well as data assimilation and dissemination. We summarize a small portion of the ongoing nuclear data efforts at Los Alamos for medium mass to heavy nuclei. We begin with

Marshall Mueller, Shuchin Aeron, James M. Murphy, Abiy Tasissa

Wasserstein dictionary learning is an unsupervised approach to learning a collection of probability distributions that generate observed distributions as Wasserstein barycentric combinations. Existing methods for Wasserstein dictionary learning optimize an objective that seeks a dictionary with sufficient representation capacity via barycentric interpolation

Pranay Dighe, Prateeth Nayak, Oggi Rudovic, Erik Marchi

Accurate prediction of the user intent to interact with a voice assistant (VA) on a device (e.g. on the phone) is critical for achieving naturalistic, engaging, and privacy-centric interactions with the VA. To this end, we present a novel approach to predict the user's intent (the user speaking to the device or not) directly from acoustic and textual informa

Darius Jurčiukonis, Luís Lavoura

The most usual option to stabilize Dark Matter (DM) is a $Z_2$ symmetry. In general, though, DM may be stabilized by any $Z_N$ with $N \ge 2$. We consider the way $Z_N$ is a subgroup of the internal-symmetry group $G$ of a model; we entertain the possibility that $Z_N$ is the center of $G$, yet $G$ is not of the form $Z_N \times G^\prime$, where $G^\prime$ i

S. Johnston, E. Khatami, R. T. Scalettar

Numerical approaches to the correlated electron problem have achieved considerable success, yet are still constrained by several bottlenecks, including high order polynomial or exponential scaling in system size, long autocorrelation times, challenges in recognizing novel phases, and the Fermion sign problem. Methods in machine learning (ML), artificial inte

Regina Garcia-Mendez, Jingxu Zheng, David C. Bock, Cherno Jaye

Aluminum is the most earth-abundant metal, is trivalent, is inert in ambient humid air, and has a density approximately four-times that of lithium at room temperature. These attributes make it an attractive material for cost-effective, long-duration storage of electrical energy in batteries. Scientific discoveries in the past decade have established that sec

Song Wang, Chen Chen, Jundong Li

Graph few-shot learning is of great importance among various graph learning tasks. Under the few-shot scenario, models are often required to conduct classification given limited labeled samples. Existing graph few-shot learning methods typically leverage Graph Neural Networks (GNNs) and perform classification across a series of meta-tasks. Nevertheless, thes

Giulia Carigi, Tobias Kuna, Jochen Bröcker

A framework to establish response theory for a class of nonlinear stochastic partial differential equations (SPDEs) is provided. More specifically, it is shown that for a certain class of observables, the averages of those observables against the stationary measure of the SPDE are differentiable (linear response) or, under weaker conditions, locally H\"older

Marni Mishna, Stefan Trandafir

We study the Kronecker coefficients $g_{\lambda, \mu, \nu}$ via a formula that was described by Mishna, Rosas, and Sundaram, in which the coefficients are expressed as a signed sum of vector partition function evaluations. In particular, we use this formula to determine formulas to evaluate, bound, and estimate $g_{\lambda, \mu, \nu}$ in terms of the lengths

Lin Chen, Haochen Zhang, Kaixin Ji, Ce Shen

We show that the path-integral of conformal field theories in $D$ dimensions (CFT$_D$) can be constructed by solving for eigenstates of an RG operator following from the Turaev-Viro formulation of a topological field theory in $D+1$ dimensions (TQFT$_{D+1}$), explicitly realising the holographic sandwich relation between a symmetric theory and a TQFT. Generi

Valts Blukis, Taeyeop Lee, Jonathan Tremblay, Bowen Wen

We present a unified and compact scene representation for robotics, where each object in the scene is depicted by a latent code capturing geometry and appearance. This representation can be decoded for various tasks such as novel view rendering, 3D reconstruction (e.g. recovering depth, point clouds, or voxel maps), collision checking, and stable grasp predi

Victoriano Carmona, Fernando Fernández-Sánchez, Douglas D. Novaes

The existence of a uniform upper bound for the maximum number of limit cycles of planar piecewise linear differential systems with two zones separated by a straight line has been subject of interest of hundreds of papers. After more than 30 years of investigation since Lum-Chua's work, it has remained an open question whether this uniform upper bound exists

