March 2023 arXiv papers — page 72

Li Li, Hubert P. H. Shum, Toby P. Breckon

Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve se

Katja Kohopää, Alberto Ronzani, Robab Najafi Jabdaraghi, Arijit Bera

We demonstrate ion irradiation by argon or gallium as a wafer-scale post-processing method to increase disorder in superconducting thin films. We study several widely used superconductors, both single-elements and compounds. We show that ion irradiation increases normal-state resistivity in all our films, which is expected to enable tuning their superconduct

Henrik Rose, Nina Kunert, Tim Dietrich, Peter T. H. Pang

Three-nucleon forces are crucial for the accurate description of nuclear systems, including dense matter probed in neutron stars. We explore nuclear Hamiltonians that reproduce two-nucleon scattering data and properties of light nuclei, but differ in the three-nucleon interactions among neutrons. While no significantly improved constraints can be obtained fr

Davide Rattacaso, Gianluca Passarelli, Angelo Russomanno, Procolo Lucignano

Finding a local Hamiltonian $\hat{\mathcal{H}}$ having a given many-body wavefunction $|\psi\rangle$ as its ground state, i.e. a parent Hamiltonian, is a challenge of fundamental importance in quantum technologies. Here we introduce a numerical method, inspired by quantum annealing, that efficiently performs this task through an artificial inverse dynamics:

A. Erpenbeck, W. -T. Lin, T. Blommel, L. Zhang

The simulation of strongly correlated quantum impurity models is a significant challenge in modern condensed matter physics that has multiple important applications. Thus far, the most successful methods for approaching this challenge involve Monte Carlo techniques that accurately and reliably sample perturbative expansions to any order. However, the cost of

Andrei Brazhuk

For a long time threat modeling was treated as a manual, complicated process. However modern agile development methodologies and cloud computing technologies require adding automatic threat modeling approaches. This work considers two challenges: creating a set of machine-readable data flow diagrams that represent real cloud based applications; and usage dom

Max Bolderman, Mircea Lazar, Hans Butler

Feedforward controllers typically rely on accurately identified inverse models of the system dynamics to achieve high reference tracking performance. However, the impact of the (inverse) model identification error on the resulting tracking error is only analyzed a posteriori in experiments. Therefore, in this work, we develop an approach to feedforward contr

Sangchul Park

As debates on potential societal harm from artificial intelligence (AI) culminate in legislation and international norms, a global divide is emerging in both AI regulatory frameworks and international governance structures. In terms of local regulatory frameworks, the European Union (E.U.), Canada, and Brazil follow a horizontal or lateral approach that post

Sophie Meuret, Hugo Lourenço-Martins, Sébastien Weber, Florent Houdellier

We investigate - both experimentally and theoretically - the inelastic interaction between fast electrons and the electromagnetic field scattered by metallic apertures and nanostructures on dielectric membranes using photon induced near-field electron microscopy. The experiments - performed in a high brightness ultrafast transmission electron microscope - on

Andrea Bianchi, Jeremy Miller

For a finite group $G$ and a conjugation-invariant subset $Q\subseteq G$, we consider the Hurwitz space $\mathrm{Hur}_n(Q)$ parametrising branched covers of the plane with $n$ branch points, monodromies in $G$ and local monodromies in $Q$. For $i\ge0$ we prove that $\bigoplus_n H_i(\mathrm{Hur}_n(Q))$ is a finitely generated module over the ring $\bigoplus_n

Ulrich Bauer, Fabian Lenzen, Michael Lesnick

Clearing is a simple but effective optimization for the standard algorithm of persistent homology (PH), which dramatically improves the speed and scalability of PH computations for Vietoris--Rips filtrations. Due to the quick growth of the boundary matrices of a Vietoris--Rips filtration with increasing dimension, clearing is only effective when used in conj

Vilém Zouhar, Sunit Bhattacharya, Ondřej Bojar

The Shannon game has long been used as a thought experiment in linguistics and NLP, asking participants to guess the next letter in a sentence based on its preceding context. We extend the game by introducing an optional extra modality in the form of image information. To investigate the impact of multimodal information in this game, we use human participant

André Correia, Luís A. Alexandre

With the fast improvement of machine learning, reinforcement learning (RL) has been used to automate human tasks in different areas. However, training such agents is difficult and restricted to expert users. Moreover, it is mostly limited to simulation environments due to the high cost and safety concerns of interactions in the real world. Demonstration Lear

