July 2019 arXiv papers — page 112

Fritjof Freise, Norbert Gaffke, Rainer Schwabe

For a nonlinear regression model the information matrices of designs depend on the parameter of the model. The adaptive Wynn-algorithm for D-optimal design estimates the parameter at each step on the basis of the employed design points and observed responses so far, and selects the next design point as in the classical Wynn-algorithm for D-optimal design. Th

Anatoli Juditsky, Alexander Nazin, Arkadi Nemirovsky, Alexandre Tsybakov

We propose an approach to construction of robust non-Euclidean iterative algorithms for convex composite stochastic optimization based on truncation of stochastic gradients. For such algorithms, we establish sub-Gaussian confidence bounds under weak assumptions about the tails of the noise distribution in convex and strongly convex settings. Robust estimates

Unn Dahlen, Johan Linström, Marko Scholze

Atmospheric inverse modelling is a method for reconstructing historical fluxes of green-house gas between land and atmosphere, using observed atmospheric concentrations and an atmospheric tracer transport model. The small number of observed atmospheric concentrations in relation to the number of unknown flux components makes the inverse problem ill-condition

Robert Brunngräber, Sebastian Wolf

The investigation of polarized light of protoplanetary disks is key for constraining the dust properties, disk morphology and embedded magnetic fields. However, different polarization mechanisms and the diversity of dust grain shapes and compositions lead to ambiguities in the polarization pattern. The so-called "self-scattering" of thermal, re-emitt

Yong Min, Tingjun Jiang, Cheng Jin, Qu Li

The boom of online social media and microblogging platforms has rapidly alter the way we consume news and exchange opinions. Even though considerable efforts try to recommend various contents to users, loss of information diversity and the polarization of interest groups are still an enormous challenge for industry and academia. Here, we take advantage of be

Jonggwon Park, Kyoyun Choi, Sungwook Jeon, Dokyun Kim

Chord recognition is an important task since chords are highly abstract and descriptive features of music. For effective chord recognition, it is essential to utilize relevant context in audio sequence. While various machine learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been employed for the task, most

Glenn Bitar, Vegard N. Vestad, Anastasios M. Lekkas, Morten Breivik

We consider warm-started optimized trajectory planning for autonomous surface vehicles (ASVs) by combining the advantages of two types of planners: an A* implementation that quickly finds the shortest piecewise linear path, and an optimal control-based trajectory planner. A nonlinear 3-degree-of-freedom underactuated model of an ASV is considered, along with

Ross Street

The construction of a category of spans can be made in some categories $\CC$ which do not have pullbacks in the traditional sense. The PROP for monoids is a good example of such a $\CC$. The 2012 book concerning homological algebra by Marco Grandis gives the proof of associativity of relations in a Puppe-exact category based on a 1967 paper of M.Š. Calenko.

S. Charlès, Jean-Benoit Le Cam

In this paper, a new inverse identification method is developed from full kinematic and thermal field measurements. It consists in reconstructing the heat source from two approaches, a first one that requires the measurement of the temperature field and the value of the thermophysical parameters, and a second one based on the measurement of the kinematics fi

G Bastos, A Tayeb, N. Di Cesare, Jean-Benoit Le Cam

In the present study, a numerical method based on a metaheuristic parametric algorithm has been developed to identify the constitutive parameters of hyperelastic models, by using FE simulations and full kinematic field measurements. The full kinematic field is measured at the surface of a cruciform specimen submitted to equibiaxial tension. The sample is rec

Antoine Joux, Cecile Pierrot

Elliptic bases, introduced by Couveignes and Lercier in 2009, give an elegant way of representing finite field extensions. A natural question which seems to have been considered independently by several groups is to use this representation as a starting point for small characteristic finite field discrete logarithm algorithms. This idea has been recently pro

B. Ruellan, Jean-Benoit Le Cam, E. Robin, I. Jeanneau

Natural rubber (NR) is the most commonly used elastomer in the automotive industry thanks to its outstanding fatigue resistance. Strain-induced crystallization (SIC) is found to play a role of paramount importance in the great crack growth resistance of NR [1]. Typically, NR exhibits a lifetime reinforcement for non-relaxing loadings [2-3]. At the microscopi

