October 2022 arXiv papers — page 77

Cihan Okay, Ho Yiu Chung, Selman Ipek

Mermin square scenario provides a simple proof for state-independent contextuality. In this paper, we study polytopes $\text{MP}_\beta$ obtained from the Mermin scenario, parametrized by a function $\beta$ on the set of contexts. Up to combinatorial isomorphism, there are two types of polytopes $\text{MP}_0$ and $\text{MP}_1$ depending on the parity of $\bet

Marcello Guarro, Ricardo G. Sanfelice

This paper presents an innovative hybrid systems approach to the sender-receiver synchronization of timers. Via the hybrid systems framework, we unite the traditional sender-receiver algorithm for clock synchronization with an online, adaptive strategy to achieve synchronization of the clock rates to exponentially synchronize a pair of clocks connected over

Edward Hutter, Edgar Solomonik

Performance tuning, software/hardware co-design, and job scheduling are among the many tasks that rely on models to predict application performance. We propose and evaluate low-rank tensor decomposition for modeling application performance. We discretize the input and configuration domains of an application using regular grids. Application execution times ma

Salome Mtchedlidze, Paola Domínguez-Fernández, Xiaolong Du, Wolfram Schmidt

Primordial magnetic fields (PMFs) are possible candidates for explaining the observed magnetic fields in galaxy clusters. Two competing scenarios of primordial magnetogenesis have been discussed in the literature: inflationary and phase-transitional. We study the amplification of both large- and small-scale correlated magnetic fields, corresponding to inflat

Samuel Price, Sobhan Soleymani, Nasser M. Nasrabadi

Morph images threaten Facial Recognition Systems (FRS) by presenting as multiple individuals, allowing an adversary to swap identities with another subject. Morph generation using generative adversarial networks (GANs) results in high-quality morphs unaffected by the spatial artifacts caused by landmark-based methods, but there is an apparent loss in identit

Mrigank Rochan, Xingxin Chen, Alaap Grandhi, Eduardo R. Corral-Soto

We consider the problem of domain adaptation in LiDAR-based 3D object detection. Towards this, we propose a simple yet effective training strategy called Gradual Batch Alternation that can adapt from a large labeled source domain to an insufficiently labeled target domain. The idea is to initiate the training with the batch of samples from the source and tar

Alfredo Braunstein, Giovanni Catania, Luca Dall'Asta, Matteo Mariani

Computing observables from conditioned dynamics is typically computationally hard, because, although obtaining independent samples efficiently from the unconditioned dynamics is usually feasible, generally most of the samples must be discarded (in a form of importance sampling) because they do not satisfy the imposed conditions. Sampling directly from the co

Ch. Cobollo, A. J. Guirao, V. Montesinos

A subspace $X$ of a Banach space $Y$ has $\textit{Property U}$ whenever every continuous linear functional on $X$ has a unique norm-preserving (i.e., Hahn$-$Banach) extension to $Y$ (Phelps, 1960). Throughout this document we introduce and develop a systematic study of the existence of $\textit{U-embeddings}$ between Banach spaces $X$ and $Y$, that is, isome

Tyler Genao

We show there exist polynomial bounds on torsion of elliptic curves which come from a fixed geometric isogeny class. More precisely, for an elliptic curve $E_0$ defined over a number field $F_0$, for each $\epsilon>0$ there exist constants $c_\epsilon:=c_\epsilon(E_0,F_0),C_\epsilon:=C_\epsilon(E_0,F_0)>0$ such that for any elliptic curve $E_{/F}$ geometrica

Jean-Samuel Leboeuf, Frédéric LeBlanc, Mario Marchand

We revisit binary decision trees from the perspective of partitions of the data. We introduce the notion of partitioning function, and we relate it to the growth function and to the VC dimension. We consider three types of features: real-valued, categorical ordinal and categorical nominal, with different split rules for each. For each feature type, we upper

Jialin Wu, Raymond J. Mooney

Most Outside-Knowledge Visual Question Answering (OK-VQA) systems employ a two-stage framework that first retrieves external knowledge given the visual question and then predicts the answer based on the retrieved content. However, the retrieved knowledge is often inadequate. Retrievals are frequently too general and fail to cover specific knowledge needed to

