July 2022 arXiv papers — page 149

Tobias Goodwin-Allcock, Jason McEwen, Robert Gray, Parashkev Nachev

This paper demonstrates spherical convolutional neural networks (S-CNN) offer distinct advantages over conventional fully-connected networks (FCN) at estimating scalar parameters of tissue microstructure from diffusion MRI (dMRI). Such microstructure parameters are valuable for identifying pathology and quantifying its extent. However, current clinical pract

G. Dréau, Y. Lebreton, B. Mosser, D. Bossini

In the 90's, theoretical studies motivated the use of the asymptotic-giant branch bump (AGBb) as a standard candle given the weak dependence between its luminosity and stellar metallicity. Because of the small size of observed asymptotic-giant branch (AGB) samples, detecting the AGBb is not an easy task. However, this is now possible thanks to the wealth of

Milan Korda, Jean-Bernard Lasserre, Alexey Lazarev, Victor Magron

A classical result from topology called Uryshon's lemma asserts the existence of a continuous separator of two disjoint closed sets in a sufficiently regular topological space. In this work we make a search for this separator constructive and efficient in the context of real algebraic geometry. Namely, given two compact disjoint basic semialgebraic sets whic

Gustav Uhre Jakobsen, Gustav Mogull, Jan Plefka, Benjamin Sauer

We exhibit an initial-value formulation of the worldline quantum field theory (WQFT) approach to the classical two-body problem in general relativity. We show that the Schwinger-Keldysh (in-in) formalism leads to purely retarded propagators in the evaluation of observables in the WQFT. Integration technology for retarded master integrals is introduced at thi

Aldo Riello, Michele Schiavina

We study gauge theories on spacetime manifolds with a codimension-$1$ submanifold with boundary. We characterise the reduced phase space of the theory whenever it is described by a local momentum map for the action of the gauge group $\mathcal{G}$, by means of Fr\'echet reduction by stages. The momentum map decomposes into a bulk term called constraint map,

Joseph P. Conlon, Filippo Revello

We study the overshoot problem in the context of post-inflationary string cosmology (in particular LVS). LVS cosmology features a long kination epoch as the volume modulus rolls down the exponential slope towards the final minimum, with an energy density that scales as $m_s^4$. This roll admits attractor tracker solutions, and if these are located the oversh

David Brizuela, Sara F. Uria

According to the Belinski-Khalatnikov-Lifshitz conjecture, close to a spacelike singularity different spatial points decouple, and the dynamics can be described in terms of the Mixmaster (vacuum Bianchi IX) model. In order to understand the role played by quantum-gravity effects in this context, in the present work we consider the semiclassical behavior of t

Eric J. Tervo, Ryan M. France, Daniel J. Friedman, Madhan K. Arulanandam

Thermophotovoltaics are promising solid-state energy converters for a variety of applications such as grid-scale energy storage, concentrating solar-thermal power, and waste heat recovery. Here, we report the design, fabrication, and testing of large area (0.8 cm$^2$), scalable, single junction 0.74-eV GaInAs thermophotovoltaic devices reaching an efficiency

Charlee Stefanski, Vanio Markov, Constantin Gonciulea

Extracting the outcome of a quantum computation is a difficult task. In many cases, the quantum phase estimation algorithm is used to digitally encode a value in a quantum register whose amplitudes' magnitudes reflect the discrete sinc function. In the standard implementation the value is approximated by the most frequent outcome, however, using the frequenc

Achim Blumensath

We study power-set operations on classes of trees and tree algebras. Our main result consists of a distributive law between the tree monad and the upwards-closed power-set monad, in the case where all trees are assumed to be linear. For non-linear ones, we prove that such a distributive law does not exist.

Katharine Patterson, Kevin Wilson, Scott Wisdom, John R. Hershey

We propose the novel task of distance-based sound separation, where sounds are separated based only on their distance from a single microphone. In the context of assisted listening devices, proximity provides a simple criterion for sound selection in noisy environments that would allow the user to focus on sounds relevant to a local conversation. We demonstr

Gábor Domokos, Krisztina Regős

We examine geophysical crack patterns using the mean field theory of convex mosaics. We assign the pair $(\bar n^*,\bar v^*)$ of average corner degrees to each crack pattern and we define two local, random evolutionary steps $R_0$ and $R_1$, corresponding to secondary fracture and rearrangement of cracks, respectively. Random sequences of these steps result

