April 2023 arXiv papers — page 102

Gopal Chandra Pal, Siddharth SS, Manish Agarwal, Chander Shekhar Sharma

Sessile droplets coalescing on superhydrophobic substrates spontaneously jump from the surface. In this process, the excess surface energy available at the initiation of coalescence overcomes the minimal surface adhesion and manifests as sufficient kinetic energy to propel the droplets away from the substrate. Here, we show that the coalescence induced dropl

Nikolas A. Wittek, Mekhi Dhesi, Leor Barack, Harald P. Pfeiffer

Binary black hole simulations become increasingly more computationally expensive with smaller mass ratios, partly because of the longer evolution time, and partly because the lengthscale disparity dictates smaller time steps. The program initiated by Dhesi et al. (arXiv:2109.03531) explores a method for alleviating the scale disparity in simulations with mas

Aurore Boitrel

In this paper we study the classification of del Pezzo surfaces $X$ of degree $5$ over any perfect field $\mathbf{k}$ in explicit geometric terms. More precisely, in each case we use the Petersen graph to illustrate the $\operatorname{Gal}(\overline{\mathbf{k}}/\mathbf{k})$-action on the $(-1)$-curves of $X$ and we describe explicitly its group of automorphi

Christof Bless, Ildar Baimuratov, Oliver Karras

Scientific knowledge graphs have been proposed as a solution to structure the content of research publications in a machine-actionable way and enable more efficient, computer-assisted workflows for many research activities. Crowd-sourcing approaches are used frequently to build and maintain such scientific knowledge graphs. To contribute to scientific knowle

Stefan Riesenberger, Christian Krieg

While in the past decade there has been significant progress in open-source synthesis and verification tools and flows, one piece is still missing in the open-source design automation ecosystem: a tool to estimate the power consumption of a design on specific target technologies. We discuss a work-in-progress method to characterize target technologies using

Morakot Choetkiertikul, Apirak Hoonlor, Chaiyong Ragkhitwetsagul, Siripen Pongpaichet

Nowadays, numerous industries have exceptional demand for skills in data science, such as data analysis, data mining, and machine learning. The computational notebook (e.g., Jupyter Notebook) is a well-known data science tool adopted in practice. Kaggle and GitHub are two platforms where data science communities are used for knowledge-sharing, skill-practici

Arpita Chatterjee

We formulate the density matrices of a quantum state obtained by first adding multi-photons to and then subtracting multi-photons from any arbitrary state as well as performing the same process in the reverse order. Considering the field to be initially in a thermal (or in an even coherent) state, we evaluate the photon number distribution, Wigner function a

Philippe Boileau, Ning Leng, Nima S. Hejazi, Mark van der Laan

Heterogeneous treatment effects are driven by treatment effect modifiers, pre-treatment covariates that modify the effect of a treatment on an outcome. Current approaches for uncovering these variables are limited to low-dimensional data, data with weakly correlated covariates, or data generated according to parametric processes. We resolve these issues by d

Naga Mallika Gunturu

API proliferation is expected to grow in the coming years. This growth is further increased by the recent trends in digital transformation efforts undertaken by organizations across the spectrum. This paper discusses the benefits organizations can leverage by moving towards an API economy and proposes a framework for organizations to execute API transformati

Abhijeet Dutta

Higher spin (HS) fields naturally occur in string theory, they are considered as a candidate for dark matter and may also appear as a collective excitation in condensed matter systems. In some cases one may study the HS fields in the non-relativistic settings. Thus, it is of interest to know the non-relativistic limit of HS fields and how to find the Schroed

Yiran Wang

We study streaking artifacts caused by beam-hardening effects in X-ray computed tomography (CT). The effect is known to be nonlinear. We show that the nonlinearity can be recovered from the observed artifacts for strictly convex bodies. The result provides a theoretical support for removal of the artifacts.

Hung Viet Chu, Nurettin Irmak, Steven J. Miller, Laszlo Szalay

Inspired by the surprising relationship (due to A. Bird) between Schreier sets and the Fibonacci sequence, we introduce Schreier multisets and connect these multisets with the $s$-step Fibonacci sequences, defined, for each $s\geqslant 2$, as: $F^{(s)}_{2-s} = \cdots = F^{(s)}_0 = 0$, $F^{(s)}_1 = 1$, and $F^{(s)}_{n} = F^{(s)}_{n-1} + \cdots + F^{(s)}_{n-s}

Mitchell R. Whittam, Aristeidis G. Lamprianidis, Yannick Augenstein, Carsten Rockstuhl