Joaquim Borges, Josep Rifà, Victor Zinoviev

In two previous papers we constructed new families of completely regular codes by concatenation methods. Here we determine cases in which the new codes are completely transitive. For these cases we also find the automorphism groups of such codes. For the remaining cases, we show that the codes are not completely transitive assuming an upper bound on the orde

Andreas Hohl, Pierre Schapira

We prove that various morphisms related to the six Grothendieck operations on sheaves become isomorphisms when restricted to (weakly) constructible sheaves. To this end, we first study some properties of weakly cohomologically constructible sheaves. We then deduce several compatibilities of the six operations in the context of (weakly) $\mathbb{R}$-construct

Stefan Clarke, Gabriele Dragotto, Jaime Fernández Fisac, Bartolomeo Stellato

We propose a stochastic first-order algorithm to learn the rationality parameters of simultaneous and non-cooperative potential games, i.e., the parameters of the agents' optimization problems. Our technique combines (i.) an active-set step that enforces that the agents play at a Nash equilibrium and (ii.) an implicit-differentiation step to update the estim

Siyu Chen, Yitan Wang, Zhaoran Wang, Zhuoran Yang

We study the offline contextual bandit problem, where we aim to acquire an optimal policy using observational data. However, this data usually contains two deficiencies: (i) some variables that confound actions are not observed, and (ii) missing observations exist in the collected data. Unobserved confounders lead to a confounding bias and missing observatio

José Martins, Sandro Pinto

In this paper, we aim to understand the properties and guarantees of static partitioning hypervisors (SPH) for Arm-based mixed-criticality systems (MCS). To this end, we performed a comprehensive empirical evaluation of popular open-source SPH, i.e., Jailhouse, Xen (Dom0-less), Bao, and seL4 CAmkES VMM, focusing on two key requirements of modern MCS: real-ti

Samet Gelincik, Charles Pillet, Pascal Giard

In this letter, we propose to add dynamic frozen bits to underlying polar codes with a Reed-Muller information set with the aim of maintaining the same sub-decoding structure in Automorphism Ensemble (AE) and lowering the Maximum Likelihood (ML) bound by reducing the number of minimum weight codewords. We provide the dynamic freezing constraint matrix that r

A. D. Biggs

We present an analysis of two seasons of archival, multi-frequency VLA monitoring of the quad lens system JVAS B1422+231, the 15-GHz data of which have previously been published. The 8.4- and 15-GHz variability curves show significant variability, especially in polarization, but lack features on short time-scales that would be necessary for an accurate measu

Jiaqing Yuan, Munindar P. Singh

Conversations among online users sometimes derail, i.e., break down into personal attacks. Such derailment has a negative impact on the healthy growth of cyberspace communities. The ability to predict whether ongoing conversations are likely to derail could provide valuable real-time insight to interlocutors and moderators. Prior approaches predict conversat

Zixuan Hu, Li Shen, Zhenyi Wang, Tongliang Liu

The goal of data-free meta-learning is to learn useful prior knowledge from a collection of pre-trained models without accessing their training data. However, existing works only solve the problem in parameter space, which (i) ignore the fruitful data knowledge contained in the pre-trained models; (ii) can not scale to large-scale pre-trained models; (iii) c

B. Shridharan, Blesson Mathew, R. Arun, T. B. Cysil

Herbig Ae/Be stars are intermediate-mass pre-main sequence stars undergoing accretion through their circumstellar disk. The optical and infrared (IR) spectra of HAeBe stars show HI emission lines belonging to Balmer, Paschen and Brackett series. We use the archival X-Shooter spectra available for 109 HAeBe stars from Vioque et al. (2018) and analyse the vari

Haaroon Yousaf, Naomi A. Arnold, Renaud Lambiotte, Timothy LaRock

Recent years have witnessed the availability of richer and richer datasets in a variety of domains, where signals often have a multi-modal nature, blending temporal, relational and semantic information. Within this context, several works have shown that standard network models are sometimes not sufficient to properly capture the complexity of real-world inte

Zhehan Kan, Shuoshuo Chen, Ce Zhang, Yushun Tang

A central challenge in human pose estimation, as well as in many other machine learning and prediction tasks, is the generalization problem. The learned network does not have the capability to characterize the prediction error, generate feedback information from the test sample, and correct the prediction error on the fly for each individual test sample, whi

