May 2022 arXiv papers — page 149

Junyi Li, Tianyi Tang, Zheng Gong, Lixin Yang

Nowadays, pretrained language models (PLMs) have dominated the majority of NLP tasks. While, little research has been conducted on systematically evaluating the language abilities of PLMs. In this paper, we present a large-scale empirical study on general language ability evaluation of PLMs (ElitePLM). In our study, we design four evaluation dimensions, i.e.

Ramya Gurunathan, Suchismita Sarker, Christopher K. H. Borg, James Saal

Interest in high entropy alloy thermoelectric materials is predicated on achieving ultralow lattice thermal conductivity $κ\sub{L}$ through large compositional disorder. However, here we show that for a given mechanism, such as mass contrast phonon scattering, $κ\sub{L}$ will be minimized along the binary alloy with the highest mass contrast, such that addin

Timo Kist, Anna Ijjas

We use numerical relativity simulations to explore the conditions for a canonical scalar field $ϕ$ minimally coupled to Einstein gravity to generate an extended phase of slow contraction that robustly smooths the universe for a wide range of initial conditions and then sets the conditions for a graceful exit stage. We show that to achieve robustness it suffi

Ranjan Maitra

Functional Magnetic Resonance Imaging~(fMRI) is a popular non-invasive modality to investigate activation in the human brain. The end result of most fMRI experiments is an activation map corresponding to the given paradigm. These maps can vary greatly from one study to the next, so quantifying the reliability of identified activation over several fMRI studie

Nikolas Ebert, Patrick Mangat, Oliver Wasenmüller

In order to ensure safe autonomous driving, precise information about the conditions in and around the vehicle must be available. Accordingly, the monitoring of occupants and objects inside the vehicle is crucial. In the state-of-the-art, single or multiple deep neural networks are used for either object recognition, semantic segmentation, or human pose esti

Nicolas Resch, Chen Yuan

In this work, we prove new results concerning the combinatorial properties of random linear codes. Firstly, we prove a lower bound on the list-size required for random linear codes over $\mathbb F_q$ $\varepsilon$-close to capacity to list-recover with error radius $ρ$ and input lists of size $\ell$. We show that the list-size $L$ must be at least $\frac{\lo

Daniele Fakhoury, Emanuele Fakhoury, Hendrik Speleers

In this paper we present ExSpliNet, an interpretable and expressive neural network model. The model combines ideas of Kolmogorov neural networks, ensembles of probabilistic trees, and multivariate B-spline representations. We give a probabilistic interpretation of the model and show its universal approximation properties. We also discuss how it can be effici

Dongnan Liu, Mariano Cabezas, Dongang Wang, Zihao Tang

Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks, due to variance in lesion characteristics imparted by different scanners and acquisition parameter

Weichao Lan, Yiu-ming Cheung, Juyong Jiang

Unstructured pruning has the limitation of dealing with the sparse and irregular weights. By contrast, structured pruning can help eliminate this drawback but it requires complex criterion to determine which components to be pruned. To this end, this paper presents a new method termed TissueNet, which directly constructs compact neural networks with fewer we

Nayla Escribano, Jon Ander González, Julen Orbegozo-Terradillos, Ainara Larrondo-Ureta

Parliamentary transcripts provide a valuable resource to understand the reality and know about the most important facts that occur over time in our societies. Furthermore, the political debates captured in these transcripts facilitate research on political discourse from a computational social science perspective. In this paper we release the first version o

Anirban Patra, Namit Pai, Parhitosh Sharma

This work proposes a dislocation density-based strain gradient $J_2$ plasticity framework that models the strength contribution due to Geometrically Necessary Dislocations (GNDs) using a lower order, Taylor hardening backstress model. An anisotropy factor is introduced to phenomenologically represent the differential hardening between grains in this $J_2$ pl

Nuno Arala

We study the problem of existence of one-parameter, linear families of polynomials of degree n all of whose polynomials have Galois group A_n. The methods we use have a strong geometric flavour.