Yumeng Li, Dan Zhang, Margret Keuper, Anna Khoreva

The generalization with respect to domain shifts, as they frequently appear in applications such as autonomous driving, is one of the remaining big challenges for deep learning models. Therefore, we propose an intra-source style augmentation (ISSA) method to improve domain generalization in semantic segmentation. Our method is based on a novel masked noise e

Emmanuel Wend-Benedo Zongo, Bernhard Ruf

In this paper, we analyze an eigenvalue problem for nonlinear elliptic operators involving homogeneous Dirichlet boundary conditions in a open smooth bounded domain. We prove bifurcation results from trivial solutions and from infinity for the considered nonlinear eigenvalue problem. We also show the existence of multiple solutions of the nonlinear problem u

Ran Duan, Hongxun Wu, Renfei Zhou

Fast matrix multiplication is one of the most fundamental problems in algorithm research. The exponent of the optimal time complexity of matrix multiplication is usually denoted by $\omega$. This paper discusses new ideas for improving the laser method for fast matrix multiplication. We observe that the analysis of higher powers of the Coppersmith-Winograd t

Timothy M. Chan, Da Wei Zheng

We revisit the classic problem of simplex range searching and related problems in computational geometry. We present a collection of new results which improve previous bounds by multiple logarithmic factors that were caused by the use of multi-level data structures. Highlights include the following: $\bullet$ For a set of $n$ points in a constant dimension $

Samir Khan, Johan Ugander

A popular method for variance reduction in observational causal inference is propensity-based trimming, the practice of removing units with extreme propensities from the sample. This practice has theoretical grounding when the data are homoscedastic and the propensity model is parametric (Yang and Ding, 2018; Crump et al. 2009), but in modern settings where

Urs Frauenfelder, Joa Weber

Critical points of a function subject to a constraint can be either detected by restricting the function to the constraint or by looking for critical points of the Lagrange multiplier functional. Although the critical points of the two functionals, namely the restriction and the Lagrange multiplier functional are in natural one-to-one correspondence this doe

Robin Feldmann, Alberto Baiardi, Markus Reiher

This work presents a general framework for deriving exact and approximate Newton self-consistent field (SCF) orbital optimization algorithms by leveraging concepts borrowed from differential geometry. Within this framework, we extend the augmented Roothaan--Hall (ARH) algorithm to unrestricted electronic and nuclear-electronic calculations. We demonstrate th

Eugene Larsen-Hallock, Adam Rej, David Thesmar

We empirically analyze a large sample of firm sales growth expectations. We find that the relationship between forecast errors and lagged revision is non-linear. Forecasters underreact to typical (positive or negative) news about future sales, but overreact to very significant news. To account for this non-linearity, we propose a simple framework, where (1)

Ethan Ancell, Christopher Bennett, Bert Debusschere, Sapan Agarwal

Neural networks have revolutionized the field of machine learning with increased predictive capability. In addition to improving the predictions of neural networks, there is a simultaneous demand for reliable uncertainty quantification on estimates made by machine learning methods such as neural networks. Bayesian neural networks (BNNs) are an important type

Rohit Singh, Alexander P. Wu, Bonnie Berger

When a dynamical system can be modeled as a sequence of observations, Granger causality is a powerful approach for detecting predictive interactions between its variables. However, traditional Granger causal inference has limited utility in domains where the dynamics need to be represented as directed acyclic graphs (DAGs) rather than as a linear sequence, s

Yu Hamada, Nobuyuki Matsumoto

We consider how the energy can be stored in the boundary of spacetime, in particular in a spherical bubble that can be made by a quantum gravitational process. Our calculation is performed within the framework of classical Einstein gravity by identifying the Gibbons-Hawking-York term as the membrane action. We show that the energy of the bubble can be given

Tayfun Sönmez, Utku Ünver

In a 3-2 split verdict, the Supreme Court approved the exclusion of India's socially and economically backward classes from its affirmative action measures to address economic deprivation. Dissenting justices, including the Chief Justice of India, protested the Majority Opinion for sanctioning "an avowedly exclusionary and discriminatory principle." To justi

Vardan G. Elbakyan, Sergei Nayakshin, Dominique M. -A. Meyer, Eduard I. Vorobyov

3D simulations of high mass young stellar object (HMYSO) growth show that their circumstellar discs fragment onto multiple self-gravitating objects. Accretion of these by HMYSO may explain episodic accretion bursts discovered recently. We post-process results of a previous 3D simulation of a HMYSO disc with a 1D code that resolves the disc and object dynamic

Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz

Although perfect copying of an unknown quantum state is not possible, approximate cloning is possible in quantum mechanics. Quantum telecloning is a variant of approximate quantum cloning which uses quantum teleportation to allow for the use of classical communication to create physically separate clones of a quantum state. We present results of a of $1 \rig

Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, Jimeng Sun

Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction. However, medical image-text datasets are orders of magnitude below the general images and captions from the internet. Moreover, previous methods

Manuel Cossio

Computational pathology is a field that has complemented various subspecialties of diagnostic pathology over the last few years. In this article a brief analyzis the different applications in nephrology is developed. To begin, an overview of the different forms of image production is provided. To continue, the most frequent applications of computer vision mo

Wenlu Tang, Guohao Shen, Yuanyuan Lin, Jian Huang

We propose a nonparametric quantile regression method using deep neural networks with a rectified linear unit penalty function to avoid quantile crossing. This penalty function is computationally feasible for enforcing non-crossing constraints in multi-dimensional nonparametric quantile regression. We establish non-asymptotic upper bounds for the excess risk

Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhong, Cho-Jui Hsieh

Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain has only focused on problems with small label spaces and ignored eXtreme Multi-label Classification (XMC), which is an essential task in the era of big data for web-scale machine lea

Guillaume Chapuy, Jean-François Marckert

The integrated super-Brownian excursion (ISE) is the occupation measure of the spatial component of the head of the Brownian snake with lifetime process the normalized Brownian excursion. It is a random probability measure on $\mathbb{R}$, and it is known to describe the continuum limit of the distribution of labels in various models of random discrete label

Shiliang Gao, Yibo Gao

We provide a type-uniform formula for the degree of the stretched Kostka quasi-polynomial $K_{\lambda,\mu}(N)$ in all classical types, improving a previous result by McAllister in $\mathfrak{sl}_r(\mathbb{C})$. Our proof relies on a combinatorial model for the weight multiplicity by Berenstein and Zelevinsky.

Gabriel Moldovan, Guillaume Lehnasch, Laurent Cordier, Marcello Meldi

Essential features of the Multigrid Ensemble Kalman Filter (G. Moldovan, G. Lehnasch, L. Cordier, M. Meldi, A multigrid/ensemble Kalman filter strategy for assimilation of unsteady flows, Journal of Computational Physics 443-110481) recently proposed for Data Assimilation of fluid flows are investigated and assessed in this article. The analysis is focused o

Martijn S. S. L. Oei, Reinout J. van Weeren, Martin J. Hardcastle, Franco Vazza

The warm-hot intergalactic medium (warm-hot IGM, or WHIM) pervades the filaments of the Cosmic Web and harbours half of the Universe's baryons. The WHIM's thermodynamic properties are notoriously hard to measure. Here we estimate a galaxy group - WHIM boundary temperature using a new method. In particular, we use a radio image of the giant radio galaxy (gian

Eamonn A. Gaffney, Andrew L. Krause, Philip K. Maini, Chenyuan Wang

Motivated by bacterial chemotaxis and multi-species ecological interactions in heterogeneous environments, we study a general one-dimensional reaction-cross-diffusion system in the presence of spatial heterogeneity in both transport and reaction terms. Under a suitable asymptotic assumption that the transport is slow over the domain, while gradients in the r

Remigiusz Durka, Jerzy Kowalski-Glikman

We consider two BF formulations of the theory of gravity with a negative cosmological constant, of Plebanski and of MacDowell-Mansouri. Both give the standard Einstein equations in the bulk but differ in expressions of edge charges. We compute the asymptotic charges explicitly in both theories for AdS-Schwarzschild, AdS-Kerr, and AdS-Taub--NUT solutions. We

Razvan C. Fetecau, Hansol Park

We investigate the long-time behaviour of solutions to a nonlocal partial differential equation on smooth Riemannian manifolds of bounded sectional curvature. The equation models self-collective behaviour with intrinsic interactions that are modelled by an interaction potential. We consider attractive interaction potentials and establish sufficient condition

Anwesh Ray

Let $p\geq 7$ be a prime and $n>1$ be a natural number. We show that there exist infinitely many Galois representations $\varrho:Gal(\bar{\mathbb{Q}}/\mathbb{Q})\rightarrow GL_{n}(\mathbb{Z}_p)$ which are unramified outside $\{p, \infty\}$ with large image. More precisely, the Galois representations constructed have image containing the kernel of the mod-$p^