Ekaterina Voloshina, Oleg Serikov, Tatiana Shavrina

The probing methodology allows one to obtain a partial representation of linguistic phenomena stored in the inner layers of the neural network, using external classifiers and statistical analysis. Pre-trained transformer-based language models are widely used both for natural language understanding (NLU) and natural language generation (NLG) tasks making them

Elham E Khoda, Dylan Rankin, Rafael Teixeira de Lima, Philip Harris

Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). In this paper we present an implem

Joshua Holden

Public-key cryptography has become a popular way to motivate the teaching of concepts in elementary number theory, abstract algebra, and introduction to proof courses, as well as in cryptography courses. Unfortunately, many experts expect quantum computers to make common forms of public-key cryptography obsolete in the near future. Fortunately, there are sev

Reinier van der Meer, Zichang Huang, Malaquias Correa Anguita, Dongxue Qu

The unification of general relativity and quantum theory is one of the fascinating problems of modern physics. One leading solution is Loop Quantum Gravity (LQG). Simulating LQG may be important for providing predictions which can then be tested experimentally. However, such complex quantum simulations cannot run efficiently on classical computers, and quant

Gideon Dresdner, Dmitrii Kochkov, Peter Norgaard, Leonardo Zepeda-Núñez

Despite their ubiquity throughout science and engineering, only a handful of partial differential equations (PDEs) have analytical, or closed-form solutions. This motivates a vast amount of classical work on numerical simulation of PDEs and more recently, a whirlwind of research into data-driven techniques leveraging machine learning (ML). A recent line of w

Yeonghyeon Lee, Kangwook Jang, Jahyun Goo, Youngmoon Jung

Large-scale speech self-supervised learning (SSL) has emerged to the main field of speech processing, however, the problem of computational cost arising from its vast size makes a high entry barrier to academia. In addition, existing distillation techniques of speech SSL models compress the model by reducing layers, which induces performance degradation in l

Anna Neufeld, Lucy L. Gao, Joshua Popp, Alexis Battle

In the analysis of single-cell RNA sequencing data, researchers often characterize the variation between cells by estimating a latent variable, such as cell type or pseudotime, representing some aspect of the individual cell's state. They then test each gene for association with the estimated latent variable. If the same data are used for both of these steps

James R. Wootton

Quantum error correcting codes are designed to pinpoint exactly when and where errors occur in quantum circuits. This feature is the foundation of their primary task: to support fault-tolerant quantum computation. However, this feature could used as the basis of benchmarking: By analyzing the outputs of even small-scale quantum error correction circuits, a d

Mukhlis Amien, Feng Chong, Huang Heyan

Indonesian is an agglutinative language since it has a compounding process of word-formation. Therefore, the translation model of this language requires a mechanism that is even lower than the word level, referred to as the sub-word level. This compounding process leads to a rare word problem since the number of vocabulary explodes. We propose a strategy to

Vinitra Swamy, Bahar Radmehr, Natasa Krco, Mirko Marras

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We implement five state-of-the-art methodologies for explaining black-box machine learning models (LIME, Perm

Abdullah-Al-Mamun, Ch. Md. Rakin Haider, Jianguo Wang, Walid G. Aref

The emerging class of instance-optimized systems has shown potential to achieve high performance by specializing to a specific data and query workloads. Particularly, Machine Learning (ML) techniques have been applied successfully to build various instance-optimized components (e.g., learned indexes). This paper investigates to leverage ML techniques to enha

William Chang, Andrew Manion

We equip the basic local crossing bimodules in Ozsv\'ath-Szab\'o's theory of bordered knot Floer homology with the structure of 1-morphisms of 2-representations, categorifying the $U_q(\mathfrak{gl}(1|1)^+)$-intertwining property of the corresponding maps between ordinary representations. Besides yielding a new connection between bordered knot Floer homology

Paolo Turri, Jessica R. Lu, Gunther Witzel, Anna Ciurlo

Adaptive optics images from the W. M. Keck Observatory have delivered numerous influential scientific results, including detection of multi-system asteroids, the supermassive black hole at the center of the Milky Way, and directly imaged exoplanets. Specifically, the precise and accurate astrometry these images yield was used to measure the mass of the super