Ziyuan An, Ya Su, Shuang Ni, Zhaoyong Guan

The Mn2Ge2Te6 shows intrinsic ferromagnetic (FM) order, with Curie temperature (Tc) of 316 K. The FM order origins from superexchange interaction between Mn and Te atoms. Mn2Ge2Te6 is half-metal (HM), and spin-\b{eta} electron is a semiconductor with gap of 1.462 eV. Mn2Ge2Te6 tends in-plane anisotropy (IPA), with magnetic anisotropy energy (MAE) of -13.2 me

Katherine Stasaski, Marti A. Hearst

Generating diverse, interesting responses to chitchat conversations is a problem for neural conversational agents. This paper makes two substantial contributions to improving diversity in dialogue generation. First, we propose a novel metric which uses Natural Language Inference (NLI) to measure the semantic diversity of a set of model responses for a conver

Riccardo Molle, Donato Passaseo

In this paper we present a new variational characteriztion of the first nontrival curve of the Fuč\'ık spectrum for elliptic operators with Dirichlet boundary conditions. Moreover, we describe the asymptotic behaviour and some properties of this curve and of the corresponding eigenfunctions. In particular, this new characterization allows us to compare t

Simon Caron-Huot, Yue-Zhou Li, Julio Parra-Martinez, David Simmons-Duffin

Do gravitational interactions respect the basic principles of relativity and quantum mechanics? We show that any graviton S-matrix that satisfies these assumptions cannot significantly differ from General Relativity at low energies. We provide sharp bounds on the size of potential corrections in terms of the mass M of new higher-spin states, in spacetime dim

Alexandros Vasilopoulos, Nikolaos G. Fytas, Erol Vatansever, Anastasios Malakis

We study the question of universality in the two-dimensional spin-$1$ Baxter-Wu model in the presence of a crystal field $Δ$. We employ extensive numerical simulations of two types, providing us with complementary results: Wang-Landau sampling at fixed values of $Δ$ and a parallelized variant of the multicanonical approach performed at constant temperature $

Thibaut Kulak, Anthony Fillion, François Blayo

We propose a unified view on two widely used data visualization techniques: Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE). We show that they can both be derived from a common mathematical framework. Leveraging this formulation, we propose to compare SOM and SNE quantitatively on two datasets, and discuss possible avenues for future work

Eugen Salzmann, Florian Zwicke, Stefanie Elgeti

One approach with rising popularity in analyzing time-dependent problems in science and engineering is the so-called space-time finite-element method that utilized finiteelements in both space and time. A common ansatz in this context is to divide the mesh in temporal direction into so-called space-time slabs, which are subsequently weakly connected in time

Gong Chen, Fabio Pusateri

We consider the $1d$ cubic nonlinear Schrödinger equation with an external potential $V$ that is non-generic. Without making any parity assumption on the data, but assuming that the zero energy resonance of the associated Schrödinger operator is either odd or even, we prove global-in-time quantitative bounds and asymptotics for small solutions. First, we use

Thomas Lartigue, Sach Mukherjee

In many applications, data can be heterogeneous in the sense of spanning latent groups with different underlying distributions. When predictive models are applied to such data the heterogeneity can affect both predictive performance and interpretability. Building on developments at the intersection of unsupervised learning and regularised regression, we prop

Zhimeng Ouyang, Lei Wu, Qinghua Xiao

In this paper, we study the local-in-time validity of the Hilbert expansion for the relativistic Landau equation. We justify that solutions of the relativistic Landau equation converge to small classical solutions of the limiting relativistic Euler equations as the Knudsen number shrinks to zero in a weighted Sobolev space. The key difficulty comes from the

Daniel A. Spielman, Peng Zhang

Marcus, Spielman and Srivastava (Annals of Mathematics 2014) solved the Kadison--Singer Problem by proving a strong form of Weaver's conjecture: they showed that for all $α> 0$ and all lists of vectors of norm at most $\sqrtα$ whose outer products sum to the identity, there exists a signed sum of those outer products with operator norm at most $\sqrt{8 α

Moritz Businger, Louis Nicolas, Théo Sanchez Mejia, Alban Ferrier

Quantum repeaters based on heralded entanglement require quantum nodes that are able to generate multimode quantum correlations between memories and telecommunication photons. The communication rate scales linearly with the number of modes, yet highly multimode quantum storage remains challenging. In this work, we demonstrate an atomic frequency comb quantum

Alexander Varchenko

We show that the $p$-adic KZ connection associated with the family of curves $y^q=(t-z_1)\dots (t-z_{qg+1})$ has an invariant subbundle of rank $g$, while the corresponding complex KZ connection has no nontrivial proper subbundles due to the irreducibility of its monodromy representation. The construction of the invariant subbundle is based on new Dwork--typ

