November 2022 arXiv papers — page 111

Wei Zhou, Haotian Wu, Jingjing Xu, Mohammad Zeineldeen

ASR can be improved by multi-task learning (MTL) with domain enhancing or domain adversarial training, which are two opposite objectives with the aim to increase/decrease domain variance towards domain-aware/agnostic ASR, respectively. In this work, we study how to best apply these two opposite objectives with speaker labels to improve conformer-based ASR. W

Yi Yu, Feipeng Da

With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By mapping the rotational periodicity of different cycles into the

C. Malacaria, L. Ducci, M. Falanga, D. Altamirano

When accreting X-ray pulsars (XRPs) undergo bright X-ray outbursts, their luminosity-dependent spectral and timing features can be analysed in detail. The XRP GRO J1750-27 recently underwent one of such episodes, during which it was observed with $NuSTAR$ and monitored with $NICER$. Such a data set is rarely available, as it samples the outburst over more th

Md Zobaer Hossain, Ahnaf Mozib Samin

The extent to which men and women use language differently has been questioned previously. Finding clear and consistent gender differences in language is not conclusive in general, and the research is heavily influenced by the context and method employed to identify the difference. In addition, the majority of the research was conducted in written form, and

Tobias Schnabel, Mengting Wan, Longqi Yang

With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry. Even though progress on improving model design has been rapid in research, we argue that many advances fail to translate into practice because of two limiting assumptions. First, most approaches focus on a transduc

Jan Reislöhner, Xiao Chen, Doyeong Kim, Silvana Botti

The deep ultraviolet, the bandgap region of dielectrics, is not readily accessible for established methods of ultrafast spectroscopy. Here, a miniature beamline, where a tailored deep ultraviolet field is used immediately after the noncollinear generation without subsequent optical elements, is introduced for transient absorption and dispersion spectroscopy.

Huda Alalawi, Jacob Boyd, Chun Shen, Michael Strickland

We compute bottomonium suppression and elliptic flow within the pNRQCD effective field theory using an open quantum systems approach. For the hydrodynamical background, we use 2+1D MUSIC second-order viscous hydrodynamics with IP-Glasma initial conditions and evolve bottom/antibottom quantum wave packets in real time in these backgrounds. We find that the im

Hannah Alpert

We use Papasoglu's method of area-minimizing separating sets to give an alternative proof, and explicit constants, for the following theorem of Guth and Braun--Sauer: If $M$ is a closed, oriented, $n$-dimensional manifold, with a Riemannian metric such that every ball of radius $1$ in the universal cover of $M$ has volume at most $V_1$, then the simplicial v

Tanay Dey, Purba Bhattacharya, Supratik Mukhopadhyay, Nayana Majumdar

Numerical simulation of avalanches, saturated avalanches, and streamers can help us understand the detector physics of Resistive Plate Chambers (RPC). 3D Monte Carlo simulation of an avalanche inside an RPC, the transition from avalanche to saturated avalanche to streamer may help the search for the optimum voltage and alternate gas mixtures. This task is da

Danial Dervovic, Nicolas Marchesotti, Freddy Lecue, Daniele Magazzeni

We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in General Additive Models (GAMs); and SubscaleHedge, an expert advice algorithm for combining base models trained on subsets of features called subscales. LAMs can augment any additiv

Guillermo Flores, Gustavo Garrigos, Teresa Signes, Beatriz Viviani

When $L$ is the Hermite or the Ornstein-Uhlenbeck operator, we find minimal integrability and smoothness conditions on a function $f$ so that the fractional power $L^\sigma f(x_0)$ is well-defined at a given point $x_0$. We illustrate the optimality of the conditions with various examples. Finally, we obtain similar results for the fractional operators $(-\D

Vladimir Dokchitser, Holly Green, Alexandros Konstantinou, Adam Morgan

We investigate Selmer groups of Jacobians of curves that admit an action of a non-trivial group of automorphisms, and give applications to the study of the parity of Selmer ranks. Under the Shafarevich--Tate conjecture, we give an expression for the parity of the Mordell--Weil rank of an arbitrary Jacobian in terms of purely local invariants; the latter can

Wolfgang Lucha, Dmitri Melikhov, Hagop Sazdjian

Aiming at self-consistent descriptions of multiquark hadrons (such as tetraquarks, pentaquarks, hexaquarks) by means of QCD sum rules, we note that the totality of contributions to two-point or three-point correlation functions that involve, respectively, either two or just a single operator capable of interpolating the particular multiquark under study can