Albert Zijlstra

A number of late [WC] stars have unique infrared properties, not found among the non-[WC] planetary nebulae, and together define a class of IR-[WC] stars. They have unusual IRAS colours, resembling stars in the earliest post-AGB evolution and possibly related to PAH formation. Most or all show a double chemistry, with both a neutral (molecular) oxygen-rich a

Kevin Schnelli, Yuanyuan Xu

We show that the fluctuations of the largest eigenvalue of any generalized Wigner matrix $H$ converge to the Tracy-Widom laws at a rate nearly $O(N^{-1/3})$, as the matrix dimension $N$ tends to infinity. We allow the variances of the entries of $H$ to have distinct values but of comparable sizes such that $\sum_{i} \mathbb{E}|h_{ij}|^2=1$. Our result improv

Renato Sortino, Simone Palazzo, Concetto Spampinato

Generating images from semantic visual knowledge is a challenging task, that can be useful to condition the synthesis process in complex, subtle, and unambiguous ways, compared to alternatives such as class labels or text descriptions. Although generative methods conditioned by semantic representations exist, they do not provide a way to control the generati

Weina Wu, Jianliang Zhai

We establish a large deviation principle (LDP) for a class of stochastic porous media equations driven by L\'{e}vy-type noise on a $\sigma$-finite measure space $(E,\mathcal{B}(E),\mu)$, with the Laplacian replaced by a negative definite self-adjoint operator. One of the main contributions of this paper is that we do not assume the compactness of embeddings

Peter Humphries, Rizwanur Khan

We prove an upper bound for the twelfth moment of Hecke $L$-functions associated to holomorphic Hecke cusp forms of weight $k$ in a dyadic interval $T \leq k \leq 2T$ as $T$ tends to infinity. This bound recovers the Weyl-strength subconvex bound $L(1/2,f) \ll_{\varepsilon} k^{1/3 + \varepsilon}$ and shows that for any $\delta > 0$, the sub-Weyl subconvex bo

Amar Kumar Banerjee, Anirban Paul

In this paper, we have studied first the idea of rough continuity of real valued functions of real variables and then we have discussed some important properties of rough continuity. Then we study the idea of rough $I$-continuity of real valued functions and find the relation between rough $I$-continuity and rough continuity. We also introduce the notion of

Marek Czachor

A new dynamical paradigm merging quantum dynamics with cosmology is discussed. Time evolution involves a genuine passage of time, which distinguishes the formalism from those where dynamics in space is equivalent to statics in space-time. Hyperbolic spatial sections occur as asymptotic large-cosmic-time supports of quantum wave functions. For simplicity, the

Miguel García-Bravo, Tapio Rajala, Jyrki Takanen

We give a necessary condition for a domain to have a bounded extension operator from $L^{1,p}(\Omega)$ to $L^{1,p}(\mathbb R^n)$ for the range $1 < p < 2$. The condition is given in terms of a power of the distance to the boundary of $\Omega$ integrated along the measure theoretic boundary of a set of locally finite perimeter and its extension. This generali

S. Ravichandran, Rama Govindarajan

This article describes the dynamics of small inertial particles centrifuging out of a single vortex. It shows the importance of caustics formation in the vicinity of a single vortex: both for particle collisions and void formation. From these single-vortex studies we provide estimates of the role of caustics in high Reynolds number turbulence, and in the cas

Alexander R. Klotz, Everett Sullivan

A growing self-avoiding walk (GSAW) is a stochastic process that starts from the origin on a lattice and grows by occupying an unoccupied adjacent lattice site at random. A sufficiently long GSAW will reach a state in which all adjacent sites are already occupied by the walk and become trapped, terminating the process. It is known empirically from simulation

Ibrahim Aldulijan, Jacob Beal, Sonja Billerbeck, Jeff Bouffard

Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, r

Giorgio Torrieri, David Montenegro

We formulate a lagrangian hydrodynamics including shear and bulk viscosity in the presence of spin density, and investigate it using the linear response functional formalism. The result is a careful accounting of all sound and vortex interactions close to local equilibrium.

