November 2022 arXiv papers — page 25

Orin Levy, Asaf Cassel, Alon Cohen, Yishay Mansour

We present the E-UC$^3$RL algorithm for regret minimization in Stochastic Contextual Markov Decision Processes (CMDPs). The algorithm operates under the minimal assumptions of realizable function class and access to \emph{offline} least squares and log loss regression oracles. Our algorithm is efficient (assuming efficient offline regression oracles) and enj

Atefeh H. Arani, Peng Hu, Yeying Zhu

Recent technological advancements in space, air, and ground components have made possible a new network paradigm called space-air-ground integrated network (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAG

Mariia Mykhailova

This report describes a variety of programming assignments that can be used to teach quantum computing in a practical manner. These assignments let the learners get hands-on experience with all stages of quantum software development process, from solving quantum computing problems and implementing the solutions to debugging the programs, performing resource

Philipp Mertsch

Observations of cosmic ray electrons have made great strides in the last decade and direct observations of the all-electron flux as well as separate electron and positron spectra are now available up to ~ 1 TeV. In this invited contribution to the 2022 edition of the Rencontres de Moriond on Very High Energy Phenomena in the Universe, we review the data on c

Aravind Sasidharan Pillai

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in

Wenhao Sun, Grace Li Zhang, Huaxi Gu, Bing Li

In deep neural networks (DNNs), there are a huge number of weights and multiply-and-accumulate (MAC) operations. Accordingly, it is challenging to apply DNNs on resource-constrained platforms, e.g., mobile phones. Quantization is a method to reduce the size and the computational complexity of DNNs. Existing quantization methods either require hardware overhe

Zhihuang Zhang, Meng Xu, Wenqiang Zhou, Tao Peng

Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual semantic localization neural network using m

Wenhao Sun, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo

Deep neural networks (DNNs) have successfully been applied in many fields in the past decades. However, the increasing number of multiply-and-accumulate (MAC) operations in DNNs prevents their application in resource-constrained and resource-varying platforms, e.g., mobile phones and autonomous vehicles. In such platforms, neural networks need to provide acc

Steven D Miller

We consider a finite-volume domain $\mathfrak{D}\subset\mathbb{R}^{3}$ of size $\mathrm{Vol}(\mathfrak{D})\sim \mathrm{L}^{3}$ containing a viscous fluid of kinematic viscosity $\nu$ with velocity field $U_{a}(x,t)$ satisfying the Navier--Stokes equations with prescribed boundary data. We introduce a zero-centred homogeneous-isotropic Gaussian field $\mathsc

Sauradip Nag, Xiatian Zhu, Yi-Zhe Song, Tao Xiang

Existing Temporal Action Detection (TAD) methods typically take a pre-processing step in converting an input varying-length video into a fixed-length snippet representation sequence, before temporal boundary estimation and action classification. This pre-processing step would temporally downsample the video, reducing the inference resolution and hampering th

Jiayu Song, Iman Munire Bilal, Adam Tsakalidis, Rob Procter

Opinion summarisation synthesises opinions expressed in a group of documents discussing the same topic to produce a single summary. Recent work has looked at opinion summarisation of clusters of social media posts. Such posts are noisy and have unpredictable structure, posing additional challenges for the construction of the summary distribution and the pres

R. Dey, P. K. Netrakanti, D. K. Mishra, S. P. Behera

We report the measurement of the non-reactor environmental backgrounds and the detector response with the Indian Scintillator Matrix for Reactor Anti-Neutrinos (ISMRAN), which is $\sim$1 ton detector setup by volume, consisting of 10$\times$9 (10 rows and 9 columns) Plastic Scintillator Bars (PSBs) array at BARC, Mumbai, India. ISMRAN is an above-ground anti

Nicholas J. Cecchi, Ashlyn A. Callan, Landon P. Watson, Yuzhe Liu

Protective headgear effects measured in the laboratory may not always translate to the field. In this study, we evaluated the impact attenuation capabilities of a commercially available padded helmet shell cover in the laboratory and field. In the laboratory, we evaluated the efficacy of the padded helmet shell cover in attenuating impact magnitude across si

Bhavesh Laddagiri, Yash Raj, Anshuman Dash

Real-world tasks are largely composed of multiple models, each performing a sub-task in a larger chain of tasks, i.e., using the output from a model as input for another model in a multi-model pipeline. A model like MATRa performs the task of Crosslingual Transliteration in two stages, using English as an intermediate transliteration target when transliterat