Gabriel Arenas-Henriquez, Felipe Diaz, Yerko Novoa

We extend the charged Renyi entropy to a more general holographic scenario. Coupling an arbitrary non-linear electrodynamics Lagrangian density to AdS gravity, we analyse the thermodynamic features of non-linearly charged hyperbolic black holes and the thermal fluctuations in the grand canonical ensemble. We provide a general form for the relevant holographi

J. Wu, D. Scolnic, A. G. Riess, G. S. Anand

The Tip of the Red Giant Branch (TRGB) is an apparent discontinuity in the color-magnitude diagram (CMD) along the giant branch due to the end of the red giant evolutionary phase and is used to measure distances in the local universe. In practice, the tip is often fuzzy and its localization via edge detection response (EDR) relies on several methods applied

Changye Li, Trevor Cohen, Serguei Pakhomov

Linguistic anomalies detectable in spontaneous speech have shown promise for various clinical applications including screening for dementia and other forms of cognitive impairment. The feasibility of deploying automated tools that can classify language samples obtained from speech in large-scale clinical settings depends on the ability to capture and automat

Pablo D. Bergamasco, Gabriel G. Carlo, Alejandro M. F. Rivas

The out-of-time order correlator (OTOC) has been widely studied in closed quantum systems. However, there are very few studies for open systems and they are mainly focused on isolating the effects of scrambling from those of decoherence. Adopting a different point of view, we study the interplay between these two processes. This proves crucial in order to ex

Sabyasachi Basu, Suman Kalyan Bera, C. Seshadhri

A fundamental problem in mathematics and network analysis is to find conditions under which a graph can be partitioned into smaller pieces. The most important tool for this partitioning is the Fiedler vector or discrete Cheeger inequality. These results relate the graph spectrum (eigenvalues of the normalized adjacency matrix) to the ability to break a graph

Michał Bortkiewicz, Jakub Łyskawa, Paweł Wawrzyński, Mateusz Ostaszewski

Hierarchical decomposition of control is unavoidable in large dynamical systems. In reinforcement learning (RL), it is usually solved with subgoals defined at higher policy levels and achieved at lower policy levels. Reaching these goals can take a substantial amount of time, during which it is not verified whether they are still worth pursuing. However, due

Paul Pöpperl, Igor V. Gornyi, Yuval Gefen

We study the dynamics of a monitored single particle in a one-dimensional, Anderson-localized system. The time evolution is governed by Hamiltonian dynamics for fixed time intervals, interrupted by local, projective measurements. The competition between disorder-induced localization and measurement-induced jumps leads to interesting behaviour of readout-aver

Felix Huber, Nikolai Wyderka

The spectral variant of the quantum marginal problem asks: Given prescribed spectra for a set of overlapping quantum marginals, does there exist a compatible joint state? The main idea of this work is a symmetry-reduced semidefinite programming hierarchy that detects when no such joint state exists. The hierarchy is complete, in the sense that it detects eve

Esther Rolf, Ben Packer, Alex Beutel, Fernando Diaz

Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predictive performance. In this work, we seek to better understand how we might characterize, detect, and design for data-model synergies. We focus on a particular type of data-model inef

J. M. Hardin

Tests of models for new physics appearing in neutrino experiments often involve global fits to a quantum mechanical effect called neutrino oscillations. This paper introduces students to methods commonly used in these global fits starting from an understanding of more conventional fitting methods using log-likelihood and $\chi^2$ minimization. Specifically,

Wenjun Jiang, Xiaojun Yuan

This paper investigates the potential of reconfigurable intelligent surface (RIS) for passive information transfer in a RIS-aided multiple-input multiple-output (MIMO) system. We propose a novel simultaneous active and passive information transfer (SAPIT) scheme. In SAPIT, the transmitter (Tx) and the RIS deliver information simultaneously, where the RIS inf

Reed Milewicz, Jeffrey Carver, Samuel Grayson, Travis Atkison

Journalists, public policy analysts, and economists have called attention to the growing importance that high-performance and scientific computing have to national security and industrial leadership. As computing continues to power scientific advances in virtually every discipline, so too does it improve our economic productivity and quality of life. The inc

