September 2022 arXiv papers — page 3

Oscar Castillo-Felisola, Dominic T. Price, Mattia Scomparin

Computer Algebra Systems (CASs) like Cadabra Software play a prominent role in a wide range of research activities in physics and related fields. We show how Cadabra language is easily implemented in the well established Python programming framework, gaining excellent flexibility and customization to address the issue of tensor perturbations in General Relat

Jonathon Hales, Ghaith Hiary

A new deterministic algorithm for finding square divisors, and finding $r$-power divisors in general, is presented. This algorithm is based on Lehman's method for integer factorization and is straightforward to implement. While the theoretical complexity of the new algorithm is far from best known, the algorithm becomes especially effective if even a loose b

Takuya Kurihana, Ian Foster, Rebecca Willett, Sydney Jenkins

We present a framework for cloud characterization that leverages modern unsupervised deep learning technologies. While previous neural network-based cloud classification models have used supervised learning methods, unsupervised learning allows us to avoid restricting the model to artificial categories based on historical cloud classification schemes and ena

Karina Ruzaeva, Kira Küsters, Wolfgang Wiechert, Benjamin Berkels

We here propose an automated pipeline for the microscopy image-based characterization of catalytically active inclusion bodies (CatIBs), which includes a fully automatic experimental high-throughput workflow combined with a hybrid approach for multi-object microbial cell segmentation. For automated microscopy, a CatIB producer strain was cultivated in a micr

Ross Hollyman, Fotios Petropoulos, Michael E. Tipping

We examine the problem of making reconciled forecasts of large collections of related time series through a behavioural/Bayesian lens. Our approach explicitly acknowledges and exploits the 'connectedness' of the series in terms of time-series characteristics and forecast accuracy as well as hierarchical structure. By making maximal use of the available infor

Vladimir Mitankin, Masahiro Nakahara, Sam Streeter

We study local-global principles for two notions of semi-integral points, termed Campana points and Darmon points. In particular, we develop a semi-integral version of the Brauer-Manin obstruction interpolating between Manin's classical version for rational points and the integral version developed by Colliot-Th\'el\`ene and Xu. We determine the status of lo

Diksha Garg, Sameer Patel, Mary Hall Reno, Alexander Reustle

Ultra-high-energy neutrinos serve as messengers of some of the highest energy astrophysical environments. Given that neutrinos are neutral and only interact via weak interactions, neutrinos can emerge from sources, traverse astronomical distances, and point back to their origins. Their weak interactions require large target volumes for neutrino detection. Us

Kiarash Jamali, Dari Kimanius, Sjors H. W. Scheres

Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. Along with knowledge about the imaged molecules, cryo-EM maps allow de novo atomic modelling, which is typically done through a laborious manual process. Taking inspiration from recent advances in machine le

James P. Horwath, Colin Lehman-Chong, Aleksandra Vojvodic, Eric A. Stach

Heterogeneous catalysts consisting of supported metallic nanoparticles typically derive exceptional catalytic activity from their large proportion of under-coordinated surface sites which promote adsorption of reactant molecules. Simultaneously, these high energy surface configurations are unstable, leading to nanoparticle growth or degradation, and eventual

J. H. Mclean, M. R. Jones, B. J. O'Connell, A. E Maguire

A wind turbines' power curve is easily accessible damage sensitive data, and as such is a key part of structural health monitoring in wind turbines. Power curve models can be constructed in a number of ways, but the authors argue that probabilistic methods carry inherent benefits in this use case, such as uncertainty quantification and allowing uncertainty p

Oscar Castillo-Felisola, Dominic T. Price, Mattia Scomparin

The aim of this work is to present a series of concrete examples which illustrate how the computer algebra system Cadabra can be used to manipulate expressions appearing in General Relativity and other gravitational theories. We highlight the way in which Cadabra's philosophy differs from other systems with related functionality. The use of various new built

Kezi Li, Jeremy D. Brown

Upper-extremity amputees who use myoelectric prostheses currently lack the haptic sensory information needed to perform dexterous activities of daily living. While considerable research has focused on restoring this haptic information, these approaches often rely on single-modality feedback schemes which are necessary but insufficient for the feedforward and

Hans U. Boden, Ceyhun Elmacioglu, Anshul Guha, Homayun Karimi

We define a knot to be half ribbon if it is the cross-section of a ribbon 2-knot, and observe that ribbon implies half ribbon implies slice. We introduce the half ribbon genus of a knot K, the minimum genus of a ribbon knotted surface of which K is a cross-section. We compute this genus for all prime knots up to 12 crossings, and many 13-crossing knots. The