Chunyao Song, Lei Jin, Pengbo Song, Hongtao Rong

The discovery of the topological states has become a key topic in condensed matter physics with the focus evolving from the Dirac or Weyl points to high-dimension topological states of the nodal lines and nodal surfaces. For a topological material to manifest its quantum properties and become useful in applications, the topological states need to be genuine

Chunyao Song, Lulu Liu, Shengtao Cui, Jingjing Gao

The superconductor SnTaS$_{2}$ is theoretically predicted to be an intriguing topological nodal line semimetal without consideration of spin-orbit coupling. By carrying out angle-resolved photoemission (ARPES) and spin-resolved ARPES measurements combined with band structure calculations, we have provided a complete picture of the electronic structure and sp

Lennart Brocki, Neo Christopher Chung

Despite the unprecedented performance of deep neural networks (DNNs) in computer vision, their practical application in the diagnosis and prognosis of cancer using medical imaging has been limited. One of the critical challenges for integrating diagnostic DNNs into radiological and oncological applications is their lack of interpretability, preventing clinic

Mengqi Fang, Eui-Hyeok Yang

Transition metal dichalcogenides (TMDs) are two-dimensional (2D) materials with remarkable electrical, optical and chemical properties. One promising strategy to tailor TMD properties of TMDs is to create alloys through dopant-induced modification. Dopants can introduce additional states within the bandgap of TMDs, leading to changes in their optical, electr

Jeremy Karam, Alison Sills

The formation of star clusters involves the growth of smaller, gas-rich subclusters through accretion of gas from the giant molecular cloud within which the subclusters are embedded. The two main accretion mechanisms responsible for this are accretion of gas from dense filaments, and from the ambient background of the cloud. We perform simulations of both of

Rong Wang, Yao Yao, Zhen-Qiang Yin

Quantum discord represents a correlation beyond classicality that could be useful for many quantum information tasks, and therefore is viewed as a valuable quantum resource. Theoretically, whether a bipartite quantum state has a non-zero amount of quantum discord or not can be determined once its density matrix is given. Experimentally, however, it is not ea

Junsu Kim, Younggyo Seo, Sungsoo Ahn, Kyunghwan Son

Recently, graph-based planning algorithms have gained much attention to solve goal-conditioned reinforcement learning (RL) tasks: they provide a sequence of subgoals to reach the target-goal, and the agents learn to execute subgoal-conditioned policies. However, the sample-efficiency of such RL schemes still remains a challenge, particularly for long-horizon

Ameya Prabhu, Hasan Abed Al Kader Hammoud, Puneet Dokania, Philip H. S. Torr

Continual Learning (CL) aims to sequentially train models on streams of incoming data that vary in distribution by preserving previous knowledge while adapting to new data. Current CL literature focuses on restricted access to previously seen data, while imposing no constraints on the computational budget for training. This is unreasonable for applications i

Priyanka Das, Richard Plesh, Veeru Talreja, Natalia Schmid

Iris is an established modality in biometric recognition applications including consumer electronics, e-commerce, border security, forensics, and de-duplication of identity at a national scale. In light of the expanding usage of biometric recognition, identity clash (when templates from two different people match) is an imperative factor of consideration for

Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury

Given an abstract, deformed, ordinary sketch from untrained amateurs like you and me, this paper turns it into a photorealistic image - just like those shown in Fig. 1(a), all non-cherry-picked. We differ significantly from prior art in that we do not dictate an edgemap-like sketch to start with, but aim to work with abstract free-hand human sketches. In doi

Adrien Scalea

(Chern--Simons) vector models exhibit an infinite-dimensional symmetry, the slightly-broken higher-spin symmetry with the unbroken higher-spin symmetry being the first approximation. In this note, we compute the $n$-point correlation functions of the higher-spin currents as higher-spin invariants directly on the CFT side, which complements earlier results th

Tiziano Binzoni, Alain Mazzolo

Monte Carlo (MC) simulations allowing to describe photons propagation in statistical mixtures represent an interest that goes way beyond the domain of optics, and can cover, e.g., nuclear reactor physics, image analysis or life science just to name a few. MC simulations are considered a ``gold standard'' because they give exact solutions (in the statistical

Theophilus Quachie Asenso, Manuela Zucknick

In this paper, we consider the regularized multi-response regression problem where there exists some structural relation within the responses and also between the covariates and a set of modifying variables. To handle this problem, we propose MADMMplasso, a novel regularized regression method. This method is able to find covariates and their corresponding in