C. Edson Utazi, Warren C. Jochem, Marta Gacic-Dobo, Padraic Murphy

Estimates of national immunization coverage are crucial for guiding policy and decision-making in national immunization programs and setting the global immunization agenda. WHO and UNICEF estimates of national immunization coverage (WUENIC) are produced annually for various vaccine-dose combinations and all WHO Member States using information from multiple d

Meghann Moriah Lugar, Micah B. Milinovich, Emily Quesada-Herrera

Assuming the Riemann hypothesis, we prove estimates for the variance of the real and imaginary part of the logarithm of the Riemann zeta-function in short intervals. We give three different formulations of these results. Assuming a conjecture of Chan for how often gaps between zeros can be close to a fixed nonzero value, we prove a conjecture of Berry (1988)

Amro Eldebiky, Grace Li Zhang, Georg Boecherer, Bing Li

The last decade has witnessed the breakthrough of deep neural networks (DNNs) in many fields. With the increasing depth of DNNs, hundreds of millions of multiply-and-accumulate (MAC) operations need to be executed. To accelerate such operations efficiently, analog in-memory computing platforms based on emerging devices, e.g., resistive RAM (RRAM), have been

Joseph R. Cruise, Alexander Seidel

We introduce and prove the "root theorem", which establishes a condition for families of operators to annihilate all root states associated with zero modes of a given positive semi-definite $k$-body Hamiltonian chosen from a large class. This class is motivated by fractional quantum Hall and related problems, and features generally long-ranged, one-dimension

Luke Rowe, Martin Ethier, Eli-Henry Dykhne, Krzysztof Czarnecki

Predicting the future motion of road agents is a critical task in an autonomous driving pipeline. In this work, we address the problem of generating a set of scene-level, or joint, future trajectory predictions in multi-agent driving scenarios. To this end, we propose FJMP, a Factorized Joint Motion Prediction framework for multi-agent interactive driving sc

Suraj Ravindran, Vatsa Gandhi, Barry Lawlor, Guruswami Ravichandran

Multiscale experiments in heterogeneous materials and the knowledge of their physics under shock compression are limited. This study examines the multiscale shock response of particulate composites comprised of soda-lime glass particles in a PMMA matrix using full-field high-speed digital image correlation (DIC) for the first time. Normal plate impact experi

Dilawaiz, Shahid Qamar, Muhammad Irfan

We present a scheme to generate distant bipartite and tripartite entanglement between an atomic ensemble and an yttrium iron garnet (YIG) sphere in coupled microwave cavities. We consider an atomic ensemble in a single-mode microwave cavity which is coupled with a second single-mode cavity having a YIG sphere. Our system, therefore, has five excitation modes

Alessandro Artale, Luca Geatti, Nicola Gigante, Andrea Mazzullo

Linear Temporal Logic (LTL) is the de-facto standard temporal logic for system specification, whose foundational properties have been studied for over five decades. Safety and cosafety properties define notable fragments of LTL, where a prefix of a trace suffices to establish whether a formula is true or not over that trace. In this paper, we study the compl

Juan Uriel Legaria-Peña, Félix Sánchez-Morales, Yuriria Cortés-Poza

Cell-based models provide a helpful approach for simulating complex systems that exhibit adaptive, resilient qualities, such as cancer. Their focus on individual cell interactions makes them a particularly appropriate strategy to study the effects of cancer therapies, which often are designed to disrupt single-cell dynamics. In this work, we also propose the

Shuvendu Roy, Ali Etemad

In semi-supervised representation learning frameworks, when the number of labelled data is very scarce, the quality and representativeness of these samples become increasingly important. Existing literature on semi-supervised learning randomly sample a limited number of data points for labelling. All these labelled samples are then used along with the unlabe

Mario Malerba, Simone Sotgiu, Andrea Schirato, Leonetta Baldassarre

Recently, the concept of strong light-matter coupling has been demonstrated in semiconductor structures, and it is poised to revolutionize the design and implementation of components, including solid state lasers and detectors. We demonstrate an original nanospectroscopy technique that permits to study the light-matter interaction in single subwavelength-siz