Jesús Contreras, Victor Rivero

For a spectrally negative L\'evy process, scale functions appear in the solution of two-sided exit problems, and in particular in relation with the Laplace transform of the first time it exits a closed interval. In this paper, we consider the Laplace transform of more general functionals, which can depend simultaneously on the values of the process and its s

Yan Gao, Javier Fernandez-Marques, Titouan Parcollet, Pedro P. B. de Gusmao

Self-supervised learning (SSL) has proven vital in speech and audio-related applications. The paradigm trains a general model on unlabeled data that can later be used to solve specific downstream tasks. This type of model is costly to train as it requires manipulating long input sequences that can only be handled by powerful centralised servers. Surprisingly

Abdullah Alhadlaq, Said Kerrache, Hatim Aboalsamh

Online stores and service providers rely heavily on recommendation softwares to guide users through the vast amount of available products. Consequently, the field of recommender systems has attracted increased attention from the industry and academia alike, but despite this joint effort, the field still faces several challenges. For instance, most existing w

Tobias Wenzel

This article illustrates how open hardware solutions are implemented by researchers as a strategy to access technology for cutting-edge research. Specifically, it is discussed what kind of open technologies are most enabling in scientific environments characterized by economic and infrastructural constraints. It is demonstrated that do-it-yourself (DIY) tech

Jie Chu, Mikael Vejdemo-Johansson, Ping Ji

In this paper, we present an algorithm that computes the generalized \v{C}ech complex for a finite set of disks where each may have a different radius in 2D space. An extension of this algorithm is also proposed for a set of balls in 3D space with different radius. To compute a $k$-simplex, we leverage the computation performed in the round of $(k-1)$-simpli

Benjamin Russo, M. Paul Laiu

In this paper, we give an in-depth error analysis for surrogate models generated by a variant of the Sparse Identification of Nonlinear Dynamics (SINDy) method. We start with an overview of a variety of non-linear system identification techniques, namely, SINDy, weak-SINDy, and the occupation kernel method. Under the assumption that the dynamics are a finite

Mar Bastero-Gil, António Torres Manso

Inflaton-vector interactions of the type $\phi F\tilde{F}$ have provided interesting phenomenology to tackle some of current problems in cosmology, namely the vectors could constitute the dark matter component. It could also lead to possible signatures imprinted in a gravitational wave spectrum. Through this coupling, a rolling inflaton induces an exponentia

Michael S. Albergo, Eric Vanden-Eijnden

A generative model based on a continuous-time normalizing flow between any pair of base and target probability densities is proposed. The velocity field of this flow is inferred from the probability current of a time-dependent density that interpolates between the base and the target in finite time. Unlike conventional normalizing flow inference methods base

Majdi I. Radaideh, Chris Pappas, Mark Wezensky, Pradeep Ramuhalli

Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, the authors successfully conducted 21 fa

Matheus V. X. Ferreira, David C. Parkes

Trading on decentralized exchanges has been one of the primary use cases for permissionless blockchains with daily trading volume exceeding billions of U.S.~dollars. In the status quo, users broadcast transactions and miners are responsible for composing a block of transactions and picking an execution ordering -- the order in which transactions execute in t

Will Hide, Joe Thomas

We study the number of short geodesics and small eigenvalues on Weil-Petersson random genus zero hyperbolic surfaces with $n$ cusps in the regime $n\to\infty$. Inspired by work of Mirzakhani and Petri \cite{Mi.Pe19}, we show that the random multi-set of lengths of closed geodesics converges, after a suitable rescaling, to a Poisson point process with explici

Gian Marco Visani, Michael N. Pun, Arman Angaji, Armita Nourmohammad

Group-equivariant neural networks have emerged as a data-efficient approach to solve classification and regression tasks, while respecting the relevant symmetries of the data. However, little work has been done to extend this paradigm to the unsupervised and generative domains. Here, we present Holographic-(Variational) Auto Encoder (H-(V)AE), a fully end-to

Angelo Bratta, Avadesh Meduri, Michele Focchi, Ludovic Righetti

In legged logomotion, online trajectory optimization techniques generally depend on heuristic-based contact planners in order to have low computation times and achieve high replanning frequencies. In this work, we propose ContactNet, a fast acyclic contact planner based on a multi-output regression neural network. ContactNet ranks discretized stepping region