Yihao Li, Hassan Al Hajj, Pierre-Henri Conze, Mostafa EI Habib Daho

Diabetes is a chronic disease characterized by excess sugar in the blood and affects 422 million people worldwide, including 3.3 million in France. One of the frequent complications of diabetes is diabetic retinopathy (DR): it is the leading cause of blindness in the working population of developed countries. As a result, ophthalmology is on the verge of a r

Xiao-Yu Xiong, Xi-Dan Hu, Qizhong Zhu, Zhi Li

We investigate the dynamical properties of quasiparticles in graphene superlattices with three typical Kekul\'{e} distortions (i.e., Kekul\'{e}-O, Kekul\'{e}-Y and Kekul\'{e}-M). On the one hand, we numerically show the visualized evolution process of Kekul\'{e} quasiparticles; while on the other hand, we analytically obtain the centroid trajectory of the qu

B. T. Dullo, J. H. Knapen, R. J. Beswick, R. D. Baldi

[Abridged] We use high-resolution $HST$ imaging and $e$-MERLIN 1.5-GHz observations of galaxy cores from the LeMMINGs survey to investigate the relation between optical structural properties and nuclear radio emission for a large sample of galaxies. We perform accurate, multi-component decompositions of new surface brightness profiles extracted from $HST$ im

Yuquan Xiao, Qinghe Du

Age of information (AoI) is a powerful metric to evaluate the freshness of information, where minimization of average statistics, such as the average AoI and average peak AoI, currently prevails in guiding freshness optimization for related applications. Although minimizing the statistics does improve the received information's freshness for status update sy

Gu Zhang, Zhan Cao, Dong E. Liu

The interplay between a topological degeneracy and the residue degeneracy (also known as the residue entropy) of quantum criticality remains as an important but not thoroughly understood topic. We find that this topological degeneracy, provided by a Majorana zero mode pair, relaxes the otherwise strictly requested symmetry requirement, to observe the boundar

Rahul R Nair, Grzegorz Wilk, Zbigniew Włodarczyk

We show that the multiplicity distribution of photons produced with enhanced void probability in inelastic proton-proton collisions at $\sqrt{s} =$ 900 GeV, 2.76 TeV, and 7 TeV, measured at forward rapidities by the ALICE experiment at LHC, can be described by the geometric Poisson distribution. The traditionally used negative binomial distribution fails to

Benjamin Doerr, Anatolii Kostrygin

We develop a simple and generic method to analyze randomized rumor spreading processes in fully connected networks. In contrast to all previous works, which heavily exploit the precise definition of the process under investigation, we only need to understand the probability and the covariance of the events that uninformed nodes become informed. This universa

Ankush, Rishu Verma, Sahil Kumar, B. C. Chauhan

In our recent work we identify the hybrid textures of neutrino mass matrix which simultaneously account for dark matter (DM) and neutrinoless double beta decay ($0\nu\beta\beta$). We also obtained the bounds on dark matter mass and effective Majorana mass $|M_{ee}|$. In this work we look for those hybrid textures which altogether accounts for DM, $0\nu\beta\

P. Reimann, P. Vorndamme, J. Schnack

Isotropic but otherwise largely arbitrary Heisenberg models in the presence of a homogeneous magnetic field are considered, including various integrable, non-integrable, as well as disordered examples, and not necessarily restricted to one dimension or short-range interactions. Taking for granted that the non-equilibrium initial condition and the spectrum of

Amin Mekacher, Max Falkenberg, Andrea Baronchelli

Deplatforming, or banning malicious accounts from social media, is a key tool for moderating online harms. However, the consequences of deplatforming for the wider social media ecosystem have been largely overlooked so far, due to the difficulty of tracking banned users. Here, we address this gap by studying the ban-induced platform migration from Twitter to

Kamil Malinka, Martin Perešíni, Anton Firc, Ondřej Hujňák

In late 2022, OpenAI released a new version of ChatGPT, a sophisticated natural language processing system capable of holding natural conversations while preserving and responding to the context of the discussion. ChatGPT has exceeded expectations in its abilities, leading to extensive considerations of its potential applications and misuse. In this work, we

William Barker

This paper is concerned with the existence of traveling wave solutions for diffusive two-species Lotka-Volterra systems with delay in both the reaction and diffusion terms without monotonicity. We extend the partial or cross monotone iteration method to systems that satisfy the partial quasi-monotone condition via construction appropriate upper and lower sol