David Burrell, William Cocke, Ryan McCulloch

We investigate the question of how many subgroups of a finite group are not in its Chermak-Delgado lattice. The Chermak-Delgado lattice for a finite group is a self-dual lattice of subgroups with many intriguing properties. Fasol\u{a} and T\u{a}rn\u{a}uceanu asked how many subgroups are not in the Chermak-Delgado lattice and classified all groups with two or

Vuong Bui

Let $P(n)$ be the number of polyominoes of $n$ cells and $\lambda$ be Klarner's constant, that is, $\lambda=\lim_{n\to\infty} \sqrt[n]{P(n)}$. We show that there exist some positive numbers $A,T$, so that for every $n$ \[ P(n) \ge An^{-T\log n} \lambda^n. \] This is somewhat a step toward the well known conjecture that there exist positive $C,\theta$ so that

Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas

The kernel Maximum Mean Discrepancy~(MMD) is a popular multivariate distance metric between distributions that has found utility in two-sample testing. The usual kernel-MMD test statistic is a degenerate U-statistic under the null, and thus it has an intractable limiting distribution. Hence, to design a level-$\alpha$ test, one usually selects the rejection

Keith Paarporn, Philip N. Brown

Can revealing one's competitive capabilities to an opponent offer strategic benefits? In this paper, we address this question in the context of General Lotto games, a class of two-player competitive resource allocation models. We consider an asymmetric information setting where the opponent is uncertain about the resource budget of the other player, and hold

Nurudin Alvarez-Gonzalez, Andreas Kaltenbrunner, Vicenç Gómez

Identifying similar network structures is key to capture graph isomorphisms and learn representations that exploit structural information encoded in graph data. This work shows that ego-networks can produce a structural encoding scheme for arbitrary graphs with greater expressivity than the Weisfeiler-Lehman (1-WL) test. We introduce IGEL, a preprocessing st

Sauradip Nag, Mengmeng Xu, Xiatian Zhu, Juan-Manuel Perez-Rua

Few-shot (FS) and zero-shot (ZS) learning are two different approaches for scaling temporal action detection (TAD) to new classes. The former adapts a pretrained vision model to a new task represented by as few as a single video per class, whilst the latter requires no training examples by exploiting a semantic description of the new class. In this work, we

S. A. Yost, Y. Liu, B. Shakerin, B. F. L. Ward

We present an overview of the use of IR-improvement of unintegrable singularities in the infrared regime via amplitude-based resummation in $\mathrm{QED} \times \mathrm{QCD}$ $\subset$ $\mathrm{SU}(2)_L \times \mathrm{U}_1 \times \mathrm{SU}(3)^c$. We work in the context of precision LHC/FCC physics. While illustrating such IR-improvement in specific example

Yuehao Bai, Jizhou Liu, Azeem M. Shaikh, Max Tabord-Meehan

This paper studies inference in cluster randomized trials where treatment status is determined according to a "matched pairs" design. Here, by a cluster randomized experiment, we mean one in which treatment is assigned at the level of the cluster; by a "matched pairs" design, we mean that a sample of clusters is paired according to baseline, cluster-level co

Animesh Karnewar, Oliver Wang, Tobias Ritschel, Niloy Mitra

We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene. Such a model can be used to produce 3D "remixes" of a given scene, by mapping spatial latent codes into a 3D volumetric representation, which can subsequently be rendered from arbitrary views using physically based volume rendering. By construc

Houshang Ardavan

The spectrum derived here for the most tightly-focused component of the radiation generated by the superluminally moving current sheet in the magnetrosphere of a non-aligned neutron star has a distribution function that fits the entire gamma-ray spectrum of the Crab pulsar on its own. This is the first time that the undivided breadth of this spectrum, from 1

Theodoros Papanikolaou, Andreas Lymperis, Smaragda Lola, Emmanuel N. Saridakis

Primordial black holes (PBHs) can generically form in inflationary setups through the collapse of enhanced cosmological perturbations, providing us access to the early Universe through their associated observational signatures. In the current work we propose a new mechanism of PBH production within non-canonical inflation, using a class of steep-deformed inf

Giovanni Cabass, Mikhail M. Ivanov, Oliver H. E. Philcox, Marko Simonovic

We forecast the constraints on single-field inflation from the bispectrum of future high-redshift surveys such as MegaMapper. Considering non-local primordial non-Gaussianity (NLPNG), we find that current methods will yield constraints of order $\sigma(f_{\rm NL}^{\rm eq})\approx 23$, $\sigma(f_{\rm NL}^{\rm orth})\approx 12$ in a joint power-spectrum and bi