A Tayeb, Jean-Benoit Le Cam, M. Grédiac, E. Toussaint

In this study, the Virtual Fields Method (VFM) is applied to identify constitutive parameters of hyperelastic models from a heterogeneous test. Digital image correlation (DIC) was used to estimate the displacement and strain fields required by the identification procedure. Two different hyperelastic models were considered: the Mooney model and the Ogden mode

Gayathri Viswanath, C S Stalin, Suvendu Rakshit, Kshama S. Kurian

Narrow line Seyfert 1 galaxies (NLS1s) are believed to be powered by accretion of matter onto low mass black holes (BHs) in spiral host galaxies with BH masses M_BH of 10^6 - 10^8 M_sun . However, the broad band spectral energy distribution of the gamma-ray emitting NLS1s are found to be similar to flat spectrum radio quasars. This challenges our current not

Stephanie M. Bohaichuk, Miguel Muñoz Rojo, Gregory Pitner, Connor J. McClellan

Vanadium dioxide (VO2) has been widely studied for its rich physics and potential applications, undergoing a prominent insulator-metal transition (IMT) near room temperature. The transition mechanism remains highly debated, and little is known about the IMT at nanoscale dimensions. To shed light on this problem, here we use ~1 nm wide carbon nanotube (CNT) h

Zenan Zhai, Dat Quoc Nguyen, Saber A. Akhondi, Camilo Thorne

Chemical patents are an important resource for chemical information. However, few chemical Named Entity Recognition (NER) systems have been evaluated on patent documents, due in part to their structural and linguistic complexity. In this paper, we explore the NER performance of a BiLSTM-CRF model utilising pre-trained word embeddings, character-level word re

Yang Cao, Jingling Yuan, Song Xiao, Qing Xie

With the development of big data and artificial intelligence, the technology of urban computing becomes more mature and widely used. In urban computing, using GPS-based trajectory data to discover urban dense areas, extract similar urban trajectories, predict urban traffic, and solve traffic congestion problems are all important issues. This paper presents a

Anupam Golder, Debayan Das, Josef Danial, Santosh Ghosh

Power side-channel analysis (SCA) has been of immense interest to most embedded designers to evaluate the physical security of the system. This work presents profiling-based cross-device power SCA attacks using deep learning techniques on 8-bit AVR microcontroller devices running AES-128. Firstly, we show the practical issues that arise in these profiling-ba

Chia-Jung Yeh, Po-Han Lin, Xiwen Zhang, Olga Kocharovskaya

Two kinds of spectral control over $γ$-ray echo using a nuclear frequency comb system are theoretically investigated. A nuclear frequency comb system is composed of multiple nuclear targets under magnetization (hyperfine splitting), mechanical motion (Doppler shift) or both, namely, moving and magnetized targets. In frequency domain the unperturbed single ab

Trevor Brown, Srivatsan Ravi

State-of-the-art \emph{software transactional memory (STM)} implementations achieve good performance by carefully avoiding the overhead of \emph{incremental validation} (i.e., re-reading previously read data items to avoid inconsistency) while still providing \emph{progressiveness} (allowing transactional aborts only due to \emph{data conflicts}). Hardware t

Zhun Gou, Ming-hui Wang, Nan-jing Huang

In this paper, the existence and pathwise uniqueness of strong solutions for jump-type stochastic differential equations are investigated under non-Lipschitz conditions. A sufficient condition is obtained for ensuring the non-confluent property of strong solutions of jump-type stochastic differential equations. Moreover, some examples are given to illustrate

Huiling Yuan, Yong Zhou, Zhiyuan Zhang, Xiangyu Cui

Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security's volatility. In this paper, we propose two econometric models, which integrate three information sources. In GARCH-Itô-OI model, we assume that the option-implied volatility can influence the securi

Weixia Zhang, Kede Ma, Jia Yan, Dexiang Deng

We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions. Our model consists of two convolutional neural networks (CNN), each of which specializes in one distortion scenario. For synthetic distortions, we pre-train a CNN to classify image distortion type and level, where we enjoy large-s

Weicheng Cai, Haiwei Wu, Danwei Cai, Ming Li

This paper describes our DKU replay detection system for the ASVspoof 2019 challenge. The goal is to develop spoofing countermeasure for automatic speaker recognition in physical access scenario. We leverage the countermeasure system pipeline from four aspects, including the data augmentation, feature representation, classification, and fusion. First, we int