L. F. Urrutia

We summarize recent work dealing with the characterization of effective actions giving the electromagnetic response of some topological materials, arising from their microscopic structure. The case of weakly tilted Weyl semimetals in the limit of zero temperature, but nonzero chemical potential, is presented as a subset of a specific choice of terms in the f

Thierno-Moussa Bah, Stanislav Didenko, Di Zhou, Tianqi Zhu

Herein, we report the use of nanostructured crystalline Si as a thermoelectric material and its integration into thermoelectric harvesters. The proof-of-concept relies on the partial suppression of lattice thermal transport by introducing pores with dimensions scaling between the electron mean free path and the phonon mean free path. In other words, we artif

Munshi G. Mustafa

In this article an introduction to the thermal field theory within imaginary time has been discussed in details. The imaginary time formalism has been introduced through both the operatorial and the functional integration method. The prescription to perform frequency sum for boson and fermion has been discussed. Green's function both in Minkowski time as wel

Emily Feller, Robert Hochberg

A famous result of D. Walkup is that an $m\times n$ rectangle may be tiled by T-tetrominos if and only if both $m$ and $n$ are multiples of 4. The "if" portion may be proved by tiling a $4\times 4$ block, and then copying that block to fill the rectangle; but, this leads to regular, periodic tilings. In this paper we investigate how much "order" must be pres

Babak Badnava, Sravan Reddy Chintareddy, Morteza Hashemi

In this paper, we consider the multi-user scheduling problem in millimeter wave (mmWave) video streaming networks, which comprise a streaming server and several users, each requesting a video stream with a different resolution. The main objective is to optimize the long-term average quality of experience (QoE) for all users. We tackle this problem by conside

Georg Hess, Johan Jaxing, Elias Svensson, David Hagerman

Masked autoencoding has become a successful pretraining paradigm for Transformer models for text, images, and, recently, point clouds. Raw automotive datasets are suitable candidates for self-supervised pre-training as they generally are cheap to collect compared to annotations for tasks like 3D object detection (OD). However, the development of masked autoe

John W. Jackson, Yea-Jen Hsu, Raquel C. Greer, Romsai T. Boonyasai

We present a conceptual model to measure disparity--the target study--where social groups may be similarly situated (i.e., balanced) on allowable covariates. Our model, based on a sampling design, does not intervene to assign social group membership or alter allowable covariates. To address non-random sample selection, we extend our model to generalize or tr

Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljačić

Symbolic regression is a machine learning technique that can learn the governing formulas of data and thus has the potential to transform scientific discovery. However, symbolic regression is still limited in the complexity and dimensionality of the systems that it can analyze. Deep learning on the other hand has transformed machine learning in its ability t

Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell, Bamshad Mobasher

Competitive online games use rating systems for matchmaking; progression-based algorithms that estimate the skill level of players with interpretable ratings in terms of the outcome of the games they played. However, the overall experience of players is shaped by factors beyond the sole outcome of their games. In this paper, we engineer several features from

Javier M. Lizana

$B$-anomalies may suggest New Physics at the TeV scale breaking flavor universality. In particular, 4321 gauge models can successfully explain them in a consistent way. In this talk we explore how to UV complete the 4321 model in a 5D warped background to solve simultaneously the Higgs hierarchy problem too, finding interesting connections with the flavor pu

Matthew Earnshaw, Paweł Sobociński

We introduce regular languages of morphisms in free monoidal categories, with their associated grammars and automata. These subsume the classical theory of regular languages of words and trees, but also open up a much wider class of languages over string diagrams. We use the algebra of monoidal and cartesian restriction categories to investigate the properti

Riccardo Argurio, Matteo Bertolini, Sebastián Franco, Eduardo García-Valdecasas

Recently, the first instance of a model of D-branes at Calabi-Yau singularities where supersymmetry is broken dynamically into stable vacua has been proposed. This construction was based on a system of $N$ regular and $M=1$ fractional branes placed at the tip of the so-called (orientifolded) Octagon singularity. In this paper we show that this model admits a

Weilong Fu, Ali Hirsa

We develop an unsupervised deep learning method to solve the barrier options under the Bergomi model. The neural networks serve as the approximate option surfaces and are trained to satisfy the PDE as well as the boundary conditions. Two singular terms are added to the neural networks to deal with the non-smooth and discontinuous payoff at the strike and bar

Adam Gregory, Zachary Hamaker

Pipe dreams and bumpless pipe dreams for vexillary permutations are each known to be in bijection with certain semistandard tableaux via maps due to Lenart and Weigandt, respectively. Recently, Gao and Huang have defined a bijection between the former two sets. In this note we show for vexillary permutations that the Gao-Huang bijection preserves the associa