Farha Yasmin, Jan Sperling

Research in quantum information science aims to surpass the scaling limitations of classical information processing. From a physicist's perspective, performance improvement involves a physical speedup in the quantum domain, achieved by dynamically exploiting quantum correlations. In this study, speed limits in interacting quantum systems are derived by compa

Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml, Peter Bühlmann

We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment are unknown. We assume a linear structural causal model with additive Gaussian noise and consider interventions that perturb their targets while maintaining the causal relationships

Gabriel Laverghetta

Robotic agents often perform tasks that transform sets of input objects into output objects through functional motions. This work describes the FOON knowledge representation model for robotic tasks. We define the structure and key components of FOON and describe the process we followed to create our universal FOON dataset. The paper describes various search

Shiwang Ma, Vitaly Moroz

We study asymptotic behavior of positive ground state solutions of the nonlinear Kirchhoff equation $$ -\Big(a+b\int_{\mathbb R^N}|\nabla u|^2\Big)\Delta u+ \lambda u= u^{q-1}+ u^{p-1} \quad {\rm in} \ \mathbb R^N, $$ as $\lambda\to 0$ and $\lambda\to +\infty$, where $N=3$ or $N= 4$, $2<q\le p\le 2^*$, $2^*=\frac{2N}{N-2}$ is the Sobolev critical exponent, $

Maria Filipkovska

Two combined methods for computing solutions of time-varying semilinear differential-algebraic equations (descriptor systems) are obtained. When constructing the methods, time-varying spectral projectors which can be found numerically are used. This enables one to numerically solve the differential-algebraic equation (DAE) in the original form without additi

Edoardo Arbib, Paolo Biscari, Clara Patriarca, Giovanni Zanzotto

By using modular functions on the upper complex half-plane, we study a class of strain energies for crystalline materials whose global invariance originates from the full symmetry group of the underlying lattice. This follows Ericksen's suggestion which aimed at extending the Landau-type theories to encompass the behavior of crystals undergoing structural ph

K. Sreeman Reddy

It has been argued that there are no islands in FLRW cosmologies with $\Lambda>0$ and $k=0$ arXiv:2008.01022. We argue that there is a timelike separated island at the initial singularity, and it will resolve the cosmological information paradox. The information about the particles that went beyond the horizon is not lost for our observer. By measuring Hawki

Artyom V. Astashenok, Sergey D. Odintsov, Vasilis Oikonomou

We investigate the Chandrasekhar mass limit for white dwarfs in various models of $f(R)$ gravity. Two equations of state for stellar matter are used: simple relativistic polytropic equation with polytropic index $n=3$ and the realistic Chandrasekhar equation of state. For calculations it is convenient to use the equivalent scalar-tensor theory in the Einstei

Álvaro del Pino, Aldo Witte

We describe a procedure, called regularisation, that allows us to study geometric structures on Lie algebroids via foliated geometric structures on a manifold of higher dimension. This procedure applies to various classes of Lie algebroids; namely, those whose singularities are of b^k, complex-log, or elliptic type, possibly with self-crossings. One of our m

Ourong Lin, Tian Yu, Yuhan Hou, Yi Zhu

This paper presents the design of a wireless sensor network for detecting and alerting the freezing of gait (FoG) symptoms in patients with Parkinson's disease. Three sensor nodes, each integrating a 3-axis accelerometer, can be placed on a patient at ankle, thigh, and truck. Each sensor node can independently detect FoG using an on-device deep learning (DL)

Mannque Rho

Unlike the octet baryons for $N_f=3$, there is no skyrmion coming from the $\eta^\prime$ meson. It is instead described as a fractional quantum Hall droplet, a pancake or a pita involving a singular $\eta^\prime$ ring in which Chern-Simons fields live. By incorporating hidden local symmetry and hidden scale symmetry in nuclear dynamics, I describe how to acc

Hari Prasanna Das, Yu-Wen Lin, Utkarsha Agwan, Lucas Spangher

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy c

Tien-Tai Nguyen

In this paper, we consider an incompressible viscous fluid in an infinitely deep ocean, being bounded above by a free moving boundary. The governing equations are the gravity-driven incompressible Navier-Stokes equations with variable density and no surface tension is taken into account on the free surface. After using the Lagrangian transformation, we write

Noga Alon, Noah Kravitz

Answering a recent question of Patchell and Spiro, we show that when a $d$-dimensional cube of side length $n$ is filled with letters, the word $\mathsf{CAT}$ can appear contiguously at most $(3^{d-1}/2)n^d$ times (allowing diagonals); we also characterize when equality occurs and extend our results to words other than $\mathsf{CAT}$.