Amit Rege, Claire Monteleoni

We evaluate the distribution learning capabilities of generative adversarial networks by testing them on synthetic datasets. The datasets include common distributions of points in $R^n$ space and images containing polygons of various shapes and sizes. We find that by and large GANs fail to faithfully recreate point datasets which contain discontinous support

Rebecca Coulson

Metrically homogeneous graphs are connected graphs which, when endowed with the path metric, are homogeneous as metric spaces. Here we consider a class of countable metrically homogeneous graphs. The algebra of an age is a concept introduced by Cameron and is closely connected to the profile of the automorphism group of the associated countable structure. Ca

Steven V Sam, Andrew Snowden

Given r elements in an integral domain over an algebraically closed field such that any two are linearly independent, we show that there is an integer e between 1 and r! such the eth powers of these elements are linearly independent.

Jun Gu, Tzonelih Hwang

Recently, Yang et al. (Quantum Inf Process:17:129, 2018) proposed a secure multi-party quantum summation protocol allowing the involved participants to sum their secrets privately. They claimed that the proposed protocol can prevent each participant's secret from being known by others. However, this study shows that the participant who prepares the initi

David J. W. Simpson

For piecewise-linear maps, the phenomenon that a branch of a one-dimensional unstable manifold of a periodic solution is completely contained in its stable manifold is codimension-two. Unlike codimension-one homoclinic corners, such `subsumed' homoclinic connections can be associated with stable periodic solutions. The purpose of this paper is to determi

Owen Marschall, Kyunghyun Cho, Cristina Savin

We present a framework for compactly summarizing many recent results in efficient and/or biologically plausible online training of recurrent neural networks (RNN). The framework organizes algorithms according to several criteria: (a) past vs. future facing, (b) tensor structure, (c) stochastic vs. deterministic, and (d) closed form vs. numerical. These axes

Joshua E. Siegel, Maria F. Beemer, Steven M. Shepard

Manufacturers struggle to produce low-cost, robust and complex components at manufacturing lot-size one. Additive processes like Fused Filament Fabrication (FFF) inexpensively produce complex geometries, but defects limit viability in critical applications. We present an approach to high-accuracy, high-throughput and low-cost automated non-destructive testin

Xiangxi Yan, Xiangkun Kong, Qi Wang, Lei Xing

In this paper, a novel water-based reconfigurable frequency selective rasorber (FSR) at microwave band is proposed, which has a thermally tunable absorption band above the transmission band. The water-based FSR consists of a bandpass type frequency selective surface (FSS) and a 3D printing container. The water substrate is filled into the sealed space constr

Bruno G. M. Vieira, Niclas S. Mueller, Eduardo B. Barros, Stephanie Reich

The self-assembly of metallic nanoparticles is a promising route to metasurfaces with unique properties for many optical applications, such as surface-enhanced spectroscopy, light manipulation, and sensing. We present an in-depth theoretical study of the optical properties of mono- and bilayers assembled from gold and silver nanoparticles. With finite-differ

Dimitrie Culcer, Attila Geresdi

Topological phases are characterised by a topological invariant that remains unchanged by deformations in the Hamiltonian. Materials exhibiting topological phases include topological insulators, superconductors exhibiting strong spin-orbit coupling, transition metal dichalcogenides, which can be made atomically thin and have direct band gaps, as well as high

Yuan Lin, John McPhee, Nasser L. Azad

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. T

Jacob Goodman, Leonardo Colombo

The Poincaré map is widely used to study the qualitative behavior of dynamical systems. For instance, it can be used to describe the existence of periodic solutions. The Poincaré map for dynamical systems with impulse effects was introduced in the last decade and mainly employed to study the existence of limit cycles (periodic gaits) for the locomotion of bi

Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid

Predicting the future trajectories of multiple interacting agents in a scene has become an increasingly important problem for many different applications ranging from control of autonomous vehicles and social robots to security and surveillance. This problem is compounded by the presence of social interactions between humans and their physical interactions w

Evgeny Gelfer, Alexander Fedotov, Stefan Weber

Radiation friction can have a substantial impact on electron dynamics in a transparent target exposed to a strong laser pulse. In particular, by modifying quiver electron motion, it can strongly enhance the longitudinal charge separation field, thus stimulating ion acceleration. We present a model and simulation results for such a radiation induced ion accel

S. Banerjee, W. A. Atkinson, A. P. Kampf

Charge-density wave order is now understood to be a widespread feature of underdoped cuprate high-temperature superconductors, although its origins remain unclear. While experiments suggest that the charge-ordering wavevector is determined by Fermi-surface nesting, the relevant sections of the Fermi surface are featureless and provide no clue as to the under

