August 2022 arXiv papers — page 49

Masahito Hayashi, Yuxiang Yang

The ability to extract relevant information is critical to learning. An ingenious approach as such is the information bottleneck, an optimisation problem whose solution corresponds to a faithful and memory-efficient representation of relevant information from a large system. The advent of the age of quantum computing calls for efficient methods that work on

B. G. Zakharov

We study the medium modification factor $I_{pp}$ within the light-cone path integral approach to induced gluon emission. We use parametrization of the running coupling $\alpha_s(Q,T)$ which has a plateau around $Q\sim \kappa T$. We calculate $I_{pp}$ with no free parameters using $\kappa$ fitted to the LHC data on the nuclear modification factor $R_{AA}$. We

Tong Han, David J. Hill, Yue Song

Network topology has significant impacts on operational performance of power systems. While extensive research efforts have been devoted to optimization of network topology for improving various system performances, the problem of how to transition from the initial topology to the desired optimal topology requires study. To address this problem, we propose t

Alessandra Rossi, Patrick Holthaus, Sìlvia Moros, Gabriella Lakatos

The Trust, Acceptance and Social Cues in Human-Robot Interaction - SCRITA is the 5th edition of a series of workshops held in conjunction with the IEEE RO-MAN conference. This workshop focuses on addressing the challenges and development of the dynamics between people and robots in order to foster short interactions and long-lasting relationships in differen

Gerrit Grutzeck

The goal of this thesis was to develop a procedure to optimize the mirror suppression, which reduces the dynamic range of the Fast Fourier Transform spectrometers (FFTS), and implement this procedure in the Field Programmable Gate Array (FPGA) of the FFTS. This is achieved by applying a calibration on the complex amplitude spectrum. Modern multi-beam heterod

Anton Nazarov, Pavel Nikitin, Daniil Sarafannikov

We consider the decomposition into irreducible components of the exterior algebra $\bigwedge\left(\mathbb{C}^{n}\otimes \left(\mathbb{C}^{k}\right)^{*}\right)$ regarded as a $GL_{n}\times GL_{k}$ module. Irreducible $GL_{n}\times GL_{k}$ representations are parameterized by pairs of Young diagrams $(\lambda,\bar{\lambda}')$, where $\bar{\lambda}'$ is the com

Stephan Thaler, Maximilian Stupp, Julija Zavadlav

Neural network (NN) potentials are a natural choice for coarse-grained (CG) models. Their many-body capacity allows highly accurate approximations of the potential of mean force, promising CG simulations at unprecedented accuracy. CG NN potentials trained bottom-up via force matching (FM), however, suffer from finite data effects: They rely on prior potentia

Ya-Rong Wang, Ting-Yan Li, Zheng-Yuan Fang, Hao Chen

We study the excited states of $\omega$ and $\omega_3$ by comparison with the $\rho$ and $\rho_3$ families, and discuss the possibility of $X(2220)$ as $\omega$ excitation by analyzing the mass spectra and strong decay behaviors. In addition, we predict the masses and widths of $\omega(2D)$ and $\omega_3$and $\rho_3(4D)$, $\rho_3(1G)$, $\omega_3$ and $\rho_3

Alexander Kalinowski, Yuan An

The majority of knowledge graph embedding techniques treat entities and predicates as separate embedding matrices, using aggregation functions to build a representation of the input triple. However, these aggregations are lossy, i.e. they do not capture the semantics of the original triples, such as information contained in the predicates. To combat these sh

Pablo Barros, Ozge Nilay Yalcın, Ana Tanevska, Alessandra Sciutti

Recent advances in reinforcement learning with social agents have allowed such models to achieve human-level performance on specific interaction tasks. However, most interactive scenarios do not have a version alone as an end goal; instead, the social impact of these agents when interacting with humans is as important and largely unexplored. In this regard,

Igor A. Spiridonov

The Torelli group of a genus $g$ oriented surface $\Sigma_g$ is the subgroup $\mathcal{I}_g$ of the mapping class group ${\rm Mod}(\Sigma_g)$ consisting of all mapping classes that act trivially on ${\rm H}_1(\Sigma_g, \mathbb{Z})$. The quotient group ${\rm Mod}(\Sigma_g) / \mathcal{I}_g$ is isomorphic to the symplectic group ${\rm Sp}(2g, \mathbb{Z})$. The