Koki Inoue, Shuichiro Ogake, Hayato Kawamura, Naoki Igo

Today, as seen in smart speakers, spoken dialogue technology is rapidly advancing to enable human-like interaction. However, current dialogue systems cannot pay attention not only to the content of speech, but also to the way of speaking and eye contact and facial expressions, while watching the facial expressions of the person with whom one is speaking. The

Reza Ghazalian, Hui Chen, George C. Alexandropoulos, Gonzalo Seco-Granados

The recent research in the emerging technology of reconfigurable intelligent surfaces (RISs) has identified its high potential for localization and sensing. However, to accurately localize a user placed in the area of influence of an RIS, the RIS location needs to be known a priori and its phase profile is required to be optimized for localization. In this p

Hans Lindblad, Mihai Tohaneanu

We study a system of semilinear wave equations on Kerr backgrounds that satisfies the weak null condition. Under the assumption of small initial data, we prove global existence and pointwise decay estimates.

Xinlu Zhang, Shiyang Li, Zhiyu Chen, Xifeng Yan

Health conditions among patients in intensive care units (ICUs) are monitored via electronic health records (EHRs), composed of numerical time series and lengthy clinical note sequences, both taken at irregular time intervals. Dealing with such irregularity in every modality, and integrating irregularity into multimodal representations to improve medical pre

Jane H. Lee, Saeid Haghighatshoar, Amin Karbasi

Spiking neural networks (SNN) have recently emerged as alternatives to traditional neural networks, owing to energy efficiency benefits and capacity to better capture biological neuronal mechanisms. However, the classic backpropagation algorithm for training traditional networks has been notoriously difficult to apply to SNN due to the hard-thresholding and

Jorge Delgado, Plamen Koev, Ana Marco, Jose-Javier Martinez

We present a method to derive new explicit expressions for bidiagonal decompositions of Vandermonde and related matrices such as the (q-, h-) Bernstein-Vandermonde ones, among others. These results generalize the existing expressions for nonsingular matrices to matrices of arbitrary rank. For totally nonnegative matrices of the above classes, the new decompo

Swastika Roy, Hatim Chergui, Christos Verikoukis

Sixth-generation (6G) networks anticipate intelligently supporting a massive number of coexisting and heterogeneous slices associated with various vertical use cases. Such a context urges the adoption of artificial intelligence (AI)-driven zero-touch management and orchestration (MANO) of the end-to-end (E2E) slices under stringent service level agreements (

Daniel S. Seara, Akash Piya, A. Pasha Tabatabai

Motivated by the anisotropic interactions between fish, we implement spatially anisotropic and therefore non-reciprocal interactions in the 2D Ising model. First, we show that the model with non-reciprocal interactions alters the system critical temperature away from that of the traditional 2D Ising model. Further, local perturbations to the magnetization in

Varun Mittal, Laura P. Schaposnik

Through the reinterpretation of housing data as candlesticks, we extend Nature Scientific Reports' article by Liang and Unwin [LU22] on stock market indicators for COVID-19 data, and utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market, comparing the findings to those one would obtain

Krzysztof P. Wójcik, Piotr Majek

We perform a theoretical analysis of the fate of local magnetic moment of a quantum dot coupled to a normal metallic lead and a topological superconducting wire hosting Majorana modes at the ends. By means of simple analytical tools and numerical renormalization group calculations we show that the proximity of Majorana mode reduces the magnetic moment from $

Phillip Howard, Arden Ma, Vasudev Lal, Ana Paula Simoes

The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text. Existing approaches for this task have yielded impressive results when the training and testing data are from the same domain. However, these methods show a drastic decrease in performance when applied to cross-domain settings where the domain of the testing data di

Yusuf Alnawakhtha, Atul Mantri, Carl A. Miller, Daochen Wang

Trapdoor claw-free functions (TCFs) are immensely valuable in cryptographic interactions between a classical client and a quantum server. Typically, a protocol has the quantum server prepare a superposition of two-bit strings of a claw and then measure it using Pauli-$X$ or $Z$ measurements. In this paper, we demonstrate a new technique that uses the entire

Chenyue Liu, Chao Fan, Ali Mostafavi

Understanding the determinants underlying variations in urban health status is important for informing urban design and planning, as well as public health policies. Multiple heterogeneous urban features could modulate the prevalence of diseases across different neighborhoods in cities and across different cities. This study examines heterogeneous features re

