April 2023 arXiv papers — page 27

Wenhao Lu, Xufeng Zhao, Sven Magg, Martin Gromniak

Explaining the behaviour of intelligent agents learned by reinforcement learning (RL) to humans is challenging yet crucial due to their incomprehensible proprioceptive states, variational intermediate goals, and resultant unpredictability. Moreover, one-step explanations for RL agents can be ambiguous as they fail to account for the agent's future behaviour

Roberto Verdel, Guo-Yi Zhu, Markus Heyl

The fission of a string connecting two charges is an astounding phenomenon in confining gauge theories. The dynamics of this process have been studied intensively in recent years, with plenty of numerical results yielding a dichotomy: the confining string can decay relatively fast or persist up to extremely long times. Here, we put forward a dynamical locali

Lena M. Hansen, Lorenzo Carosini, Lennart Jehle, Francesco Giorgino

Temporal-to-spatial demultiplexing routes non-simultaneous events of the same spatial mode to distinct output trajectories. This technique has now been widely adopted because it gives access to higher-number multi-photon states when exploiting solid-state quantum emitters. However, implementations so far have required an always-increasing number of active el

Brian DuSell

Human language is full of compositional syntactic structures, and although neural networks have contributed to groundbreaking improvements in computer systems that process language, widely-used neural network architectures still exhibit limitations in their ability to process syntax. To address this issue, prior work has proposed adding stack data structures

Lauren Bandklayder, Julia E. Bergner, Rhiannon Griffiths, Brenda Johnson

Model structures for many different kinds of functor calculus can be obtained by applying a theorem of Bousfield to a suitable category of functors. In this paper, we give a general criterion for when model categories obtained via this approach are cofibrantly generated. Our examples recover the homotopy functor and $n$-excisive model structures of Biederman

Jiadong Liang, Yuze Han, Xiang Li, Zhihua Zhang

In this paper we consider linearly constrained optimization problems and propose a loopless projection stochastic approximation (LPSA) algorithm. It performs the projection with probability $p_n$ at the $n$-th iteration to ensure feasibility. Considering a specific family of the probability $p_n$ and step size $\eta_n$, we analyze our algorithm from an asymp

Fernando Iglesias-Suarez, Pierre Gentine, Breixo Solino-Fernandez, Tom Beucler

Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid-scale processes, particularly clouds and convection. Deep learning can learn these subgrid-scale processes from computationally expensive storm-resolving m

Steffen van Bergerem, Martin Grohe, Sandra Kiefer, Luca Oeljeklaus

The fixed-point logic LREC= was developed by Grohe et al. (CSL 2011) in the quest for a logic to capture all problems decidable in logarithmic space. It extends FO+C, first-order logic with counting, by an operator that formalises a limited form of recursion. We show that for every LREC=-definable property on relational structures, there is a constant k such

Florian Frank, Stefan Milius, Henning Urbat

Positive data languages are languages over an infinite alphabet closed under possibly non-injective renamings of data values. Informally, they model properties of data words expressible by assertions about equality, but not inequality, of data values occurring in the word. We investigate the class of positive data languages recognizable by nondeterministic o

Yue Song, T. Anderson Keller, Nicu Sebe, Max Welling

Despite the significant recent progress in deep generative models, the underlying structure of their latent spaces is still poorly understood, thereby making the task of performing semantically meaningful latent traversals an open research challenge. Most prior work has aimed to solve this challenge by modeling latent structures linearly, and finding corresp

Jérôme Bétrisey, Patrick Eggenberger, Gaël Buldgen, Othman Benomar

Asteroseismology has become a powerful tool to study the internal rotation of stars, and its study allows to constrain the internal AM transport processes and better understand their physical nature. In this context, we compared the rotation rates predicted by asteroseismology and by starspots measurements for four main-sequence stars from the Kepler LEGACY

S. Drew Chojnowski, Swetlana Hubrig, David L. Nidever, Ewa Niemczura

Despite the universe containing primordial thorium (Th) of sufficient abundance to appear in stellar spectra, detection of Th has to date been tentative and based on just a few weak and blended lines. Here, we present convincing evidence not only for the first Th detection in a magnetic chemically peculiar Ap star but also for the first detection of Th III i

David Swinarski

Mukai showed that the GIT quotient $\operatorname{Gr}(7,16) /\!/ \operatorname{Spin}(10)$ is a birational model of the moduli space of Deligne-Mumford stable genus 7 curves $\overline{M}_7$. The key observation is that a general smooth genus 7 curve can be realized as the intersection of the orthogonal Grassmannian $\operatorname{OG}(5,10)$ in $\mathbb{P}^{1