Gary C. F. Lee, Amir Weiss, Alejandro Lancho, Jennifer Tang

We study the problem of single-channel source separation (SCSS), and focus on cyclostationary signals, which are particularly suitable in a variety of application domains. Unlike classical SCSS approaches, we consider a setting where only examples of the sources are available rather than their models, inspiring a data-driven approach. For source models with

Alexander Dobrick, Jochen Glück

We consider systems of parabolic linear equations, subject to Neumann boundary conditions on bounded domains in $\mathbb{R}^d$, that are coupled by a matrix-valued potential $V$, and investigate under which conditions each solution to such a system converges to an equilibrium as $t \to \infty$. While this is clearly a fundamental question about systems of pa

Raphael César de Souza Pimenta, Anibal Thiago Bezerra

With the advent of near-term quantum computers, the simulation of properties of solids using quantum algorithms becomes possible. By an adequate description of the system's Hamiltonian, variational methods enable to fetch the band structure and other fundamental properties as transition probabilities. Here, we use k$\cdot$p Hamiltonians to describe semicondu

Shanshan Zhong, Wushao Wen, Jinghui Qin

Recently many effective attention modules are proposed to boot the model performance by exploiting the internal information of convolutional neural networks in computer vision. In general, many previous works ignore considering the design of the pooling strategy of the attention mechanism since they adopt the global average pooling for granted, which hinders

Ashish Cherukuri, Alireza Zolanvari, Goran Banjac, Ashish R. Hota

This paper focuses on solving a data-driven distributionally robust optimization problem over a network of agents. The agents aim to minimize the worst-case expected cost computed over a Wasserstein ambiguity set that is centered at the empirical distribution. The samples of the uncertainty are distributed across the agents. Our approach consists of reformul

Shahab Aslani, Watjana Lilaonitkul, Vaishnavi Gnanananthan, Divya Raj

During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions. This variation is seen in the CXR projections used, image annotations added and in the inspiratory effort and degree of rotation of clinical images. The image analysis community ha

Karl Friedrich Siburg, Christopher Strothmann, Gregor Weiß

We introduce a new stochastic order for the tail dependence between random variables. We then study different measures of tail dependence which are monotone in the proposed order, thereby extending various known tail dependence coefficients from the literature. We apply our concepts in an empirical study where we investigate the tail dependence for different

Tomasz Andrearczyk, Janusz Sadowski, Krzysztof Dybko, Tadeusz Figielski

Pulsed current-induced magnetization reversal is investigated in the layer of (Ga,Mn)(Bi,As) dilute ferromagnetic semiconductor (DFS) epitaxially grown under tensile misfit strain causing perpendicular magnetic anisotropy in the layer. The magnetization reversal, recorded through measurements of the anomalous Hall effect, appearing under assistance of a stat

Amir Bahrami, Zoé-Lise Deck-Léger, Christophe Caloz

Space-time varying metamaterials based on uniform-velocity modulation have spurred considerable interest over the past decade. We present here the first extensive investigation of accelerated modulation space-time metamaterials. Using the tools of general relativity, we establish their electrodynamic principles and describe their fundamental phenomena, in co

Artem Ryzhikov, Mikhail Hushchyn, Denis Derkach

Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linea

Niels M. P. Neumann, Robert S. Wezeman

Quantum computers can solve specific complex tasks for which no reasonable-time classical algorithm is known. Quantum computers do however also offer inherent security of data, as measurements destroy quantum states. Using shared entangled states, multiple parties can collaborate and securely compute quantum algorithms. In this paper we propose an approach f

Cyprien Gille, Frederic Guyard, Michel Barlaud

In this paper we present a new approach to solve semi-supervised classification tasks for biomedical applications, involving a supervised autoencoder network. We create a network architecture that encodes labels into the latent space of an autoencoder, and define a global criterion combining classification and reconstruction losses. We train the Semi-Supervi

Yongjian Wang, Thomas Boemerich, Jinhong Park, Henry F. Legg

The Dirac material ZrTe$_5$ at very low carrier density was recently found to be a nodal-line semimetal, where ultra-flat bands are expected to emerge in magnetic fields parallel to the nodal-line plane. Here we report that in very low carrier-density samples of ZrTe$_5$, when the current and the magnetic field are both along the crystallographic $a$ axis, t

Florian Otterpohl, Peter Nalbach, Michael Thorwart

A quantum two-level system immersed in a sub-Ohmic bath experiences enhanced low-frequency quantum statistical fluctuations which render the nonequilibrium quantum dynamics highly non-Markovian. Upon using the numerically exact time-evolving matrix product operator approach, we investigate the phase diagram of the polarization dynamics. In addition to the kn

Yury Kaminsky, Irina Deeva

Learning a Bayesian network is an NP-hard problem and with an increase in the number of nodes, classical algorithms for learning the structure of Bayesian networks become inefficient. In recent years, some methods and algorithms for learning Bayesian networks with a high number of nodes (more than 50) were developed. But these solutions have their disadvanta

Adam Mair, Kabe Moen

We prove certain two weight bump conditions are sufficient for the compactness of the commutator $[b,T]$ where $b\in CMO$ and $T$ is a Calder\'on- Zygmund operator. This is the first result for compactness in the two weight setting without additional assumptions on the individual weights.