Martin Law

A multi-arm multi-stage trial is a multi-arm trial which includes interim analyses - analysing the data at certain specified points, generally discontinuing treatments which are concluded to not work and proceeding with the remainder. It is possible that the advantages of multi-arm trials over single-arm trials may be enhanced further by considering the allo

Ilaria Gianani, Silvia Gentilini, Iole Venditti, Chiara Battocchio

High-performing nanomaterials are paramount for advanced photonic technologies, like sensing, lasing, imaging, data storage, processing, and medical and biological applications. Metal nanoparticles play a key role, because the localized surface plasmonic resonances enhance the linear and nonlinear optical properties of hosting materials, thus increasing the

Donato Bini, Thibault Damour

We compute the variation of the Fokker-Wheeler-Feynman total linear and angular momentum of a gravitationally interacting binary system under the second post-Minkowskian retarded dynamics. The resulting $O(G^2)$ equations-of-motion-based, total change in the system's angular momentum is found to agree with existing computations that assumed balance with angu

Mohammad Alsalti, Victor G. Lopez, Julian Berberich, Frank Allgöwer

We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems. The scheme uses a non-parametric predictive model based only on input and noisy output data along with a set of basis functions that approximate the unknown nonlinearities. Despite the noisy output da

Yves Ismaël Ngounou Bakam, Denys Pommeret

We propose a clustering procedure to group K populations into subgroups with the same dependence structure. The method is adapted to paired population and can be used with panel data. It relies on the differences between orthogonal projection coefficients of the K density copulas estimated from the K populations. Each cluster is then constituted by populatio

Qi Guo, Andrés F. Barrientos, Víctor Peña

Confidential data, such as electronic health records, activity data from wearable devices, and geolocation data, are becoming increasingly prevalent. Differential privacy provides a framework to conduct statistical analyses while mitigating the risk of leaking private information. Compositional data, which consist of vectors with positive components that add

Klaus G. Strassmeier, Matthias Steffen

A spectroscopic investigation of the lithium resonance doublet in $\xi$ Boo A and $\xi$ Boo B in terms of both abundance and isotopic ratio is presented. We obtained new $R$=130\,000 spectra with a signal-to-noise ratio (S/N) per pixel of up to 3200 using the 11.8m LBT and PEPSI. From fits with synthetic line profiles based on 1D-LTE MARCS model atmospheres

Sheena Panthaplackel, Milos Gligoric, Junyi Jessy Li, Raymond J. Mooney

Automatically fixing software bugs is a challenging task. While recent work showed that natural language context is useful in guiding bug-fixing models, the approach required prompting developers to provide this context, which was simulated through commit messages written after the bug-fixing code changes were made. We instead propose using bug report discus

Jie Peng, Jianing Tang, Pinghua Tang, Zhongzhou Ren

Deterministic single-photon sources are important and ubiquitous in quantum information protocols. However, to the best of our knowledge, none of them work in the ultrastrong light-matter coupling regime, and each excitation process can only emit one photon. We propose a deterministic single-photon source in circuit QED which can work in the ultrastrong coup

Joshua Springer

Landing is a challenging part of autonomous drone flight and a great research opportunity. This PhD proposes to improve on fiducial autonomous landing algorithms by making them more flexible. Further, it leverages its location, Iceland, to develop a method for landing on lava flows in cooperation with analog Mars exploration missions taking place in Iceland

Marina Zapater, José L. Risco-Martín, Patricia Arroba, José L. Ayala

Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the temperature a

Andrei Comăneci, Michael Joswig

We study a parametric version of the Fermat-Weber problem with respect to an asymmetric distance function, which occurs naturally in tropical geometry. Our results yield a method for constructing phylogenetic supertrees.

Dai-Wei Qu, Qiaoyi Li, Shou-Shu Gong, Yang Qi

Recently, robust $d$-wave superconductive (SC) order has been unveiled in the ground state of the 2D $t$-$t'$-$J$ model -- with both nearest-neighbor ($t$) and next-nearest-neighbor ($t'$) hoppings -- by density matrix renormalization group studies. However, there is currently a debate on whether the $d$-wave SC holds up strong on both $t'/t>0$ and $t'/t<0$

Paul Jerabek, Anthony Burrows, Peter Schwerdtfeger

Density functional calculations for metallic lithium along a cuboidal bcc-to-fcc transformation path demonstrate that the bcc phase is quasi-degenerate with the fcc phase with a very small activation barrier of 0.1~kJ/mol, but becomes the dominant phase at higher temperatures in accordance with Landau theory. This resolves the long-standing controversy about