Baijian Yang, Carolyn Ellis, Preston Smith, Huyunting Huang

In order to operate in a regulated world, researchers need to ensure compliance with ever-evolving landscape of information security regulations and best practices. This work explains the concept of Controlled Unclassified Information (CUI) and the challenges it brings to the research institutions. Survey from the user perceptions showed that most researcher

Syed Mohammed Arshad Zaidi, Varun Chandola, EunHye Yoo

Deep learning approaches for spatio-temporal prediction problems such as crowd-flow prediction assumes data to be of fixed and regular shaped tensor and face challenges of handling irregular, sparse data tensor. This poses limitations in use-case scenarios such as predicting visit counts of individuals' for a given spatial area at a particular temporal resol

Mark J. Ablowitz, Justin T. Cole, Gennady A. El, Mark A. Hoefer

The propagation of localized solitons in the presence of large-scale waves is a fundamental problem, both physically and mathematically, with applications in fluid dynamics, nonlinear optics and condensed matter physics. Here, the evolution of a soliton as it interacts with a rarefaction wave or a dispersive shock wave, examples of slowly varying and rapidly

Ze Li, Zi-Qing Xiao, Run-Qiu Yang

In order to study the pseudo entropy of time-like subregions holographically, the previous smooth space-like extremal surface was recently generalized to mix space-like and time-like segments and the area becomes complex value. This paper finds that, if one tries to use such kind of piecewise smooth extremal surfaces to compute time-like entanglement entropy

Prodromos-Vasileios Mekikis, Dimitrios Tyrovolas, Sotiris Tegos, Alexandros Papadopoulos

Reconfigurable intelligent surfaces (RISs) are artificial planar structures able to offer a unique way of manipulating propagated wireless signals. Commonly composed of a number of reconfigurable passive cell components and basic electronic circuits, RISs can almost freely perform a set of wave modification functionalities, in order to realize programmable w

Guojun Zhang, Yancheng Yuan, Defeng Sun

In this paper, we propose and analyze an efficient Halpern-Peaceman-Rachford (HPR) algorithm for solving the Wasserstein barycenter problem (WBP) with fixed supports. While the Peaceman-Rachford (PR) splitting method itself may not be convergent for solving the WBP, the HPR algorithm can achieve an $O(1/\varepsilon)$ non-ergodic iteration complexity with res

Maximilian Kimmich, Andrea Bartezzaghi, Jasmina Bogojeska, Cristiano Malossi

Neural approaches have become very popular in Question Answering (QA), however, they require a large amount of annotated data. In this work, we propose a novel approach that combines data augmentation via question-answer generation with Active Learning to improve performance in low-resource settings, where the target domains are diverse in terms of difficult

Kyriaki-Margarita Bintsi, Robert Jarolim, Benoit Tremblay, Miraflor Santos

Extreme Ultraviolet (EUV) light emitted by the Sun impacts satellite operations and communications and affects the habitability of planets. Currently, EUV-observing instruments are constrained to viewing the Sun from its equator (i.e., ecliptic), limiting our ability to forecast EUV emission for other viewpoints (e.g. solar poles), and to generalize our know

Attilio L. Stella, Aleksei Chechkin, Gianluca Teza

Inhomogeneous environments are rather ubiquitous in nature, often implying anomalies resulting in deviation from Gaussianity of diffusion processes. While sub- and superdiffusion are usually due to conversing environmental features (hindering or favoring the motion, respectively), they are both observed in systems ranging from the micro- to the cosmological

Xingyi Yang, Daquan Zhou, Jiashi Feng, Xinchao Wang

Despite the recent visually-pleasing results achieved, the massive computational cost has been a long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits their applications on resource-limited platforms. Prior methods towards efficient DPM, however, have largely focused on accelerating the testing yet overlooked their huge