Jivnesh Sandhan, Ashish Gupta, Hrishikesh Terdalkar, Tushar Sandhan

The phenomenon of compounding is ubiquitous in Sanskrit. It serves for achieving brevity in expressing thoughts, while simultaneously enriching the lexical and structural formation of the language. In this work, we focus on the Sanskrit Compound Type Identification (SaCTI) task, where we consider the problem of identifying semantic relations between the comp

Fan Wang, Dachun Yang, Wen Yuan

Let $X$ be a ball quasi-Banach function space satisfying some mild assumptions and $H_X(\mathbb{R}^n)$ the Hardy space associated with $X$. In this article, the authors introduce both the Hardy space $H_X(\mathbb{R}^{n+1}_+)$ of harmonic functions and the Hardy space $\mathbb{H}_X(\mathbb{R}^{n+1}_+)$ of harmonic vectors, associated with $X$, and then establ

Hermann Kroll, Niklas Mainzer, Wolf-Tilo Balke

Designing keyword-based access paths is a common practice in digital libraries. They are easy to use and accepted by users and come with moderate costs for content providers. However, users usually have to break down the search into pieces if they search for stories of interest that are more complex than searching for a few keywords. After searching for ever

Ingemar Bengtsson, Karol Zyczkowski

We review selected achievements of the late Bogdan Mielnik in the field of theoretical physics, with an emphasis on his attempts to go beyond quantum mechanics. Some of his original views on the problems of contemporary society and organization of science are also recalled.

Terrence George, Sanjay Ramassamy

Cluster integrable systems are a broad class of integrable systems modelled on bipartite dimer models on the torus. Many discrete integrable dynamics arise by applying sequences of local transformations, which form the cluster modular group of the cluster integrable system. This cluster modular group was recently characterized by the first author and Inchios

Bassam Shayya

Suppose $S$ is a smooth compact hypersurface in $\Bbb R^n$ and $\sigma$ is an appropriate measure on $S$. If $Ef= \hat{fd\sigma}$ is the extension operator associated with $(S,\sigma)$, then the Mizohata-Takeuchi conjecture asserts that $\int |Ef(x)|^2 w(x) dx \leq C (\sup_T w(T)) \| f \|_{L^2(\sigma)}^2$ for all functions $f \in L^2(\sigma)$ and weights $w

Cecilia Jarne, Mariano Caruso

In order to comprehend and enhance models that describes various brain regions is important to study the dynamics of trained recurrent neural networks. Including Dales law in such models usually presents several challenges. However, this is an important aspect that allows computational models to better capture the characteristics of the brain. Here we presen

Xin Wang, Jian Sun, Gang Wang, Frank Allgöwer

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a.k.a. network systems). To this end, we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling, under which a model-based stability criterion for the closed-loop network system is

Fengying Dang, Dong Chen, Jun Chen, Zhaojian Li

Event-triggered model predictive control (eMPC) is a popular optimal control method with an aim to alleviate the computation and/or communication burden of MPC. However, it generally requires priori knowledge of the closed-loop system behavior along with the communication characteristics for designing the event-trigger policy. This paper attempts to solve th

Shun'ya Mizoguchi

We study the relation between the instanton expansion of the Seiberg-Witten prepotential for $D=4$, ${\cal N}=2$ $SU(2)$ SUSY gauge theory for $N_f=0$ and $1$ and the monstrous moonshine. By utilizing a newly developed simple method to obtain the SW prepotential, it is shown that the coefficients of the expansion of $q=e^{2\pi \tau}$ in terms of $A^2=\frac{\

Farha A. Khan, Tanmay Chakraborty, Jörg P. Dietrich, Christian Wirth

State-of-the-art multi-objective optimization often assumes a known utility function, learns it interactively, or computes the full Pareto front-each requiring costly expert input.~Real-world problems, however, involve implicit preferences that are hard to formalize. To reduce expert involvement, we propose an offline, interpretable utility learning method t