Shane Rupert Koscielniak

The stability and convergence of an Iterative Learning Controller (ILC) may be assessed either by directly iterating the equations for a variety of inputs, or by finding the eigenvalues of the iterated system, or by forming the Z-transform and applying pole-zero or equivalent root locus. Two often-used criteria are (i) Asymptotic Convergence (AC) of the diff

Darius Muglich, Christian Schroeder de Witt, Elise van der Pol, Shimon Whiteson

Successful coordination in Dec-POMDPs requires agents to adopt robust strategies and interpretable styles of play for their partner. A common failure mode is symmetry breaking, when agents arbitrarily converge on one out of many equivalent but mutually incompatible policies. Commonly these examples include partial observability, e.g. waving your right hand v

Di Cao, Noah J. Cowan, James S. Freudenberg

Given a plant subject to delayed sensor measurement, there are several approaches to compensate for the delay. An obvious approach is to address this problem in state space, where the $n$-dimensional plant state is augmented by an $N$-dimensional (Pad\'e) approximation to the delay, affording (optimal) state estimate feedback vis-\`a-vis the separation princ

Christopher Tosh, Mauricio Tec, Wesley Tansey

A fundamental task in science is to design experiments that yield valuable insights about the system under study. Mathematically, these insights can be represented as a utility or risk function that shapes the value of conducting each experiment. We present PDBAL, a targeted active learning method that adaptively designs experiments to maximize scientific ut

Aida Behmard, Fei Dai, John M. Brewer, Travis A. Berger

Dynamical evolution within planetary systems can cause planets to be engulfed by their host stars. Following engulfment, the stellar photosphere abundance pattern will reflect accretion of rocky material from planets. Multi-star systems are excellent environments to search for such abundance trends because stellar companions form from the same natal gas clou

J. -G. Ducoin, B. Desoubrie, F. Daigne, N. Leroy

The SVOM satellite, to be launched in early 2024, is primarily devoted to the multi-wavelength observation of gamma-ray bursts and other higher-energy transients. Thanks to its onboard Microchannel X-ray Telescope and Visible-band Telescope, it is also very well adapted to the electromagnetic follow-up of gravitational wave events. We discuss the SVOM rapid

Roman Kuzmin, Nitish Mehta, Nicholas Grabon, Vladimir E. Manucharyan

It is customary to use arrays of superconducting quantum interference devices (SQUIDs) for implementing magnetic field-tunable inductors. Here, we demonstrate an equivalent tunability in a (SQUID-free) array of single Al/AlOx/Al Josephson tunnel junctions. With the proper choice of junction geometry, a perpendicularly applied magnetic field bends along the p

Paolo Di Vecchia, Carlo Heissenberg, Rodolfo Russo, Gabriele Veneziano

We propose two possible eikonal operators encoding the effects of classical radiation as coherent states of gravitons and show how to compute from them different classical observables. In the first proposal, only genuinely propagating gravitons are included, while, in the second, zero-frequency modes are added in order to recover the effects of a static grav

Kaizhen Guo, Junyao Ye, Shuyue Guan, Shuang Jia

We report magnetization, heat capacity and electrical resistivity for a newly discovered heavy fermion (HF) compound $\mathrm{YbV_6Sn_6}$ which is crystallized in a hexagonal $\mathrm{HfFe_6Ge_6}$-type structure, highlighted by the stacking of triangular ytterbium sublattice and kagome vanadium sublattice. Above 2 K, $\mathrm{YbV_6Sn_6}$ shows typical HF pro

Alex Kaltenbach

In the present paper, we examine a Crouzeix-Raviart approximation for non-linear partial differential equations having a $(p,\delta)$-structure for some $p\in (1,\infty)$ and $\delta\ge 0$. We establish a priori error estimates, which are optimal for all $p\in (1,\infty)$ and $\delta\ge 0$, medius error estimates, i.e., best-approximation results, and a prim

Steven Nguyen, Zillur Rahman, Brendan Tan Morris

In the last decade, research in the field of autonomous vehicles has grown immensely, and there is a wealth of information available for researchers to rapidly establish an autonomous vehicle platform for basic maneuvers. In this paper, we design, implement, and test, in ten weeks, a PD approach to longitudinal control for pedestrian emergency braking. We al