Rindranirina Ramamonjison, Haley Li, Timothy T. Yu, Shiqi He

We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formulation of an optimization problem based on its description. To facilitate this process, we build an intuitive user interface system that enables the users to validate and edit the su

Florentin Münch

We give a new upper bound for the average graph distance in terms of the average Ollivier curvature. Here, the average Ollivier curvature is weighted with the edge betweenness centrality. Moreover, we prove that equality is attained precisely for the reflective graphs which have been classified as Cartesian products of cocktail party graphs, Johnson graphs,

William Dorrell, Peter E. Latham, Timothy E. J. Behrens, James C. R. Whittington

To afford flexible behaviour, the brain must build internal representations that mirror the structure of variables in the external world. For example, 2D space obeys rules: the same set of actions combine in the same way everywhere (step north, then south, and you won't have moved, wherever you start). We suggest the brain must represent this consistent mean

Tianxiang Gao, Hongyang Gao

Implicit neural networks have become increasingly attractive in the machine learning community since they can achieve competitive performance but use much less computational resources. Recently, a line of theoretical works established the global convergences for first-order methods such as gradient descent if the implicit networks are over-parameterized. How

Sylvianne D. C. Roscam Abbing, Filippo Campi, Brian de Keijzer, Corentin Morice

The emission of high-order harmonics from solids \cite{ghimire11a,schubert14a,luu15a,golde08a} under intense laser-pulse irradiation is revolutionizing our understanding of strong-field solid-light interactions \cite{ghimire11a,schubert14a,luu15a,vampa15b,yoshikawa17a,hafez18a,jurgens20a}, while simultaneously opening avenues towards novel, all-solid, cohere

Rahul Mishra, Hari Prabhat Gupta

Automated feature extraction capability and significant performance of Deep Neural Networks (DNN) make them suitable for Internet of Things (IoT) applications. However, deploying DNN on edge devices becomes prohibitive due to the colossal computation, energy, and storage requirements. This paper presents a novel approach for designing and training lightweigh

Keshaan Singh, Isaac Nape, Wagner Tavares Buono, Angela Dudley

Increasing the information capacity of communication channels is a pressing need, driven by growing data demands and the consequent impending data crunch with existing modulation schemes. In this regard, mode division multiplexing (MDM), where the spatial modes of light form the encoding basis, has enormous potential and appeal, but is impeded by modal noise

Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna

Machine learning algorithms typically assume independent and identically distributed samples in training and at test time. Much work has shown that high-performing ML classifiers can degrade significantly and provide overly-confident, wrong classification predictions, particularly for out-of-distribution (OOD) inputs. Conditional language models (CLMs) are p

Pedro Gomes, Silvia Rossi, Laura Toni

This paper aims at bringing some light and understanding to the field of deep learning for dynamic point cloud processing. Specifically, we focus on the hierarchical features learning aspect, with the ultimate goal of understanding which features are learned at the different stages of the process and what their meaning is. Last, we bring clarity on how hiera

Hamed Pezeshki, Pingzhi Li, Reinoud Lavrijsen, Martijn Heck

We introduce a novel integrated hybrid plasmonic-photonic device for all-optical switching and reading of nanoscale ferrimagnet bits. The racetrack memory made of synthetic ferrimagnetic material with a perpendicular magnetic anisotropy is coupled on to a photonic waveguide onto the indium phosphide membrane on silicon platform. The device which is composed

Vladimir Li, Atsuto Maki

Knowledge transfer between artificial neural networks has become an important topic in deep learning. Among the open questions are what kind of knowledge needs to be preserved for the transfer, and how it can be effectively achieved. Several recent work have shown good performance of distillation methods using relation-based knowledge. These algorithms are e

Martin W. McCall, Stefanos Fr. Koufidis

The axial propagation of circularly polarized light in an optically active structurally chiral medium is exactly solved via full electromagnetic analysis. Some symmetries of the system's characteristic matrix reveal new insights, which are confirmed by coupled wave theory. For extreme values of chirality, now accessible via metamaterials, a reverse circular

Rajenki Das, Mark Muldoon, Mark Lunt, John McBeth

It is well-known that mood and pain interact with each other, however individual-level variability in this relationship has been less well quantified than overall associations between low mood and pain. Here, we leverage the possibilities presented by mobile health data, in particular the "Cloudy with a Chance of Pain" study, which collected longitudinal dat

A. Esposito, F. S. Patacchini, A. Schlichting

Motivated by applications in data science, we study partial differential equations on graphs. By a classical fixed-point argument, we show existence and uniqueness of solutions to a class of nonlocal continuity equations on graphs. We consider general interpolation functions, which give rise to a variety of different dynamics, e.g., the nonlocal interaction

Connor Mooney, Yang Yang

We construct nonlinear entire anisotropic minimal graphs over $\mathbb{R}^4$, completing the solution to the anisotropic Bernstein problem. The examples we construct have a variety of growth rates, and our approach both generalizes to higher dimensions and recovers and elucidates known examples of entire minimal graphs over $\mathbb{R}^{n},\, n \geq 8$.