Mohamad Mestoukirdi, Matteo Zecchin, David Gesbert, Qianrui Li

Statistical heterogeneity across clients in a Federated Learning (FL) system increases the algorithm convergence time and reduces the generalization performance, resulting in a large communication overhead in return for a poor model. To tackle the above problems without violating the privacy constraints that FL imposes, personalized FL methods have to couple

Guozhen Hu, Zhengyi Shao, Lu Li

We investigate the angular momentum of mono-abundance populations (MAPs) of the Milky Way thick disk by using a sample of 26,076 giant stars taken from APOGEE DR17 and Gaia EDR3. The vertical and perpendicular angular momentum components, $L_Z$ and $L_P$, of MAPs in narrow bins have significant variations across the [$\alpha$/M]-[M/H] plane. $L_Z$ and $L_P$

A. Yamakage, T. Sato, R. Okuyama, T. Funato

We construct a general theoretical framework for describing curvature-induced spin-orbit interactions on the basis of group theory. Our theory can systematically determine the emergence of spin splitting in the band structure according to symmetry in the wavenumber space and the bending direction of the material. As illustrative examples, we derive the curva

Laureano González-Vega, J. Rafael Sendra, Juana Sendra

In this paper, using Descartes law of sign, we provide elementary proof of results on the number of real eigenvalues of real matrices of which certain properties on the signs of their principal minors are known. More precisely, we analyze P, N, Q, R, PN and $QR$ matrices as well as their variants "almost", "weak" and "sub-zero" matrices.

Humberto da Silva, Qian Yao, Masato Morita, Brian K. Kendrick

The calcium monofluoride (CaF) molecule has emerged as a promising candidate for precision measurements, quantum simulation, and ultracold chemistry experiments. Inelastic and reactive collisions of laser cooled CaF molecules in optical tweezers have recently been reported and collisions of cold Li atoms with CaF are of current experimental interest. In this

Deepak Gupta, Sabine H. L. Klapp, David A. Sivak

Designing a protocol to efficiently drive a stochastic system is an active field of research. Here we extend such control theory to an active Ornstein-Uhlenbeck particle (AOUP) in a bistable potential, driven by a harmonic trap. We find that protocols designed to minimize the excess work (up to linear-response) perform better than naive protocols with consta

Gui-Qiang G. Chen, Jie Kuang, Wei Xiang, Yongqian Zhang

We establish the optimal convergence rate to the hypersonic similarity law, which is also called the Mach number independence principle, for steady compressible full Euler flows over two-dimensional slender Lipschitz wedges. The problem can be formulated as the comparison of the entropy solutions in $BV\cap L^{1}$ between the two initial-boundary value probl

Md Hanif Ali, Adityanarayan Pandey, Rowtu Srinu, Paritosh Meihar

Ferroelectricity in sputtered undoped-HfO$_2$ is attractive for composition control for low power and non-volatile memory and logic applications. Unlike doped HfO$_2$, evolution of ferroelectricity with annealing and film thickness effect in sputter deposited undoped HfO$_2$ on Si is not yet reported. In present study, we have demonstrated the impact of post

Frederic Rapp, Marco Roth

Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on parameterized quantum circuits. By employing a hardware-efficient feature map and careful regularization of the Gram matrix, we demonstrate that the variance information of the resulting q

M. Thakor, K. H. Seh, S. R. Gladson, M. L. Fernandez

This study numerically examines the influences of transverse annulation around a cone surface on the characteristics of a flow over an orthocone. This work is inspired by Spyroceras, a fossilized genus of the nautiloid family during the Paleozoic era, whose method of locomotion is understudied. As a baseline case, a flow over a smooth orthoconic model with a

Philipp Rieger, Markus Weißenhofer, Ulrich Nowak

Defects, i.e. inhomogeneities of the underlying lattice, are ubiquitous in magnetic materials and can have a crucial impact on their applicability in spintronic devices. For magnetic skyrmions, localized and topologically non-trivial spin textures, they give rise to a spatially inhomogeneous energy landscape and can lead to pinning, resulting in an exponenti

Jingquan Luo, Qisheng Wang, Lvzhou Li

We explore potential quantum speedups for the fundamental problem of testing the properties of closeness and $k$-wise uniformity of probability distributions. Closeness testing is the problem of distinguishing whether two $n$-dimensional distributions are identical or at least $\varepsilon$-far in $\ell^1$- or $\ell^2$-distance. We show that the quantum quer

Igor Grzelec, Monika Pilśniak, Mariusz Woźniak

Let $C_{n_1}\cup C_{n_2}\cup \ldots \cup C_{n_k}$ be a 2-factor i.e. a vertex-disjoint union of cycles. In this note we completely characterize those 2-factors that are uniquely embeddeble in their complement.