Vincent Wall, Gabriel Zöller, Oliver Brock

We propose a sensorization method for soft pneumatic actuators that uses an embedded microphone and speaker to measure different actuator properties. The physical state of the actuator determines the specific modulation of sound as it travels through the structure. Using simple machine learning, we create a computational sensor that infers the corresponding

Tianpeng Gao, Jianzhong Li

The approximate sorting for big data is considered in this paper. The goal of approximate sorting for big data is to generate an approximate sorted result, but using less CPU and I/O cost. For big data, we consider the approximate sorting in I/O model. The existing metrics on permutation space are not available for external approximate sorting algorithms. Th

Siyuan Wang, Zhongyu Wei, Zhihao Fan, Qi Zhang

Multi-hop reasoning requires aggregating multiple documents to answer a complex question. Existing methods usually decompose the multi-hop question into simpler single-hop questions to solve the problem for illustrating the explainable reasoning process. However, they ignore grounding on the supporting facts of each reasoning step, which tends to generate in

Shucheng Yang, Xiaoping Gao, Jie Ren

Rapid Single Flux Quantum (RSFQ) logic is a promising technology to supersede Complementary metal-oxide-semiconductor (CMOS) logic in some specialized areas due to providing ultra-fast and energy-efficient circuits. To realize a large-scale integration design, electronic design automation (EDA) tools specialized for RSFQ logic are required due to the diverge

Wouter Jansen, Nico Huebel, Jan Steckel

Designing and validating sensor applications and algorithms in simulation is an important step in the modern development process. Furthermore, modern open-source multi-sensor simulation frameworks are moving towards the usage of video-game engines such as the Unreal Engine. Simulation of a sensor such as a LiDAR can prove to be difficult in such real-time so

Rouven Schmidt, Thomas Kneib

The primary objective of Stochastic Frontier (SF) Analysis is the deconvolution of the estimated composed error terms into noise and inefficiency. Assuming a parametric production function (e.g. Cobb-Douglas, Translog, etc.), might lead to false inefficiency estimates. To overcome this limiting assumption, the production function can be modelled utilizing P-

Matthew Chen, Adela YiYu Zhang

We provide a short proof that the dimensions of the mod $p$ homology groups of the unordered configuration space $B_k(T)$ of $k$ points in a torus are the same as its Betti numbers for $p>2$ and $k\leq p$. Hence the integral homology has no $p$-power torsion. The same argument works for the punctured genus $g$ surface with $g>0$, thereby recovering a result

Michelangelo Domina, Urvesh Patil, Matteo Cobelli, Stefano Sanvito

We introduce a compact cluster expansion method, constructed over Jacobi and Legendre polynomials, to generate highly accurate and flexible machine-learning force fields. The constituent many-body contributions are separated, interpretable and adaptable to replicate the physical knowledge of the system. In fact, the flexibility introduced by the use of the J

Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li

Planning-based reinforcement learning has shown strong performance in tasks in discrete and low-dimensional continuous action spaces. However, planning usually brings significant computational overhead for decision-making, and scaling such methods to high-dimensional action spaces remains challenging. To advance efficient planning for high-dimensional contin

Peter Belcak, Roger Wattenhofer

Owing to their versatility, graph structures admit representations of intricate relationships between the separate entities comprising the data. We formalise the notion of connection between two vertex sets in terms of edge and vertex features by introducing graph-walking programs. We give two algorithms for mining of deterministic graph-walking programs tha

Xiao-Yun Wang, Chen Dong, Quanjin Wang

In this work, we systematically study the $\phi$ meson and nucleus interaction by analyzing and fitting the cross sections of $\gamma N$$\rightarrow \phi$$N$ ($N$ represent the nucleus) reactions near the threshold. With the help of vector meson dominant model, the distribution of $\phi$-$N$ scattering length as a function of energy is presented, and the res

Matthew Badger, Sean McCurdy

We prove that in any Banach space the set of windows in which a rectifiable curve resembles two or more straight line segments is quantitatively small with constants that are independent of the curve, the dimension of the space, and the choice of norm. Together with Part I, this completes the proof of the necessary half of the Analyst's Traveling Salesman th

Louise Coppieters de Gibson, Philip N. Garner

Current speech recognition architectures perform very well from the point of view of machine learning, hence user interaction. This suggests that they are emulating the human biological system well. We investigate whether the inference can be inverted to provide insights into that biological system; in particular the hearing mechanism. Using SincNet, we conf