Jakob R. Elias, Ryan Chard, Maksim Levental, Zhengchun Liu

Advancements in scientific instrument sensors and connected devices provide unprecedented insight into ongoing experiments and present new opportunities for control, optimization, and steering. However, the diversity of sensors and heterogeneity of their data result in make it challenging to fully realize these new opportunities. Organizing and synthesizing

Peter Emil Carstensen, Jacob Bendsen, Asbjørn Thode Reenberg, Tobias K. S. Ritschel

We propose a whole-body model of the metabolism in man as well as a generalized approach for modeling metabolic networks. Using this approach, we are able to write a large metabolic network in a systematic and compact way. We demonstrate the approach using a whole-body model of the metabolism of the three macronutrients, carbohydrates, proteins and lipids. T

Sungwon Park, Sungwon Han, Donghyun Ahn, Jaeyeon Kim

High-resolution daytime satellite imagery has become a promising source to study economic activities. These images display detailed terrain over large areas and allow zooming into smaller neighborhoods. Existing methods, however, have utilized images only in a single-level geographical unit. This research presents a deep learning model to predict economic in

Zhigang Yan, Dong Li, Zhichao Zhang, Jiguang He

In federated learning (FL), a number of devices train their local models and upload the corresponding parameters or gradients to the base station (BS) to update the global model while protecting their data privacy. However, due to the limited computation and communication resources, the number of local trainings (a.k.a. local update) and that of aggregations

Yurong Chen, Xiaotie Deng, Chenchen Li, David Mguni

Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in $n$-player games, which builds the foundation for modern multi-agent learning algorithms. Although FP has provable convergence guarantees on zero-sum games and potential games, many real-world problems are often a mixture of both and the

Zhihong Zhang, Linwei Zhou, Zhaoxi Chen, Antonín Jaroš

Direct growth of large-area vertically stacked two-dimensional (2D) van der Waal (vdW) materials is a prerequisite for their high-end applications in integrated electronics, optoelectronics and photovoltaics. Currently, centimetre- to even metre-scale monolayers of single-crystal graphene (MLG) and hexagonal boron nitride (h-BN) have been achieved by epitaxi

Patrick Hemmer, Max Schemmer, Niklas Kühl, Michael Vössing

Over the last years, the rising capabilities of artificial intelligence (AI) have improved human decision-making in many application areas. Teaming between AI and humans may even lead to complementary team performance (CTP), i.e., a level of performance beyond the ones that can be reached by AI or humans individually. Many researchers have proposed using exp

Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum

Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word alignments. These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints. Parsers also train on a point-estimate of the alignment pipeline,

M. Akramov, F. Khashimova, D. Matrasulov

We consider PT-symmetric, discrete nonlocal nonlinear Schrödinger equation on metric graphs. Soliton solutions are obtained for simplest graph topologies, such as star and tree graphs. Integrability of the problem is shown by proving existence of infinite number of conservation laws.

Zhao Zhang, Davide Dreon, Tilman Esslinger, Dieter Jaksch

We propose an experiment with a driven quantum gas coupled to a dissipative optical cavity that realizes a novel kind of far-from-equilibrium phase transition between spatial and temporal order. The control parameter of the transition is the detuning between the drive frequency and the cavity resonance. For negative detunings, the system features a spatially

Simon Bultmann, Sven Behnke

We present a system for 3D semantic scene perception consisting of a network of distributed smart edge sensors. The sensor nodes are based on an embedded CNN inference accelerator and RGB-D and thermal cameras. Efficient vision CNN models for object detection, semantic segmentation, and human pose estimation run on-device in real time. 2D human keypoint esti

Decheng Ma, Enrique Solano, Chenglong Jia, Lucas Chibebe Céleri

Analogue gravity stands today as an important tool for the investigation of gravitational phenomena that would be otherwise out of reach considering our technology. We consider here the analogue Penrose process in a rotating acoustic black hole based on a quantum fluid described by the draining bathtub model. Because of the rotating nature of this acoustic s

Jakob Bæk Tejs Houen, Mikkel Thorup

Simple tabulation hashing dates back to Zobrist in 1970 and is defined as follows: Each key is viewed as $c$ characters from some alphabet $Σ$, we have $c$ fully random hash functions $h_0, \ldots, h_{c - 1} \colon Σ\to \{0, \ldots, 2^l - 1\}$, and a key $x = (x_0, \ldots, x_{c - 1})$ is hashed to $h(x) = h_0(x_0) \oplus \ldots \oplus h_{c - 1}(x_{c - 1})$ w