Yongming Luo

We consider the Cauchy problem for the defocusing cubic nonlinear Schr\"odinger equation (NLS) on the waveguide manifold $\mathbb{R}^3\times\mathbb{T}$ and establish almost sure scattering for random initial data, where no symmetry conditions are imposed and the result is available for arbitrarily rough data $f\in H^s$ with $s\in\mathbb{R}$. The main new ing

L. Cavallo, G. Cescutti, F. Matteucci

Context. The origin of the large star-to-star variation of the [Eu/Fe] ratios observed in the extremely metal-poor (at [Fe/H]$\leq-3$) stars of the Galactic halo is still a matter of debate.\\ Aims. In this paper, we explore this problem by putting our stochastic chemical evolution model in the hierarchical clustering framework, with the aim of explaining th

Dong Wang, Wen-Xi Huang, Bo Zhou, Wenduo Yu

The eigenvalue of a non-Hermitian Hamiltonian often forms a self-intersecting Riemann surface, leading to a unique mode conversion phenomenon when the Hamiltonian evolves along certain loop paths around an exceptional point (EP). However, two fundamental problems exist with the conventional scheme of EP encircling: the speed of mode conversion is restricted

Evangelos Afxonidis, Alessio Caddeo, Carlos Hoyos, Daniele Musso

We introduce a family of quantum field theories for fields carrying monopole and dipole charges. In contrast to previous realizations, fields have quadratic two-derivative kinetic terms. The dipole symmetry algebra is realized in a discretized internal space and connected to the physical space through a background gauge field. We study spontaneous symmetry b

Lea Boßmann, Nikolai Leopold, David Mitrouskas, Sören Petrat

We consider a gas of N bosons with interactions in the mean-field scaling regime. We review a recent proof of the asymptotic expansion of its spectrum and eigenstates and two applications of this result, namely the derivation of an Edgeworth expansion for fluctuations of one-body operators and the computation of the binding energy of an inhomogeneous Bose ga

Eugenia Boffo

Through their respective sigma models, a bosonic string and a superstring can be coupled to (super)gravity fields. These are subsequently forced to satisfy their right classical equation of motions, as a consequence of quantization of the string. There are indications that particle models with extended supersymmetry can replicate this behavior. The bosonic s

Ricard Bordalba, Tobias Schoels, Lluís Ros, Josep M. Porta

Direct collocation methods are powerful tools to solve trajectory optimization problems in robotics. While their resulting trajectories tend to be dynamically accurate, they may also present large kinematic errors in the case of constrained mechanical systems, i.e., those whose state coordinates are subject to holonomic or nonholonomic constraints, like loop

Shao-Ping Li, Xun-Jie Xu

Light weakly interacting particles could be copiously produced in the Sun which, as a well-understood star, could provide severe constraints on such new physics. In this work, we calculate the solar production rates of light gauge bosons (e.g. dark photon) arising from various $U(1)$ extensions of the standard model. It is known that the dark photon producti

Rossen Nenov, Dang-Khoa Nguyen, Peter Balazs

The phase retrieval problem is found in various areas of applications of engineering and applied physics. It is also a very active field of research in mathematics, signal processing and machine learning. In this paper, we present an accelerated version of the well known Fast Griffin-Lim algorithm (FGLA) for the phase retrieval problem in a general setting.

Connor Mattson, Daniel S. Brown

Robot swarms often exhibit emergent behaviors that are fascinating to observe; however, it is often difficult to predict what swarm behaviors can emerge under a given set of agent capabilities. We seek to efficiently leverage human input to automatically discover a taxonomy of collective behaviors that can emerge from a particular multi-agent system, without

Fahad Al Basir, Jahangir Chowdhury, Delfim F. M. Torres

We formulate an integrated pest management model to control natural pests of the crop through the periodic application of biopesticide and chemical pesticides. In a theoretical analysis of the system pest eradication, a periodic solution is found and established. All the system variables are proved to be bounded. Our main goal is then to ensure that pesticid

Carlos Lassance, Stéphane Clinchant

The MS MARCO-passage dataset has been the main large-scale dataset open to the IR community and it has fostered successfully the development of novel neural retrieval models over the years. But, it turns out that two different corpora of MS MARCO are used in the literature, the official one and a second one where passages were augmented with titles, mostly d