Andrea Atzori, Gianni Fenu, Mirko Marras

Face biometrics are playing a key role in making modern smart city applications more secure and usable. Commonly, the recognition threshold of a face recognition system is adjusted based on the degree of security for the considered use case. The likelihood of a match can be for instance decreased by setting a high threshold in case of a payment transaction v

Carlos P. Herrero, Rafael Ramirez

Two-dimensional (2D) silicon carbide is an emergent direct band-gap semiconductor, recently synthesized, with potential applications in electronic devices and optoelectronics. Here, we study nuclear quantum effects in this 2D material by means of path-integral molecular dynamics (PIMD) simulations in the temperature range from 25 to 1500~K. Interatomic inter

Matteo Brogi, Vanessa Emeka-Okafor, Michael R. Line, Siddharth Gandhi

We present high-resolution dayside thermal emission observations of the exoplanet WASP-18b using IGRINS on Gemini South. We remove stellar and telluric signatures using standard algorithms, and we extract the planet signal via cross correlation with model spectra. We detect the atmosphere of WASP-18b at a signal-to-noise ratio (SNR) of 5.9 using a full chemi

Gregory Ashton

Interferometric gravitational-wave observatories have opened a new era in astronomy. The rich data produced by an international network enables detailed analysis of the curved space-time around black holes. With nearly one hundred signals observed so far and thousands expected in the next decade, their population properties enable insights into stellar evolu

Richard C. Brower, Evan K. Owen

We demonstrate that the Ising model on a general triangular graph with 3 distinct couplings $K_1,K_2,K_3$ corresponds to an affine transformed conformal field theory (CFT). Full conformal invariance of the $c= 1/2$ minimal CFT is restored by introducing a metric on the lattice through the map $\sinh(2K_i) = \ell^*_i/ \ell_i$ which relates critical couplings

Jean-Paul Blaizot, Marco Claudio Traini

We consider the diffractive photo-production of vector mesons on a proton, in the dipole model. We take into account the effect of the fluctuations of the the dipole size, whose magnitude is controlled by the overlap between the photon and the vector meson wave functions. Our predictions for the incoherent diffractive cross section, obtained within the Impac

Juergen Luettin, Sebastian Monka, Cory Henson, Lavdim Halilaj

Automated driving is one of the most active research areas in computer science. Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD)in particular. However, there are still unsolved problems to guarantee reliability and safety of automated systems, especially to effectively incorporate all avail

Tania Robens

In this proceeding, I discuss several models that extend the scalar sector of the Standard Model by additional matter states. I here focus on results for models with singlet extensions, which have been obtained recently and update some of the results presented in previous work. In more detail, I will briefly review the option to test a strong first-order ele

Stephen Brown, William L. Rodi, Marco Seracini, Chen Gu

We consider the application of machine learning to the evaluation of geothermal resource potential. A supervised learning problem is defined where maps of 10 geological and geophysical features within the state of Nevada, USA are used to define geothermal potential across a broad region. We have available a relatively small set of positive training sites (kn

Boris Springborn

We classify and enumerate all rational numbers with approximation constant at least $\frac{1}{3}$ using hyperbolic geometry. Rational numbers correspond to geodesics in the modular torus with both ends in the cusp, and the approximation constant measures how far they stay out of the cusp neighborhood in between. Compared to the original approach, the geometr

Christopher Couzens, Niall T. Macpherson, Achilleas Passias

We construct multiple embeddings of all solutions of $d=5$ minimal (un)gauged supergravity into massive Type IIA supergravity. The internal spaces and warpings of such embeddings are the same as those of the $\mathcal{N}=1$ supersymmetric (Mink$_5$) AdS$_5$ vacua, with the slight modification that the U(1) R-symmetry direction becomes fibered over the extern