Erick Almaraz, Baojiu Li, Axel de la Macorra

We study nonlinear structure formation in the Bound Dark Energy model (BDE), where dark energy (DE) corresponds to a light scalar meson particle $ϕ$ dynamically formed at a condensation energy scale $Λ_c$. The evolution of this dark-energy meson is determined by the potential $V(ϕ)=Λ_c^{4+2/3}ϕ^{-2/3}$, with a distinguishing phenomenology from other quintess

Vitali Kapovitch, Andrea Mondino

We establish topological regularity and stability of N-dimensional RCD(K,N) spaces (up to a small singular set), also called non-collapsed RCD(K,N) in the literature. We also introduce the notion of a boundary of such spaces and study its properties, including its behavior under Gromov-Hausdorff convergence.

Elmer Ramirez Barreto, David Romero Abad

It was explored a new $SU(3)_{L} \otimes U(1)_{X}$ electroweak extension of the Standard Model, where new particles arise with peculiar signatures as heavy fractionally charged leptons and electrical neutral quarks, among others. We examine the branching fraction and the lifetime of new heavy leptons in order to discriminate if they could be identified as lo

René Alvarez-Donado, Alex Antonelli

We applied an efficient methodology to separate vibrational and configurational entropies in bulk metallic glasses by means of molecular dynamics simulation based on a combination of non-equilibrium adiabatic switching and reversible scaling methods. This approach involves calculating the vibrational free energy using the Einstein crystal as a reference for

Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan

Adversarial training is an effective methodology for training deep neural networks that are robust against adversarial, norm-bounded perturbations. However, the computational cost of adversarial training grows prohibitively as the size of the model and number of input dimensions increase. Further, training against less expensive and therefore weaker adversar

C. Tserkezis, A. I. Fernández-Domínguez, P. A. D. Gonçalves, F. Todisco

Rooted in quantum optics and benefiting from its well-established foundations, strong coupling in nanophotonics has experienced increasing popularity in recent years. With nanophotonics being an experiment-driven field, the absence of appropriate theoretical methods to describe ground-breaking advances has often emerged as an important issue. To address this

Dominik Suszalski, Grzegorz Rut, Adam Rycerz

The Corbino geometry allows one to investigate the propagation of electric current along a p-n interface in ballistic graphene in the absence of edge states appearing for the familiar Hall-bar geometry. Using the transfer matrix in the angular-momentum space we find that for sufficiently strong magnetic fields the current propagates only in one direction, de

Laurent Côté, Ikshu Neithalath

We prove that the ($τ$-weighted, sheaf-theoretic) SL(2,C) Casson-Lin invariant introduced by Manolescu and the first author in [CM19] is generically independent of the parameter $τ$ and additive under connected sums of knots in integral homology 3-spheres. This addresses two questions asked in [CM19]. Our arguments involve a mix of topology, microlocal analy

Michal Pavelka, Ilya Peshkov, Vaclav Klika

Continuum mechanics can be formulated in the Lagrangian frame (addressing motion of individual continuum particles) or in the Eulerian frame (addressing evolution of fields in an inertial frame). There is a canonical Hamiltonian structure in the Lagrangian frame. By transformation to the Eulerian frame we find the Poisson bracket for Eulerian continuum mecha

Abdurrahman Fouda, Ahmed S. Ibrahim, Ismail Guvenc, Monisha Ghosh

An integrated access and backhaul (IAB) network architecture can enable flexible and fast deployment of next-generation cellular networks. However, mutual interference between access and backhaul links, small inter-site distance and spatial dynamics of user distribution pose major challenges in the practical deployment of IAB networks. To tackle these proble

Hadi Hosseini, Sujoy Sikdar, Rohit Vaish, Jun Wang

Envy-freeness up to one good (EF1) is a well-studied fairness notion for indivisible goods that addresses pairwise envy by the removal of at most one good. In the worst case, each pair of agents might require the (hypothetical) removal of a different good, resulting in a weak aggregate guarantee. We study allocations that are nearly envy-free in aggregate, a

Derek Howard, Marta Maslej, Justin Lee, Jacob Ritchie

Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data which can be mined to predict mental health states using machine learning methods. We benchmark multiple methods of text feature representation for social media