Thuc Nguyen Huu, Vinh Van Duong, Jonghoon Yim, Byeungwoo Jeon

Plenoptic images and videos bearing rich information demand a tremendous amount of data storage and high transmission cost. While there has been much study on plenoptic image coding, investigations into plenoptic video coding have been very limited. We investigate the motion compensation for plenoptic video coding from a slightly different perspective by loo

Dhruvit Patel, Edward Ott

In this paper we consider the machine learning (ML) task of predicting tipping point transitions and long-term post-tipping-point behavior associated with the time evolution of an unknown (or partially unknown), non-stationary, potentially noisy and chaotic, dynamical system. We focus on the particularly challenging situation where the past dynamical state t

Xiaolong Li

The purpose of this article is to initiate the investigation of the curvature operator of the second kind on K\"ahler manifolds. The main result asserts that a closed K\"ahler surface with six-positive curvature operator of the second kind is biholomorphic to $\mathbb{CP}^2$. It is also shown that a closed non-flat K\"ahler surface with six-nonnegative curva

Oliver H. E. Philcox, Salvatore Torquato

Studies of disordered heterogeneous media and galaxy cosmology share a common goal: analyzing the distribution of particles at `microscales' to predict physical properties at `macroscales', whether for a liquid, composite material, or entire Universe. The former theory provides an array of techniques to characterize a wide class of microstructures; in this w

Wei Guo, Jing-Mei Qiu

In this paper, we propose a novel Local Macroscopic Conservative (LoMaC) low rank tensor method for simulating the Vlasov-Poisson (VP) system. The LoMaC property refers to the exact local conservation of macroscopic mass, momentum and energy at the discrete level. This is a follow-up work of our previous development of a conservative low rank tensor approach

Daniel Hausmann, Nir Piterman

Algorithms for model checking and satisfiability of the modal $\mu$-calculus start by converting formulas to alternating parity tree automata. Thus, model checking is reduced to checking acceptance by tree automata and satisfiability to checking their emptiness. The first reduces directly to the solution of parity games but the second is more complicated. We

Michele Reticcioli, Zhichang Wang, Michael Schmid, Dominik Wrana

Excess charge on polar surfaces of ionic compounds is commonly described by the two-dimensional electron gas (2DEG) model, a homogeneous distribution of charge, spatially-confined in a few atomic layers. Here, by combining scanning probe microscopy with density functional theory calculations, we show that excess charge on the polar TaO$_2$ termination of KTa

Ashwin Ganesan

An assumption that researchers have often used to model interference in a wireless network is the unit disk graph model. While many theoretical results and performance guarantees have been obtained under this model, an open research direction is to extend these results to hypergraph interference models. Motivated by recent results that the worst-case perform

Andrey Prokopenko, Piyush Sao, Damien Lebrun-Grandié

Computing the Euclidean minimum spanning tree (EMST) is a computationally demanding step of many algorithms. While work-efficient serial and multithreaded algorithms for computing EMST are known, designing an efficient GPU algorithm is challenging due to a complex branching structure, data dependencies, and load imbalances. In this paper, we propose a single

Sherzod R. Otajonov

The properties of 3D Bose-Einstein condensate have been studied with variational and numerical methods. In the variational approach, we use the super-Gaussian trial function, and it is demonstrated that this trial function gives a good approach for the descriptions of the quantum droplets. The analytical equations for the variational parameters are obtained.

Finn E. Christensen, Brian D. Ramsey

Grazing-incidence X-ray optics have revolutionized X-ray astrophysics. The ability to concentrate flux to a tiny detection region provides a dramatic reduction in background and a consequent very large improvement in sensitivity. The X-ray optics also permit use of small-format, high-performance focal plane detectors and, of course, especially for high-angul

T. J. Burns, E. S. Swanson

We develop a model for the production of the Pc states observed at LHCb in Lambdab - Jpsi p K- decays. With fewer parameters than other approaches, we obtain excellent fits to the Jpsi-p invariant mass spectrum, capturing both the prominent peaks, and broader features over the full range of invariant mass. A distinguishing feature of our model is that wherea

Moritz Herrmann, Daniyal Kazempour, Fabian Scheipl, Peer Kröger

We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can considerably enhance cluster detection: theoretical arguments and empirical evidence show that clustering embedding vectors, representing the structure of a data manifold instead of the observed feature vectors themselves