Jia-xin Qian, Yan-qing Lu

We study the collective motion of self-propelled particles affected by the spatial-dependent noise based on the Vicsek rules. Only the particles inside the special region will affected by noise. The consideration of the spatial-dependent noise is closer to reality because of the complexity of the environment. Interestingly, we find that there exists an optim

Wayne Xin Zhao, Jing Liu, Ruiyang Ren, Ji-Rong Wen

Text retrieval is a long-standing research topic on information seeking, where a system is required to return relevant information resources to user's queries in natural language. From classic retrieval methods to learning-based ranking functions, the underlying retrieval models have been continually evolved with the ever-lasting technical innovation. To des

Nghi D. Q. Bui, Yue Wang, Steven Hoi

Automated software debugging is a crucial task for improving the productivity of software developers. Many neural-based techniques have been proven effective for debugging-related tasks such as bug localization and program repair (or bug fixing). However, these techniques often focus only on either one of them or approach them in a stage-wise manner, ignorin

Kevin Voogd, Jean Pierre Allamaa, Javier Alonso-Mora, Tong Duy Son

Reinforcement learning (RL) is a promising solution for autonomous vehicles to deal with complex and uncertain traffic environments. The RL training process is however expensive, unsafe, and time consuming. Algorithms are often developed first in simulation and then transferred to the real world, leading to a common sim2real challenge that performance decrea

Swati Gupta, Jai Moondra, Mohit Singh

Fair facility location problems try to balance access costs to open facilities borne by different groups of people by minimizing the $L_p$ norm of these group distances. However, there is no clear choice of "$p$" in the current literature. We present a novel approach to address the challenge of choosing the right notion of fairness. We introduce the concept

Timur Abbiasov, Cate Heine, Edward Glaeser, Carlo Ratti

Americans travel 7 to 9 miles on average for shopping and recreational activities, which is far longer than the 15-minute (walking) city advocated by ecologically-oriented urban planners. This paper provides a comprehensive analysis of local trip behavior in US cities using GPS data on individual trips from 40 million mobile devices. We define local usage as

Duncan Earl, K Karunaratne, Jason Schaake, Ryan Strum

We present the architecture and near-term use cases for a first-generation commercial quantum network. We define the foundational hardware and software elements required to operate and manage the network. Finally, we discuss the configuration of this network for near-term consumer applications and propose how the network can support the broader technical goa

Yuya Sasaki, Yulong Wang

Policy analysts are often interested in treating the units with extreme outcomes, such as infants with extremely low birth weights. Existing changes-in-changes (CIC) estimators are tailored to middle quantiles and do not work well for such subpopulations. This paper proposes a new CIC estimator to accurately estimate treatment effects at extreme quantiles. W

J. M. Jenkins, C. M. J. Osborne, R. Keppens

Aims. We here demonstrate how the recently developed Lightweaver framework makes non-LTE (NLTE) spectral synthesis feasible on a new 3D ab-initio magnetohydrodynamic (MHD) filament/prominence simulation, in a post-processing step. Methods. We clarify the need to introduce filament/prominent-specific Lightweaver boundary conditions that accurately model incid

Graham Smith

In the study of immersed surfaces of constant positive extrinsic curvature in space-forms, it is natural to substitute completeness for a weaker property, which we here call quasicompleteness. We determine the global geometry of such surfaces under the hypotheses of quasicompleteness. In particular, we show that, for $k>\text{Max}(0,-c)$, the only quasicompl

Jiajian Guan, Qin Jiang, Yue He, Xu Zhang

The nanocrystalline-amorphous textures are commonly observed in the coatings synthesized by energetic deposition.This work reports a theoretical study towards binary Cu-C phase separation.By performing a MD simulation using the LAMMPS instead of classical PFK methodology, we theoretically explained how the initial pressure and Cu concentration fundamentally

Jiabao Gao, Caijun Zhong, Geoffrey Ye Li, Joseph B. Soriaga

In terahertz (THz) massive multiple-input multiple-output (MIMO) systems, the combination of huge bandwidth and massive antennas results in severe beam split, thus making the conventional phase-shifter based hybrid precoding architecture ineffective. With the incorporation of true-time-delay (TTD) lines in the hardware implementation of the analog precoders,