Nina Golyandina

Singular spectrum analysis (SSA), starting from the second half of the XX century, has been a rapidly developing method of time series analysis. Since it can be called principal component analysis for time series, SSA will definitely be a standard method in time series analysis and signal processing in the future. Moreover, the problems solved by SSA are con

Diego Barragán Guerrero, Minh Au, Ghyslain Gagnon, François Gagnon

Modern wireless machine-to-machine-type communications aim to provide both ultra reliability and low latency, stringent requirements that appear to be mutually exclusive. From the noisy channel coding theorem, we know that reliable communications mandate transmission rates that are lower than the channel capacity. To guarantee arbitrarily-low error probabili

Kyle Luh, Sean Meehan, Hoi H. Nguyen

In this note we give various characterizations of random walks with possibly different steps that have relatively large discrepancy from the uniform distribution modulo a prime p, and use these results to study the distribution of the rank of random matrices over F_p and the equi-distribution behavior of normal vectors of random hyperplanes. We also study th

V. A. Okorokov

Estimations of some geometrical and bulk parameters are presented for the matter produced in various type collisions with ultra-high energy cosmic ray (UHECR) particles. Results for multiplicity density at midrapidity, decoupling time, and energy density are discussed for small and larger collision systems. Based on the analytic functions suggested previousl

Pedro M. Pasinetti, Lucia S. Ramirez, Paulo M. Centres, Antonio J. Ramirez-Pastor

Irreversible adsorption of objects of different shapes and sizes on Euclidean, fractal and random lattices is studied. The adsorption process is modeled by using random sequential adsorption (RSA) algorithm. Objects are adsorbed on one-, two-, and three-dimensional Euclidean lattices, on Sierpinski carpets having dimension $d$ between 1 and 2, and on Erdos-R

Giovanni Colavizza, Iain Hrynaszkiewicz, Isla Staden, Kirstie Whitaker

Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements. As a consequence of this, there has been a strong uptake of data availability statements in recent literature. Nevertheless, it is still unclear what proportion of these statements act

Alex Giacomini, Marcello Ortaggio

We consider a class of ansätze for the construction of exact solutions of the Einstein-nonlinear $σ$-model system with an arbitrary cosmological constant in (3+1) dimensions. Exploiting a geometric interplay between the $SU(2)$ field and Killing vectors of the spacetime reduces the matter field equations to a single scalar equation (identically satisfied in

Sara Dal Cengio, Demian Levis, Ignacio Pagonabarraga

We address the question of how interacting active systems in a non-equilibrium steady-state respond to an external perturbation. We establish an extended fluctuation-dissipation theorem for Active Brownian Particles (ABP) which highlights the role played by the local violation of detailed balance due to activity. By making use of a Markovian approximation we

Sudhakar Mishra, U. S. Tiwary

The focus of the efforts for defining and modelling emotion is broadly shifting from classical definite marker theory to statistically context situated conceptual theory. However, the role of context processing and its interaction with the affect is still not comprehensively explored and modelled. With the help of neural decoding of functional networks, we h

David K. Tuckett, Stephen D. Bartlett, Steven T. Flammia, Benjamin J. Brown

Noise in quantum computing is countered with quantum error correction. Achieving optimal performance will require tailoring codes and decoding algorithms to account for features of realistic noise, such as the common situation where the noise is biased towards dephasing. Here we introduce an efficient high-threshold decoder for a noise-tailored surface code

Gilad Gour, Carlo Maria Scandolo

The most general quantum object that can be shared between two distant parties is a bipartite channel, as it is the basic element to construct all quantum circuits. In general, bipartite channels can produce entangled states, and can be used to simulate quantum operations that are not local. While much effort over the last two decades has been devoted to the

V. Vilasini, Roger Colbeck

Understanding cause-effect relationships is a crucial part of the scientific process. As Bell's theorem shows, within a given causal structure, classical and quantum physics impose different constraints on the correlations that are realisable, a fundamental feature that has technological applications. However, in general it is difficult to distinguish th

Javier Fuentes-Martin, Mario Reig, Avelino Vicente

We analyze the strong CP problem and the implications for axion physics in the context of $U_1$ vector leptoquark models, recently put forward as an elegant solution to the hints of lepton flavor universality violation in B-meson decays. It is shown that in minimal gauge models containing the $U_1$ as a gauge boson, the Peccei-Quinn solution of the strong CP