Yaroslav Shchur, Guillermo Beltramo, Anatolii S. Andrushchak, Svetlana Vitusevich

The lattice dynamics of preferentially aligned nanocrystals formed upon drying of aqueous Ba(NO$_3$)$_2$ solutions in a mesoporous silica glass traversed by tubular pores of approximately 12 nm are explored by Raman scattering. To interpret the experiments on the confined nanocrystals polarized Raman spectra of bulk single crystals and X-ray diffraction expe

Raheem Karim Hashmani, Maxim Konyushikhin, Baosong Shan, Xudong Cai

The Alpha Magnetic Spectrometer (AMS) is constantly exposed to harsh condition on the ISS. As such, there is a need to constantly monitor and perform adjustments to ensure the AMS operates safely and efficiently. With the addition of the Upgraded Tracker Thermal Pump System, the legacy monitoring interface was no longer suitable for use. This paper describes

Shengyu Huang, Chih-Hung Liu, Daniel Rutschman

Given $n$ elements, an integer $k$ and a parameter $\varepsilon$, we study to select an element with rank in $(k-n\varepsilon,k+n\varepsilon]$ using unreliable comparisons where the outcome of each comparison is incorrect independently with a constant error probability, and multiple comparisons between the same pair of elements are independent. In this fault

Xin Zhou, Huang Huang, He Zhang, Xin Huang

Context: DevOps and microservices are acknowledged to be important new paradigms to tackle contemporary software demands and provide capabilities for rapid and reliable software development. Industrial reports show that they are quickly adopted together in massive software companies. However, because of the technical and organizational requirements, many dif

Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang

We study the first gradient descent step on the first-layer parameters $\boldsymbol{W}$ in a two-layer neural network: $f(\boldsymbol{x}) = \frac{1}{\sqrt{N}}\boldsymbol{a}^\topσ(\boldsymbol{W}^\top\boldsymbol{x})$, where $\boldsymbol{W}\in\mathbb{R}^{d\times N}, \boldsymbol{a}\in\mathbb{R}^{N}$ are randomly initialized, and the training objective is the emp

Taras Bodnar, Vilhelm Niklasson, Erik Thorsén

In this paper, a new way to integrate volatility information for estimating value at risk (VaR) and conditional value at risk (CVaR) of a portfolio is suggested. The new method is developed from the perspective of Bayesian statistics and it is based on the idea of volatility clustering. By specifying the hyperparameters in a conjugate prior based on two diff

Taotao He, Mohit Tawarmalani

In this paper, we develop new continuous and discrete relaxations for nonlinear expressions in an MINLP. In contrast to factorable programming, our techniques utilize the inner-function structure by encapsulating it in a polyhedral set, using a technique first proposed in [12]. We tighten the relaxations derived in [33,13] and obtain new relaxations for func

Florent Voisin, Gérald Lelong, Jean-Michel Guigner, Thomas Bizien

The assembly and stabilization of a finite number of nanocrystals in contact in water could maximize the optical absorption per unit of material. Some local plasmonic properties exploited in applications, such as photothermia and optical signal amplification, would also be maximized which is important in the perspective of mass producing nanostructures at a

Verena Ebert, Daniel Graziotin, Stefan Wagner

Communication is essential in software development, and even more in distributed settings. Communication activities need to be organized and coordinated to defend against the threat of productivity losses, increases in cognitive load, and stress among team members. With a plethora of communication channels that were identified by previous research in open-so

Gabriel E. Bittencourt Moraes, Guilherme de Loreno, Fábio Natali

New results concerning the orbital stability of periodic traveling wave solutions for the "abcd" Boussinesq model will be shown in this manuscript. For the existence of solutions, we use basic tools of ordinary differential equations to show that the corresponding periodic wave depends on the Jacobi elliptic function of cnoidal type. The spectral ana

Jongkuk Kim

Recently CDF II Collaboration reported that they measured W boson mass precisely. The measurement is deviated from the Standard Model (SM) Prediction at 7$σ$. Also, the recent FNAL measurement of the muon magnetic moment shows a $4.2σ$ deviation. To resolve the W boson as well as the muon $(g-2)$ anomalies, we explore the type-X two Higgs doublet model (2HDM

Javier López Prol, Karl W. Steininger, Keith Williges, Wolf D. Grossmann

Electrification of all economic sectors and solar photovoltaics (PV) becoming the lowest-cost electricity generation technology in ever more regions give rise to new potential gains of trade. We develop a stylized analytical model to minimize unit energy cost in autarky, open it to different trade configurations, and evaluate it empirically. We identify larg