Noémie Jaquier, Tamim Asfour

Riemannian geometry is a mathematical field which has been the cornerstone of revolutionary scientific discoveries such as the theory of general relativity. Despite early uses in robot design and recent applications for exploiting data with specific geometries, it mostly remains overlooked in robotics. With this blue sky paper, we argue that Riemannian geome

Silvan Schwarz

Let $\mathfrak{g}$ be a curved $L_\infty$-algebra endowed with a complete filtration $\mathfrak{F}\mathfrak{g}$. Suppose there exists an integer $r \in \mathbb{N}_0$ for which the curvature $\mu_0$ satisfies $\mu_0 \in \mathfrak{F}_{2r+1} \mathfrak{g}$ and the spectral sequence yields $E_{r+1}^{p,q} =0$ for $p,q$ with $p+q=2$. We prove that then a Maurer-Car

Saray Arteaga, Ling-Yun Dai, Adolfo Guevara, Pablo Roig

We show the discrepancy between the isospin-rotated $e^+e^-\to\eta\pi^-\pi^+$ cross-section -- measured by various collaborations -- and the Belle $\tau^-\to\eta\pi^-\pi^0\nu_\tau$ spectrum, which cannot be explained by heavy new physics non-standard interactions. We give for the first time the framework needed to study these beyond the standard model contri

Lyes Khacef, Philipp Klein, Matteo Cartiglia, Arianna Rubino

Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learning models have recently been proposed following different approaches. However, it is difficult to assess if a

Luke McConnell

The LHCb detector at the LHC offers unique coverage of forward rapidities for studies of Central Exclusive Production (CEP) and soft QCD. CEP measurements allow the investigation of the nature of pomerons, and provide constraints on low-x gluon phenomenology, probing potential saturation effects. Moreover LHCb can test phenomenological models of soft QCD pro

M. N. Chernodub, V. A. Goy, A. V. Molochkov

We present the results of first-principle numerical simulations of Euclidean SU(3) Yang-Mills plasma rotating with a high imaginary angular frequency. The rigid Euclidean rotation is introduced via ``rotwisted'' boundary conditions along imaginary time direction. The Polyakov loop in the co-rotating Euclidean reference frame shows the emergence of a spatiall

Stefano Pozza, Niel Van Buggenhout

A new method for solving non-autonomous ordinary differential equations is proposed, the method achieves spectral accuracy. It is based on a new result which expresses the solution of such ODEs as an element in the so called $\star$-algebra. This algebra is equipped with a product, the $\star$-product, which is the integral over the usual product of two biva

Li-on Raviv, Amir Leshem

Cellular networks provide communication for different applications. Some applications have strict and very short latency requirements, while others require high bandwidth with varying priorities. The challenge of satisfying the requirements grows in congested traffic where some packets might miss their deadlines. Unfortunately, we prove that the problem is N

Habib Alizadeh

We show that if a diffeomorphism of a symplectic manifold $(M^{2n},\omega)$ preserves the form $\omega^{k}$ for $0 < k < n$ and is connected to identity through such diffeomorphisms then it is indeed a symplectomorphism.

Spyridon Dendrinos, Andrei Mustata, Marco Vitturi

We draw a connection between the affine invariant surface measures constructed by P. Gressman and the boundedness of a certain geometric averaging operator associated to surfaces of codimension $2$ and related to the Fourier Restriction Problem for such surfaces. For a surface given by $(\xi, Q_1(\xi), Q_2(\xi))$, with $Q_1,Q_2$ quadratic forms on $\mathbb{R

Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande

Nanodiamonds have a wide range of applications including catalysis, sensing, tribology and biomedicine. To leverage nanodiamond design via machine learning, we introduce the new dataset ND5k, consisting of 5,089 diamondoid and nanodiamond structures and their frontier orbital energies. ND5k structures are optimized via tight-binding density functional theory

Anton Obukhov, Mikhail Usvyatsov, Christos Sakaridis, Konrad Schindler

Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations. In this paper, we introduce a novel low-rank representation termed Tensor Train Neural Fields (TT-NF) for learning neural fields on dense regular grids and efficient methods for sampling from them.