Hugo Masson, Amran Bhuiyan, Le Thanh Nguyen-Meidine, Mehrsan Javan

Recent years have witnessed a substantial increase in the deep learning (DL)architectures proposed for visual recognition tasks like person re-identification,where individuals must be recognized over multiple distributed cameras. Althoughthese architectures have greatly improved the state-of-the-art accuracy, thecomputational complexity of the CNNs commonly

Weiwei Sun, Wei Jiang, Eduard Trulls, Andrea Tagliasacchi

Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds. Permutation-equivariant networks have become a popular solution-they operate on individual data points with simple perceptrons and extract contextual information with global pooling. This can be achieved with a simple normalization of the feature maps, a

Jeff Donahue, Karen Simonyan

Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improv

Matteo M. Wauters, Angelo Russomanno, Roberta Citro, Giuseppe E. Santoro

We investigate the transition induced by disorder in a periodically-driven one-dimensional model displaying quantized topological transport. We show that, while instantaneous eigenstates are necessarily Anderson localized, the periodic driving plays a fundamental role in delocalizing Floquet states over the whole system, henceforth allowing for a steady stat

Shelley J. Cheng, Alec M. Vinson, Smadar Naoz

The binary star Par 1802 in the Orion Nebula presents an interesting puzzle in the field of stellar dynamics and evolution. Binary systems such as Par 1802 are thought to form from the same natal material and thus the stellar members are expected to have very similar physical attributes. However, Par 1802's stars have significantly different temperatures

André de Gouvêa, Wei-Chih Huang, Johannes König, Manibrata Sen

Lepton-number violation (LNV), in general, implies nonzero Majorana masses for the Standard Model neutrinos. Since neutrino masses are very small, for generic candidate models of the physics responsible for LNV, the rates for almost all experimentally accessible LNV observables -- except for neutrinoless double-beta decay -- are expected to be exceedingly sm

P. J. Pessi, G. Folatelli, J. P. Anderson, M. Bersten

Type II supernovae (SNe II) show strong hydrogen features in their spectra throughout their whole evolution while type IIb supernovae (SNe IIb) spectra evolve from dominant hydrogen lines at early times to increasingly strong helium features later on. However, it is currently unclear whether the progenitors of these supernova (SN) types form a continuum in p

Toshifumi Noumi, Toshiaki Takeuchi, Siyi Zhou

We study the spectrum of semiclassical rotating strings in de Sitter space and its consistency. Even though a naive extrapolation of the linear Regge trajectory on flat space implies a violation of the Higuchi bound (a unitarity bound on the mass of higher-spin particles in de Sitter space), the curved space effects turn out to modify the trajectory to respe

Peter B. Denton, Stephen J. Parke, Xining Zhang

We present a new method of exactly calculating neutrino oscillation probabilities in matter. We leverage the "eigenvector-eigenvalue identity" to show that, given the eigenvalues, all mixing angles in matter follow surprisingly simply. The CP violating phase in matter can then be determined from the Toshev identity. Then, to avoid the cumbersome expr

O. Matsyshyn, I. Sodemann

Electrons moving in a Bloch band are known to acquire an anomalous Hall velocity proportional to the Berry curvature of the band which is responsible for the intrinsic linear Hall effect in materials with broken time-reversal symmetry. Here, we demonstrate that there is also an anomalous correction to the electron acceleration which is proportional to the Be

Yen-Ta Huang, Lokman Tsui, Dung-Hai Lee

The non-regularizability of free fermion field theories, which is the root of various quantum anomalies, plays a central role in particle physics and modern condensed matter physics. In this paper, we generalize the Nielsen-Ninomiya theorem to all minimal nodal free fermion field theories protected by the time reversal, charge conservation, and charge conjug

Xiao Yuan, Yunchao Liu, Qi Zhao, Bartosz Regula

Quantum memory -- the capacity to store and faithfully recover unknown quantum states -- is essential for quantum-enhanced technology. There is thus a pressing need for operationally meaningful means to benchmark candidate memories across diverse physical platforms. Here we introduce a universal benchmark distinguished by its relevance across multiple key op

Bruno Le Floch, Márk Mezei

Two-dimensional CFTs and integrable models have an infinite set of conserved KdV higher spin currents. These currents can be argued to remain conserved under the $T\bar{T}$ deformation and its generalizations. We determine the flow equations the KdV charges obey under the $T\bar{T}$ deformation: they behave as probes "riding the Burgers flow" of the