Daniel Remenik

The KPZ fixed point is a scaling invariant Markov process which arises as the universal scaling limit of a broad class of models of random interface growth in one dimension, the one-dimensional KPZ universality class. In this survey we review the construction of the KPZ fixed point and some of the history that led to it, in particular through the exact solut

Zihao Wang, Dongjue Liu, Hau Tian Teo, Qiang Wang

The recent discovery of higher-order topology has largely enriched the classification of topological materials. Theoretical and experimental studies have unveiled various higher-order topological insulators that exhibit topologically protected corner or hinge states. More recently, higher-order topology has been introduced to topological semimetals. Thus far

Charalambos D. Charalambous, Stelios Louka

In this paper it is shown that the nonfeedback capacity of multiple-input multiple-output (MIMO) additive Gaussian noise (AGN) channels, when the noise is nonstationary and unstable, is characterized by an asymptotic optimization problem that involves, a generalized matrix algebraic Riccati equation (ARE) of filtering theory, and a matrix Lyapunov equation o

Alessandro Berti

The scalability of process mining techniques is one of the main challenges to tackling the massive amount of event data produced every day in enterprise information systems. To this purpose, filtering and sampling techniques are proposed to keep a subset of the behavior of the original log and make the application of process mining techniques feasible. While

Mohammad H. Alhakami, Numa A. Althubiti, Nwuyer A. Al-shammari

We re-examine the hadronic loop effects to the masses of $D^*_0$ and $D^*_{s0}$ calculated in quark models in the framework of heavy meson chiral perturbation theory (HMCHPT). The inaccuracy in the choice of the argument of the chiral loop functions in previous works is corrected. Our calculations consider the full one-loop corrections that appear at leading

Oliver Buchholz, Eric Raidl

Machine learning operates at the intersection of statistics and computer science. This raises the question as to its underlying methodology. While much emphasis has been put on the close link between the process of learning from data and induction, the falsificationist component of machine learning has received minor attention. In this paper, we argue that t

Patrick Musau, Nathaniel Hamilton, Diego Manzanas Lopez, Preston Robinette

Recent advances in machine learning technologies and sensing have paved the way for the belief that safe, accessible, and convenient autonomous vehicles may be realized in the near future. Despite tremendous advances within this context, fundamental challenges around safety and reliability are limiting their arrival and comprehensive adoption. Autonomous veh

Anna Arutyunova, Heiko Röglin

Hierarchical Clustering is a popular tool for understanding the hereditary properties of a data set. Such a clustering is actually a sequence of clusterings that starts with the trivial clustering in which every data point forms its own cluster and then successively merges two existing clusters until all points are in the same cluster. A hierarchical cluster

Ilya Kuprov, David Wilkowski, Nikolay Zheludev

It is commonly believed that electromagnetic spectra of atoms and molecules can be fully described by interactions of electric and magnetic multipoles. However, it has recently become clear that interactions between light and matter also involve toroidal multipoles - toroidal absorption lines have been observed in electromagnetic metamaterials. Here we show

Luca Chirolli, Norman Y. Yao, Joel E. Moore

High fidelity quantum information processing requires a combination of fast gates and long-lived quantum memories. In this work, we propose a hybrid architecture, where a parity-protected superconducting qubit is directly coupled to a Majorana qubit, which plays the role of a quantum memory. The superconducting qubit is based upon a $π$-periodic Josephson ju

Mitsuhiro Miyazaki

Let $R$ be the Ehrhart ring of the stable set polytope of a cycle graph which is not Gorenstein. We describe the non-Gorenstein locus of $\mathrm{Spec} R$. Further, we show that $R$ is almost Gorenstein. Moreover, we show that the conjecture of Hibi and Tsuchiya is true.

