May 2022 arXiv papers — page 106

Victor J. H. Trees, Daphne M. Stam

Numerical simulations of starlight that is reflected by Earth-like exoplanets predict habitability signatures that can be searched for with future telescopes. We explore signatures of water oceans in the flux and polarization spectra of this reflected light. With an adding-doubling algorithm, we compute the total flux F, polarized flux Q and degree of polari

Zhi Li

The complexity of a quantum gate, defined as the minimal number of elementary gates to build it, is an important concept in quantum information and computation. It is shown recently that the complexity of quantum gates built from random quantum circuits almost surely grows linearly with the number of building blocks. In this article, we provide two short pro

Alejandro D. Somoza, Nicola Lorenzoni, James Lim, Susana F. Huelga

Electron-hole pairs in organic photovoltaics dissociate efficiently despite their Coulomb-binding energy exceeding thermal energy at room temperature. The electronic states involved in charge separation couple to structured vibrational environments containing multiple underdamped modes. The non-perturbative simulations of such large, spatially extended elect

Marek Lewicki, Ville Vaskonen, Hardi Veermäe

We present a new approach to studies of bubble dynamics in fluids. Relying on particle-based simulations, this method is general and suitable for cases where the commonly used perfect fluid description fails. We study expanding true vacuum bubbles surrounded by free or self-interacting particles and quantify how self-interactions affect the terminal bubble w

Catherine Wong, William P. McCarthy, Gabriel Grand, Yoni Friedman

Our understanding of the visual world goes beyond naming objects, encompassing our ability to parse objects into meaningful parts, attributes, and relations. In this work, we leverage natural language descriptions for a diverse set of 2K procedurally generated objects to identify the parts people use and the principles leading these parts to be favored over

A. N. Yan

We analyze the Kaluza-Klein type procedure in AdS$_3$ space called the dimensional degression. The topological theory of the Fronsdal field in AdS$_3$ is reformulated in terms of the fields propagating in AdS$_2$. We find that the Fronsdal field in AdS$_3$ leads to finitely many Kaluza-Klein modes. Namely, the obtained spectrum is the massive Klein-Gordon an

Pratik Kumar, Ankita Nandi, Shantanu Chakrabartty, Chetan Singh Thakur

Analog computing is attractive compared to digital computing due to its potential for achieving higher computational density and higher energy efficiency. However, unlike digital circuits, conventional analog computing circuits cannot be easily mapped across different process nodes due to differences in transistor biasing regimes, temperature variations and

Nikita Kolganov, Dmitrii A. Trunin

We establish the correspondence between the classical and quantum butterfly effects in nonlinear vector mechanics with the broken $O(N)$ symmetry. On one hand, we analytically calculate the out-of-time ordered correlation functions and the quantum Lyapunov exponent using the augmented Schwinger-Keldysh technique in the large-$N$ limit. On the other hand, we

Wuyang Chen, Wei Huang, Xinyu Gong, Boris Hanin

Advanced deep neural networks (DNNs), designed by either human or AutoML algorithms, are growing increasingly complex. Diverse operations are connected by complicated connectivity patterns, e.g., various types of skip connections. Those topological compositions are empirically effective and observed to smooth the loss landscape and facilitate the gradient fl

Divya Ramesh, Vaishnav Kameswaran, Ding Wang, Nithya Sambasivan

Accountability, a requisite for responsible AI, can be facilitated through transparency mechanisms such as audits and explainability. However, prior work suggests that the success of these mechanisms may be limited to Global North contexts; understanding the limitations of current interventions in varied socio-political conditions is crucial to help policyma

Frank V. Kowalski

The decay of the electrical energy in a resistor-vacuum capacitor circuit is shown to involve multiple relaxation processes, with dramatically different time constants. This is measured using a vacuum capacitor to eliminate the effect of a dielectric between the plates (polypropylene capacitors are shown to exhibit similar behavior). A simple phenomenologica

Lily Xu, Arpita Biswas, Fei Fang, Milind Tambe

Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land. Combinatorial bandits have been used to allocate limited patrol resources, but existing approaches overlook the fact that each location is home to multiple species in varying proportions, so a patrol benefi