Uri Stemmer

We design a new algorithm for the Euclidean $k$-means problem that operates in the local model of differential privacy. Unlike in the non-private literature, differentially private algorithms for the $k$-means objective incur both additive and multiplicative errors. Our algorithm significantly reduces the additive error while keeping the multiplicative error

Tomas Kamencek, Natalia Bedoya-Martínez, Egbert Zojer

Metal-organic frameworks (MOFs) are crystalline materials consisting of metal centers and organic linkers forming open and porous structures. They have been extensively studied due to various possible applications exploiting their large amount of internal surface area. Phonon properties of MOFs are, however, still largely unexplored, despite their relevance

Bernardo Araneda

The Teukolsky equations are currently the leading approach for analysing stability of linear massless fields propagating in rotating black holes. It has recently been shown that the geometry of these equations can be understood in terms of a connection constructed from the conformal and complex structure of Petrov type D spaces. Since the study of linear mas

Finn Larsen, Jun Nian, Yangwenxiao Zeng

We analyze AdS$_5$ black holes that are nearly supersymmetric. They depart from the BPS limit in two distinct ways: a temperature takes them above extremality and a potential violates a certain constraint. We study the thermodynamics of these deformations and their interplay in detail. We discuss recent microscopic computations of BPS black hole entropy in $

Lorenzo Casarin, Arkady A. Tseytlin

A classically scale-invariant 6d analog of the 4d Yang-Mills theory is the 4-derivative $ (\nabla F)^2 + F^3$ gauge theory with two independent couplings. Motivated by a search for a perturbatively conformal but possibly non-unitary 6d models we compute the one-loop $β$-functions in this theory. A systematic way of doing this using the background field metho

Stephen P. Martin, David G. Robertson

We present an implementation and numerical study of the Standard Model couplings, masses, and vacuum expectation value (VEV), using the pure $\overline{\rm{MS}}$ renormalization scheme based on dimensional regularization. Here, the $\overline{\rm{MS}}$ Lagrangian parameters are treated as the fundamental inputs, and the VEV is defined as the minimum of the L

Carl Doersch, Andrew Zisserman

Synthetic visual data can provide practically infinite diversity and rich labels, while avoiding ethical issues with privacy and bias. However, for many tasks, current models trained on synthetic data generalize poorly to real data. The task of 3D human pose estimation is a particularly interesting example of this sim2real problem, because learning-based app

Jonas Azzam, Steve Hofmann, José María Martell, Mihalis Mourgoglou

It is well-known that quantitative, scale invariant absolute continuity (more precisely, the weak-$A_\infty$ property) of harmonic measure with respect to surface measure, on the boundary of an open set $ Ω\subset \mathbb{R}^{n+1}$ with Ahlfors-David regular boundary, is equivalent to the solvability of the Dirichlet problem in $Ω$, with data in $L^p(\partia

Ruo-Yu Liu, Huirong Yan

Deep observation of the High Energy Stereoscopic System (HESS) on the most extended pulsar wind nebula HESS J1825-137 reveals an enhanced energy-dependent morphology, providing useful information on the particle transport mechanism in the nebula. We find that the energy-dependent morphology is consistent with a diffusion-dominated transport of electrons/posi

Cyran Aouameur, Philippe Esling, Gaëtan Hadjeres

In this work, we introduce a system for real-time generation of drum sounds. This system is composed of two parts: a generative model for drum sounds together with a Max4Live plugin providing intuitive controls on the generative process. The generative model consists of a Conditional Wasserstein autoencoder (CWAE), which learns to generate Mel-scaled magnitu

Timur Nasybullov

Let $F$ be an algebraically closed field of zero characteristic. If the transcendence degree of $F$ over $\mathbb{Q}$ is finite, then all Chevalley groups over $F$ are known to possess the $R_{\infty}$-property. If the transcendence degree of $F$ over $\mathbb{Q}$ is infinite, then Chevalley groups of type $A_n$ over $F$ do not possess the $R_{\infty}$-prope