Gregory Kopnov, Alexander Gerber

Perpendicular magnetic anisotropy was found to be highly sensitive to the nominal thickness and morphology of laterally heterogeneous CoPd films in the vicinity of the metal insulator transition. We used the effect to tune the anisotropy by the width of lithographically patterned stripes with non-uniform cross-sectional thickness profiles. The phenomenon and

H. M. Tedila, R. Yuen, N. Wang, J. P. Yuan

We report on the variation in the single-pulse emission from PSR J1900+4221 (CRAFTS 19C10) observed at frequency centered at 1.25 GHz using the Five-hundred-meter Aperture Spherical radio Telescope. The integrated pulse profile shows two distinct components, referred to here as the leading and trailing components, with the latter component also containing a

Victoria Gould, Georgia Schneider

Let $Q$ be an inverse semigroup. A subsemigroup $S$ of $Q$ is a left I-order in $Q$ and $Q$ is a semigroup of left I-quotients of $S$ if every element in $Q$ can be written as $a^{-1}b$, where $a, b \in S$ and $a^{-1}$ is the inverse of $a$ in the sense of inverse semigroup theory. If we insist on being able to take $a$ and $b$ to be $\mathcal{R}$-related in

Subba Reddy Oota, Jashn Arora, Veeral Agarwal, Mounika Marreddy

Several popular Transformer based language models have been found to be successful for text-driven brain encoding. However, existing literature leverages only pretrained text Transformer models and has not explored the efficacy of task-specific learned Transformer representations. In this work, we explore transfer learning from representations learned for te

Stefan Dominicus, Amit Kumar Mishra

Sea ice concentration is an important metric used to characterize polar sea ice behavior. Understanding this behavior and accurately representing it is of critical importance for climate science research, and also has important uses in the context of maritime navigation. An end-to-end workflow for generating learned concentration estimation models from synth

Dmitriy Kim, Georgi Georgiev, Natalya Markovskaya

We consider a classical multiple access system with a single transmission channel, finite number of users (users), and randomized transmission protocol (ALOHA). We assume that every user sends messages to the base station with various intensities. Due to the overlapping of messages during their sending there are restrictions on the time between the messages

Pulkit Vyas, Chirag Saxena, Anwesh Badapanda, Anurag Goswami

Depth estimation is an important task, applied in various methods and applications of computer vision. While the traditional methods of estimating depth are based on depth cues and require specific equipment such as stereo cameras and configuring input according to the approach being used, the focus at the current time is on a single source, or monocular, de

John D. Ilee, Catherine Walsh, Jeff Jennings, Richard A. Booth

The radial extent of millimetre dust in protoplanetary discs is often far smaller than that of their gas, mostly due to processes such as dust growth and radial drift. However, it has been suggested that current millimetre continuum observations of discs do not trace their full extent due to limited sensitivity. In this Letter, we present deep (19 $μ$Jy beam

Aida Slavic, Ronald Siebes, Andrea Scharnhorst

Linked data (LD) technology is hailed as a long-awaited solution in web-based information exchange. Linked Open Data (LOD) bring this to another level by enabling meaningful linking of resources and creating a global, openly accessible knowledge graph. Our case is the Universal Decimal Classification (UDC) and the challenges for a KOS service provider to mai

Esteban Catté, Mohamed Sana, Mickael Maman

6G networks require a flexible infrastructure to dynamically provide ubiquitous network coverage. Mobile Access Points (MAP) deployment is a promising solution. In this paper, we formulate the joint 3D MAP deployment and user association problem over a dynamic network under interference and mobility constraints. First, we propose an iterative algorithm to op

Michael Pantic, Cesar Cadena, Roland Siegwart, Lionel Ott

This work investigates the use of Neural implicit representations, specifically Neural Radiance Fields (NeRF), for geometrical queries and motion planning. We show that by adding the capacity to infer occupancy in a radius to a pre-trained NeRF, we are effectively learning an approximation to a Euclidean Signed Distance Field (ESDF). Using backward different

Emilio Soria-Olivas, José E. Vila Gisbert, Regino Barranquero Cardeñosa, Yolanda Gomez

This paper presents a sandbox example of how the integration of models borrowed from Behavioral Economic (specifically Protection-Motivation Theory) into ML algorithms (specifically Bayesian Networks) can improve the performance and interpretability of ML algorithms when applied to Behavioral Data. The integration of Behavioral Economics knowledge to define

Claudiu Vinte, Ion Smeureanu, Titus-Felix Furtuna, Marcel Ausloos

This article introduces an intrinsic entropy model that can be used as an indicator to gauge investor interest in a given exchange-traded security, along with the state of the general market corroborated by individual security trade data. Although the syntagma of intrinsic entropy might sound somehow pleonastic, since entropy itself characterizes the fundame

Clarice Poon, Gabriel Peyré

Non-smooth optimization is a core ingredient of many imaging or machine learning pipelines. Non-smoothness encodes structural constraints on the solutions, such as sparsity, group sparsity, low-rank and sharp edges. It is also the basis for the definition of robust loss functions and scale-free functionals such as square-root Lasso. Standard approaches to de