Fulvio Melia

The search for alternative cosmological models is largely motivated by the growing discordance between the predictions of LCDM and the ever improving observations, such as the disparity in the value of H_0 measured at low and high redshifts. One model, in particular, known as the R_h=ct universe, has been highly successful in mitigating or removing all of th

Jeff Maki, Tilman Enss

The scattering properties of spin-polarized Fermi gases are dominated by p-wave interactions. Besides their inherent angular dependence, these interactions differ from their s-wave counterparts as they also require the presence of a finite effective range in order to understand the low-energy properties of the system. In this article we examine how the shear

Kunal Bhagat, Shiva Rudraraju

Microstructure evolution in metal additive manufacturing (AM) is a complex multi-physics and multi-scale problem. Understanding the impact of AM process conditions on the microstructure evolution and the resulting mechanical properties of the printed part is an active area of research. At the meltpool scale, the thermo-fluidic governing equations have been e

Pierre-Emmanuel Jabin, Datong Zhou

We investigate the properties of discretizations of advection equations on non-cartesian grids and graphs in general. Advection equations discretized on non-cartesian grids have remained a long-standing challenge as the structure of the grid can lead to strong oscillations in the solution, even for otherwise constant velocity fields. We introduce a new metho

Rick Schieni, Chengwei Zhao, Michael Malisoff, Laurent Burlion

Reference governors are add-on schemes that are used to modify trajectories to prevent controlled dynamical systems from violating constraints and so are playing an increasingly important role in aerospace, robotic, and other engineering applications. Here we present a novel reference governor design for systems whose polynomial constraints depend on unknown

Dharmaraj Ramachandran, Radhika Vathsan

While several measures exist for entanglement of multipartite pure states, a true entanglement measure for mixed states still eludes us. A deeper study of the geometry of quantum states may be the way to address this issue, on which context we come up with a measure for pure states based on a geodesic distance on the space of quantum states. Our measure sati

Joachim Börger, Marc Patrick Zapf, Marat Kopytjuk, Xinrun Li 2

Sensor visibility is crucial for safety-critical applications in automotive, robotics, smart infrastructure and others: In addition to object detection and occupancy mapping, visibility describes where a sensor can potentially measure or is blind. This knowledge can enhance functional safety and perception algorithms or optimize sensor topologies. Despite it

Sualeh Khurshid, Diego Donzis, Katepalli R. Sreenivasan

Universal properties of turbulence have been associated traditionally with very high Reynolds numbers, but recent work has shown that the onset of the power-laws in derivative statistics occurs at modest microscale Reynolds numbers of the order of 10, with the corresponding exponents being consistent with those for the inertial range structure functions at v

Luigi Barletti, Lucio Demeio, Sara Nicoletti

The Chapman-Enskog method, in combination with the quantum maximum entropy principle, is applied to the Wigner equation in order to obtain quantum Navier-Stokes equations for electrons in graphene in the isothermal case. The derivation is based on the quantum version of the maximum entropy principle and follows the lines of Ringhofer-Degond-M\'ehats' theory

Andrei Margeloiu, Nikola Simidjievski, Pietro Lio, Mateja Jamnik

Neural networks often struggle with high-dimensional but small sample-size tabular datasets. One reason is that current weight initialisation methods assume independence between weights, which can be problematic when there are insufficient samples to estimate the model's parameters accurately. In such small data scenarios, leveraging additional structures ca

Cristina Flaut, Dana Piciu

The aim of this paper is to analize the structure of BL-algebras using commutative rings. From computational considerations, we are very interested in the finite case. We present new ways to generate finite BL-algebras using commutative rings and we give summarizing statistics. Furthermore, we investigated BL-rings, i.e., commutative rings whose the lattice

Roxana Zeraati, Victor Buendía, Tatiana A. Engel, Anna Levina

Multiple studies of neural avalanches across different data modalities led to the prominent hypothesis that the brain operates near a critical point. The observed exponents often indicate the mean-field directed-percolation universality class, leading to the fully-connected or random network models to study the avalanche dynamics. However, the cortical netwo

Niklas Hidman, Henrik Ström, Srdjan Sasic, Gaetano Sardina

By using Direct Numerical Simulations (DNS) of bubbly flows with passive scalars, we show a transition in the scalar spectra from a $k^{-5/3}$ to a $k^{-3}$ scaling with the wavenumber $k$, in contrast with those of single-phase isotropic turbulence. For cases with a mean scalar gradient in the horizontal direction, the scalar spectrum decays faster than $k^