Nathaël Alibaud, Félix del Teso, Jørgen Endal, Espen R. Jakobsen

A result by Courrège says that linear translation invariant operators satisfy the maximum principle if and only if they are of the form $\mathcal{L}=\mathcal{L}^{σ,b}+\mathcal{L}^μ$ where $$ \mathcal{L}^{σ,b}[u](x)=\text{tr}(σσ^{\texttt{T}} D^2u(x))+b\cdot Du(x) $$ and $$ \mathcal{L}^μ[u](x)=\int \big(u(x+z)-u-z\cdot Du(x) \mathbf{1}_{|z| \leq 1}\big) \,\mat

Tomáš Masařík, Irene Muzi, Marcin Pilipczuk, Paweł Rzążewski

The celebrated Erdős-Pósa theorem states that every undirected graph that does not admit a family of $k$ vertex-disjoint cycles contains a feedback vertex set (a set of vertices hitting all cycles in the graph) of size $O(k \log k)$. After being known for long as Younger's conjecture, a similar statement for directed graphs has been proven in 1996 by Ree

Brett Altschul, Matthias R. Schindler

Lorentz and CPT violation in hadronic physics must be tied to symmetry violations at the underlying quark and gluon level. Chiral perturbation theory provides a method for translating novel operators that may appear in the Lagrange density for color-charged parton fields into equivalent forms for effective theories at the meson and baryon levels. We extend t

Gianluca Calcagni, Sachiko Kuroyanagi, Sylvain Marsat, Mairi Sakellariadou

We investigate possible signatures of quantum gravity which could be tested with current and future gravitational-wave (GW) observations. In particular, we analyze how quantum gravity can influence the GW luminosity distance, the time dependence of the effective Planck mass and the instrumental strain noise of interferometers. Using both model-dependent and

Carl Niblaeus, Jonathan M. Cornell, Joakim Edsjö

If indirect detection searches are to be used to discriminate between dark matter particle models, it is crucial to understand the expected energy spectra of secondary particles such as neutrinos, charged antiparticles and gamma-rays emerging from dark matter annihilations in the local Universe. In this work we study the effect that both the choice of event

O. V. Bogdanov, P. O. Kazinski, G. Yu. Lazarenko

The general formula for probability to record a twisted photon produced by a charged particle moving in an inhomogeneous isotropic dispersive medium is derived. The explicit formulas for probability to record a twisted photon are obtained for the radiation of a charged particle traversing a dielectric plate or an ideally conducting foil. It is shown that, in

J. L. Weiland, G. E. Addison, C. L. Bennett, M. Halpern

Angular power spectra computed from Planck HFI 353 GHz intensity and polarization maps produce a TB correlation that can be approximated by a power law. Whether the observed TB correlation is an induced systematic feature or a physical property of Galactic dust emission is of interest both for cosmological and Galactic studies. We investigate the large angul

Pierre Martinetti, Devashish Singh

We show how the twisting of spectral triples induces a transition from an euclidean to a lorentzian noncommutative geometry, at the level of the fermionic action. More specifically, we compute the fermionic action for the twisting of a closed euclidean manifold, then that of a two-sheet euclidean manifold, and finally the twisting of the spectral triple of e

Hangjin Liu, You, Zhou, Ahmad Beirami

Nonlinear function estimation is core to modern machine learning applications. In this paper, to perform nonlinear function estimation, we reduce a nonlinear inverse problem to a linear one using a polynomial kernel expansion. These kernels increase the feature set, and may result in poorly conditioned matrices. Nonetheless, we show several examples where th

Salvatore Vilella, Daniela Paolotti, Giancarlo Ruffo, Leo Ferres

The always increasing mobile connectivity affects every aspect of our daily lives, including how and when we keep ourselves informed and consult news media. By studying a DPI (deep packet inspection) dataset, provided by one of the major Chilean telecommunication companies, we investigate how different cohorts of the population of Santiago De Chile consume n

Abdul Quddus, M. Bhuyan, S. K. Patra

Starting from light to superheavy nuclei, we have calculated the effective surface properties such as the symmetry energy, neutron pressure, and symmetry energy curvature using the coherent density fluctuation model. The isotopic chains of O, Ca, Ni, Zr, Sn, Pb, and Z = 120 are considered in the present analysis, which cover nuclei over the whole nuclear cha

Vinod Subramanian, Emmanouil Benetos, Ning Xu, SKoT McDonald

Adversarial attacks refer to a set of methods that perturb the input to a classification model in order to fool the classifier. In this paper we apply different gradient based adversarial attack algorithms on five deep learning models trained for sound event classification. Four of the models use mel-spectrogram input and one model uses raw audio input. The