Yan He, Chih-Chun Chien

The Uhlmann phase, which reflects the holonomy as the purified state of a density matrix traverses a loop in the parameter space, has been used to characterize topological properties of several systems at finite temperatures. We test the Uhlmann holonomy against various system-environment couplings in quantum dynamics described by the Lindblad equations of t

Colin Bloomfield, Yoshihiro Maruyama

We extend Lawvere-Pitts prop-categories (aka. hyperdoctrines) to develop a general framework for providing "algebraic" semantics for nonclassical first-order logics. This framework includes a natural notion of substitution, which allows first-order logics to be considered as structural closure operators just as propositional logics are in abstract algebraic

Hang Dong, Víctor Suárez-Paniagua, Huayu Zhang, Minhong Wang

Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. We propose a method using ontologies and weak supervision, with recent pre-trained co

Yiming Liu, Yuhao Wang, Cen Zhang, Lei Zhang

In the Standard Model Effective Field Theory (SMEFT), operators involving the top quark are generally difficult to probe, and can generate sizable loop contributions to the electroweak precision observables, measured by past and future lepton colliders. Could the high precision of the electroweak measurements compensate the loop suppression and provide compe

Julia Holter, Jonathan Stallrich

The tuning parameter selection strategy for penalized estimation is crucial to identify a model that is both interpretable and predictive. However, popular strategies (e.g., minimizing average squared prediction error via cross-validation) tend to select models with more predictors than necessary. This paper proposes a simple, yet powerful cross-validation s

Dmitry Kovalev, Alexander Gasnikov

In this paper, we revisit the smooth and strongly-convex-strongly-concave minimax optimization problem. Zhang et al. (2021) and Ibrahim et al. (2020) established the lower bound $\Omega\left(\sqrt{\kappa_x\kappa_y} \log \frac{1}{\epsilon}\right)$ on the number of gradient evaluations required to find an $\epsilon$-accurate solution, where $\kappa_x$ and $\ka

Pavan V. Kashyap, Yohann Duguet, Olivier Dauchot

Laminar-turbulent pattern formation is a distinctive feature of the intermittency regime in subcritical plane shear flows. By performing extensive numerical simulations of the plane channel flow, we show that the pattern emerges from a spatial modulation of the turbulent flow, due to a linear instability. We sample over many realizations the linear response

Wen-Xuan Long, Rui Chen, Marco Moretti, Wei Zhang

Orbital angular momentum (OAM) radars are able to estimate the azimuth angle and the rotation velocity of multiple targets without relative motion or beam scanning. Moreover, OAM wireless communications can achieve high spectral efficiency (SE) by utilizing a set of information-bearing modes on the same frequency channel. Benefitting from the above advantage

Liudmila Bishler, Andrei Mironov, Andrey Morozov

We present a comprehensive classification of invariants of knots and links associated with irreducible representations of \uqslN{}, when the parameter of quantization $q$ is a root of unity. We demonstrate that, besides the standard HOMFLY-PT invariants, which are associated with representations with highest and lowest weights, non-trivial invariants can be

Nikolaos Tziavelis, Wolfgang Gatterbauer, Mirek Riedewald

We study ranked enumeration for Conjunctive Queries (CQs) where the answers are ordered by a given ranking function (e.g., an ORDER BY clause in SQL). We develop "any-k" algorithms, which, without knowing the number k of desired answers, push down the ranking into joins by carefully ordering the computation of intermediate tuples and avoiding materialization

Jordan Leung, Frank Permenter, Ilya Kolmanovsky

This paper introduces a computationally efficient approach for solving Model Predictive Control (MPC) reference tracking problems with state and control constraints. The approach consists of three key components: First, a log-domain interior-point quadratic programming method that forms the basis of the overall approach; second, a method of warm-starting thi

Ngoc Mai Tran, Jidong Wang

This paper studies the following question: given a piecewise-linear function, find its minimal algebraic representation as a tropical rational signomial. We put forward two different notions of minimality, one based on monomial length, the other based on factorization length. We show that in dimension one, both notions coincide, but this is not true in dimen

Xia Zeng, Arkaitz Zubiaga

As part of an automated fact-checking pipeline, the claim veracity classification task consists in determining if a claim is supported by an associated piece of evidence. The complexity of gathering labelled claim-evidence pairs leads to a scarcity of datasets, particularly when dealing with new domains. In this paper, we introduce SEED, a novel vector-based