Claudia Vanea, Jonathan Campbell, Omri Dodi, Liis Salumäe

We introduce a new benchmark dataset, Placenta, for node classification in an underexplored domain: predicting microanatomical tissue structures from cell graphs in placenta histology whole slide images. This problem is uniquely challenging for graph learning for a few reasons. Cell graphs are large (>1 million nodes per image), node features are varied (64-

Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick, Tom Rainforth

We investigate the benefit of treating all the parameters in a Bayesian neural network stochastically and find compelling theoretical and empirical evidence that this standard construction may be unnecessary. To this end, we prove that expressive predictive distributions require only small amounts of stochasticity. In particular, partially stochastic network

Katharina Höveler, Johannes Deiglmayr, Josef A. Agner, Raphaël Hahn

In 1954, Vogt and Wannier (Phys. Rev. 95, 1190) predicted that the capture rate of a polarizable neutral atom or molecule by an ion should increase by a factor of two compared to the classical Langevin rate as the collision energy approaches zero. This prediction has not been verified experimentally. The H$_2^+$ + H$_2$ reaction is ideally suited to observe

Joachim Hofer, Rudolf Gross, Gerard Higgins, Hans Huebl

We report the levitation of a superconducting lead-tin sphere with 100 micrometer diameter (corresponding to a mass of 5.6 micrograms) in a static magnetic trap formed by two coils in an anti-Helmholtz configuration, with adjustable resonance frequencies up to 240 hertz. The center-of-mass motion of the sphere is monitored magnetically using a dc superconduc

Karsten Reichold

This paper derives asymptotic theory for Breitung's (2002, Journal of Econometrics 108, 343-363) nonparameteric variance ratio unit root test when applied to regression residuals. The test requires neither the specification of the correlation structure in the data nor the choice of tuning parameters. Compared with popular residuals-based no-cointegration tes

Namya Bagree, Charles Noren, Damanpreet Singh, Matthew Travers

Practical deployments of coordinated fleets of mobile robots in different environments have revealed the benefits of maintaining small distances between robots, especially as they move at higher speeds. However, this is counter-intuitive in that as speed increases, reducing the amount of space between robots also reduces the time available to the robots to r

Huy Q. Nguyen, Ian Tice

We study the Muskat problem for one fluid in arbitrary dimension, bounded below by a flat bed and above by a free boundary given as a graph. In addition to a fixed uniform gravitational field, the fluid is acted upon by a generic force field in the bulk and an external pressure on the free boundary, both of which are posited to be in traveling wave form. We

Vitaly Gorelov, Lucia Reining, Walter R. L. Lambrecht, Matteo Gatti

In bulk and low-dimensional extended systems, the screening of excitations by the electron cloud is a key feature governing spectroscopic properties. Widely used computational approaches, especially in the framework of many-body perturbation theory, such as the GW approximation and the resulting approximate Bethe-Salpeter equation, are explicitly formulated

Yuto Watanabe, Kazunori Sakurama

This paper addresses a distributed convex optimization problem with a class of coupled constraints, which arise in a multi-agent system composed of multiple communities modeled by cliques. First, we propose a fully distributed gradient-based algorithm with a novel operator inspired by the convex projection, called the clique-based projection. Next, we scruti

Chandra Kant Jat, Jay Dave, Dirk Van Hertem, Hakan Ergun

HVDC is a critically important technology for the large-scale integration of renewable resources such as offshore wind farms. Currently, only point-to-point and multi-terminal HVDC connections exist in real-life operation. However, with the advancement of VSC-based converter technologies, future HVDC systems are foreseen to develop into meshed HVDC grids. Bi

T. Vermeij, C. J. A. Mornout, V. Rezazadeh, J. P. M. Hoefnagels

Martensite damage in Dual-Phase (DP) steel has been studied extensively, yet, the exact deformation mechanisms that trigger or inhibit damage initiation remain mostly unexplored. Whereas generally assumed to be hard and brittle, lath martensite in fact deforms in a highly anisotropic manner, showing large strains under favorable habit plane orientations, whi

D. Bazin, K. Becker, F. Bonaiti, Ch. Elster

It is a fascinating phenomenon in nuclear physics that states with a pronounced few-body structure can emerge from the complex dynamics of many nucleons. Such halo or cluster states often appear near the boundaries of nuclear stability. As such, they are an important part of the experimental program beginning at the Facility for Rare Isotope Beams (FRIB). A