Parag Pravin Dakle, SaiKrishna Rallabandi, Preethi Raghavan

We view the landscape of large language models (LLMs) through the lens of the recently released BLOOM model to understand the performance of BLOOM and other decoder-only LLMs compared to BERT-style encoder-only models. We achieve this by evaluating the smaller BLOOM model variants (\textit{350m/560m} and \textit{1b3/1b7}) on several NLP benchmark datasets an

Rui Huang, Ze Huang, Songzhi Su

Visual Place Recognition is an essential component of systems for camera localization and loop closure detection, and it has attracted widespread interest in multiple domains such as computer vision, robotics and AR/VR. In this work, we propose a faster, lighter and stronger approach that can generate models with fewer parameters and can spend less time in t

Xiao Wang, Xiaoxin Yang, Jiangyu Li

The Moore's law in the semiconducting industry has faltered as the three-dimensional (3D) Si-based transistors has approached their physical limit with the downscaling. The carrier mobility $\rm \mu $, critical to the device's performance, will be degraded when the thickness of Si is scaled into several nanometers. In contrast to the bulk counterpart, two-di

Kohei Kobayashi

We investigate the effect of stochastic control errors on the Hamiltonian that controls a closed quantum system. Quantum information technologies require careful control for preparing a desired state used as an information resource. However, because the stochastic control errors inevitably appear in realistic situation, it is difficult to completely implemen

Sofia Maragkou, Axel Jantsch

Cyber-physical systems (CPS) provide profitable surfaces for hardware attacks such as hardware Trojans. Hardware Trojans can implement stealthy attacks such as leaking critical information, taking control of devices or harm humans. In this article we review information flow tracking (IFT) methods for protecting CPS against hardware Trojans, and discuss their

Sepideh Hatamikia, Ingo Gulyas, Wolfgang Birkfellner, Gernot Kronreif

Medical imaging phantoms are widely used for validation and verification of imaging systems and algorithms in surgical guidance and radiation oncology procedures. Especially, for the performance evaluation of new algorithms in the field of medical imaging, manufactured phantoms need to replicate specific properties of the human body, e.g., tissue morphology

Snir Vitrack Tamam, Raz Lapid, Moshe Sipper

Deep neural networks (DNNs) have greatly impacted numerous fields over the past decade. Yet despite exhibiting superb performance over many problems, their black-box nature still poses a significant challenge with respect to explainability. Indeed, explainable artificial intelligence (XAI) is crucial in several fields, wherein the answer alone -- sans a reas

Cehao Yu, Maarten Wijntjes, Elmar Eisemann, Sylvia Pont

We present a method to capture the 7-dimensional light field structure, and translate it into perceptually-relevant information. Our spectral cubic illumination method quantifies objective correlates of perceptually relevant diffuse and directed light components, including their variations over time, space, in color and direction, and the environment's respo

André Artelt, Barbara Hammer

Counterfactual explanations are a popular type of explanation for making the outcomes of a decision making system transparent to the user. Counterfactual explanations tell the user what to do in order to change the outcome of the system in a desirable way. However, it was recently discovered that the recommendations of what to do can differ significantly in

Daniel Lazarev

Entropy and information can be considered dual: entropy is a measure of the subspace defined by the information constraining the given ambient space. Negative entropies, arising in na\"ive extensions of the definition of entropy from discrete to continuous settings, are byproducts of the use of probabilities, which only work in the discrete case by a fortuna

Maria C. DiMarco, Sierra L. Jess, Robie A. Hennigar, Robert B. Mann

Johnson has shown [arXiv:1703.06119] that in the vicinity of a critical point the efficiency of a black hole heat engine can approach the Carnot efficiency while maintaining finite power. We characterize and extend this result in several ways, and we show how the rate of approach to the Carnot efficiency is governed by the critical exponents. We apply these

Maria Laura Filippetti, Javier Andreu-Perez, Carina de Klerk, Chloe Richmond

In the last decade, fNIRS has provided a non-invasive method to investigate neural activation in developmental populations. Despite its increasing use in developmental cognitive neuroscience, there is little consistency or consensus on how to pre-process and analyse infant fNIRS data. With this registered report, we investigated the feasibility of applying m

Yongdan Yang, Zongkang Zhang, Xiaosi Xu, Bing-Nan Lu

A common situation in quantum many-body physics is that the underlying theories are known but too complicated to solve efficiently. In such cases one usually builds simpler effective theories as low-energy or large-scale alternatives to the original theories. Here the central tasks are finding the optimal effective theories and proving their equivalence to t