G. G. A. de Souza, T. S. Abelha, T. B. Saramela, A. F. V. Cortez

In this work we present the latest developments in the SAMPA-SRS integration. A software was developed to improve the acquisition configuration, acquisition, and decoding of the data. The complete framework was tested using a triple GEM-based position sensitive detector for X-rays. The detector was operated in Ar/CO$_2$ (70/30) in continuous flow, at atmosph

Gianluca Capozzi, Daniele Cono D'Elia, Giuseppe Antonio Di Luna, Leonardo Querzoni

In recent years, binary analysis gained traction as a fundamental approach to inspect software and guarantee its security. Due to the exponential increase of devices running software, much research is now moving towards new autonomous solutions based on deep learning models, as they have been showing state-of-the-art performances in solving binary analysis p

Lucas Benigni, Nixia Chen, Patrick Lopatto, Xiaoyu Xie

We study the eigenvector mass distribution of an $N\times N$ Wigner matrix on a set of coordinates $I$ satisfying $| I | \ge c N$ for some constant $c >0$. For eigenvectors corresponding to eigenvalues at the spectral edge, we show that the sum of the mass on these coordinates converges to a Gaussian in the $N \rightarrow \infty$ limit, after a suitable resc

Hongbo Wang, Weimin Xiong, Yifan Song, Dawei Zhu

Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction. However, most existing works focus on sentence-level coarse-grained JERE, which have limitations in real-world scenarios. In this paper, we construct a large-scale document-level fine-grained JERE dataset DocRED-FE, which improves DocRED with Fine-Graine

David Carchedi

On one hand, together with Pelle Steffens, we recently characterized the infinity category of derived manifolds up to equivalence by a universal property. On the other hand, it is shown in recent work of Behrend-Liao-Xu that the category of differential graded manifolds admits a homotopy theory. In this paper, we prove that the associated infinity category o

Ilya Kovalenko, Kira Barton, James Moyne, Dawn M. Tilbury

Rapid advances in artificial intelligence (AI) have the potential to significantly increase the productivity, quality, and profitability in future manufacturing systems. Traditional mass-production will give way to personalized production, with each item made to order, at the low cost and high-quality consumers have come to expect. Manufacturing systems will

Zachary Morrison, Benjamin P. Russo, Yingzhao Lian, Rushikesh Kamalapurkar

The reliable operation of automatic systems is heavily dependent on the ability to detect faults in the underlying dynamical system. While traditional model-based methods have been widely used for fault detection, data-driven approaches have garnered increasing attention due to their ease of deployment and minimal need for expert knowledge. In this paper, we

Yu Cao, Xiangqiao Meng, P. Y. Mok, Xueting Liu

It is a time-consuming and tedious work for manually colorizing anime line drawing images, which is an essential stage in cartoon animation creation pipeline. Reference-based line drawing colorization is a challenging task that relies on the precise cross-domain long-range dependency modelling between the line drawing and reference image. Existing learning m

Shouhei Honda

We provide relationships between the spectral convergences in B\'erard-Besson-Gallot sense, in Kasue-Kumura sense and the measured Gromov-Hausdorff convergence, for compact finite dimensional RCD spaces. As an independent interest, a canonical (spectral) approximation map between such spaces constructed by given spectral data is obtained.

Ziquan Liu, Yi Xu, Xiangyang Ji, Antoni B. Chan

Recent years have seen the ever-increasing importance of pre-trained models and their downstream training in deep learning research and applications. At the same time, the defense for adversarial examples has been mainly investigated in the context of training from random initialization on simple classification tasks. To better exploit the potential of pre-t

Ke Gao, Lei-Hua Liu

The study focuses on the impact of microlensing in modern cosmology and introduces a new framework for the static spherically symmetrical wormhole in terms of the radial equation of state. Following a standard procedure, the study calculates the lensing equation, magnification, and event rate based on the the radial equation of state. The analysis highlights

Pierre Béaur, Benjamin Hellouin de Menibus

Given an $\omega$-automaton and a set of substitutions, we look at which accepted words can also be defined through these substitutions, and in particular if there is at least one. We introduce a method using desubstitution of $\omega$-automata to describe the structure of preimages of accepted words under arbitrary sequences of homomorphisms: this takes the