Niklas Küchler, Jürgen Horbach

In particle-based computer simulations of polydisperse glassforming systems, the particle diameters $\sigma = \sigma_1, \dots, \sigma_N$ of a system with $N$ particles are chosen with the intention to approximate a desired distribution density $f$ with the corresponding histogram. One method to accomplish this is to draw each diameter randomly from the densi

Hanying Chen, Tianlin Li, Yifei Hao, Anil Rajapitamahuni

We report the effect of remote surface optical (RSO) phonon scattering on carrier mobility in monolayer graphene gated by ferroelectric oxide. We fabricate monolayer graphene transistors back-gated by epitaxial (001) Ba$_{0.6}$Sr$_{0.4}$TiO$_{3}$ films, with field effect mobility up to 23,000 cm$^{2}$V$^{-1}$s$^{-1}$ achieved. Switching the ferroelectric pol

Masaya Tsujii, Kousuke Ishida, Shigeyuki Ishida, Yuta Mizukami

Recent studies in heavily hole-doped iron-based superconductor RbFe$_2$As$_2$ have suggested the emergence of novel electronic nematicity directed along the Fe-As direction, 45$^\circ$ rotated from the usual nematicity ubiquitously found in BaFe$_2$As$_2$ and related materials. This motivates us to study the physical properties of Ba$_{1-x}$Rb$_{x}$Fe$_{2}$A

Shuai Zhao, Linchao Zhu, Xiaohan Wang, Yi Yang

Self-supervised learning makes significant progress in pre-training large models, but struggles with small models. Mainstream solutions to this problem rely mainly on knowledge distillation, which involves a two-stage procedure: first training a large teacher model and then distilling it to improve the generalization ability of smaller ones. In this work, we

Elena Righetti, Alice Antonello, Luca Marchetti, Enrico Domenici

Parkinson's disease (PD) is the second most common neurodegenerative disorder worldwide, yet there is no disease-modifying therapy up to this date. The biological complexity underlying PD hampers the investigation of the principal contributors to its pathogenesis. In this context, mechanistic models grounded in molecular-level knowledge provide virtual labs

Yusuke Kimura, Hidetoshi Nishimori

Simulated quantum annealing is a generic classical protocol to simulate some aspects of quantum annealing and is sometimes regarded as a classical alternative to quantum annealing in finding the ground state of a classical Ising model. We derive a generic condition for simulated quantum annealing to converge to thermal equilibrium at a given, typically low,

T W J Kwok, T P McAuliffe, A K Ackerman, B H Savitzky

A Twinning Induced Plasticity (TWIP) steel with a nominal composition of Fe-16.4Mn-0.9C-0.5Si-0.05Nb-0.05V was deformed to an engineering strain of 6\%. The strain around the deformation twins were mapped using the 4D-STEM technique. Strain mapping showed a large average elastic strain of approximately 6\% in the directions parallel and perpendicular to the

Wei Tao, Ruilin Zhu, Zhen-Jun Xiao

We present the next-to-next-to-leading order (NNLO) QCD corrections to the decay constants for both the pseudoscalar and vector beauty-charmed mesons $B_{c}$ and $B^*_{c}$ in nonrelativistic QCD effective theory. Explicit NNLO calculation verified that the $B_c$ decay constant from pseudoscalar current is identical with the $B_c$ decay constant from axial-ve

Federico Vismara, Tommaso Benacchio

We introduce an extended discontinuous Galerkin discretization of hyperbolic-parabolic problems on multidimensional semi-infinite domains. Building on previous work on the one-dimensional case, we split the strip-shaped computational domain into a bounded region, discretized by means of discontinuous finite elements using Legendre basis functions, and an unb

Gabriella Carini, Mitch Newcomer, John Parsons

This writeup summarizes the work of the Topical Working Group 7 of the Instrumentation Frontier group of the Snowmass 2021 process. Group 'IF07' dealt with issues pertaining to ASICs and Readout Electronics. The community efforts as part of IF07 were organized across 7 white papers submitted to the Snowmass process and available in the arXiv.

Weishi Yuan, Jiaming Wang, Philip M. Singer, Rebecca W. Smaha

Kagome lattice Heisenberg antiferromagnets are known to be highly sensitive to perturbations caused by structural disorder. NMR is a local probe ideally suited for investigating such disorder-induced effects, but in practice large distributions in the conventional one-dimensional NMR data make it difficult to distinguish the intrinsic behavior expected for p

Ziyuan Qin, Huahui Yi, Qicheng Lao, Kang Li

The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images. However, it remains unknown whether this capability can also apply to the medical image domain. This paper thoroughly studies the knowledge transferability of pre-trained VLMs to the medical domain, where we show that well-designed med