Belle II Collaboration, F. Abudinén, I. Adachi, L. Aggarwal

The dark photon $A^\prime$ and the dark Higgs boson $h^\prime$ are hypothetical particles predicted in many dark sector models. We search for the simultaneous production of $A^\prime$ and $h^\prime$ in the dark Higgsstrahlung process $e^{+}e^{-}\rightarrow A^\prime \, h^\prime$ with $A^\prime \rightarrow \mu^+\mu^-$ and $ h^\prime$ invisible in electron-posi

Jiachen Li, Ye Jin, Neil Qiang Su, Weitao Yang

We applied localized orbital scaling correction (LOSC) in Bethe-Salpeter equation (BSE) to predict accurate excitation energies for molecules. LOSC systematically eliminates the delocalization error in the density functional approximation and is capable of approximating quasiparticle (QP) energies with accuracy similar or better than the $GW$ Green's functio

Jonas Fritzsch, Justus Bogner, Markus Haug, Stefan Wagner

The euphoria around microservices has decreased over the years, but the trend of modernizing legacy systems to this novel architectural style is unbroken to date. A variety of approaches have been proposed in academia and industry, aiming to structure and automate the often long-lasting and cost-intensive migration journey. However, our research shows that t

Sauradip Nag, Nisarg Shah, Anran Qi, Raghavendra Ramachandra

In this paper we present a novel self-supervised method to anticipate the depth estimate for a future, unobserved real-world urban scene. This work is the first to explore self-supervised learning for estimation of monocular depth of future unobserved frames of a video. Existing works rely on a large number of annotated samples to generate the probabilistic

A. Frasca, G. Catanzaro, I. Busà, P. Guillout

In this paper we present a spectroscopic study of six double-lined binaries, five of which were recently discovered in a high-resolution spectroscopic survey of optical counterparts of stellar X-ray sources. Thanks to high-resolution spectra acquired with CAOS spectropolarimeter during seven years, we were able to measure the radial velocities of their compo

Alfredo Braunstein, Louise Budzynski, Stefano Crotti, Federico Ricci-Tersenghi

We consider a high-dimensional random constrained optimization problem in which a set of binary variables is subjected to a linear system of equations. The cost function is a simple linear cost, measuring the Hamming distance with respect to a reference configuration. Despite its apparent simplicity, this problem exhibits a rich phenomenology. We show that d

David Grass, Georgia M. Beasley, Martin C. Fischer, M. Angelica Selim

Pump-probe microscopy of melanin in tumors has been proposed to improve diagnosis of malignant melanoma, based on the hypothesis that aggressive cancers disaggregate melanin structure. However, measured signals of melanin are complex superpositions of multiple nonlinear processes, which makes interpretation challenging. Polarization control during measuremen

C. Stuardi, A. Bonafede, K. Rajpurohit, M. Brüggen

Radio relics are Mpc-size synchrotron sources located in the outskirts of some merging galaxy clusters. Binary-merging systems with favorable orientation may host two almost symmetric relics, named double radio relics. Double radio relics are seen preferentially edge-on and, thus, constitute a privileged sample for statistical studies. Their polarization and

Benjamin Schumacher, Michael D. Westmoreland

We present a critique of the many-world interpretation of quantum mechanics, based on different ``pictures'' that describe the time evolution of an isolated quantum system. Without an externally imposed frame to restrict these possible pictures, the theory cannot yield non-trivial interpretational statements. This is analogous to Goodman's famous ``grue-blee

Raheleh Salehi, Ario Sadafi, Armin Gruber, Peter Lienemann

Diagnosing hematological malignancies requires identification and classification of white blood cells in peripheral blood smears. Domain shifts caused by different lab procedures, staining, illumination, and microscope settings hamper the re-usability of recently developed machine learning methods on data collected from different sites. Here, we propose a cr

Christian Berger, Hans P. Reiser, Franz J. Hauck, Florian Held

Byzantine fault tolerance (BFT) can preserve the availability and integrity of IoT systems where single components may suffer from random data corruption or attacks that can expose them to malicious behavior. While state-of-the-art BFT state-machine replication (SMR) libraries are often tailored to fit a standard request-response interaction model with dedic

Arij Bouazizi, Adrian Holzbock, Ulrich Kressel, Klaus Dietmayer

In this work, we present MotionMixer, an efficient 3D human body pose forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D body pose dependencies by sequentially mixing both modalities. Given a stacked sequence of 3D body poses, a spatial-MLP extracts fine grained spatial dependencies of the body joints