Mary Sánchez-Gordón, Ricardo Colomo-Palacios, Muhammad Azeem Akbar, Monica Kristiansen Holone

Context: Most software companies strive to have high-performing teams and mitigate withdrawal behaviors like being present but unproductive. In this context, psychological safety and developers perceived impact are suggested as potential drivers of voice and silence behaviors. However, understanding these social aspects of software development entails the in

Shiksha Singhal, Veeraruna Kavitha, Jayakrishnan Nair

We analyse a coalition formation game between strategic service providers of a congestible service. The key novelty of our formulation is that it is a constant sum game, i.e., the total payoff across all service providers (or coalitions of providers) is fixed, and dictated by the size of the market. The game thus captures the tension between resource pooling

Thomas P. Baumeler, Essa A. Alharbi, George Kakavelakis, George C. Fish

Metal halide perovskites (MHPs) have shown an incredible rise in efficiency, reaching as high as 25.7%, which now competes with traditional photovoltaic technologies. Herein, we excluded CsX and RbX, the most commonly used cations to stabilize FAPbI3, from the bulk of perovskite thin films and applied them on the surface, as passivation agents. Extensive dev

Christopher Battarbee, Delaram Kahrobaei, Ludovic Perret, Siamak F. Shahandashti

In this paper, we present a new diverse class of post-quantum group-based Digital Signature Schemes (DSS). The approach is significantly different from previous examples of group-based digital signatures and adopts the framework of group action-based cryptography: we show that each finite group defines a group action relative to the semidirect product of the

Michael Gutperle, Nicholas Klein, Dikshant Rathore

We consider the uplift of co-dimension two defect solutions of seven dimensional gauged supergravity to eleven dimensions, previously found by two of the authors. The uplifted solutions are expressed as Lin-Lunin-Maldacena solutions and an infinite family of regular solutions describing holographic defects is found using the electrostatic formulation of LLM

Wei Nie, Tao Shi, Yu-xi Liu, Franco Nori

Optical mirrors determine cavity properties by means of light reflection. Imperfect reflection gives rise to open cavities with photon loss. We study an open cavity made of atom-dimer mirrors with a tunable reflection spectrum. We find that the atomic cavity shows anti-$\mathcal{PT}$ symmetry. The anti-$\mathcal{PT}$ phase transition controlled by atomic cou

Dimitrios Tsagkarogiannis

We review some recent progress on applications of Cluster Expansions. We focus on a system of classical particles living in a continuous medium and interacting via a stable and tempered pair potential. We review the cluster expansion in both the canonical and the grand canonical ensemble and compute thermodynamic quantities such as the pressure, the free ene

Mihai Babiac, Karolis Martinkus, Roger Wattenhofer

We provide a novel approach to construct generative models for graphs. Instead of using the traditional probabilistic models or deep generative models, we propose to instead find an algorithm that generates the data. We achieve this using evolutionary search and a powerful fitness function, implemented by a randomly initialized graph neural network. This bri

L. Bartolomeo Koninckx, M. A. De Vito, O. G. Benvenuto

V404 Cyg is a Low Mass X-Ray Binary (LMXB) system that has undergone outbursts in 1938, 1989, and 2015. During these events, it has been possible to determine relevant data of the system; such as the masses of the compact object (a black hole, BH) and its companion, the orbital period, the companion spectral type, and luminosity class, among others. Remarkab

Ruiwen Dong

We consider semigroup algorithmic problems in finitely generated metabelian groups. Our paper focuses on three decision problems introduced by Choffrut and Karhum\"{a}ki (2005): the Identity Problem (does a semigroup contain a neutral element?), the Group Problem (is a semigroup a group?) and the Inverse Problem (does a semigroup contain the inverse of a gen

S. Heinemeyer, F. von der Pahlen

High-precision predictions in BSM models require calculations at the loop-level and thus a renormalization of (some of) the BSM parameter. Here many choices for the renormalization scheme (RS) are possible. A given RS can be well suited to yield ``stable'' and ``well behaved'' higher-order corrections in one part of the BSM parameter space, but can fail comp

Jussi Leinonen, Ulrich Hamann, Daniele Nerini, Urs Germann

Diffusion models have been widely adopted in image generation, producing higher-quality and more diverse samples than generative adversarial networks (GANs). We introduce a latent diffusion model (LDM) for precipitation nowcasting - short-term forecasting based on the latest observational data. The LDM is more stable and requires less computation to train th