Jianghui Ji, Haitao Li, Junbo Zhang, Liang Fang

The Closeby Habitable Exoplanet Survey (CHES) mission is proposed to discover habitable-zone Earth-like planets of the nearby solar-type stars ($\sim 10~\mathrm{pc}$ away from our solar system) via micro-arcsecond relative astrometry. The major scientific objectives of CHES are: to search for Earth Twins or terrestrial planets in habitable zones orbiting 100

Ema Dimastrogiovanni, Matteo Fasiello, Ameek Malhotra, Gianmassimo Tasinato

Gravitational wave (GW) backgrounds of cosmological origin are expected to be nearly isotropic, with small anisotropies resembling those of the cosmic microwave background. We analyse the case of a scalar-induced GW background and clarify in the process the relation between two different approaches to calculating GW anisotropies. We focus on GW scenarios sou

Raffaele Giancarlo, Giovanni Manzini, Antonio Restivo, Giovanna Rosone

Introduced about thirty years ago in the field of Data Compression, the Burrows-Wheeler Transform (BWT) is a string transformation that, besides being a booster of the performance of memoryless compressors, plays a fundamental role in the design of efficient self-indexing compressed data structures. Finding other string transformations with the same remarkab

K. M. Dasyra, G. F. Paraschos, T. Bisbas, F. Combes

The jets launched by actively accreting black holes are capable of launching several of the massive (million or billion solar mass) molecular outflows observed in galaxies. These outflows could suppress or enhance star formation in galaxies. To investigate the stability of clouds capable to form stars in outflows, we modeled CO and HCO+ ALMA data of the gala

Bianka Woloncewicz, Tamoghna Das, Marek Żukowski

Better versions of separability conditions for four mode optical fields, i.e. two beams with two modes of mutually orthogonal polarization are given. Our conditions involve variances. Their meaning is intuitive and their implementation is feasible. Namely, if for a given quantum state the spread of the data around its mean value is smaller than the minimal s

Yaqi Hu, Mingsheng Yin, Sundeep Rangan, Marco Mezzavilla

High-rank line-of-sight (LOS) MIMO systems have attracted considerable attention for millimeter wave and THz communications. The small wavelengths in these frequencies enable spatial multiplexing with massive data rates at long distances. Such systems are also being considered for multi-path non-LOS (NLOS) environments. In these scenarios, standard channel m

Tarek Mealy, Filippo Capolino

We propose a degenerate version of the Bragg condition, associated to a degenerate band edge (DBE). A standard Bragg condition can be implemented using a periodic grating that operates at the regular band edge. The structure we propose to realize a DBE is made of two stacked identical gratings that form two coupled periodic waveguides with broken mirror symm

Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta

Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input. ICL incurs substantial computational, memory, and storage costs because it involves processing all of the training examples every time a prediction

Bruce Allen

Gravitational waves (GWs) create correlations in the arrival times of pulses from different pulsars. The expected correlation $\mu(\gamma)$ as a function of the angle $\gamma$ between the directions to two pulsars was calculated by Hellings and Downs for an isotropic and unpolarized GW background, and several pulsar timing array (PTA) collaborations are work

S. Alvi, T. Brinckmann, M. Gerbino, M. Lattanzi

The stability of particles in the cosmic soup is an important property that can affect the cosmic evolution. In this work, we update the constraints on the decaying cold dark matter scenario, when the decay products are effectively massless. We assume, as a base case, that all of dark matter is unstable and it can decay on cosmological time scales. We then e

Andres Iturriaga, Carlos A. Sing Long, Alejandro Jara

We study Dirichlet process-based models for sets of predictor-dependent probability distributions, where the domain and predictor space are general Polish spaces. We generalize the definition of dependent Dirichlet processes, originally constructed on Euclidean spaces, to more general Polish spaces. We provide sufficient conditions under which dependent Diri

Zi Hian Tan, Kian Yan Yong, Jian-Jun Shu

Although lessons have been learned from previous severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) outbreaks, the rapid evolution of the viruses means that future outbreaks of a much larger scale are possible, as shown by the current coronavirus disease 2019 (COVID-19) outbreak. Therefore, it is necessary to better understa

Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld

Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely hampers the use of modern ready-made machine learning models as they rely heavily on the paradigm, 'the bigger the data the better'. Presenting similarity based machine learning (SML), we show an approach to select

Xiaoyun Li, Belhal Karimi, Ping Li

We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce the communication cost in the gradient transmission process. Our convergence analysis of COMP-AMS shows that such compressed gradient averaging strategy yields same convergence rate

K. V. Harsha, Jithin Ravi, Tobias Koch

We consider a binary statistical hypothesis testing problem, where $n$ independent and identically distributed random variables $Z^n$ are either distributed according to the null hypothesis $P$ or the alternate hypothesis $Q$, and only $P$ is known. For this problem, a well-known test is the Hoeffding test, which accepts $P$ if the Kullback-Leibler (KL) dive

Jiadi Zhang, Mohammad Reza Amini, Ilya Kolmanovsky, Munechika Tsutsumi

This paper investigates options to complement a diesel engine airpath feedback controller with a feedforward. The control objective is to track the intake manifold pressure and exhaust gas recirculation (EGR) rate targets by manipulating the EGR valve and variable geometry turbine (VGT) while satisfying state and input constraints. The feedback controller is

Carlos A. Argüelles, Diksha Garg, Sameer Patel, Mary Hall Reno

The neutrino interaction length scales with energy, and becomes comparable to Earth's diameter above 10's of TeV energies. Over terrestrial distances, the tau's short lifetime leads to an energetic regenerated tau neutrino flux, tau neutrino to tau to tau neutrino, within the Earth. The next generation of neutrino experiments aim to detect ultra-high energy

Steven Jorgensen, John Holodnak, Jensen Dempsey, Karla de Souza

With the increasing prevalence of encrypted network traffic, cyber security analysts have been turning to machine learning (ML) techniques to elucidate the traffic on their networks. However, ML models can become stale as new traffic emerges that is outside of the distribution of the training set. In order to reliably adapt in this dynamic environment, ML mo

Patrizio Angelini, Steven Chaplick, Sabine Cornelsen, Giordano Da Lozzo

In an upward-planar L-drawing of a directed acyclic graph (DAG) each edge $e$ is represented as a polyline composed of a vertical segment with its lowest endpoint at the tail of $e$ and of a horizontal segment ending at the head of $e$. Distinct edges may overlap, but not cross. Recently, upward-planar L-drawings have been studied for $st$-graphs, i.e., plan

Mohammad Dehghani Soltani, Hossein Kazemi, Elham Sarbazi, Taisir E. H. El-Gorashi

The design of a compact high-speed and wide field of view (FOV) receiver is challenging due to the presence of two well-known trade-offs. The first one is the area-bandwidth trade-off of photodetectors (PDs) and the second one is the gain-FOV trade-off due to the use of optics. The combined effects of these two trade-offs imply that the achievable data rate

Guangxi Li, Xuanqiang Zhao, Xin Wang

An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing (NLP). Although some efforts based on syntactic analysis have opened the door to research in Quantum NLP (QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent n

Angela Y. Zhu, Nandita Mitra, Karla Hemming, Michael O. Harhay

Cluster-randomized trials (CRTs) involve randomizing entire groups of participants -- called clusters -- to treatment arms but are often comprised of a limited or fixed number of available clusters. While covariate adjustment can account for chance imbalances between treatment arms and increase statistical efficiency in individually-randomized trials, analyt

Matthew van Niekerk, Anthony Rizzo, Hector Rubio Rivera, Gerald Leake

As computing resource demands continue to escalate in the face of big data, cloud-connectivity and the internet of things, it has become imperative to develop new low-power, scalable architectures. Neuromorphic photonics, or photonic neural networks, have become a feasible solution for the physical implementation of efficient algorithms directly on-chip. Thi

J. M. Casas, L. Bonavera, J. González-Nuevo, C. Baccigalupi

Component separation is the process with which emission sources in astrophysical maps are generally extracted by taking multi-frequency information into account. It is crucial to develop more reliable methods for component separation for future CMB experiments. We aim to develop a new method based on fully convolutional neural networks called the Cosmic micr

Benjamin Decardi-Nelson, Jinfeng Liu

In this work, we present a distributed framework based on the graph algorithm for computing control invariant set for nonlinear cascade systems. The proposed algorithm exploits the structure of the interconnections within a process network. First, the overall system is decomposed into several subsystems with overlapping states. Second, the control invariant