Dan A. Lee, Martin Lesourd, Ryan Unger

We generalize Y. Shi and L.-F.\ Tam's \cite{ShiTam} nonnegativity result for the Brown-York mass, by considering nonnegative scalar curvature (NNSC) fill-ins that need only be complete rather than compact. Moreover, the NNSC fill-ins need not even be complete as long the incompleteness is ``shielded'' by a region with positive scalar curvature and occurs occ

Lucian Nita, Eric C. Kerrigan, Eduardo M. G. Vila, Yuanbo Nie

Solutions to optimal control problems can be discontinuous, even if all the functionals defining the problem are smooth. This can cause difficulties when numerically computing solutions to these problems. While conventional numerical methods assume state and input trajectories are continuous and differentiable or smooth, our method is able to capture discont

Seif Alwan, Subhajit Sarkar, Amos Sharoni, Yonatan Dubi

The temperature dependence of the chirality-induced spin selectivity (CISS) effect can be used to discriminate between different theoretical proposals for the mechanism of the CISS effect. Here we briefly review key experimental results and discuss the effect of temperature in different models for the CISS effect. We then focus on the recently suggested spin

Laura State, Hadrien Salat, Stefania Rubrichi, Zbigniew Smoreda

Explainable artificial intelligence (XAI) provides explanations for not interpretable machine learning (ML) models. While many technical approaches exist, there is a lack of validation of these techniques on real-world datasets. In this work, we present a use-case of XAI: an ML model which is trained to estimate electrification rates based on mobile phone da

Yikai Yan, Chaoyue Niu, Fan Wu, Qinya Li

The mainstream workflow of image recognition applications is first training one global model on the cloud for a wide range of classes and then serving numerous clients, each with heterogeneous images from a small subset of classes to be recognized. From the cloud-client discrepancies on the range of image classes, the recognition model is desired to have str

Lukáš Gráf, Brian Henning, Xiaochuan Lu, Tom Melia

We expand Hilbert series technologies in effective field theory for the inclusion of massive particles, enabling, among other things, the enumeration of operator bases for non-linearly realized gauge theories. We find that the Higgs mechanism is manifest at the level of the Hilbert series, as expected for the partition function of an $S$-matrix that is subje

Thomas Auzelle, Miriam Oliva, Philipp John, Manfred Ramsteiner

The self-assembly of heteroepitaxial GaN nanowires using either molecular beam epitaxy (MBE) or metal-organic vapor phase epitaxy (MOVPE) mostly results in wafer-scale ensembles with ultrahigh ($>10$ $\mu$m$^{-2}$) or ultralow ($<1$ $\mu$m$^{-2}$) densities, respectively. A simple means to tune the density of well-developed nanowire ensembles between these t

Wen-Yuan Ai, Jean Alexandre, Sarben Sarkar

We study the renormalisation of the non-Hermitian $\mathcal{P}\mathcal{T}$-symmetric scalar field theory with the interaction $\phi^2(i\phi)^\varepsilon$ using the Wilsonian approach and without any expansion in $\varepsilon$. Specifically, we solve the Wetterich equation in the local potential approximation, both in the ultraviolet regime and with the loop

Sebastian Franz, Natalia Kopteva

Time-fractional parabolic equations with a Caputo time derivative are considered. For such equations, we explore and further develop the new methodology of the a-posteriori error estimation and adaptive time stepping proposed in [7]. We improve the earlier time stepping algorithm based on this theory, and specifically address its stable and efficient impleme

Lev Titarchuk, Elena Seifina

The outburst activity in the MAXI~J1348--630 has sparked a great deal of controversy, whether the source contains a black hole (BH). Here, we present the results of our analysis of the outburst of MAXI J348--630 using Swift/XRT data. We find that energy spectra in all spectral states can be modeled using a combination of Comptonization and Gaussian iron-line

Dmitry Gorbunov, Dmitry Kalashnikov

Future $c$-$\tau$ factories are natural places to study extensions of the Standard Model of particle physics (SM) with new long-lived feebly interacting particles light enough to be produced in electron-positron collisions. We investigate prospects of these machines in exploring such extensions emphasizing the role of polarized beams in getting rid of the SM