Mónica Clapp, Mayra Soares

We study the system of semilinear elliptic equations $$-\Delta u_i+ u_i = \sum_{j=1}^\ell \beta_{ij}|u_j|^p|u_i|^{p-2}u_i, \qquad u_i\in H^1(\mathbb{R}^N),\qquad i=1,\ldots,\ell,$$ where $N\geq 4$, $1<p<\frac{N}{N-2}$, and the matrix $(\beta_{ij})$ is symmetric and admits a block decomposition such that the entries within each block are positive or zero and

Philipp Spitzer, Niklas Kühl, Marc Goutier

Across a multitude of work environments, expert knowledge is imperative for humans to conduct tasks with high performance and ensure business success. These humans possess task-specific expert knowledge (TSEK) and hence, represent subject matter experts (SMEs). However, not only demographic changes but also personnel downsizing strategies lead and will conti

Yu Yang, Hongzhi Yin, Jiannong Cao, Tong Chen

Dynamic graphs refer to graphs whose structure dynamically changes over time. Despite the benefits of learning vertex representations (i.e., embeddings) for dynamic graphs, existing works merely view a dynamic graph as a sequence of changes within the vertex connections, neglecting the crucial asynchronous nature of such dynamics where the evolution of each

Han Huang, Yijie Dong, Xiaohong Jia, Jianqiao Zhou

Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both transverse and longitudinal views using thyroid ultrasound. However, the appearance of the thyroid gland and lesions can vary dramatic

Ezzat Elokda, Saverio Bolognani, Andrea Censi, Florian Dörfler

This paper presents karma mechanisms, a novel approach to the repeated allocation of a scarce resource among competing agents over an infinite time. Examples include deciding which ride hailing trip requests to serve during peak demand, granting the right of way in intersections or lane mergers, or admitting internet content to a regulated fast channel. We s

Rabih Zbib, Lucas Alvarez Lacasa, Federico Retyk, Rus Poves

Measuring semantic similarity between job titles is an essential functionality for automatic job recommendations. This task is usually approached using supervised learning techniques, which requires training data in the form of equivalent job title pairs. In this paper, we instead propose an unsupervised representation learning method for training a job titl

Weilong Fu, Ali Hirsa, Jörg Osterrieder

Financial time series simulation is a central topic since it extends the limited real data for training and evaluation of trading strategies. It is also challenging because of the complex statistical properties of the real financial data. We introduce two generative adversarial networks (GANs), which utilize the convolutional networks with attention and the

Tian Yu, Qing Chang

Robots are good at performing repetitive tasks in modern manufacturing industries. However, robot motions are mostly planned and preprogrammed with a notable lack of adaptivity to task changes. Even for slightly changed tasks, the whole system must be reprogrammed by robotics experts. Therefore, it is highly desirable to have a flexible motion planning metho

Camille Scalliet, Benjamin Guiselin, Ludovic Berthier

We combine the swap Monte Carlo algorithm to long multi-CPU molecular dynamics simulations to analyse the equilibrium relaxation dynamics of model supercooled liquids over a time window covering ten orders of magnitude for temperatures down to the experimental glass transition temperature $T_g$. The analysis of \rev{several} time correlation functions couple

Emanuel Hubenschmid, Thiago L. M. Guedes, Guido Burkard

We propose a multi-channel version of quantum electro-optic sampling involving monochromatic field modes. It allows for multiple simultaneous measurements of arbitrarily many $\hat{X}$ and $\hat{Y}$ field-quadrature for a single quantum-state copy, while independently tuning the interaction strengths at each channel. In contrast to standard electro-optic sam

Mykola Makhortykh, Ernesto de León, Aleksandra Urman, Clara Christner

The growing availability of data about online information behaviour enables new possibilities for political communication research. However, the volume and variety of these data makes them difficult to analyse and prompts the need for developing automated content approaches relying on a broad range of natural language processing techniques (e.g. machine lear

Mohammad Akil, Abdelaziz Soufyane, Youssef Belhamadia

In this paper, we investigate the stabilization of a one-dimensional Lorenz piezoelectric (Stretching system) with partial viscous dampings. First, by using Lorenz gauge conditions, we reformulate our system to achieve the existence and uniqueness of the solution. Next, by using General criteria of Arendt-Batty, we prove the strong stability in different cas