Minal Suresh Patil

The intent of control argumentation frameworks is to specifically model strategic scenarios from the perspective of an agent by extending the standard model of argumentation framework in a way that takes unquantified uncertainty regarding arguments and attacks into account. They do not, however, adequately account for coalition formation and interactions amo

Pouria Rouzrokh, Bardia Khosravi, Shahriar Faghani, Mana Moassefi

Despite the ever-increasing interest in applying deep learning (DL) models to medical imaging, the typical scarcity and imbalance of medical datasets can severely impact the performance of DL models. The generation of synthetic data that might be freely shared without compromising patient privacy is a well-known technique for addressing these difficulties. I

Oded Hupert, Idan Schwartz, Lior Wolf

We seek to semantically describe a set of images, capturing both the attributes of single images and the variations within the set. Our procedure is analogous to Principle Component Analysis, in which the role of projection vectors is replaced with generated phrases. First, a centroid phrase that has the largest average semantic similarity to the images in t

Vikram Mahamiya, Alok Shukla, Nandini Garg, Brahmananda Chakraborty

Pristine graphene is not suitable for hydrogen storage at ambient conditions since it binds the hydrogen molecules only by van der Waals interactions. However, the adsorption energy of the hydrogen molecules can be improved by doping or decorating metal atoms on the graphene monolayer. The doping and decoration processes are challenging due to the oxygen int

Mateusz Mazelanik, Adam Leszczyński, Tomasz Szawełło, Michał Parniak

Magnetic resonance imaging is a three-dimensional imaging technique, where a gradient of the magnetic field is used to interrogate spin resonances with spatial resolution. The application of this technique to probe the coherence of atoms with good three-dimensional resolution is a challenging application. We propose and demonstrate an optical method to probe

Federico Rottoli, Sara Murciano, Erik Tonni, Pasquale Calabrese

We study the ground-state entanglement Hamiltonian of several disjoint intervals for the massless Dirac fermion on the half-line. Its structure consists of a local part and a bi-local term that couples each point to another one in each other interval. The bi-local operator can be either diagonal or mixed in the fermionic chiralities and it is sensitive to th

Emilian Postolache, Jordi Pons, Santiago Pascual, Joan Serrà

Universal sound separation consists of separating mixes with arbitrary sounds of different types, and permutation invariant training (PIT) is used to train source agnostic models that do so. In this work, we complement PIT with adversarial losses but find it challenging with the standard formulation used in speech source separation. We overcome this challeng

Antonella Iuliano, Claudio Macci

A telegraph process with an elastic barrier at the origin was studied in [5]; in particular the number of visits of the origin before the absorption is a geometric distributed random variable M. Some asymptotic results (large and moderate deviations) for that model were obtained in [17]. In this paper we study large and moderate deviations for a generalized

Joshua C. Kynaston, Christian A Yates, Anna Hekkink, Chris Guiver

There exist several methods for simulating biological and physical systems as represented by chemical reaction networks. Systems with low numbers of particles are frequently modelled as discrete-state Markov jump processes and are typically simulated via a stochastic simulation algorithm (SSA). An SSA, while accurate, is often unsuitable for systems with lar

M. R. Mumpower, D. Nuedecker, H. Sasaki, T. Kawano

The pre-equilibrium reaction mechanism is considered in the context of the exciton model. A modification to the one-particle one-hole state density is studied which can be interpreted as a collective enhancement. The magnitude of the collective enhancement is set by simulating the Lawrence Livermore National Laboratory (LLNL) pulsed-spheres neutron-leakage s

Simon Letzgus

Accurate wind turbine power curve models, which translate ambient conditions into turbine power output, are crucial for wind energy to scale and fulfill its proposed role in the global energy transition. While machine learning (ML) methods have shown significant advantages over parametric, physics-informed approaches, they are often criticised for being opaq

Michelle Delcourt, Reaz Huq, Pawel Pralat

In 1972 Tutte famously conjectured that every 4-edge-connected graph has a nowhere zero 3-flow; this is known to be equivalent to every 5-regular, 4-edge-connected graph having an edge orientation in which every in-degree is either 1 or 4. Jaeger conjectured a generalization of Tutte's conjecture, namely, that every $4p+1$-regular, $4p$-edge-connected graph