Muhammad Shafique, Sizhuo Liu, Philip Schniter, Rizwan Ahmad

Objective: Acquiring fully sampled training data is challenging for many MRI applications. We present a self-supervised image reconstruction method, termed ReSiDe, capable of recovering images solely from undersampled data. Materials and Methods: ReSiDe is inspired by plug-and-play (PnP) methods, but unlike traditional PnP approaches that utilize pre-trained

Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammad Saidur Rahman, Mohammed Atiquzzaman

This paper proposes a blockchain-based Federated Learning (FL) framework with Intel Software Guard Extension (SGX)-based Trusted Execution Environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model generated from the tampered local models can be e

Qiang Liu, Junfei Wu, Shu Wu, Liang Wang

Evidence-aware fake news detection aims to conduct reasoning between news and evidence, which is retrieved based on news content, to find uniformity or inconsistency. However, we find evidence-aware detection models suffer from biases, i.e., spurious correlations between news/evidence contents and true/fake news labels, and are hard to be generalized to Out-

Simran Kumari, Ashish R. Hota, Siddhartha Mukhopadhyay

In this paper, we simultaneously address the problems of energy optimal and safe motion planning of electric vehicles (EVs) in a data-driven robust optimization framework. Safe maneuvers, especially in urban traffic, are characterized by frequent lateral motions, such as lane changes, overtakes and turning along curved roads. Motivated by our previous work w

Fanghui Liu, Luca Viano, Volkan Cevher

In online reinforcement learning (RL), instead of employing standard structural assumptions on Markov decision processes (MDPs), using a certain coverage condition (original from offline RL) is enough to ensure sample-efficient guarantees (Xie et al. 2023). In this work, we focus on this new direction by digging more possible and general coverage conditions,

ATLAS Collaboration

A search is presented for displaced production of Higgs bosons or $Z$ bosons, originating from the decay of a neutral long-lived particle (LLP) and reconstructed in the decay modes $H\rightarrow \gamma\gamma$ and $Z\rightarrow ee$. The analysis uses the full Run 2 data set of proton$-$proton collisions delivered by the LHC at an energy of $\sqrt{s}=13$ TeV b

Tomás Alvim, Margarida M. Telo da Gama, Mykola Tasinkevych

Liquid crystal skyrmions are topologically protected spatially localized distortions of the director field which are fascinating from both fundamental mathematics and applied physics points of view. Skyrmions are realized in experimental setups which are identical to those used in display technology. This opens exciting opportunities for designing advanced e

Abolfazl Mohajer

In this paper, we describe Galois covers of algebraic curves and their families by using local systems associated to push-forward of sheaves by the structure morphism. More precisely, if $f:C\to Y$, we consider the sheaves $f_*(\C)$. The group action by the Galois group $G$, yields a decomposition of this sheaf into irreducible local systems corresponding to

B. Das, V. Petit, Y. Nazé, M. F. Corcoran

We report detailed X-ray observations of the unique binary system $\epsilon$ Lupi, the only known short-period binary consisting of two magnetic early-type stars. The components have comparably strong, but anti-aligned magnetic fields. The orbital and magnetic properties of the system imply that the magnetospheres overlap at all orbital phases, suggesting th

Mutsumi Kimura, Takuma Akimoto

Local diffusivity of a protein depends crucially on the conformation, and the conformational fluctuations are often non-Markovian. Here, we investigate the Langevin equation with non-Markovian fluctuating diffusivity, where the fluctuating diffusivity is modeled by a generalized Langevin equation under a double-well potential. We find that non-Markovian fluc

Christian Tantardini, Hayk A. Zakaryan, Zhong-Kang Han, Tariq Altalhi

Hardness is a materials' property with implications in several industrial fields, including oil and gas, manufacturing, and others. However, the relationship between this macroscale property and atomic (i.e., microscale) properties is unknown and in the last decade several models have unsuccessfully tried to correlate them in a wide range of chemical space.