The far-field back-scattering amplitude of an electric field from a relativistically-moving sphere is analyzed. Contrary to prior research, we do so by expressing the fields in the helicity basis, and we highlight here its advantages when compared to the commonly-considered parity basis. With the purpose of exploring specific scattering phenomena considering

Alexander E. Black, Kevin Liu, Alex Mcdonough, Garrett Nelson

A tanglegram consists of two rooted binary trees and a perfect matching between their leaves, and a planar tanglegram is one that admits a layout with no crossings. We show that the problem of generating planar tanglegrams uniformly at random reduces to the corresponding problem for irreducible planar tanglegram layouts, which are known to be in bijection wi

Zhongyipan Lin

Let $F$ be an arbitrary $p$-adic field and let $G$ be an arbitrary reductive group over $F$ with Langlands dual group $^LG$. We show that the change-of-group morphism of Emerton-Gee stacks $\mathcal{X}_{^LG}\to\mathcal{X}_{GL_d}$ is relatively representable by algebraic stacks of finite presentation over $\operatorname{Spf}\mathbf{Z}_p$ for any embedding $^L

Yunpeng Zhang, Zheng Zhu, Dalong Du

The vision-based perception for autonomous driving has undergone a transformation from the bird-eye-view (BEV) representations to the 3D semantic occupancy. Compared with the BEV planes, the 3D semantic occupancy further provides structural information along the vertical direction. This paper presents OccFormer, a dual-path transformer network to effectively

Antonin Chodron de Courcel, Matthew Rosenzweig, Sylvia Serfaty

We consider conservative and gradient flows for $N$-particle Riesz energies with mean-field scaling on the torus $\mathbb{T}^d$, for $d\geq 1$, and with thermal noise of McKean-Vlasov type. We prove global well-posedness and relaxation to equilibrium rates for the limiting PDE. Combining these relaxation rates with the modulated free energy of Bresch et al.

Viet-Anh Le, Hao M. Wang, Gabor Orosz, Andreas A. Malikopoulos

In this paper, we present an optimal control framework to address motion coordination of connected automated vehicles (CAVs) in the presence of human-driven vehicles (HDVs) in merging scenarios. Our framework combines an unconstrained trajectory solution of a low-level energy-optimal control problem with an upper-level optimization problem that yields the mi

Igor Furtat

The paper considers some class of dynamical systems that called density systems. For such systems the derivative of quadratic function depends on so-called density function. The density function is used to set the properties of phase space, therefore, it influences the behaviour of investigated systems. A particular class of such systems is previously consid

Riley Kiefer, Jacob Stevens, Ashok Patel

Fingerprint recognition and matching is a common form of user authentication. While a fingerprint is unique to each individual, authentication is vulnerable when an attacker can forge a copy of the fingerprint (spoof). To combat these spoofed fingerprints, spoof detection and liveness detection algorithms are currently being researched as countermeasures to

Kimet Jusufi, Emmanuel Moulay, Jonas Mureika, Ahmed Farag Ali

We use a string T-duality corrected pair of regular black holes to construct an Einstein-Rosen (ER) bridge with the wormhole throat proportional to the zero-point (Planck) length. This may be a geometric realization of quantum entanglement for particle/antiparticle pairs. We point out that for an extreme mass configuration consisting of a black hole pair, on

Pablo Catalán, Juan Antonio García-Martín, Jacobo Aguirre, José A. Cuesta

All possible phenotypes are not equally accessible to evolving populations. In fact, only phenotypes of large size, i.e. those resulting from many different genotypes, are found in populations of sequences, presumably because they are easier to discover and maintain. Genotypes that map to these phenotypes usually form mostly connected genotype networks that

Qunxi Zhu, Yao Guo, Wei Lin

Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with representative datasets. Recently, an augmented framework has been developed to overcome some limitations that emerged in the application of the original framework. In this paper, we propose a

Álvaro García López

We show that contextual hidden variables including the effect of the measuring devices can be backward-propagated by means of the Green's function to initial Cauchy hidden data. If this data is uncorrelated in spacelike-disjoint sets, the CHSH-Bell inequality can be derived. However, the correlation loophole remains unclosed.