L. Ivzhenko, A. Girich, M. Baranowski, A. Kharchenko

The spectrum of electromagnetic waves in periodic linear structures, such as periodic waveguides or chains of microelements i.e. spheres, cavities, exhibit the sequence of stop bands for propagating waves. Breaking the translational symmetry of the periodic microstrip can also lead to the localization of the microwaves at the microstrip edge. In this paper,

Avraham Aizenbud, Dmitry Gourevitch

Let a complex algebraic reductive group $\bf G$ act on a complex algebraic manifold $\bf X$. For a $\bf G$-invariant subvariety $\Xi$ of the nilpotent cone $\mathcal{N}(\mathfrak{g}^*)\subset \mathfrak{g}^*$ we define a notion of $\Xi$-symplectic complexity of $\bf X$. This notion generalizes the notion of complexity defined in [Vin86]. We prove several prop

Maryam Fazel-Zarandi, Wei-Ning Hsu

Self-supervised learning leverages unlabeled data effectively, improving label efficiency and generalization to domains without labeled data. While recent work has studied generalization to more acoustic/linguistic domains, languages, and modalities, these investigations are limited to single-source speech with one primary speaker in the recording. This pape

Michael J. Sharkey, Krit Dwivedi, Samer Alabed, Andrew J. Swift

Purpose: Lung disease assessment in precapillary pulmonary hypertension (PH) is essential for appropriate patient management. This study aims to develop an artificial intelligence (AI) deep learning model for lung texture classification in CT Pulmonary Angiography (CTPA), and evaluate its correlation with clinical assessment methods. Materials and Methods: I

Sarka Necasova, Maria Angeles Rodriguez-Bellido, Tong Tang

The paper deals with the problem of the energy conservation for the weak solutions to the compressible Primitive Equations (CPE) system with degenerate viscosity. The sufficient conditions on the regularity of weak solutions for the energy equality are obtained even for the case when the solutions may include vacuum. In this paper, we show two theorems, the

Alexander J. B. Russell

Braginskii MHD provides a more accurate description of many plasma environments than classical MHD since it actively treats the stress tensor using a closure derived from physical principles. Stress tensor effects nonetheless remain relatively unexplored for solar MHD phenomena, especially in nonlinear regimes. This paper analytically examines nonlinear damp

Xiaoting Wang, Yanxiang Zhang

Spiking neural networks (SNNs) present a promising energy efficient alternative to traditional Artificial Neural Networks (ANNs) due to their multiplication-free operations enabled by binarized intermediate activations. However, this binarization leads to precision loss, hindering the SNN performance. In this paper, we introduce Multiple Threshold (MT) appro

Yong Guo, David Stutz, Bernt Schiele

Despite the success of vision transformers (ViTs), they still suffer from significant drops in accuracy in the presence of common corruptions, such as noise or blur. Interestingly, we observe that the attention mechanism of ViTs tends to rely on few important tokens, a phenomenon we call token overfocusing. More critically, these tokens are not robust to cor

Ly V. Nguyen, Lu Liu, Nguyen Linh-Trung, A. Lee Swindlehurst

One-bit digital-to-analog converters (DACs) are a practical and promising solution for reducing cost and power consumption in massive multiple-input multiple-output (MIMO) systems. However, the one-bit precoding problem is NP-hard and even more challenging in frequency-selective fading channels compared to the flat-fading scenario. While block-wise processin

Babak Miraftab, Dave Witte Morris

A graph is said to be uniquely hamiltonian if it has a unique hamiltonian cycle. For a natural extension of this concept to infinite graphs, we find all uniquely hamiltonian vertex-transitive graphs with finitely many ends, and also discuss some examples with infinitely many ends. In particular, we show each nonabelian free group $F_n$ has a Cayley graph of

Riyan Yang, Lin Li, Wensheng Gan, Zefeng Chen

In the era of the Web of Things, the Metaverse is expected to be the landing site for the next generation of the Internet, resulting in the increased popularity of related technologies and applications in recent years and gradually becoming the focus of Internet research. The Metaverse, as a link between the real and virtual worlds, can provide users with im

Mohsen Sotoudeh, Manuel Dillenz, Johannes Döhn, Julian Hansen

Mg batteries with oxide cathodes have the potential to significantly surpass existing Li-ion technologies in terms of sustainability, abundance, and energy density. However, Mg intercalation at the cathode is often severely hampered by the sluggish kinetics of Mg$^{2+}$ migration within oxides. Here we report a combined theoretical and experimental study add