Yuxiang Wei, Tengfei Xue, Yogesh Rathi, Nikos Makris

The brain's white matter (WM) undergoes developmental and degenerative processes during the human lifespan. To investigate the relationship between WM anatomical regions and age, we study diffusion magnetic resonance imaging tractography that is finely parcellated into fiber clusters in the deep, superficial, and cerebellar WM. We propose a deep-learning-bas

Domenico Giordano, Pierluigi Amodio, Felice Iavernaro

We address two questions regarding square-well potentials from a didactic perspective. The first question concerns whether or not the justification of the standard a priori omission of the potential's vertical segments in the analysis of the eigenvalue problem is licit. The detour we follow to find out the answer considers a trapezoidal potential, includes t

Francesco Bajardi, Rocco D'Agostino, Micol Benetti, Vittorio De Falco

Discrepancies between observations at early and late cosmic epochs, and the vacuum energy problem associated with the interpretation of cosmological constant, are questioning the $\Lambda$CDM model. Motivated by these conceptual and observational facts, extensions of Einstein's gravity are recently intensively considered in view of curing unsolved issues suf

Tobias Friedrich, Davis Issac, Nikhil Kumar, Nadym Mallek

We prove an approximate max-multiflow min-multicut theorem for bounded treewidth graphs. In particular, we show the following: Given a treewidth-$r$ graph, there exists a (fractional) multicommodity flow of value $f$, and a multicut of capacity $c$ such that $ f \leq c \leq \mathcal{O}(\ln (r+1)) \cdot f$. It is well known that the multiflow-multicut gap on

Andreas Hausoel, Markus Wallerberger, Josef Kaufmann, Karsten Held

We describe an aberration of the resampling estimator for the Green's function customarily used in hybridization expansion continuous-time quantum Monte Carlo. It occurs due to Pauli principle constraints in calculations of Anderson impurity models with baths consisting of a discrete energy spectrum. We identify the missing Feynman diagrams, characterize the

Bruce Boghosian, Christoph Börgers, Natasa Dragovic, Anna Haensch

We derive a differential-integral equation akin to the Hegselmann-Krause model of opinion dynamics, and propose a particle method for solving the equation. Numerical experiments demonstrate second-order convergence of the method in a weak sense. We also show that our differential-integral equation can equivalently be stated as a system of differential equati

George Dickinson

We prove an upper bound on the density of zeros very close to the critical line of the family of Dirichlet $L$-functions of modulus $q$ at height $T$. To do this, we derive an asymptotic for the twisted second moment of Dirichlet $L$-functions uniformly in $q$ and $t$. As a second application of the asymptotic formula we prove that, for every integer $q$, at

Shayan Halder

Economy is severely dependent on the stock market. An uptrend usually corresponds to prosperity while a downtrend correlates to recession. Predicting the stock market has thus been a centre of research and experiment for a long time. Being able to predict short term movements in the market enables investors to reap greater returns on their investments. Stock

Yi Ren, Yanyang Xiao, Guo-Qiang Bi, Pek-Ming Lau

In modern neuroscience, memory has been postulated to stored in neural circuits as sequential spike train and Reverberation is one of the specific example.Former research has made much progress on phenomenon description. However, the mechanism of reverberation has been unclear yet. In this study, combining electrophysiological record and numerical simulation

Mahdi Zahedi, Taha Shahroodi, Stephan Wong, Said Hamdioui

Applications of Binary Neural Networks (BNNs) are promising for embedded systems with hard constraints on computing power. Contrary to conventional neural networks with the floating-point datatype, BNNs use binarized weights and activations which additionally reduces memory requirements. Memristors, emerging non-volatile memory devices, show great potential

Rui Li, ST John, Arno Solin

Gaussian process training decomposes into inference of the (approximate) posterior and learning of the hyperparameters. For non-Gaussian (non-conjugate) likelihoods, two common choices for approximate inference are Expectation Propagation (EP) and Variational Inference (VI), which have complementary strengths and weaknesses. While VI's lower bound to the mar

Mahboubeh Keyvanara, Arman Goshtasbi, Irene A. Kuling

Soft robots are interesting examples of hyper-redundancy in robotics, however, the nonlinear continuous dynamics of these robots and the use of hyper-elastic and visco-elastic materials makes modeling of these robots more complicated. This study presents a geometric Inverse Kinematic (IK) model for trajectory tracking of multi-segment extensible soft robots,