Jean Pierre Allamaa, Panagiotis Patrinos, Herman Van der Auweraer, Tong Duy Son

Orthogonal collocation methods are direct approaches for solving optimal control problems (OCP). A high solution accuracy is achieved with few optimization variables, making it more favorable for embedded and real-time NMPC applications. However, collocation approaches lack a guarantee about the safety of the resulting trajectory as inequality constraints ar

Cédric Josz, Lexiao Lai

We provide sufficient conditions for instability of the subgradient method with constant step size around a local minimum of a locally Lipschitz semi-algebraic function. They are satisfied by several spurious local minima arising in robust principal component analysis and neural networks.

Akshat Bhandari, Sriya Rallabandi, Sanchit Singhal, Aditya Kasliwal

Contrails, short for condensation trails, are line-shaped ice clouds produced by aircraft engine exhaust when they fly through cold and humid air. They generate a greenhouse effect by absorbing or directing back to Earth approximately 33% of emitted outgoing longwave radiation. They account for over half of the climate change resulting from aviation activiti

Cédric Josz, Lexiao Lai

We consider the subgradient method with constant step size for minimizing locally Lipschitz semi-algebraic functions. In order to analyze the behavior of its iterates in the vicinity of a local minimum, we introduce a notion of discrete Lyapunov stability and propose necessary and sufficient conditions for stability.

Anar Rustamov, Joachim Stroth, Romain Holzmann

We develop an innovative and unbiased procedure, based on event mixing, to account for unavoidable contributions from volume (or system size) fluctuations to experimentally measured moments of particle multiplicity distributions produced in relativistic nuclear collisions. Within the wounded-nucleon model they are characterized by fluctuations of the number

Cédric Josz, Lexiao Lai

We provide the first positive result on the nonsmooth optimization landscape of robust principal component analysis, to the best of our knowledge. It is the object of several conjectures and remains mostly uncharted territory. We identify a necessary and sufficient condition for the absence of spurious local minima in the rank-one case. Our proof exploits th

Heather D. Couture

Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies

Jin-Xin Hu, Zi-Ting Sun, Ying-Ming Xie, K. T. Law

Recently, the Josephson diode effect (JDE), in which the superconducting critical current magnitudes differ when the currents flow in opposite directions, has attracted great interest. In particular, it was demonstrated that gate-defined Josephson junctions based on magic-angle twisted bilayer graphene showed a strong nonreciprocal effect when the weak-link

Einan Gardi, Franz Herzog, Stephen Jones, Yao Ma

We study the application of the method of regions to Feynman integrals with massless propagators contributing to off-shell Green's functions in Minkowski spacetime (with non-exceptional momenta) around vanishing external masses, $p_i^2\to 0$. This on-shell expansion allows us to identify all infrared-sensitive regions at any power, in terms of infrared subgr

Huan Qing

Community detection in weighted networks has been a popular topic in recent years. However, while there exist several flexible methods for estimating communities in weighted networks, these methods usually assume that the number of communities is known. It is usually unclear how to determine the exact number of communities one should use. Here, to estimate t

Chuang Lin, Peize Sun, Yi Jiang, Ping Luo

Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding box annotations. Natural language supervision is an attractive alternative for its annotation-free attributes and broader object concepts. However, learning open-vocabulary object

Minghui Hu, Chuanxia Zheng, Heliang Zheng, Tat-Jen Cham

The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals. In this work, we harness these traits and present a unified multimodal generation model that can conduct both the "modality translation" and "multi-modality generation" tasks using a sin

S. Gomé, L. S. Tuckerman, D. Barkley

Low Reynolds number turbulence in wall-bounded shear flows en route to laminar flow takes the form of spatially intermittent turbulent structures. In plane shear flows, these appear as a regular pattern of alternating turbulent and quasi-laminar flow. Both the physical and the spectral energy balance of a turbulent-laminar pattern in plane Couette flow are c

Kyung-Won Suh

We investigate infrared properties of OGLE4 Mira variables in our Galaxy. For each object, we cross-identify the AllWISE, 2MASS, Gaia, and IRAS counterparts. We present various IR two-color diagrams (2CDs) and period-magnitude and period-color relations for the Mira variables. Generally, the Mira variables with longer periods are brighter in the IR fluxes an