Laura Ciobanu, Alex Evetts

In this paper we generalise and unify the results and methods used by Benson, Liardet, Evetts, and Evetts & Levine, to show that rational sets in a virtually abelian group G have rational (relative) growth series with respect to any generating set for G. We prove equivalences between the structures used in the literature, and establish the rationality of imp

Constantine Yannouleas, Uzi Landman

It is demonstrated that exact diagonalization of the microscopic many-body Hamiltonian via systematic full configuration-interaction (FCI) calculations is able to predict the spectra as a function of detuning of three-electron hybrid qubits based on GaAs asymmetric double quantum dots. It is further shown that, as a result of strong inter-electron correlatio

Shuvojit Paul, Ashreya Jayaram, N Narinder, Thomas Speck

We experimentally determine the force exerted by a bath of active particles onto a passive probe as a function of its distance to a wall and compare it to the measured averaged density distribution of active particles around the probe. Within the framework of an active stress, we demonstrate that both quantities are - up to a factor - directly related to eac

Paolo Campeti, Eiichiro Komatsu

We derive a new upper bound on the tensor-to-scalar ratio parameter $r$ using the frequentist profile likelihood method. We vary all the relevant cosmological parameters of the $\Lambda$CDM model, as well as the nuisance parameters. Unlike the Bayesian analysis using Markov Chain Monte Carlo (MCMC), our analysis is independent of the choice of priors. Using

Luís Duarte

Let $(R,\mathfrak{m})$ be a Noetherian local ring and $I$ an ideal of $R$. We study how local cohomology modules with support in $\mathfrak{m}$ change for small perturbations $J$ of $I$, that is, for ideals $J$ such that $I\equiv J\bmod \mathfrak{m}^N$ for large $N$, under the hypothesis that $I$ and $J$ share the same Hilbert function. As one of our main re

Roza Aceska, McKenna Kaczanowski

We introduce the notion of a cross-frame potential function, which takes one frame as input and returns its cross-frame potential value with respect to another frame. We analyze the behavior of this new function to determine what information the cross-frame potential value can reveal about the relationship between the two frames.

Yifan Cui, Jan Hannig

Inferential models have recently gained in popularity for valid uncertainty quantification. In this paper, we investigate inferential models by exploring relationships between inferential models, fiducial inference, and confidence curves. In short, we argue that from a certain point of view, inferential models can be viewed as fiducial distribution based con

Truc Nguyen, Phuc Thai, Tre' R. Jeter, Thang N. Dinh

Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. How

V. Miralles, O. Eberhardt, H. Gisbert, A. Pich

We consider a simple extension of the electroweak theory, incorporating one $SU(2)_L$ doublet of colour-octet scalars with Yukawa couplings satisfying the principle of minimal flavour violation. Using the HEPfit package, we perform a global fit to the available data, including all relevant theoretical constraints, and extract the current bounds on the model

Simon Jenni, Markus Woodson, Fabian Caba Heilbron

We propose a method for generating a temporally remapped video that matches the desired target duration while maximally preserving natural video dynamics. Our approach trains a neural network through self-supervision to recognize and accurately localize temporally varying changes in the video playback speed. To re-time videos, we 1. use the model to infer th

Sayali Phadke, Matthew D Beckman, Kari Lock Morgan

The central role of statistical literacy has been discussed extensively, emphasizing its importance as a learning outcome and in promoting a citizenry capable of interacting with the world in an informed and critical manner. Our work contributes to the growing literature on assessing and improving people's statistical literacy vis-a-vis contexts important in

Xiao-Qi Han, Sheng-Song Xu, Zhen Feng, Rong-Qiang He

A main task in condensed-matter physics is to recognize, classify, and characterize phases of matter and the corresponding phase transitions, for which machine learning provides a new class of research tools due to the remarkable development in computing power and algorithms. Despite much exploration in this new field, usually different methods and technique

Jiening Zhu, Harini Veeraraghavan, Larry Norton, Joseph O. Deasy

The Histogram of Oriented Gradient is a widely used image feature, which describes local image directionality based on numerical differentiation. Due to its ill-posed nature, small noise may lead to large errors. Conventional HOG may fail to produce meaningful directionality results in the presence of noise, which is common in medical radiographic imaging. W