Muhammad Zohaib

In software industry, the DevOps is an increasingly adopting software development paradigm. Towards the sustainable DevOps adoption, there is a need to transform the organization Culture, Automation, Measurement and Sharing (CAMS) aspects concerning to core theme of continues development and operations. The software organizations face several complexities wh

Francesco Giuliari, Gianluca Scarpellini, Stuart James, Yiming Wang

Positional reasoning is the process of ordering unsorted parts contained in a set into a consistent structure. We present Positional Diffusion, a plug-and-play graph formulation with Diffusion Probabilistic Models to address positional reasoning. We use the forward process to map elements' positions in a set to random positions in a continuous space. Positio

Masaaki Fukasawa, Basile Maire, Marcus Wunsch

We consider Geometric Mean Market Makers -- a special type of Decentralized Exchange -- with two types of users: liquidity takers and arbitrageurs. Liquidity takers trade at prices that can create arbitrage opportunities, while arbitrageurs align the exchange's price with the external market price. We show that in Geometric Mean Market Makers charging propor

Yingjian Liu, Jiang Li, Xiaoping Wang, Zhigang Zeng

Emotion Recognition in Conversation (ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper, we propose an emotional inertia and contagion-driven dependency modeling approach (EmotionIC) for ERC task. Our EmotionIC consists of three main components, i.e.,

Anton Pakhomov, Nikolay Rosanov, Mikhail Arkhipov, Rostislav Arkhipov

We theoretically demonstrate the possibility to tune the temporal waveform of optical unipolar pulses upon their coherent interaction with a multi-level resonant medium. This is achieved through the coherent control of the response of a multi-level resonant medium by means of half-cycle unipolar pulses. We show that despite the ultrabroad spectrum of half-cy

Laura Duvidovich, Alberto Petriella

Aims: We provide new insights into the gamma-ray emission from HESS J1912+101, a TeV supernova remnant candidate probably associated with the radio pulsar PSR J1913+1011. Methods: We obtained new observations at 1.5 GHz using the VLA in the D configuration, with the purpose of detecting the radio shell of the putative remnant. In addition, we observed a sing

Song Park, Sanghyuk Chun, Byeongho Heo, Wonjae Kim

We need billion-scale images to achieve more generalizable and ground-breaking vision models, as well as massive dataset storage to ship the images (e.g., the LAION-4B dataset needs 240TB storage space). However, it has become challenging to deal with unlimited dataset storage with limited storage infrastructure. A number of storage-efficient training method

Francesco Malaspina

We generalize the results by Eisenbud and Schreyer about Ulrich bundles over Veronese varieties to Segre-Veronese varieties. We discuss the range where we have natural cohomology and we construct multigraded resolutions and monads for Ulrich bundles of any rank. Moreover we give cohomological characterizations for significant families of bundles.

Jürg Fröhlich, Zhou Gang, Alessandro Pizzo

A proposal of how to complete non-relativistic quantum mechanics to a physically meaningful, mathematically precise and logically coherent theory is reviewed. Our proposal leads to a general, non-linear stochastic law for the time-evolution of states of individual physical systems. An application of the general formalism to the quantum theory of fluorescence

Sebastian Kreutzer, Christian Iwainsky, Marta Garcia-Gasulla, Victor Lopez

Automated code instrumentation, i.e. the insertion of measurement hooks into a target application by the compiler, is an established technique for collecting reliable, fine-grained performance data. The set of functions to instrument has to be selected with care, as instrumenting every available function typically yields too large a runtime overhead, thus sk

Yonadav Shavit

As advanced machine learning systems' capabilities begin to play a significant role in geopolitics and societal order, it may become imperative that (1) governments be able to enforce rules on the development of advanced ML systems within their borders, and (2) countries be able to verify each other's compliance with potential future international agreements

Tuo Wan, Kai Zhang, Junkai Li, Zhesen Yang

The non-Hermitian skin effect is a distinctive phenomenon in non-Hermitian systems, which manifests as the anomalous localization of bulk states at the boundary. To understand the physical origin of the non-Hermitian skin effect, a bulk band characterization based on the dynamical degeneracy on an equal frequency contour is proposed, which reflects the stron

Xing Cui, Zekun Li, Peipei Li, Yibo Hu

This paper explores interactive facial image editing via dialogue and introduces the ChatEdit benchmark dataset for evaluating image editing and conversation abilities in this context. ChatEdit is constructed from the CelebA-HQ dataset, incorporating annotated multi-turn dialogues corresponding to user edit requests on the images. The dataset is challenging,