Mohammad Bagher Heydari, Majid Karimipour, Morteza Mohammadi Shirkolaei

The hybridization of hyperbolic Polaritonics with THz Plasmonics has attracted immense attention due to its fascinating applications in this region. However, to effectively enhance the performance of these coupled modes, one way is the usage of smart materials in the heterostructures. Here, we introduce a symmetric graphene-based structure containing hexagon

Seyed Morteza Habibi Khorasani, Mitul Luhar, Shervin Bagheri

Turbulent flows over porous lattices consisting of rectangular cuboid pores are investigated using scale-resolving direct numerical simulations. Beyond a certain threshold which is primarily determined by the wall-normal Darcy permeability, ${K_y}^+$, near-wall turbulence transitions from its canonical regime, marked by the presence of streak-like structures

Gianluca Costagliola, Federico Bosia, Nicola M. Pugno

Understanding the interplay between concurrent length scales is a fundamental issue in many problems involving friction between sliding interfaces, from tribology to the study of earthquakes and seismic faults. On the one hand, a macroscopic sliding event is preceded by slip precursors with a characteristic propagation length scale. On the other hand, the em

Zhimin Chen, Longlong Jing, Liang Yang, Yingwei Li

Recent state-of-the-art method FlexMatch firstly demonstrated that correctly estimating learning status is crucial for semi-supervised learning (SSL). However, the estimation method proposed by FlexMatch does not take into account imbalanced data, which is the common case for 3D semi-supervised learning. To address this problem, we practically demonstrate th

Hongwei Chen, Sathya R. Chitturi, Rajan Plumley, Lingjia Shen

The advent of next-generation X-ray free electron lasers will be capable of delivering X-rays at a repetition rate approaching 1 MHz continuously. This will require the development of data systems to handle experiments at these type of facilities, especially for high throughput applications, such as femtosecond X-ray crystallography and X-ray photon fluctuat

Hua Jiang, Shudong Yang, Shuang Li, Shengbo Liu

When reputation evaluation indicators become targets, existing indicators will lose the role of indicating the true quality; At present, the evaluation of discipline reputation mostly focuses on subjective evaluation based on objective data, and there is a dispute about reliability and validity; Due to different indicators and weight settings, it is difficul

János Barát, András Gyárfás, Géza Tóth

Two independent edges in ordered graphs can be nested, crossing or separated. These relations define six types of subgraphs, depending on which relations are forbidden. We refine a remark by Erd\H{o}s and Rado that every 2-coloring of the edges of a complete graph contains a monochromatic spanning tree. We show that forbidding one relation we always have a m

Zhenqi Hua, Azza Ben-Akacha, Qingquan He, Tianhan Liu

Metal halide perovskites possess many physical properties amenable to optoelectronic applications, whereas the realization of these potentials has been hampered by their environmental and electronic instabilities. The morphological and molecular low dimensional perovskites and perovskite related materials have shown much promise in enhancing the chemical sta

Pengzhi Huang, Thang Hoang, Yueying Li, Elaine Shi

In this paper, we propose a new secure machine learning inference platform assisted by a small dedicated security processor, which will be easier to protect and deploy compared to today's TEEs integrated into high-performance processors. Our platform provides three main advantages over the state-of-the-art: (i) We achieve significant performance improvements

Kushal Ramakrishna, Mani Lokamani, Andrew Baczewski, Jan Vorberger

We present a comprehensive investigation of the electrical and thermal conductivity of iron under high pressures at ambient temperature, employing the real-time formulation of time-dependent density functional theory (RT-TDDFT). Specifically, we examine the influence of a Hubbard correction (+\textit{U}) to account for strong electron correlations. Our calcu

Yuri Berest, Ajay C. Ramadoss

Symmetric homology is a natural generalization of cyclic homology, in which symmetric groups play the role of cyclic groups. In the case of associative algebras, the symmetric homology theory was introduced by Z. Fiedorowicz \cite{F} and was further developed in the work of S. Ault \cite{Au1, Au2}. In this paper, we show that, for algebras defined over a fie

Akshita Mittel, Shashank Tripathi

Emotion recognition aims to interpret the emotional states of a person based on various inputs including audio, visual, and textual cues. This paper focuses on emotion recognition using visual features. To leverage the correlation between facial expression and the emotional state of a person, pioneering methods rely primarily on facial features. However, fac