Dibyajyoti Saikia, Mahfooz Alam, Jayanta Bera, Atanu Betal

In recent years, halide perovskite-based solar cells have received intensive attention, and demonstrated power conversion efficiency as high as 25.8%. With regard to the toxicity of Pb and the instability of organic elements, all inorganic lead-free perovskites (ILPs) have been extensively studied to achieve comparable or greater photovoltaic performance. In

Mike Zhang, Kristian Nørgaard Jensen, Barbara Plank

Skill Classification (SC) is the task of classifying job competences from job postings. This work is the first in SC applied to Danish job vacancy data. We release the first Danish job posting dataset: Kompetencer (en: competences), annotated for nested spans of competences. To improve upon coarse-grained annotations, we make use of The European Skills, Comp

Jiaxin Li, Danfeng Hong, Lianru Gao, Jing Yao

With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way. With the joint utilization of EO data, much research on mult

Lorenzo Dello Schiavo, Kohei Suzuki

This is the second paper of a series on configuration spaces $Υ$ over singular spaces $X$. Here, we focus on geometric aspects of the extended metric measure space $(Υ, \mathsf{d}_Υ, μ)$ equipped with the $L^2$-transportation distance $\mathsf{d}_Υ$, and a mixed Poisson measure $μ$. Firstly, we establish the essential self-adjointness and the $L^p$-uniquenes

Nima Karbasizadeh, S. Hassan HosseinNia

According to the well-known loop shaping method for the design of controllers, the performance of the controllers in terms of step response, steady-state disturbance rejection and noise attenuation and robustness can be improved by increasing the gain at lower frequencies and decreasing it at higher frequencies and increasing the phase margin as much as poss

Joaquín Castañeda, Vinicio Gómez

In this paper we present a new proof of a proposition presented in the article: Complexes of tournaments, directionality filtrations and persistent homology. This paper is part of Joaquín Castañeda's undergraduate thesis.

Oscar Sainz, Itziar Gonzalez-Dios, Oier Lopez de Lacalle, Bonan Min

Recent work has shown that NLP tasks such as Relation Extraction (RE) can be recasted as Textual Entailment tasks using verbalizations, with strong performance in zero-shot and few-shot settings thanks to pre-trained entailment models. The fact that relations in current RE datasets are easily verbalized casts doubts on whether entailment would be effective i

Wenjun Wang, Feng Xie, Xiongfeng Yang

This paper concerns with the large time behavior of solutions to a diffusion approximation radiation hydrodynamics model when the initial data is a small perturbation around an equilibrium state. The global-in-time well-posedness of solutions is achieved in Sobolev spaces depending on the Littlewood-Paley decomposition technique together with certain elabora

Wenjie Yin, Arkaitz Zubiaga

While social media offers freedom of self-expression, abusive language carry significant negative social impact. Driven by the importance of the issue, research in the automated detection of abusive language has witnessed growth and improvement. However, these detection models display a reliance on strongly indicative keywords, such as slurs and profanity. T

Hafsa Syed, Adam Kinos, Chunyan Shi, Lars Rippe

Flip-flop processes due to magnetic dipole-dipole interaction between neighbouring ions in rare-earth-ion-doped crystals is one of the mechanisms of relaxation between hyperfine levels. Modeling of this mechanism has so far been macroscopic, characterized by an average rate describing the relaxation of all ions. Here however, we present a microscopic model o

Claudiu Vinte, Marcel Ausloos, Titus Felix Furtuna

Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging f

Karina Ruzaeva, Katharina Nöh, Benjamin Berkels

In this paper, we propose a hybrid approach for multi-object microbial cell segmentation. The approach combines an ML-based detection with a geometry-aware variational-based segmentation using B-splines that are parametrized based on a geometric model of the cell shape. The detection is done first using YOLOv5. In a second step, each detected cell is segment

Marija Boberg, Nadine Gdaniec, Patryk Szwargulski, Franziska Werner

Magnetic Particle Imaging (MPI) is a tomographic imaging technique for determining the spatial distribution of superparamagnetic nanoparticles. Current MPI systems are capable of imaging iron masses over a wide dynamic range of more than four orders of magnitude. In theory, this range could be further increased using adaptive amplifiers, which prevent signal