David H. Meyer, Joshua C. Hill, Paul D. Kunz, Kevin C. Cox

Electric field sensors based on Rydberg atoms offer unique capabilities, relative to traditional sensors, for detecting radio-frequency signals. In this work, we demonstrate simultaneous demodulation and detection of five rf tones spanning nearly two decades (6 octaves), from 1.7 GHz to 116 GHz. We show continuous recovery of the phase and amplitude of each

Aderik Voorspoels, Jocelyne Vreede, Enrico Carlon

All-atom simulations have become increasingly popular to study conformational and dynamical properties of nucleic acids as they are accurate and provide high spatial and time resolutions. This high resolution however comes at a heavy computational cost and within the time scales of simulations nucleic acids weakly fluctuate around their ideal structure explo

Harshdeep Singh, Sabyashachi Mishra, Sonjoy Majumder

Classical optimizers play a crucial role in determining the accuracy and convergence of variational quantum algorithms. In literature, many optimizers, each having its own architecture, have been employed expediently for different applications. In this work, we consider a few popular optimizers and assess their performance in variational quantum algorithms f

Jun Rekimoto

Interactions based on automatic speech recognition (ASR) have become widely used, with speech input being increasingly utilized to create documents. However, as there is no easy way to distinguish between commands being issued and text required to be input in speech, misrecognitions are difficult to identify and correct, meaning that documents need to be man

Brahim Tamadazte

This manuscript gives an overview of my research work carried out within the FEMTO-ST institute in Besan\c{c}on, more particularly in the Automatic and Micro-Mechatronic Systems (AS2M) department. It is above all the result of my (co)-supervision of interns, PhD students and postdocs. I would like to pay tribute to them, for their major contribution to scien

Ioannis Konidakis, Beate Krause, Gyu-Hyeon Park, Nithin Pulumati

The incorporation of carbon nanotubes (CNTs) within polymer hosts offers a great platform for the development of advanced thermoelectric (TE) composite materials. Over the years, several CNT/polymer composite formulations have been investigated on an effort to maximize the TE performance. Meanwhile, several studies focused on the decay dynamics of the charge

Jiale Fu, Yali Yuan, Jiajun He, Sichu Liang

The current security problems in cyberspace are characterized by strong and complex threats. Defenders face numerous problems such as lack of prior knowledge, various threats, and unknown vulnerabilities, which urgently need new fundamental theories to support. To address these issues, this article proposes a generic theoretical model for cyberspace defense

Ji-Cai Liu

Let $M_n$ and $T_n$ denote the $n$th Motzkin number and the $n$th central trinomial coefficient respectively. We prove that for any prime $p\ge 5$, \begin{align*} &\sum_{k=0}^{p-1}M_k^2\equiv \left(\frac{p}{3}\right)\left(2-6p\right)\pmod{p^2},\\ &\sum_{k=0}^{p-1}kM_k^2\equiv \left(\frac{p}{3}\right)\left(9p-1\right)\pmod{p^2},\\ &\sum_{k=0}^{p-1}T_kM_k\equi

Ali Waqar Azim, Ahmad Bazzi, Raed Shubair, Marwa Chafii

LoRa is a widely adopted method of utilizing chirp spread spectrum (CSS) techniques at the physical (PHY) layer to facilitate low-power wide-area network (LPWAN) connectivity. By tailoring the spreading factors, LoRa can achieve a diverse spectral and energy efficiency (EE) levels, making it amenable to a plethora of Internet-of-Things (IoT) applications tha

Murat Altunbulak, Fatma Altunbulak Aksu

We give a characterization for the binary linear constant weight codes by using the symmetric difference of the supports of the codewords. This characterization gives a correspondence between the set of binary linear constant weight codes and the set of partitions for the union of supports of the codewords. By using this correspondence, we present a formula

Wei Su

In this paper, we investigate how to measure the intelligence of systems under specific structures. Two indicators are adopted to characterize the intelligence of a given structure, namely the function diversity of the structure, and the ability to generate order under specific environments. A measure of intelligence degree is proposed, with which the intell

Marc Höll, Alon Nissan, Brian Berkowitz, Eli Barkai

First passage time statistics in disordered systems exhibiting scale invariance are studied widely. In particular, long trapping times in energy or entropic traps are fat-tailed distributed, which slow the overall transport process. We study the statistical properties of the first passage time of biased processes in different models, and employ the big jump