Ajaharul Islam, Lihua Dong, Yun Guo, Alexander Rothkopf

We introduce a method for reducing anisotropic heavy-quark potentials to isotropic potentials by using an effective screening mass that depends on the quantum numbers $l$ and $m$ of a given state. We demonstrate that, using the resulting 1D effective potential model, one can solve a 1D Schr\"odinger equation and reproduce the full 3D results for the energies

Tanya Marwah, Zachary C. Lipton, Jianfeng Lu, Andrej Risteski

A burgeoning line of research leverages deep neural networks to approximate the solutions to high dimensional PDEs, opening lines of theoretical inquiry focused on explaining how it is that these models appear to evade the curse of dimensionality. However, most prior theoretical analyses have been limited to linear PDEs. In this work, we take a step towards

Lorenzo Luzi, Paul M Mayer, Josue Casco-Rodriguez, Ali Siahkoohi

The inference stage of diffusion models can be seen as running a reverse-time diffusion stochastic differential equation, where samples from a Gaussian latent distribution are transformed into samples from a target distribution that usually reside on a low-dimensional manifold, e.g., an image manifold. The intermediate values between the initial latent space

Jonathan A. Barmak

A cop tries to capture a robber in a topological space $X$ being unable to see him. For which spaces $X$ does the cop have a strategy which allows him to capture the robber independently of his efforts to escape? In other words, when is there a curve $\gamma: \mathbb{R}_{\ge 0}\to X$ which has a coincidence with any other curve in $X$. We analyze in particul

Flavio Giobergia

Much like other learning-based models, recommender systems can be affected by biases in the training data. While typical evaluation metrics (e.g. hit rate) are not concerned with them, some categories of final users are heavily affected by these biases. In this work, we propose using multiple triplet losses terms to extract meaningful and robust representati

Duc Le, Panos P. Markopoulos

Singular-Value Decomposition (SVD) is a ubiquitous data analysis method in engineering, science, and statistics. Singular-value estimation, in particular, is of critical importance in an array of engineering applications, such as channel estimation in communication systems, electromyography signal analysis, and image compression, to name just a few. Conventi

Qi Liu, Zihuiwen Ye, Tao Yu, Phil Blunsom

The task of context-dependent text-to-SQL aims to convert multi-turn user utterances to formal SQL queries. This is a challenging task due to both the scarcity of training data from which to learn complex contextual dependencies and to generalize to unseen databases. In this paper we explore augmenting the training datasets using self-play, which leverages c

Rebeca Vétil, Clément Abi Nader, Alexandre Bône, Marie-Pierre Vullierme

We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs. To do so, volumetric segmentation masks are embedded into a common probabilistic shape space that is learned with a variational auto-encoding network. The resulting latent shape representations are leveraged to derive zeroshot and few-shot meth

Adrian E. Rubio Lopez, Vincenzo Giannini

The levitation of nanoparticles is essential in various branches of research. Casimir forces are natural candidates to tackle it but the lack of broadband metamaterials precluded repulsive forces in vacuum. We show sub-micron nanoparticle levitation in vacuum only based on the design of a broadband metamaterial perfect magnetic conductor surface, where the f

Joshua D. Herman, Rachel E. Roca, Alexandra G. O'Neill, Marcus L. Wong

Recent research has explored using neural networks to reconstruct undersampled magnetic resonance imaging (MRI) data. Because of the complexity of the artifacts in the reconstructed images, there is a need to develop task-based approaches of image quality. Common metrics for evaluating image quality like the normalized root mean squared error (NRMSE) and str

Yu Fang, Fei Li, Jianfei Hua, Bo Guo

MeV ultrafast electron diffraction (UED) is a widely used technology for ultrafast structural dynamic studies of matters in numerous areas. The development of laser wakefield accelerator (LWFA) envisions great potential of advanced all-optical electron source based on LWFA in UED applications. We experimentally demonstrated that an LWFA-based device with a m

Sihem Mesnager, Minjia Shi, Hongwei Zhu

Cyclic codes have many applications in consumer electronics, communication and data storage systems due to their efficient encoding and decoding algorithms. An efficient approach to constructing cyclic codes is the sequence approach. In their articles [Discrete Math. 321, 2014] and [SIAM J. Discrete Math. 27(4), 2013], Ding and Zhou constructed several class