Anupam Ghosh, Partha Konar, Rishav Roshan

We explore a two-component dark matter scenario in an extended Kim-Shifman-Vainshtein-Zakharov (KSVZ) axion framework. This hybrid setup incorporates an extra $SU(2)_L$ complex singlet scalar whose lightest component plays the role of one of the dark matter, while the QCD axion of the KSVZ model acts as a second dark matter candidate. In this work, we focus

Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi

A determinantal point process (DPP) is an elegant model that assigns a probability to every subset of a collection of $n$ items. While conventionally a DPP is parameterized by a symmetric kernel matrix, removing this symmetry constraint, resulting in nonsymmetric DPPs (NDPPs), leads to significant improvements in modeling power and predictive performance. Re

Yannick Sire, Xueying Yu, Haitian Yue, Zehua Zhao

We introduce new models for Schr\"odinger-type equations, which generalize standard NLS and for which different dispersion occurs depending on the directions. Our purpose is to understand dispersive properties depending on the directions of propagation, in the spirit of waveguide manifolds, but where the diffusion is of different types. We mainly consider th

Yang Xing, Wenbo Li, Xiaoyu Mo, Chen Lv

Human intention prediction provides an augmented solution for the design of assistants and collaboration between the human driver and intelligent vehicles. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on the upper limb neuromuscular Electromyography (EMG) signals. A sing

Jacopo Fiore, Mattia Udina, Marco Marciani, Goetz Seibold

Multiband superconductors can host collective excitations with marked differences with respect to their single-band counterpart. We first study the spectrum of collective amplitude fluctuations in a clean two-bands superconductor, showing that the spectral weight of the Higgs mode rapidly deviates from the naive extension of the single band case as the inter

Valentina Franceschi, Andrea Pinamonti, Giorgio Saracco, Giorgio Stefani

We consider non-negative $\sigma$-finite measure spaces coupled with a proper functional $P$ that plays the role of a perimeter. We introduce the Cheeger problem in this framework and extend many classical results on the Cheeger constant and on Cheeger sets to this setting, requiring minimal assumptions on the pair measure space-perimeter. Throughout the pap

Nigel J. Burroughs, Andrew D. McAinsh

During cell division paired (sister) chromosomes are observed to perform approximate saw-tooth oscillations across the cell mid-plane. Experimental data suggests that these oscillations are regulated through intersister tension. We propose a time dependent tension threshold model that exhibits three stable periodic solutions and the phase diagram can be gene

Jixiang Fu, Jingcao Wu

Given a fibration $f$ between two projective manifolds $X$ and $Y$, we discuss the effective generation of the higher direct images $R^{i}f_{\ast}(K^{m}_{X})$, where $K^{m}_{X}$ is the $m$-th tensor power of the canonical bundle of $X$. In particular, we answer two questions posed by Popa--Schnell in [PS14].

Romain Egele, Isabelle Guyon, Venkatram Vishwanath, Prasanna Balaprakash

Bayesian optimization (BO) is a promising approach for hyperparameter optimization of deep neural networks (DNNs), where each model training can take minutes to hours. In BO, a computationally cheap surrogate model is employed to learn the relationship between parameter configurations and their performance such as accuracy. Parallel BO methods often adopt si

Gavin P. Salam, Lian-Tao Wang, Giulia Zanderighi

The discovery of the Higgs boson, ten years ago, was a milestone that opened the door to the study of a new sector of fundamental physical interactions. We review the role of the Higgs field in the Standard Model of particle physics and explain its impact on the world around us. We summarize the insights into Higgs physics revealed so far by ten years of wor

Zinnat Hassan, Sayantan Ghosh, P. K. Sahoo, Kazuharu Bamba

In recent years there has been a growing interest in the field of Casimir wormhole. In classical general relativity (GR), it is known that the null energy condition (NEC) has to be violated to have a wormhole to be stable. The Casimir effect is an experimentally verified effect that is caused due to the vacuum field fluctuations in quantum field theory. Sinc

Karan Kheta, Claire Delgove, Ruolin Liu, Adeola Aderogba

Future airports are becoming more complex and congested with the increasing number of travellers. While the airports are more likely to become hotspots for potential conflicts to break out which can cause serious delays to flights and several safety issues. An intelligent algorithm which renders security surveillance more effective in detecting conflicts wou