Tom Farshi, Jonas Richter, Daniele Toniolo, Arijeet Pal

We analyze a Floquet circuit with random Clifford gates in one and two spatial dimensions. By using random graphs and methods from percolation theory, we prove in the two dimensional setting that some local operators grow at ballistic rate, which implies the absence of localization. In contrast, the one-dimensional model displays a strong form of localizatio

Matthias Köhler, Matthias A. Müller, Frank Allgöwer

We present a sequential distributed model predictive control (MPC) scheme for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. In the scheme, we explore the idea of using tracking MPC with artificial references to let agents coordinate their cooperation without external gu

Andrea Tagliabue, Jonathan P. How

Imitation learning (IL) can generate computationally efficient sensorimotor policies from demonstrations provided by computationally expensive model-based sensing and control algorithms. However, commonly employed IL methods are often data-inefficient, requiring the collection of a large number of demonstrations and producing policies with limited robustness

N. Furey, M. J. Hughes

Reframing certain well-known particle models in terms of normed division algebras leads to two new results for BSM physics. (1) We identify a sequence of complex structures which induces a cascade of breaking symmetries: Spin(10) $\mapsto$ Pati-Salam $\mapsto$ Left-Right symmetric $\mapsto$ Standard model + B-L (both pre- and post-Higgs-mechanism). These com

Stephen Zhao, Chris Lu, Roger Baker Grosse, Jakob Nicolaus Foerster

Learning With Opponent-Learning Awareness (LOLA) (Foerster et al. [2018a]) is a multi-agent reinforcement learning algorithm that typically learns reciprocity-based cooperation in partially competitive environments. However, LOLA often fails to learn such behaviour on more complex policy spaces parameterized by neural networks, partly because the update rule

Naum I. Gershenzon, Robert W. Ritzi, David F. Dominic, Edward Mehnert

Carbon dioxide (CO2) storage reservoirs commonly exhibit sedimentary architecture that reflects fluvial deposition. The heterogeneity in petrophysical properties arising from this architecture influences the dynamics of injected CO2. We previously used a geocellular modeling approach to represent this heterogeneity, including heterogeneity in constitutive sa

David Hart, Michael Whitney, Bryan Morse

We present a new and general framework for convolutional neural network operations on spherical (or omnidirectional) images. Our approach represents the surface as a graph of connected points that doesn't rely on a particular sampling strategy. Additionally, by using an interpolated version of SelectionConv, we can operate on the sphere while using existing

Erika M. Holmbeck, Rebecca Surman, Ian U. Roederer, G. C. McLaughlin

With the most trans-iron elements detected of any star outside the Solar System, HD 222925 represents the most complete chemical inventory among r-process-enhanced, metal-poor stars. While the abundance pattern of the heaviest elements identified in HD 222925 agrees with the scaled Solar r-process residuals, as is characteristic of its r-process-enhanced cla

Dmitry Dolgopyat, Bassam Fayad, Adam Kanigowski

We construct conservative analytic flows of zero metric entropy which satisfy the classical central limit theorem.

John A. Christian

This work introduces the problem of initial orbit determination (IOD) from only heading measurements. Such a problem occurs in practice when estimating the orbit of a spacecraft using visual odometry measurements from an optical camera. After reviewing the problem geometry, a simple solution is developed in the form of an iterative scheme on the parameters d

E. T. Akhmedov, I. V. Kochergin, M. N. Milovanova

We consider quantum field theory with selfinteractions in various patches of Minkowski and de Sitter space-times. Namely, in Minkowski space-time we consider separately right (left) Rindler wedge, past wedge and future wedge. In de Sitter space-time we consider expanding Poincare patch, static patch, contracting Poincare patch and global de Sitter itself. In

Pascal Noble, Luis Miguel Rodrigues, Changzhen Sun

The present work shows that essentially all small-amplitude periodic traveling waves of the electronic Euler-Poisson system are spectrally unstable. This instability is neither modulational nor co-periodic, and thus requires an unusual spectral analysis and, beyond specific computations, newly devised arguments. The growth rate with respect to the amplitude

Daniel Block, Moises Reyes Rivas

In recent and past works, convexity is usually assumed on each individual part of the action functional in order to demonstrate the existence and uniqueness of a Nash equilibrium on some interval [0, T] (this meant that each hessian was assumed to be nonnegative). Particularly, a certain assumption was imposed in order to quantify the smallness of T. The con