Atsuo Kuniba, Shuichiro Matsuike, Akihito Yoneyama

We present a family of new solutions to the tetrahedron equation of the form $RLLL=LLLR$, where $L$ operator may be regarded as a quantized six-vertex model whose Boltzmann weights are specific representations of the $q$-oscillator or $q$-Weyl algebras. When the three $L$'s are associated with the $q$-oscillator algebra, $R$ coincides with the known intertwi

Ondřej Chrenko, Raúl O. Chametla, David Nesvorný, Mario Flock

[Abridged] The occurrence rate of observed sub-Neptunes has a break at 0.1 au, which is often attributed to a migration trap at the inner rim of protoplanetary disks where a positive co-rotation torque prevents inward migration. We argue that conditions in inner disk regions are such that sub-Neptunes are likely to open gaps, lose the support of the co-rotat

Shuchan Wang

In this paper, we propose our information-theoretic equivalence of entropic multi-marginal optimal transport (MOT). This equivalence can be easily reduced to the case of entropic optimal transport (OT). Because OT is widely used to compare differences between knowledge or beliefs, we apply this result to the communication between agents with different belief

Balder ten Cate, Maurice Funk, Jean Christoph Jung, Carsten Lutz

This note serves three purposes: (i) we provide a self-contained exposition of the fact that conjunctive queries are not efficiently learnable in the Probably-Approximately-Correct (PAC) model, paying clear attention to the complicating fact that this concept class lacks the polynomial-size fitting property, a property that is tacitly assumed in much of the

Sebastien Perrin

Measurements of quarkonia (heavy quark and antiquark bound states) and open-heavy flavour hadrons in hadronic collisions provide a unique testing ground for understanding quantum chromodynamics (QCD). Although recently there was significant progress, our understanding of hadronic collisions has been challenged by the observation of intriguing effects in high

Luca Dal Negro, Yilin Zhu, Yuyao Chen, Marcus Prado

We investigate the localization of waves in aperiodic structures that manifest the characteristic multiscale complexity of certain arithmetic functions with a central role in number theory. In particular, we study the eigenspectra and wave localization properties of tight-binding Schr\"{o}dinger equation models with on-site potentials distributed according t

Muhammad Saad Saeed, Shah Nawaz, Muhammad Haris Khan, Sajid Javed

Recent years have seen an increased interest in establishing association between faces and voices of celebrities leveraging audio-visual information from YouTube. Prior works adopt metric learning methods to learn an embedding space that is amenable for associated matching and verification tasks. Albeit showing some progress, such formulations are, however,

Masaki Kato, Hiroshi Kori

Complex networks often possess communities defined based on network connectivity. When dynamics undergo in a network, one can also consider dynamical communities; i.e., a group of nodes displaying a similar dynamical process. We have investigated both analytically and numerically the development of dynamical community structure, where the community is referr

Chao-Yang Lu, Yuan Cao, Cheng-Zhi Peng, Jian-Wei Pan

Quantum theory has been successfully validated in numerous laboratory experiments. But would such a theory, which excellently describes the behavior of microscopic physical systems, and its predicted phenomena such as quantum entanglement, be still applicable on very large length scales? From a practical perspective, how can quantum key distribution -- where

Shin'ichi Nojiri, Sergei D. Odintsov, Valerio Faraoni

The Bekenstein-Hawking entropy is a cornerstone of horizon thermodynamics but quantum effects correct it, while inequivalent entropies arise also in non-extensive thermodynamics. Reviewing our previous work, we advocate for a new entropy construct that comprises recent and older proposals and satisfies four minimal key properties. The new proposal is then ap

Dorian Florescu, Ayush Bhandari

An alternative to conventional uniform sampling is that of time encoding, which converts continuous-time signals into streams of trigger times. This gives rise to Event-Driven Sampling (EDS) models. The data-driven nature of EDS acquisition is advantageous in terms of power consumption and time resolution and is inspired by the information representation in

Hugo Daniel Macedo, Ken Pierce

This volume contains the papers presented at the 20th International Overture Workshop, which was held in an hybrid format: online and physically at Aarhus, Denmark on 05th July 2022. This event was the latest in a series of workshops around the Vienna Development Method (VDM), the open-source project Overture, and related tools and formalisms. VDM is one of

Hannah Zohren, Tim Schrabback, Sebastian Bocquet, Martin Sommer

We present a Hubble Space Telescope (HST) weak gravitational lensing study of nine distant and massive galaxy clusters with redshifts $1.0 \lesssim z \lesssim 1.7$ ($z_\mathrm{median} = 1.4$) and Sunyaev Zel'dovich (SZ) detection significance $\xi > 6.0$ from the South Pole Telescope Sunyaev Zel'dovich (SPT-SZ) survey. We measured weak lensing galaxy shapes