Roeland ter Hoeven, Anette Messinger, Wolfgang Lechner

We present tools and methods to generalize parity compilation to digital quantum computing devices with arbitrary connectivity graphs and construct circuit implementations for the constraint Hamiltonian of higher-order constrained binary optimization problems. In particular, we show how even non-local constraints can be efficiently implemented without expens

Rebecca Cheng, Mengjie Yu, Amirhassan Shams-Ansari, Yaowen Hu

Resonator-based optical frequency comb generation is an enabling technology for a myriad of applications ranging from communications to precision spectroscopy. These frequency combs can be generated in nonlinear resonators driven using either continuous-wave (CW) light, which requires alignment of the pump frequency with the cavity resonance, or pulsed light

Georgios Tzannetos, Bárbara Gomes Ribeiro, Parameswaran Kamalaruban, Adish Singla

We consider the problem of curriculum design for reinforcement learning (RL) agents in contextual multi-task settings. Existing techniques on automatic curriculum design typically require domain-specific hyperparameter tuning or have limited theoretical underpinnings. To tackle these limitations, we design our curriculum strategy, ProCuRL, inspired by the pe

Mathieu Dumont, Kevin Hector, Pierre-Alain Moellic, Jean-Max Dutertre

Upcoming certification actions related to the security of machine learning (ML) based systems raise major evaluation challenges that are amplified by the large-scale deployment of models in many hardware platforms. Until recently, most of research works focused on API-based attacks that consider a ML model as a pure algorithmic abstraction. However, new impl

Chao Li, Junhua Zeng, Chunmei Li, Cesar Caiafa

Tensor network (TN) is a powerful framework in machine learning, but selecting a good TN model, known as TN structure search (TN-SS), is a challenging and computationally intensive task. The recent approach TNLS~\cite{li2022permutation} showed promising results for this task, however, its computational efficiency is still unaffordable, requiring too many eva

Youngju Park, Yeonju Kim, Bheema Lingam Chittari, Jeil Jung

We show that rhombohedral four-layer graphene (4LG) nearly aligned with a hexagonal boron nitride (hBN) substrate often develops nearly flat isolated low energy bands with non-zero valley Chern numbers. The bandwidths of the isolated flatbands are controllable through an electric field and twist angle, becoming as narrow as $\sim10~$meV for interlayer potent

Ulrich Faigle

Bell's inequality plays an important role with respect to the Einsteinian question about the physical reality of quantum theory. While Bell's inequality is usually viewed within the geometric framework of a Hilbert space quantum model, the present note extends the theory of Heisenberg measurements to quantum systems with representations in general orthogonal

Arthur Braida, Simon Martiel, Ioan Todinca

This paper explores the phenomenon of avoided level crossings in quantum annealing, a promising framework for quantum computing that may provide a quantum advantage for certain tasks. Quantum annealing involves letting a quantum system evolve according to the Schr\"odinger equation, with the goal of obtaining the optimal solution to an optimization problem t

Manuel Bodirsky, Simon Knäuer

We show that the problem of deciding for a given finite relation algebra A whether the network satisfaction problem for A can be solved by the k-consistency procedure, for some natural number k, is undecidable. For the important class of finite relation algebras A with a normal representation, however, the decidability of this problem remains open. We show t

Ilias Magoulas, Francesco A. Evangelista

We have recently constructed compact, CNOT-efficient, quantum circuits for fermionic and qubit excitations of arbitrary many-body rank [I. Magoulas and F.A. Evangelista, J. Chem. Theory Comput. 19, 822 (2023)]. Here, we present approximations to these circuits that substantially reduce the CNOT counts even further. Our preliminary numerical data, using the s

Gabriel Navarro, Lucas Ruhstorfer, Pham Huu Tiep, Carolina Vallejo

We determine what are the fields of values of the irreducible $p$-height zero characters of all finite groups for $p=2$; we conjecture what they should be for odd primes, and reduce this statement to a problem on blocks of quasi-simple groups.

Mansur Ziiatdinov, Aliya Khadieva, Abuzer Yakaryılmaz

Quantum fingerprinting is a technique that maps classical input word to a quantum state. The obtained quantum state is much shorter than the original word, and its processing uses less resources, making it useful in quantum algorithms, communication, and cryptography. One of the examples of quantum fingerprinting is quantum automata algorithms for MOD_p lang

ATLAS Collaboration

Searches for long-lived particles (LLPs) are among the most promising avenues for discovering physics beyond the Standard Model at the Large Hadron Collider (LHC). However, displaced signatures are notoriously difficult to identify due to their ability to evade standard object reconstruction strategies. In particular, the default ATLAS track reconstruction a

Yang Li, Wei Wang, Ming Wang, Chunmeng Dou

Deep learning needs high-precision handling of forwarding signals, backpropagating errors, and updating weights. This is inherently required by the learning algorithm since the gradient descent learning rule relies on the chain product of partial derivatives. However, it is challenging to implement deep learning in hardware systems that use noisy analog memr