Kaveh Eftekharinasab

We prove a so-called linking theorem and some of its corollaries, namely a mountain pass theorem and a three critical points theorem for Keller $ C^1$-functional on $ C^1 $- Frechet manifolds. Our approach relies on a deformation result which is not implemented by considering the negative pseudo-gradient flows. Furthermore, for mappings between Frechet manif

Amitoz Azad

Node classification on graphs can be formulated as the Dirichlet problem on graphs where the signal is given at the labeled nodes, and the harmonic extension is done on the unlabeled nodes. This paper considers a time-dependent version of the Dirichlet problem on graphs and shows how to improve its solution by learning the proper initialization vector on the

Xinwei Wang, Zirui Li, Javier Alonso-Mora, Meng Wang

Real-time safety systems are crucial components of intelligent vehicles. This paper introduces a prediction-based collision risk assessment approach on highways. Given a point mass vehicle dynamics system, a stochastic forward reachable set considering two-dimensional motion with vehicle state probability distributions is firstly established. We then develop

Andoni I. Garmendia, Josu Ceberio, Alexander Mendiburu

Neural Combinatorial Optimization attempts to learn good heuristics for solving a set of problems using Neural Network models and Reinforcement Learning. Recently, its good performance has encouraged many practitioners to develop neural architectures for a wide variety of combinatorial problems. However, the incorporation of such algorithms in the convention

Ramachandrarao Yalla, Y. Kojima, Y. Fukumoto, H. Suzuki

We experimentally demonstrate the integration of silicon-vacancy centers in nanodiamonds (SiV-NDs) with an optical nanofiber (ONF). We grow SiV-NDs on seed NDs dispersed on a quartz substrate using a microwave plasma-assisted chemical vapor deposition method. First, we search and characterize SiV-NDs on a quartz substrate using an inverted confocal microscop

Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez

This work enhances traditional authentication systems based on Personal Identification Numbers (PIN) and One-Time Passwords (OTP) through the incorporation of biometric information as a second level of user authentication. In our proposed approach, users draw each digit of the password on the touchscreen of the device instead of typing them as usual. A compl

Jiankui Li, Shan Li, Kaijia Luo

Let $\mathcal{A}$ be a unital Banach $*$-algebra and $\mathcal{M}$ be a unital $*$-$\mathcal{A}$-bimodule. If $W$ is a left separating point of $\mathcal{M}$, we show that every $*$-derivable mapping at $W$ is a Jordan derivation, and every $*$-left derivable mapping at $W$ is a Jordan left derivation under the condition $W \mathcal{A}=\mathcal{A}W$. Moreove

Fabian Heseding, Willy Scheibel, Jürgen Döllner

Software projects under version control grow with each commit, accumulating up to hundreds of thousands of commits per repository. Especially for such large projects, the traversal of a repository and data extraction for static source code analysis poses a trade-off between granularity and speed. We showcase the command-line tool pyrepositoryminer that combi

Guillaume Vanderhaegen, Pascal Szriftgiser, Alexandre Kudlinski, Matteo Conforti

We report the observation of the thermalization of the Fermi-Pasta-Ulam-Tsingou recurrence process in optical fibers. We show the transition from a reversible regime to an irreversible one, revealing a spectrally thermalized state. To do so, we actively compensate the fiber loss to make the observation of several recurrences possible. We inject into the fibe

A. Wörl, M. Garst, Y. Yamane, S. Bachus

We report on the low-temperature thermal expansion and magnetostriction of the single-impurity quadrupolar Kondo candidate Y$_{1-x}$Pr$_{x}$Ir$_2$Zn$_{20}$. In the dilute limit, we find a quadrupolar strain that possesses a singular dependence on temperature $T$, $\varepsilon_{\mathrm{u}} \sim H^2 \log 1/T$, for a small but finite magnetic field $H$. Togethe

Alexey I. Gubin, Alexander A. Barannik, Nickolay T. Cherpak, Irina A Protsenko

Studies of biochemical liquids require precise determination of their complex permittivity. We developed a microwave characterization technique on the basis of a high-quality whispering-gallery mode (WGM) sapphire resonator with a microfluidic channel filled with the liquid under test. A novel approach allows obtaining the complex permittivity of biochemical

Arkadev Roy, Rajveer Nehra, Carsten Langrock, Martin Fejer

Phase transitions and the associated symmetry breaking are at the heart of many physical phenomena. Coupled systems with multiple interacting degrees of freedom provide a fertile ground for emergent dynamics that is otherwise inaccessible in their solitary counterparts. Here we show that coupled nonlinear optical resonators can undergo self-organization in t