Changhao Liu, Fan Yang, Maokun Li, Shenheng Xu

The past decade has witnessed the advances of artificial intelligence with various applications in engineering. Recently, artificial neural network empowered inverse design for metasurfaces has been developed that can design on-demand meta-atoms with diverse shapes and high performance, where the design process based on artificial intelligence is fast and au

T. Blenski, R. Piron

We propose a model of ion-electron plasma (or nucleus-electron plasma) that accounts for the electronic structure around nuclei (i.e. ion structure) as well as for ion-ion correlations. The model equations are obtained through the minimization of an approximate free-energy functional, and it is shown that the model fulfills the virial theorem. The main hypot

Hassan Haji Mohammadi, Alireza Talebpour, Ahmad Mahmoudi Aznaveh, Samaneh Yazdani

Pronoun resolution is a challenging subset of an essential field in natural language processing called coreference resolution. Coreference resolution is about finding all entities in the text that refers to the same real-world entity. This paper presents a hybrid model combining multiple rulebased sieves with a machine-learning sieve for pronouns. For this p

Miguel Citeli de Freitas, Viktor V. Dodonov

We study numerically the coordinate wave functions and the Wigner functions of the coherent phase states (CPS), paying the main attention to their differences from the standard (Klauder--Glauber--Sudarshan) coherent states, especially in the case of high mean values of the number operator. In this case, the CPS can possess a strong coordinate (or momentum) s

Mihajlo Cekić, Gabriel P. Paternain

We study resonant differential forms at zero for transitive Anosov flows on $3$-manifolds. We pay particular attention to the dissipative case, that is, Anosov flows that do not preserve an absolutely continuous measure. Such flows have two distinguished Sinai-Ruelle-Bowen $3$-forms, $\Omega_{\text{SRB}}^{\pm}$, and the cohomology classes $[\iota_{X}\Omega_{

Sebastián Basterrech, Tarun Kumar Sharma

For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training the networks (adjusting the weights), designing network topology, optimizing global parameters, and selecting features. Here, we provide a systematic brief survey about applicatio

Stephen Lynch, Andoni Royo Abrego

Ancient solutions of the Ricci flow arise naturally as models for singularity formation. There has been significant progress towards the classification of such solutions under natural geometric assumptions. Nonnegatively curved solutions in dimensions 2 and 3, and uniformly PIC solutions in higher dimensions are now well understood. We consider ancient solut

Leonardo Colombo, Manuel de León, María Emma Eyrea Irazú, Asier López-Gordón

A hybrid system is a system whose dynamics is given by a mixture of both continuous and discrete transitions. In particular, these systems can be utilised to describe the dynamics of a mechanical system with impacts. Based on the approach by Clark, we develop a geometric Hamilton-Jacobi theory for forced and nonholonomic hybrid dynamical systems. We state th

Xianru Chen, Li Lin

Based on the Fourier extension, we propose an oversampling collocation method for solving the elliptic partial differential equations with variable coefficients over arbitrary irregular domains. This method only uses the function values on the equispaced nodes, which has low computational cost and versatility. While a variety of numerical experiments are pre

Kumud Lakara, Matias Valdenegro-Toro

Trusting the predictions of deep learning models in safety critical settings such as the medical domain is still not a viable option. Distentangled uncertainty quantification in the field of medical imaging has received little attention. In this paper, we study disentangled uncertainties in image to image translation tasks in the medical domain. We compare m

Gabriele Prato, Yale Song, Janarthanan Rajendran, R Devon Hjelm

Transformers have become one of the dominant architectures in the field of computer vision. However, there are yet several challenges when applying such architectures to video data. Most notably, these models struggle to model the temporal patterns of video data effectively. Directly targeting this issue, we introduce PatchBlender, a learnable blending funct

B. M. Reichert, R. R. Obelheiro

In recent years, there has been a growing concern with software integrity, that is, the assurance that software has not been tampered with on the path between developers and users. This path is represented by a software development pipeline and plays a pivotal role in software supply chain security. While there have been efforts to improve the security of de

Johannes Gooth, Stanislaw Galeski, Tobias Meng

The discovery of the quantum Hall effect (QHE) in 1980 marked a turning point in condensed matter physics: given appropriate experimental conditions, the Hall conductivity {\sigma}_xy of a two-dimensional (2D) electron system is exactly quantized. But what happens to the QHE in three dimensions (3D)? Experiments over the past 40 years showed that some of the