D. Yu. Akimov, I. S. Alexandrov, R. R. Alyev, V. A. Belov

The RED-100 two-phase xenon emission detector has been deployed at 19-m distance from the reactor core of the Kalinin Nuclear Power Plant (KNPP) in 2021 - 2022 for investigation of the possibility to observe reactor antineutrinos using the effect of coherent elastic neutrino-nucleus scattering (CE{\nu}NS). The performance of the main systems of the RED-100 s

R. T. K. Schock, J. Neuwald, W. Möckel, M. Kronseder

Molybdenum disulfide nanoribbons and nanotubes are quasi-1D semiconductors with strong spin-orbit interaction, a nanomaterial highly promising for quantum electronic applications. Here, it is demonstrated that a bismuth semimetal layer between the contact metal and this nanomaterial strongly improves the properties of the contacts. Two-point resistances on t

Oskar Kviman, Ricky Molén, Alexandra Hotti, Semih Kurt

In this paper, we show how the mixture components cooperate when they jointly adapt to maximize the ELBO. We build upon recent advances in the multiple and adaptive importance sampling literature. We then model the mixture components using separate encoder networks and show empirically that the ELBO is monotonically non-decreasing as a function of the number

Fatemeh Mohammadi, Job Daisie Rock, Francesca Zaffalon

In this mostly expository paper, we present recent progress on infinite (weak) cluster categories that are related to triangulations of the disk, with and without a puncture. First we recall the notion of a cluster category. Then we move to the infinite setting and survey recent work on infinite cluster categories of types $\mathbb{A}$ and $\mathbb{D}$. We c

Paul D. Nation, Matthew Treinish

We present a quantum circuit optimization technique that takes into account the variability in error rates that is inherent across present day noisy quantum computing platforms. This method can be run post qubit routing or post-compilation, and consists of computing isomorphic subgraphs to input circuits and scoring each using heuristic cost functions derive

Andreea Dogaru, Andrei Timotei Ardelean, Savva Ignatyev, Egor Zakharov

In recent years, neural distance functions trained via volumetric ray marching have been widely adopted for multi-view 3D reconstruction. These methods, however, apply the ray marching procedure for the entire scene volume, leading to reduced sampling efficiency and, as a result, lower reconstruction quality in the areas of high-frequency details. In this wo

Xiao Wang, Rohit Sharma, Petra Becker, Ladislav Bohatý

We present a study of high-quality BaCo$_2$(PO$_4$)$_2$ single crystals via magnetization, heat-capacity, thermal-expansion and magnetostriction measurements. Sharp anomalies in the thermodynamic properties at $T_N=3.4\,$K reveal a long-range antiferromagnetic order in these single-crystalline samples, which is absent in polycrystalline BaCo$_2$(PO$_4$)$_2$.

M. Parthasarathy, Marina. Kounkel, Keivan G. Stassun

The evolutionary status of 24 post-AGB stars is presented based on Gaia DR3 data. All 24 stars have parallaxes accurate to better than 3 X sigma and have RUWE values less than 1.4. Based on the Gaia DR3 distances the absolute luminosities are derived. For 14 of the stars, the luminosities confirm their post-AGB evolutionary stage. However, V1027 Cyg, which w

Sara Leardini, Yi Zhou, Andrea Tesi, Miguel Morales

Characterization of diamond-like carbon (DLC) coatings at cryogenic temperatures (down to 77 K) is presented, covering the electrical resistivity range of practical interest to gaseous and liquid particle instrumentation: 10^-1-10^5 Mohm/sq. The good behaviour observed in terms of linearity, surface uniformity and stability with time and transported charge a

Christian E. Precker, José Barzola-Quiquia, Mun K. Chan, Marcelo Jaime

In spite of 40 years of experimental studies and several theoretical proposals, an overall interpretation of the complex behavior of the magnetoresistance (MR) of multilayer graphene, i.e. graphite, at high fields ($B \lesssim 70~$T) and in a broad temperature range is still lacking. Part of the complexity is due to the contribution of stacking faults (SFs),

Irina Vorontsova, Roman Goncharov, Angelina Tarabrina, Fedor Kiselev

A theoretical research and numerical simulation of the noise influence caused by spontaneous Raman scattering, four-wave mixing, and linear channel crosstalk on the performance of QKD systems was conducted. Three types of QKD systems were considered: coherent one-way (COW) QKD protocol, subcarrier-wave (SCW) QKD system, and continuous-variable (CV) QKD integ