Nicholas Ng, Tyrel M. McQueen

Building on discoveries in graphene and two-dimensional (2D) transition metal dichalcogenides, van der Waals (VdW) layered heterostructures - stacks of such 2D materials - are being extensively explored with resulting new discoveries of novel electronic and magnetic properties in the ultrathin limit. Here we review a class of naturally occurring heterostruct

Danfu Liang, Lexter Savio Rodriguez, Haitao Zhou, Yifu Zhu

We report an experimental demonstration of optical 2DCS in cold atoms. The experiment integrates a collinear 2DCS setup with a magneto-optical trap (MOT), in which cold rubidium (Rb) atoms are prepared at a temperature of about 200 $\mu$K and a number density of $10^{10}$ cm$^{-3}$. With a sequence of femtosecond laser pulses, we first obtained one-dimension

Jie Ren, Han Xu, Yuxuan Wan, Xingjun Ma

With more people publishing their personal data online, unauthorized data usage has become a serious concern. The unlearnable strategies have been introduced to prevent third parties from training on the data without permission. They add perturbations to the users' data before publishing, which aims to make the models trained on the perturbed published datas

Janik Schönmeier-Kromer, Lode Pollet

We study the phase diagram of the one-dimensional Bose-Fermi-Hubbard model at unit filling for the scalar bosons and half filling for the $S=1/2$ fermions using quantum Monte Carlo simulations. The bare interaction between the fermions is set to zero. A central question of our study is what type of interactions can be induced between the fermions by the boso

Mo Yu, Yi Gu, Xiaoxiao Guo, Yufei Feng

Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an essential cornerstone in building general AI systems. We propose a new commonsense reasoning dataset based on human's Interactive Fiction (IF) gameplay walkthroughs as human players demonstrate plentiful and diverse commonsense reasoning. The new dat

T. Maciaszek, A. Ealet, W. Gillard, K. Jahnke

The NISP (Near Infrared Spectrometer and Photometer) is one of the two Euclid instruments. It operates in the near-IR spectral region (950-2020nm) as a photometer and spectrometer. The instrument is composed of: a cold (135 K) optomechanical subsystem consisting of a Silicon carbide structure, an optical assembly, a filter wheel mechanism, a grism wheel mech

J. M. Carmona, J. L. Cortés, J. J. Relancio, M. A. Reyes

In this work we consider the effects of Lorentz Invariance Violation over the observed flux of very high-energy neutrinos. For that, we study the neutrino propagation in a Modified Dispersion Relation scenario with a superluminal velocity. This makes the neutrino unstable and causes a cut-off in the flux of detected neutrinos. Using simple models, one can ap

Fabian Wieczorek, Shang-Ching Liu, Björn Sygo, Mykhailo Koshil

In this work, we present a three-part system that automatically sorts books on a shelf using the PR- 2 platform. The paper describes a methodology to sufficiently detect and recognize books using a multistep vision pipeline based on deep learning models as well as conventional computer vision. Furthermore, the difficulties of relocating books using a bi-manu

Zhisong Zhang, Emma Strubell, Eduard Hovy

In this work, we provide a survey of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially

Yunzhi Lin, Thomas Müller, Jonathan Tremblay, Bowen Wen

We present a parallelized optimization method based on fast Neural Radiance Fields (NeRF) for estimating 6-DoF pose of a camera with respect to an object or scene. Given a single observed RGB image of the target, we can predict the translation and rotation of the camera by minimizing the residual between pixels rendered from a fast NeRF model and pixels in t

George F R Ellis

This is a comment on both Gruber et al (2022) and Bunamano and Rovelli (2022), which discuss the relation between physical time and human time. I claim here, contrary to many views discussed there, that there is no foundational conflict between the way physics views the passage of time and the way the mind/brain perceives it. The problem rather resides in a

Seyed Mohammad Hosseiny, Hossein Rangani Jahromi, Mahdi Amniat-Talab

Effective nonlinear optical interactions are essential for many applications in modern photonics. In this paper, we investigate the role of the nonlinear response of a material to improve quantum metrology. In particular, the collective optical behavior of an atomic ensemble is applied to enhance frequency estimation through one of the atoms. Moreover, we in