Adam Chapman, Alexander Guterman, Solomon Vishkautsan, Svetlana Zhilina

We study the roots of polynomials over Cayley--Dickson algebras over an arbitrary field and of arbitrary dimension. For this purpose we generalize the concept of spherical roots from quaternion and octonion polynomials to this setting, and demonstrate their basic properties. We show that the spherical roots (but not all roots) of a polynomial $f(x)$ are also

Maryam Olyaee, José A. Cortés, F. Javier Lopez-Martinez, José F. Paris

We introduce and characterize the independent fluctuating two-ray (IFTR) fading model, a class of fading models consisting of two specular components which fluctuate independently, plus a diffuse component modeled as a complex Gaussian random variable. The IFTR model complements the popular fluctuating two-ray (FTR) model, on which the specular components ar

M. R. Sandoval

Given a (possibly singular) Riemannian foliation $\mathcal{F}$ with closed leaves on a compact manifold $M$ with an adapted metric, we investigate the wave trace invariants for the basic Laplacian about a non-zero period. We compare them to the wave invariants of the underlying Riemannian orbifold that exists when the leaves in the regular region are identif

Peter Vouras, Kumar Vijay Mishra, Alexandra Artusio-Glimpse, Samuel Pinilla

Rapid developments in synthetic aperture (SA) systems, which generate a larger aperture with greater angular resolution than is inherently possible from the physical dimensions of a single sensor alone, are leading to novel research avenues in several signal processing applications. The SAs may either use a mechanical positioner to move an antenna through sp

Tzu-Jan Li, Jack Shotton

Let $G$ be a connected reductive group defined over a finite field $\mathbb{F}_q$ of characteristic $p$, with Deligne--Lusztig dual $G^\ast$. We show that, over $\overline{\mathbb{Z}}[1/pM]$ where $M$ is the product of all bad primes for $G$, the endomorphism ring of a Gelfand--Graev representation of $G(\mathbb{F}_q)$ is isomorphic to the Grothendieck ring

Ziheng Chen

Abstract In this work, we build two environments, namely the modified QLBS and RLOP models, from a mathematics perspective which enables RL methods in option pricing through replicating by portfolio. We implement the environment specifications (the source code can be found at https://github.com/owen8877/RLOP), the learning algorithm, and agent parametrizatio

Chao Huang

This paper studies two-sided many-to-one matching in which firms have complementary preferences. We show that stable matchings exist under a balancedness condition that rules out a specific type of odd-length cycles formed by firms' acceptable sets. We also provide a class of preference profiles that satisfy this condition. Our results indicate that stable m

Julian Bellavita, Alex Sim, Kesheng Wu, Inder Monga

The XRootD system is used to transfer, store, and cache large datasets from high-energy physics (HEP). In this study we focus on its capability as distributed on-demand storage cache. Through exploring a large set of daily log files between 2020 and 2021, we seek to understand the data access patterns that might inform future cache design. Our study begins w

R. Barrena, H. Böhringer, G. Chon

We analyse the kinematical and dynamical state of the galaxy cluster RXCJ1230.7+3439, at z=0.332, using 93 new spectroscopic redshifts of galaxies acquired at the 3.6m TNG telescope and from SDSS DR16 public data. We find that RXCJ1230 appears as a clearly isolated peak in the redshift space, with a global line-of-sight velocity dispersion of $1004_{-122}^{+

E. I. Preobrazhensky, I. V. Oladyshkin, M. D. Tokman

The theory of optical effects in hydrogenated graphene (graphane) in terahertz and infrared range is developed, including the analysis of complex conductivity, reflection coefficient for graphane on a substrate and dispersion of surface plasmon-polaritons. The calculations are based on quite simple analytical approximation of graphane band structure in the v

Vassilis C. Spanos, Ioanna D. Stamou

In this paper we study four concrete models, based on no-scale supergravity with SU(2,1)/SU(2)$\times$ U(1) symmetry. We modify either the K\"ahler potential or the superpotential, which are related to the no-scale theory with this symmetry. In this scenario, the induced Gravitational Waves, are calculated to be detectable by the future space-based observati