Álvaro Corral, Mónica Minjares, Marcelo Barreiro

The Madden-Julian oscillation (MJO) is a tropical weather system having important influence in the tropics and beyond; however, many of its characteristics are poorly understood, including their initiation and completion. Here we define Madden-Julian events as the contiguous time periods with an active MJO, and we show that both the durations and the sizes o

Galien Grosjean, Scott Waitukaitis

Nominally identical materials exchange net electric charge during contact through a mechanism that is still debated. `Mosaic models', in which surfaces are presumed to consist of a random patchwork of microscopic donor/acceptor sites, offer an appealing explanation for this phenomenon. However, recent experiments have shown that global differences persist ev

Yi Huang, Dmitrii Torbunov, Brett Viren, Haiwang Yu

Deep learning algorithms often are trained and deployed on different datasets. Any systematic difference between the training and a test dataset may degrade the algorithm performance--what is known as the domain shift problem. This issue is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets.

Amin Ghadesi, Maxime Lamothe, Heng Li

Machine learning (ML), including deep learning, has recently gained tremendous popularity in a wide range of applications. However, like traditional software, ML applications are not immune to the bugs that result from programming errors. Explicit programming errors usually manifest through error messages and stack traces. These stack traces describe the cha

Tariq M. Khan, Syed S. Naqvi, Antonio Robles-Kelly, Imran Razzak

Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progression and diagnosis of such vision-threatening diseases. To this end, we propose a Multi-resolution Contextual Network (MRC-Net) that addresses these issues by extracting multi-sc

Konstantin Lion, Caterina Cocchi, Claudia Draxl

We present an x-ray absorption spectroscopy study from the carbon $K$, sulfur $K$, and sulfur $L_{2,3}$ edges of crystalline oligothiophenes of varying length, i.e., bithiophene (2T), quaterthiophene (4T), and sexithiophene (6T), performed from first principles by means of all-electron density-functional theory and many-body perturbation theory. A comprehens

Ying Shi, Yusong Gan, Yuzhong Chen, Yubin Wang

Spin or valley degrees of freedom in condensed matter have been proposed as efficient information carriers towards next generation spintronics. It is therefore crucial to develop effective strategies to generate and control spin or valley-locked spin currents, e.g., by exploiting the spin Hall or valley Hall effects. However, the scattering, and rapid dephas

Jamila Alsayed Kassem, Li Zhong, Arie Taal, Paola Grosso

Digital Twin (DT) is a prominent technology to utilise and deploy within the healthcare sector. Yet, the main challenges facing such applications are: Strict health data-sharing policies, high-performance network requirements, and possible infrastructure resource limitations. In this paper, we address all the challenges by provisioning adaptive Virtual Netwo

Christian Herglotz, Hannah Och, Anna Meyer, Geetha Ramasubbu

In this paper, we provide an in-depth assessment on the Bj{\o}ntegaard Delta. We construct a large data set of video compression performance comparisons using a diverse set of metrics including PSNR, VMAF, bitrate, and processing energies. These metrics are evaluated for visual data types such as classic perspective video, 360$^\circ$ video, point clouds, an

Afzal Badshah, Anwar Ghani, Ali Daud, Ateeqa Jalal

IoT is a fundamental enabling technology for creating smart spaces, which can assist the effective face-to-face and online education systems. The transition to smart education (integrating IoT and AI into the education system) is appealing, which has a concrete impact on learners' engagement, motivation, attendance, and deep learning. Traditional education f

Yong Liu, Jun Wang, Kun Wang, Wen Yang

The focus of our paper is to investigate the possibility of a minimizer for the Thomas-Fermi-Dirac-von Weizs\"{a}cker model on the lattice graph $\mathbb{Z}^{3}$. The model is described by the following functional: \begin{equation*} E(\varphi)=\sum_{y\in\mathbb{Z}^{3}}\left(|\nabla\varphi(y)|^2+ (\varphi(y))^{\frac{10}{3}}-(\varphi(y))^{\frac{8}{3}}\right)+

Kyuhong Shim, Jiyoung Kim, Gusang Lee, Byonghyo Shim

Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image. To overcome the limitation, deep neural networks rely on various visual hints such as size, shade, and texture extracted from RGB information. However, we observe that if such hints are overly exploited, the network can be biased on RGB infor

Unė G. Būtaitė, Christina Sharp, Michael Horodynski, Graham M. Gibson

Optical tweezers enable non-contact trapping of micro-scale objects using light. Despite their widespread use, it is currently not known how tightly it is possible to three-dimensionally trap micro-particles with a given photon budget. Reaching this elusive limit would enable maximally-stiff particle trapping for precision measurements on the nanoscale, and