Jiaxin Zhang, Yashar Moshfeghi

Numerical reasoning over text is a challenging task of Artificial Intelligence (AI), requiring reading comprehension and numerical reasoning abilities. Previous approaches use numerical reasoning programs to represent the reasoning process. However, most works do not separate the generation of operators and operands, which are key components of a numerical r

Jianxi Luo, Serhad Sarica, Kristin Wood

Data-driven conceptual design methods and tools aim to inspire human ideation for new design concepts by providing external inspirational stimuli. In prior studies, the stimuli have been limited in terms of coverage, granularity, and retrieval guidance. Here, we present a knowledge based expert system that provides design stimuli across the semantic, documen

Alejandro Corichi, Dario Núñez

The authors present concepts and mathematical developments which give rise to the Hamiltonian formulation of Einstein's general relativity, first introduced by Arnowitt, Deser, and Misner. All the geometrical quantities needed for the construction are explicitly obtained, and examples of some of the application of the formalism are given.

Raul Jimenez, Ali Rida Khalife, Daniel F. Litim, Sabino Matarrese

Evidence for almost spatial flatness of the Universe has been provided from several observational probes, including the Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillations (BAO) from galaxy clustering data. However, other than inflation, and in this case only in the limit of infinite time, there is no strong a priori motivation for a spatially

Jeremy Bernstein

The goal of this thesis is to develop the optimisation and generalisation theoretic foundations of learning in artificial neural networks. On optimisation, a new theoretical framework is proposed for deriving architecture-dependent first-order optimisation algorithms. The approach works by combining a "functional majorisation" of the loss function with "arch

T. Depastas, G. A. Souliotis, M. Veselsky, A. Bonasera

The nuclear Equation of State (EoS) lies in the center of the nuclear N-body problem as it describes the properties of the Nuclear Matter (NM) and determines the parameters of the nuclear interaction. In this work, we propose a theoretical description of the EoS of both Symmetric (SNM) and Asymmetric (ANM) nuclear matter within the framework of Fermionic Dyn

Yifei Pan, Yu Yan

We construct examples of twice differentiable functions in $\mathbb{R}^n$ with continuous Laplacian and bounded Hessian. The same construction is also applicable to higher order differentiability, the Monge-Amp\`ere equation, and mean curvature equation for hypersurfaces.

J. L. West, J. L. Campbell, P. Bhaura, R. Kothes

Using data from the Galactic Arecibo L-band Feed Array Continuum Transit Survey (GALFACTS), we report the discovery of two previously unidentified, very compressed, thin, and straight polarized filaments approximately centred at Galactic coordinates, $(l,b)=(182.5^\circ,-4.0^\circ)$, which we call G182.5--4.0. Using data from the Isaac Newton Telescope Galac

Vicente Cortés, Alejandro Gil-García, Arpan Saha

We prove that the one-loop deformation of any quaternionic K\"ahler manifold in the class of c-map spaces is locally inhomogeneous. As a corollary, we obtain that the full isometry group of the one-loop deformation of any homogeneous c-map space has precisely cohomogeneity one.

Manuel Rivera

We describe an algebraic chain level construction that models the passage from an arbitrary topological space to its free loop space. The input of the construction is a categorical coalgebra, i.e. a curved coalgebra satisfying certain properties, and the output is a chain complex. The construction is a modified version of the coHochschild complex of a differ

Lukas Braun, Joaquín Moraga

In this article, we study $G$-covers of klt varieties, where $G$ is a reductive group. First, we exhibit an example of a klt singularity admitting a $\mathbb{P}{\rm GL}_n(\mathbb{K})$-cover that is not of klt type. Then, we restrict ourselves to $G$-quasi-torsors, a special class of $G$-covers that behave like $G$-torsors outside closed subsets of codimensio

Yasas Seneviratne, Korakit Seemakhupt, Sihang Liu, Samira Khan

Persistent Memory (PM) technologies enable program recovery to a consistent state in a case of failure. To ensure this crash-consistent behavior, programs need to enforce persist ordering by employing mechanisms, such as logging and checkpointing, which introduce additional data movement. The emerging near-data processing (NDP) architec-tures can effectively

Artem Sevastopolsky, Yury Malkov, Nikita Durasov, Luisa Verdoliva

State-of-the-art face recognition systems require vast amounts of labeled training data. Given the priority of privacy in face recognition applications, the data is limited to celebrity web crawls, which have issues such as limited numbers of identities. On the other hand, self-supervised revolution in the industry motivates research on the adaptation of rel