Ivo Maffei, A. W. Roscoe

Delay Encryption (often called Timed-Release Encryption) is a scheme in which a message is sent into the future by ensuring its confidentiality only for a given amount of time. We propose a new scheme based on a novel time-lock puzzle. This puzzle relies on the assumption that repeated squaring is an inherently sequential process. We perform an extensive and

Adam Tsakalidis, Federico Nanni, Anthony Hills, Jenny Chim

Identifying changes in individuals' behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance. Most research to-date on this topic focuses on either: (a) identifying individuals at risk or with a certain mental health condition given a batch of posts or (b) providing equivalent labels at the post level. A disa

Pedro D. Alvarez, Benjamin Koch, Cristobal Laporte, Felipe Canales

We study the statefinder parameters of a cosmological model based on scale-dependent gravity. The effective Einstein field equations come from an average effective action. From the dynamical system, we derive analytical expressions that improve the convergence of the numerical solutions. We determine the statefinder parameters for moderate redshift and compa

Chiheb Ben Mahmoud, Federico Grasselli, Michele Ceriotti

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally-excited electrons, that is important in metals, and essential to the description of warm dense matter. An accurate physical descr

Kai Wei, Dillon Knox, Martin Radfar, Thanh Tran

Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems. Prosodic features such as energy and pitch have been shown to be useful for DAC. Despite their importance, little research has explored neural approaches to integrate prosodic features into end-to-end (E2E) DAC models which infer dialogue acts directly

Zhiyu Chen, Bing Liu, Seungwhan Moon, Chinnadhurai Sankar

Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. Towards building a human-like assistant that can converse naturally and seamlessly with users, it is important to build a dialogue system that conducts both types of conversations effectively. In this work, we investigate how task-oriented dial

Zhiyuan Zhou, Cameron Allen, Kavosh Asadi, George Konidaris

We study the action generalization ability of deep Q-learning in discrete action spaces. Generalization is crucial for efficient reinforcement learning (RL) because it allows agents to use knowledge learned from past experiences on new tasks. But while function approximation provides deep RL agents with a natural way to generalize over state inputs, the same

Sean C. Epstein, Timothy J. P. Bray, Margaret Hall-Craggs, Hui Zhang

Deep learning (DL) is gaining popularity as a parameter estimation method for quantitative MRI. A range of competing implementations have been proposed, relying on either supervised or self-supervised learning. Self-supervised approaches, sometimes referred to as unsupervised, have been loosely based on auto-encoders, whereas supervised methods have, to date

Otavio Braga, Takaki Makino, Olivier Siohan, Hank Liao

Traditionally, audio-visual automatic speech recognition has been studied under the assumption that the speaking face on the visual signal is the face matching the audio. However, in a more realistic setting, when multiple faces are potentially on screen one needs to decide which face to feed to the A/V ASR system. The present work takes the recent progress

Luke Lozenski, Mark A. Anastasio, Umberto Villa

Dynamic imaging is essential for analyzing various biological systems and behaviors but faces two main challenges: data incompleteness and computational burden. For many imaging systems, high frame rates and short acquisition times require severe undersampling, which leads to data incompleteness. Multiple images may then be compatible with the data, thus req

Igor Ermakov

The conception of almost complete revivals has been introduced recently. In a quantum many-body system local observable may exhibit an almost complete revival to its maximal value at the predetermined moment of time. In this paper we extend the original procedure such that the revival may be from an arbitrary point on the Bloch sphere to the arbitrary point.

Shuzhi Yu, Guanhang Wu, Chunhui Gu, Mohammed E. Fathy

Recently, one-stage trackers that use a joint model to predict both detections and appearance embeddings in one forward pass received much attention and achieved state-of-the-art results on the Multi-Object Tracking (MOT) benchmarks. However, their success depends on the availability of videos that are fully annotated with tracking data, which is expensive a

Otavio Braga, Olivier Siohan

Audio-visual automatic speech recognition is a promising approach to robust ASR under noisy conditions. However, up until recently it had been traditionally studied in isolation assuming the video of a single speaking face matches the audio, and selecting the active speaker at inference time when multiple people are on screen was put aside as a separate prob

Philipp Ernst, Soumick Chatterjee, Georg Rose, Andreas Nürnberger

In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSNR of the proposed method is increased by 10dB compared to the direct FDK reconstruction and almost 3dB compared to the modified original Prima