Martin Korth

After about a century since the first attempts by Bohr, the interpretation of quantum theory is still a field with many open questions. In this article a new interpretation of quantum theory is suggested, motivated by philosophical considerations. Based on the findings that the 'weirdness' of quantum theory can be understood to derive from a vanishing distin

Alexander Unnervik, Sébastien Marcel

Backdoor attacks allow an attacker to embed functionality jeopardizing proper behavior of any algorithm, machine learning or not. This hidden functionality can remain inactive for normal use of the algorithm until activated by the attacker. Given how stealthy backdoor attacks are, consequences of these backdoors could be disastrous if such networks were to b

Dalin Zhang, Kaixuan Chen, Yan Zhao, Bin Yang

Deep learning technologies have demonstrated remarkable effectiveness in a wide range of tasks, and deep learning holds the potential to advance a multitude of applications, including in edge computing, where deep models are deployed on edge devices to enable instant data processing and response. A key challenge is that while the application of deep models o

Lukas Pfahler, Katharina Morik

Given the increase of publications, search for relevant papers becomes tedious. In particular, search across disciplines or schools of thinking is not supported. This is mainly due to the retrieval with keyword queries: technical terms differ in different sciences or at different times. Relevant articles might better be identified by their mathematical probl

D. Kang, J. C. Arteaga-Velázquez, M. Bertaina, A. Chiavassa

KASCADE and its extension array of KASCADE-Grande were devoted to measure individual air showers of cosmic rays in the primary energy range of 100 TeV to 1 EeV. The experiment has substantially contributed to investigate the energy spectrum and mass composition of cosmic rays in the transition region from galactic to extragalactic origin of cosmic rays as we

Dimitar Trajanov, Vangel Trajkovski, Makedonka Dimitrieva, Jovana Dobreva

Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the last few years and now employs modern variants of deep neural networks to extract relevant patterns from larg

Jan Tönshoff, Berke Kisin, Jakob Lindner, Martin Grohe

We propose a universal Graph Neural Network architecture which can be trained as an end-2-end search heuristic for any Constraint Satisfaction Problem (CSP). Our architecture can be trained unsupervised with policy gradient descent to generate problem specific heuristics for any CSP in a purely data driven manner. The approach is based on a novel graph repre

Yutao Zhu, Jian-Yun Nie, Yixuan Su, Haonan Chen

Contextual information in search sessions is important for capturing users' search intents. Various approaches have been proposed to model user behavior sequences to improve document ranking in a session. Typically, training samples of (search context, document) pairs are sampled randomly in each training epoch. In reality, the difficulty to understand user'

Zhongyi Zhang, Zhenfei Wu, Chen Fang, Fu-chun Zhang

We present a new scheme for Majorana modes in systems with nonsymmporhic-symmetry-protected band degeneracy. We reveal that when the gapless fermionic excitations are encoded with conventional superconductivity and magnetism, which can be intrinsic or induced by proximity effect, topological superconductivity and Majorana modes can be obtained. We illustrate

Biao Ma

Let $S = S_g$ be a closed orientable surface of genus $g \geq 2$ and $Mod(S)$ be the mapping class group of $S$. In this paper, we show that the boundary representation of $Mod(S)$ is ergodic using statistical hyperbolicity, which generalizes the classical result of Masur on ergodicity of the action of $Mod(S)$ on the projective measured foliation space $\ma

Jin Zhang, Nicolas Tancogne-Dejean, Lede Xian, Emil Vinas Boström

Laser-induced ultrafast demagnetization is a phenomenon of utmost interest and attracts significant attention because it enables potential applications in ultrafast optoelectronics and spintronics. As a spin-orbit coupling assisted magnetic insulator, $\alpha$-$RuCl_3$ provides an attractive platform to explore the physics of electronic correlations and rela

Darshan Gera, Naveen Siva Kumar Badveeti, Bobbili Veerendra Raj Kumar, S Balasubramanian

The real-world facial expression recognition (FER) datasets suffer from noisy annotations due to crowd-sourcing, ambiguity in expressions, the subjectivity of annotators and inter-class similarity. However, the recent deep networks have strong capacity to memorize the noisy annotations leading to corrupted feature embedding and poor generalization. To handle