Sana Sabah Al-Azzawi, György Kovács, Filip Nilsson, Tosin Adewumi

In this paper, we propose a methodology for task 10 of SemEval23, focusing on detecting and classifying online sexism in social media posts. The task is tackling a serious issue, as detecting harmful content on social media platforms is crucial for mitigating the harm of these posts on users. Our solution for this task is based on an ensemble of fine-tuned t

Amit Chakraborty, Amandip De, Rohini M. Godbole, Monoranjan Guchait

Boosted top quark tagging is one of the challenging, and at the same time exciting, tasks in high energy physics experiments, in particular in the exploration of new physics signals at the LHC. Several techniques have already been developed to tag a boosted top quark in its hadronic decay channel. Recently tagging the same in the semi-leptonic channel has be

Héber H. Arcolezi, Karima Makhlouf, Catuscia Palamidessi

In recent years, Local Differential Privacy (LDP), a robust privacy-preserving methodology, has gained widespread adoption in real-world applications. With LDP, users can perturb their data on their devices before sending it out for analysis. However, as the collection of multiple sensitive information becomes more prevalent across various industries, collec

Gregor Anich, Niclas Höllrigl, Marian Kreyer, Rudolf Grimm

We report on the development of an advanced ultracold dysprosium apparatus, which incorporates a cold atom microscope (CAM) with a design resolution of a quarter micrometer. The CAM and the cooling and trapping regions are within the same vacuum glass vessel ensuring simple atom transport between them. We demonstrate the essential experimental steps of laser

Jorge Alcalde-Unzu, Marc Vorsatz

We establish that all strategy-proof social choice rules in strict preference domains follow necessarily a two-step procedure. In the first step, agents are asked to reveal some specific information about their preferences. Afterwards, a subrule that is dictatorial or strategy-proof of range 2 must be applied, and the selected subrule may differ depending on

Ming Li

Understanding the spin structure of hadrons in the small $x$ regime is an important direction to unravel the spin puzzle in hadronic physics. To include spin degrees of freedom in the small $x$ regime requires going beyond the usual eikonal approximation in high energy QCD. We developed an effective Hamiltonian approach to study spin related observables in t

Daniel B. Mayer, Dieter Braun, Thomas Franosch

We calculate the thermophoretic drift of a charged single colloidal particle with hydrodynamically slipping surface immersed in an electrolyte solution in response to a small temperature gradient. Here, we rely on a linearized hydrodynamic approach for the fluid flow and the motion of the electrolyte ions while keeping the full nonlinearity of the Poisson-Bo

Thomas Collins, Riccardo Di Clemente, Mario Gutiérrez-Roig, Federico Botta

Urban vibrancy is the dynamic activity of humans in urban locations. It can vary with urban features and the opportunities for human interactions, but it might also differ according to the underlying social conditions of city inhabitants across and within social surroundings. Such heterogeneity in how different demographic groups may experience cities has th

Mohammad N. Ivaki, Emanuel Milman

Employing a local version of the Brunn-Minkowski inequality, we give a new and simple proof of a result due to Andrews, Choi and Daskalopoulos that the origin-centred balls are the only closed, self-similar solutions of the Gauss curvature flow. Extensions to various non-linearities are obtained, assuming the centroid of the enclosed convex body is at the or

Adel Khalfallah, Mohamed Mhamdi

Suppose $\alpha,\beta \in \mathbb{R}\backslash \mathbb{Z}^-$ such that $\alpha+\beta>-1$ and $1\leq p \leq \infty$. Let $u=P_{\alpha,\beta}[f]$ be an $(\alpha,\beta)$-harmonic mapping on $\mathbb{D}$, the unit disc of $\mathbb{C}$, with the boundary $f$ being absolutely continuous and $\dot{f}\in L^p(0,2\pi)$, where $\dot{f}(e^{i\theta}):=\frac{d}{d\theta}f(

Cornelius Grunwald, Gudrun Hiller, Kevin Kröninger, Lara Nollen

We perform a global analysis of Beauty, Top, $Z$ and Drell-Yan measurements in the framework of the Standard Model effective theory (SMEFT). We work within the minimal flavor violation (MFV) hypothesis, which relates different sectors and generations beyond the $SU(2)_L$-link between left-handed top and beauty quarks. We find that the constraints on the SMEF

Jan-Christoph Klie, Ji-Ung Lee, Kevin Stowe, Gözde Gül Şahin

Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is difficult, which is why annotation tasks are often outsourced to paid crowdworkers. Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context