Satwinder Singh, Ruili Wang, Feng Hou

We propose a new meta learning based framework for low resource speech recognition that improves the previous model agnostic meta learning (MAML) approach. The MAML is a simple yet powerful meta learning approach. However, the MAML presents some core deficiencies such as training instabilities and slower convergence speed. To address these issues, we adopt m

Bin Chen, Yuefeng Liu, Boyang Yu

Reflected entropy is a newly proposed notion in quantum information. It has important implications in holography. In this work, we study the reflected entropy in the framework of the AdS$_3$/WCFT correspondence. We determine the scaling dimensions and charges of various twist operators in non-Abelian orbifold WCFT by generalizing the uniformization map and t

Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen, Mads Græsbøll Christensen

In previous work, we proposed a variational autoencoder-based (VAE) Bayesian permutation training speech enhancement (SE) method (PVAE) which indicated that the SE performance of the traditional deep neural network-based (DNN) method could be improved by deep representation learning (DRL). Based on our previous work, we in this paper propose to use $\beta$-V

Vedant Kalbag, Alexander Lerch

Harsh vocal effects such as screams or growls are far more common in heavy metal vocals than the traditionally sung vocal. This paper explores the problem of detection and classification of extreme vocal techniques in heavy metal music, specifically the identification of different scream techniques. We investigate the suitability of various feature represent

Philipp Ernst, Suhita Ghosh, Georg Rose, Andreas Nürnberger

This paper proposes an extension to the Dual Branch Prior-Net for sparse view interventional CBCT reconstruction incorporating a high quality planning scan. An additional head learns to segment interventional instruments and thus guides the reconstruction task. The prior scans are misaligned by up to +-5deg in-plane during training. Experiments show that the

Steven Finch

Each connected component of a mapping $\{1,2,...,n\}\rightarrow\{1,2,...,n\}$ contains a unique cycle. The largest such component can be studied probabilistically via either a delay differential equation or an inverse Laplace transform. The longest such cycle likewise admits two approaches: we find an (apparently new) density formula for its length. Implicat

Anna Frishman, Tobias Grafke

The subcritical transition to turbulence, as occurs in pipe flow, is believed to generically be a phase transition in the directed percolation universality class. At its heart is a balance between the decay rate and proliferation rate of localized turbulent structures, called puffs in pipe flow. Here we propose the first-ever dynamical mechanism for puff pro

Tung T. Vu, Trinh Van Chien, Canh T. Dinh, Hien Quoc Ngo

For downlink massive multiple-input multiple-output (MIMO) operating in time-division duplex protocol, users can decode the signals effectively by only utilizing the channel statistics as long as channel hardening holds. However, in a reconfigurable intelligent surface (RIS)-assisted massive MIMO system, the propagation channels may be less hardened due to t

Isaac Hobday, Paul D Stevenson, James Benstead

Model calculations of nuclear properties are peformed using quantum computing algorithms on simulated and real quantum computers. The models are a realistic calculation of deuteron binding based on effective field theory, and a simplified two-level version of the nuclear shell model known as the Lipkin-Meshkov-Glick model. A method of reducing the number of

Erik Wallin, Lennart Svensson, Fredrik Kahl, Lars Hammarstrand

Following the success of supervised learning, semi-supervised learning (SSL) is now becoming increasingly popular. SSL is a family of methods, which in addition to a labeled training set, also use a sizable collection of unlabeled data for fitting a model. Most of the recent successful SSL methods are based on pseudo-labeling approaches: letting confident mo

Hsin Chang, Jing-Yang Chang, Yi-Chieh Chang, Yu-Han Chang

This Letter reports on the first results from the Taiwan Axion Search Experiment with Haloscope, a search for axions using a microwave cavity at frequencies between 4.70750 and 4.79815 GHz. Apart from the non-axion signals, no candidates with a significance more than 3.355 were found. The experiment excludes models with the axion-two-photon coupling $\left|g

Adam Janovsky, Davide Maiorca, Dominik Macko, Vashek Matyas

Cryptography has been extensively used in Android applications to guarantee secure communications, conceal critical data from reverse engineering, or ensure mobile users' privacy. Various system-based and third-party libraries for Android provide cryptographic functionalities, and previous works mainly explored the misuse of cryptographic API in benign appli