Mattia Radice

We consider the problem of the first passage time to the origin of a spatially non-homogeneous random walk with a position-dependent drift, known as the Gillis random walk, in the presence of resetting. The walk starts from an initial site $ x_0 $ and, with fixed probability $ r $, at each step may be relocated to a given site $ x_r $. From a general perspec

Mingzhi Wang, Songbai Chen, Jiliang Jing

We studied the relativistic images caused by strong gravitational lensing in Kerr black hole images, which carry some essential signatures about the black hole space-time. We defined a new celestial coordinates whose origin is the center of black hole shadow to locate the relativistic images. Under the influences of the dragging effect caused by rotating bla

Vincent Wall, Oliver Brock

We create a virtual 2D tactile array for soft pneumatic actuators using embedded audio components. We detect contact-specific changes in sound modulation to infer tactile information. We evaluate different sound representations and learning methods to detect even small contact variations. We demonstrate the acoustic tactile sensor array by the example of a P

Zhichao Zhou, Yihua Yan, Linjie Chen, Wei Wang

The Mingantu Spectral Radioheliograph (MUSER), a new generation of solar dedicated radio imaging-spectroscopic telescope, has realized high-time, high-angular, and high-frequency resolution imaging of the sun over an ultra-broadband frequency range. Each pair of MUSER antennas measures the complex visibility in the aperture plane for each integration time an

Emilia Laiyin Yin-Grossmann, Florian Mueller, Yannick Kuhl, Franziska Schrank

The integration of PET into MRI to form a hybrid system requires often compromises for both subsystems. For example, the integration might come at the cost of a reduced PET detector height or a reduced MRI examination volume diameter. Here, we propose a so-called shared-volume concept to use the volume required for both subsystems more efficiently, in which

Neethu P. K., Ullas Chandran S. V., Julliano R. Nascimento

A set $S$ of vertices of a graph $G$ is \emph{monophonic convex} if $S$ contains all the vertices belonging to any induced path connecting two vertices of $S$. The cardinality of a maximum proper monophonic convex set of $G$ is called the \emph{monophonic convexity number} of $G$. The \emph{monophonic interval} of a set $S$ of vertices of $G$ is the set $S$

Xiaoyue Li, Xuerong Mao, Guoting Song

Since it is difficult to implement implicit schemes on the infinite-dimensional space, we aim to develop the explicit numerical method for approximating super-linear stochastic functional differential equations (SFDEs). Precisely, borrowing the truncation idea and linear interpolation we propose an explicit truncated Euler-Maruyama scheme for super-linear SF

Ignacio M. Alliati, Myrta Grüning

We present a Floquet scheme for the ab-initio calculation of nonlinear optical properties in extended systems. This entails a reformulation of the real-time approach based on the dynamical Berry-phase polarisation [Attaccalite & Gr\"uning, PRB 88, 1-9 (2013)] and retains the advantage of being non-perturbative in the electric field. The proposed method appli

P. Kapuściński, J. Dzian, A. O. Slobodeniuk, C. Rodríguez-Fernández

We perform a polarization-resolved magnetoluminescence study of excitons in ReS$_2$. We observe that two linearly polarized Rydberg series of excitons are accompanied by two other Rydberg series of dark excitons, brightened by an in-plane magnetic field. All series extrapolate to the same single-electron bandgap, indicating that the observed excitons origina

Fabien Baradel, Romain Brégier, Thibault Groueix, Philippe Weinzaepfel

Training state-of-the-art models for human pose estimation in videos requires datasets with annotations that are really hard and expensive to obtain. Although transformers have been recently utilized for body pose sequence modeling, related methods rely on pseudo-ground truth to augment the currently limited training data available for learning such models.

Fawaz Aseeri, Julian Kaspczyk

Let $p$ be a prime number, $G$ be a $p$-solvable finite group and $P$ be a Sylow $p$-subgroup of $G$. We prove that $G$ is $p$-supersolvable if $N_G(P)$ is $p$-supersolvable and if there is a subgroup $H$ of $P$ with $P' \le H \le \Phi(P)$ such that $H$ is $s$-semipermutable in $G$. As applications, we simplify the proofs of some known results and also gener

Prajwel Joseph, P. Sreekumar, C. S. Stalin, K. T. Paul

$\require{mediawiki-texvc}$ Supermassive black holes at the centre of active galactic nuclei (AGN) produce relativistic jets that can affect the star formation characteristics of the AGN hosts. Observations in the ultraviolet (UV) band can provide an excellent view of the effect of AGN jets on star formation. Here, we present a census of star formation prope