Agnieszka Ogrodnik

Relativistic heavy-ion beams at the LHC are accompanied by a large flux of equivalent photons. New measurements of exclusive dilepton production (electron, muon, and tau pairs) performed by the ATLAS experiment are discussed. We present the photon-induced production of tau pairs and constraints on the tau lepton's anomalous magnetic dipole moment. In additio

Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato

SGD with momentum is one of the key components for improving the performance of neural networks. For decentralized learning, a straightforward approach using momentum is Distributed SGD (DSGD) with momentum (DSGDm). However, DSGDm performs worse than DSGD when the data distributions are statistically heterogeneous. Recently, several studies have addressed th

Álvaro Duenas-Vidal, Jorge Segovia

The lightlike limit of boosted black hole solutions with one angular momentum is considered for $D \geq4$ dimensions. The boost is performed parallel to the angular momentum and the lightlike limit is done by means of perturbative expansions. We shown that for $D=4$ and $D> 5$ the lightlike limit cannot be extended inside the ring singularity. Then, for $D =

Marc Chemtob

We discuss Kaluza-Klein theory for type $II\ b $ supergravity on the warped deformed conifold using a large radial distance limit of Klebanov-Strassler solution where the radial coordinate separates from angle coordinates for a background asymptotic to $ AdS _5 \times T^{1,1}$ spacetime. The decomposition of field fluctuations on harmonics of the base manifo

Michael Hoyer, Shahram Eivazi, Sebastian Otte

Training recurrent neural networks is predominantly achieved via backpropagation through time (BPTT). However, this algorithm is not an optimal solution from both a biological and computational perspective. A more efficient and biologically plausible alternative for BPTT is e-prop. We investigate the applicability of e-prop to long short-term memorys (LSTMs)

Kaoutar Daoud Hiri, Matjaž Hren, Tomaž Curk

Motivation: The rapid growth of metagenomics sequencing data makes metagenomics increasingly dependent on computational and statistical methods for fast and efficient analysis. Consequently, novel analysis tools for big-data metagenomics are constantly emerging. One of the biggest challenges for researchers occurs in the analysis planning stage: selecting th

Anil Batra, Shreyank N Gowda, Frank Keller, Laura Sevilla-Lara

Understanding the steps required to perform a task is an important skill for AI systems. Learning these steps from instructional videos involves two subproblems: (i) identifying the temporal boundary of sequentially occurring segments and (ii) summarizing these steps in natural language. We refer to this task as Procedure Segmentation and Summarization (PSS)

R. Payri, F. J. Salvador, M. Carreres, C. Moreno-Montagud

Prefilming airblast atomization is widely used in aero engines. Fundamental studies on the annular configuration of airblast atomizers are difficult to realize. For this reason, researchers focused on planar configurations. In this regard, the Karlsruhe Institute of Technology (KIT) developed a test rig to conduct experimental activities, conforming a large

Stéphane Michoulier, Jean-François Gonzalez

In protoplanetary discs, the coagulation of dust grains into large aggregates still remains poorly understood. Grain porosity appears to be a promising solution to allow the grains to survive and form planetesimals. Furthermore, dust shattering has generally been considered to come only from collisional fragmentation; however, a new process was recently intr

Alexander Gräfe, Dominik Baumann, Sebastian Trimpe

The ability to detect faults is an important safety feature for event-based multi-agent systems. In most existing algorithms, each agent tries to detect faults by checking its own behavior. But what if one agent becomes unable to recognize misbehavior, for example due to failure in its onboard fault detection? To improve resilience and avoid propagation of i

Dingyi Shi, Fan Shang, Bingsheng Chen, Paul Expert

Clusters or communities can provide a coarse-grained description of complex systems at multiple scales, but their detection remains challenging in practice. Community detection methods often define communities as dense subgraphs, or subgraphs with few connections in-between, via concepts such as the cut, conductance, or modularity. Here we consider another p

Maxime Biehler, Mohamed Guermazi, Célim Starck

This article sets forth a review of knowledge distillation techniques with a focus on their applicability to retail banking contexts. Predictive machine learning algorithms used in banking environments, especially in risk and control functions, are generally subject to regulatory and technical constraints limiting their complexity. Knowledge distillation giv

Guoce Xin, Chen Zhang, Yue Zhou, Yueming Zhong

In this paper, we discover a new noncommutative algebra. We refer this algebra as the constant term algebra of type $A$, which is generated by certain constant term operators. We characterize a structural result of this algebra by establishing an explicit basis in terms of certain forests. This algebra arises when we apply the method of the iterated Laurent