Zhao-Yi Yan, Zhan Hou, Kan-Hao Xue, Tian Lu

Two-dimensional material-based field effect transistors (2DM-FETs) are playing a revolutionary role in electronic devices. However, after years of development, no device model can match the Pao-Sah model for standard silicon-based transistors in terms of physical accuracy and computational efficiency to support large-scale integrated circuit design. One rema

Dominic Enders, André Schemaitat, Aaron Tikuisis

An error in the original paper is identified and corrected. The C*-algebras with approximately inner flip, which satisfy the UCT, are identified (and turn out to be fewer than what is claimed in the original paper). The action of the flip map on K-theory turns out to be more subtle, involving a minus sign in certain components. To this end, we introduce new

Johannes Henn, Elizabeth Pratt, Anna-Laura Sattelberger, Simone Zoia

Feynman integrals are solutions to linear partial differential equations with polynomial coefficients. Using a triangle integral with general exponents as a case in point, we compare $D$-module methods to dedicated methods developed for solving differential equations appearing in the context of Feynman integrals, and provide a dictionary of the relevant conc

Shahram Jalalzadeh

The Newtonian dynamics of particles in brane gravity is investigated. Due to the coupling of the particles' energy-momentum tensor to the tension of the brane, the particle is semi-confined and oscillates along the extra dimension. We demonstrate that the frequency of these oscillations is proportional to the kinetic energy of the particle in the brane. We s

Jakob Hoydis, Fayçal Aït Aoudia, Sebastian Cammerer, Merlin Nimier-David

Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow. Since release v0.14 it integrates a differentiable ray tracer (RT) for the simulation of radio wave propagation. This unique feature allows for the computation of gradients of the channel impulse response and other related quantities with respect to many system an

Vladimir Dvorkin, Audun Botterud

While power systems research relies on the availability of real-world network datasets, data owners (e.g., system operators) are hesitant to share data due to security and privacy risks. To control these risks, we develop privacy-preserving algorithms for the synthetic generation of optimization and machine learning datasets. Taking a real-world dataset as i

Musard Balliu, Benoit Baudry, Sofia Bobadilla, Mathias Ekstedt

Software bills of materials (SBOM) promise to become the backbone of software supply chain hardening. We deep-dive into 6 tools and the accuracy of the SBOMs they produce for complex open-source Java projects. Our novel insights reveal some hard challenges for the accurate production and usage of SBOMs.

Sungnyun Kim, Sangmin Bae, Se-Young Yun

Deep learning in general domains has constantly been extended to domain-specific tasks requiring the recognition of fine-grained characteristics. However, real-world applications for fine-grained tasks suffer from two challenges: a high reliance on expert knowledge for annotation and necessity of a versatile model for various downstream tasks in a specific d

Furong Duan, Tao Zhu, Jinqiang Wang, Liming Chen

Sensor-based human activity segmentation and recognition are two important and challenging problems in many real-world applications and they have drawn increasing attention from the deep learning community in recent years. Most of the existing deep learning works were designed based on pre-segmented sensor streams and they have treated activity segmentation

Thomas Wahl, Michel Duprez, Axel Hutt

Mental disorders may exhibit pathological brain rhythms and neurostimulation promises to alleviate of patients' symptoms by modifying these rhythms. Today, most neurostimulation schemes are open-loop, i.e. administer experimental stimulation protocols independent of the patients brain activity which may yield a sub-optimal treatment. We propose a closed-loop

Roy Miles, Krystian Mikolajczyk

In this paper we revisit the efficacy of knowledge distillation as a function matching and metric learning problem. In doing so we verify three important design decisions, namely the normalisation, soft maximum function, and projection layers as key ingredients. We theoretically show that the projector implicitly encodes information on past examples, enablin

David Meister, Frank Aurzada, Mikhail A. Lifshits, Frank Allgöwer

Event-triggered control has shown the potential for providing improved control performance at the same average sampling rate when compared to time-triggered control. While this observation motivates numerous event-triggered control schemes, proving it from a theoretical perspective has only been achieved for a limited number of settings. Inspired by existing

Yi Song, Tianyu Yang, Mahdi Barzegar Khalilsarai, Giuseppe Caire

We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multi-user sum-rate, by producing a pre-beamforming matrix based on user channel covariances that maps the original channel vectors to effective channel