Mark Quinlan, Aaron Ceross, Andrew Simpson

Cyber security advice is a broad church: it is thematically expansive, comprising expert texts, user-generated data consumed by individual users via informal learning, and much in-between. While there is evidence that cyber security news articles play a role in disseminating cyber security advice, the nature and extent of that role are not clear. We present

George Pantazis, Filiberto Fele, Kostas Margellos

In this paper we focus on noncooperative games with uncertain constraints coupling the agents' decisions. We consider a setting where bounded deviations of agents' decisions from the equilibrium are possible, and uncertain constraints are inferred from data. Building upon recent advances in the so called scenario approach, we propose a randomised algorithm t

Ranran Li, Yu Wang, Ning Ding, Shuai Dong

As a recent successfully exfoliated non van der Waals layered material, AgCrS2 has received a lot of attentions. Motivated by its structure related magnetic and ferroelectric behavior, a theoretical study on its exfoliated monolayer AgCr2S4 has been carried out in the present work. Based on density functional theory, the ground state and magnetic order of mo

Miloš Grujić, Ingrid Verbauwhede

Post-processing of the raw bits produced by a true random number generator (TRNG) is always necessary when the entropy per bit is insufficient for security applications. In this paper, we derive a tight bound on the output min-entropy of the algorithmic post-processing module based on linear codes, known as linear correctors. Our bound is based on the codes'

Yifan Peng, Yian Chen, E. Miles Stoudenmire, Yuehaw Khoo

We propose a hierarchical tensor-network approach for approximating high-dimensional probability density via empirical distribution. This leverages randomized singular value decomposition (SVD) techniques and involves solving linear equations for tensor cores in this tensor network. The complexity of the resulting algorithm scales linearly in the dimension o

Juraj Szavits-Nossan, Ramon Grima

The telegraph model is the standard model of stochastic gene expression, which can be solved exactly to obtain the distribution of mature RNA numbers per cell. A modification of this model also leads to an analytical distribution of the nascent RNA numbers. These solutions are routinely used for the analysis of single-cell data, including the inference of tr

Sumin Seo, JaeWoong Shin, Jaewoo Kang, Tae Soo Kim

Deep learning has shown great potential in assisting radiologists in reading chest X-ray (CXR) images, but its need for expensive annotations for improving performance prevents widespread clinical application. Visual language pre-training (VLP) can alleviate the burden and cost of annotation by leveraging routinely generated reports for radiographs, which ex

Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang

Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models with human preferences, significantly enhancing the quality of interactions between humans and models. InstructGPT implements RLHF through several stages, including Supervised Fine-Tuning (SFT), reward model training, and Proximal Policy Optimization (PPO). H

William Won, Midhilesh Elavazhagan, Sudarshan Srinivasan, Swati Gupta

The surge of artificial intelligence, particularly large language models, has driven the rapid development of large-scale machine learning clusters. Executing distributed models on these clusters is often constrained by communication overhead, making efficient utilization of available network resources crucial. As a result, the routing algorithm employed for

Evgeny S. Asmolov, Olga I. Vinogradova

Self-propulsion of catalytic Janus swimmers in electrolyte solutions is induced by inhomogeneous ion release from their surface. Here, we consider the experimentally relevant cases of particles which emit only one type of ions (type I) or equal fluxes of cations and anions (type II). In the limit of a thin electrostatic diffuse layer we derive a nonlinear ou

Erik Panzer, Karen Yeats

For every regular graph, we define a sequence of integers, using the recursion of the Martin polynomial. This sequence counts spanning tree partitions and constitutes the diagonal coefficients of powers of the Kirchhoff polynomial. We prove that this sequence respects all known symmetries of Feynman period integrals in quantum field theory. We show that othe

Bikram Adhikari, Prabin Bhandari

The paper presents a modular approach for the estimation of a leading vehicle's velocity based on a non-intrusive stereo camera where SiamMask is used for leading vehicle tracking, Kernel Density estimate (KDE) is used to smooth the distance prediction from a disparity map, and LightGBM is used for leading vehicle velocity estimation. Our approach yields an

Chendi Ni, Yuying Li, Peter A. Forsyth

Motivated by the current global high inflation scenario, we aim to discover a dynamic multi-period allocation strategy to optimally outperform a passive benchmark while adhering to a bounded leverage limit. To this end, we formulate an optimal control problem to outperform a benchmark portfolio throughout the investment horizon. Assuming the asset prices fol

Ziyun Wang, Kenneth Chaney, Kostas Daniilidis

3D reconstruction from multiple views is a successful computer vision field with multiple deployments in applications. State of the art is based on traditional RGB frames that enable optimization of photo-consistency cross views. In this paper, we study the problem of 3D reconstruction from event-cameras, motivated by the advantages of event-based cameras in

Hrishitva Patel

Object detection is a crucial task in computer vision that aims to identify and localize objects in images or videos. The recent advancements in deep learning and Convolutional Neural Networks (CNNs) have significantly improved the performance of object detection techniques. This paper presents a comprehensive study of object detection techniques in unconstr

Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino

Accurate simulation of sea ice is critical for predictions of future Arctic sea ice loss, looming climate change impacts, and more. A key feature in Arctic sea ice is the formation of melt ponds. Each year melt ponds develop on the surface of the ice and primarily via affecting the albedo, they have an enormous effect on the energy budget and climate of the

Saranya Ganesh S., Tom Beucler, Frederick Iat-Hin Tam, Milton S. Gomez

Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigate this issue, we introduce a Multidata (M) causal feature se

Daniel Murnane, Savannah Thais, Ameya Thete

Graph neural networks (GNNs) have gained traction in high-energy physics (HEP) for their potential to improve accuracy and scalability. However, their resource-intensive nature and complex operations have motivated the development of symmetry-equivariant architectures. In this work, we introduce EuclidNet, a novel symmetry-equivariant GNN for charged particl

Jian Sun, Hiroko H. Dodge, Mohammad H. Mahoor

Deep machine learning models including Convolutional Neural Networks (CNN) have been successful in the detection of Mild Cognitive Impairment (MCI) using medical images, questionnaires, and videos. This paper proposes a novel Multi-branch Classifier-Video Vision Transformer (MC-ViViT) model to distinguish MCI from those with normal cognition by analyzing fac

Moritz Cygorek, Jonathan Keeling, Brendon W. Lovett, Erik M. Gauger

While several numerical techniques are available for predicting the dynamics of non-Markovian open quantum systems, most struggle with simulations for very long memory and propagation times, e.g., due to superlinear scaling with the number of time steps $n$. Here, we introduce a numerically exact algorithm to calculate process tensors -- compact representati

Ambra Amico, Luca Verginer, Giona Casiraghi, Giacomo Vaccario

Supply chain disruptions cause shortages of raw material and products. To increase resilience, i.e., the ability to cope with shocks, substituting goods in established supply chains can become an effective alternative to creating new distribution links. We demonstrate its impact on supply deficits through a detailed analysis of the US opioid distribution sys

Manuel Friedrich, Leonard Kreutz, Konstantinos Zemas

We derive a dimension-reduction limit for a three-dimensional rod with material voids by means of $\Gamma$-convergence. Hereby, we generalize the results of the purely elastic setting [57] to a framework of free discontinuity problems. The effective one-dimensional model features a classical elastic bending-torsion energy, but also accounts for the possibili

Wenjin Wang, Yunqing Hu, Qianglong Chen, Yin Zhang

Parameter regularization or allocation methods are effective in overcoming catastrophic forgetting in lifelong learning. However, they solve all tasks in a sequence uniformly and ignore the differences in the learning difficulty of different tasks. So parameter regularization methods face significant forgetting when learning a new task very different from le

Karma Tenzin, Arunesh Roy, Frank T. Cerasoli, Anooja Jayaraj

Efficient generation and manipulation of spin signals in a given material without invoking external magnetism remain one of the challenges in spintronics. The spin Hall effect (SHE) and Rashba-Edelstein effect (REE) are well-known mechanisms to electrically generate spin accumulation in materials with strong spin-orbit coupling (SOC), but the exact role of t

Suraj Goel, Matthew Reynolds, Matthew Girling, Will McCutcheon

Simulators can realise novel phenomena by separating them from the complexities of a full physical implementation. Here we put forward a scheme that can simulate the exotic statistics of $D(S_3)$ non-Abelian anyons with minimal resources. The qudit lattice representation of this planar code supports local encoding of $D(S_3)$ anyons. As a proof-of-principle

Nathan R. T. Lesnevich

We consider the Schur-positivity of monomial immanants of Jacobi-Trudi matrices, in particular whether a non-negative coefficient of the trivial Schur function implies non-negative coefficients for other Schur functions in said immanants. We prove that this true for hook-shape Schur functions using combinatorial methods in a representation theory setting. Ou

Valentina Ros, Felix Roy, Giulio Biroli, Guy Bunin

We consider the Generalized Lotka-Volterra system of equations with all-to-all, random asymmetric interactions describing high-dimensional, very diverse and well-mixed ecosystems. We analyze the multiple equilibria phase of the model and compute its quenched complexity, i.e., the expected value of the logarithm of the number of equilibria of the dynamical eq

Safa Khemiri, Mustafa A. Kishk, Mohamed-Slim Alouini

One of the major challenges slowing down the use of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) is the limited on-board power supply which reduces the UAV's flight time. Using a tether to provide UAVs with power can be considered a reasonable compromise that will enhance the flight time while limiting the UAV's mobility. In this work, we p

Michael Zwilich, Florian Schepers, Carsten Fallnich

A longitudinal mode-locked state can be converted to a transverse mode-locked state by exploiting the spectral and spatial filtering of an empty optical resonator. Carrier and amplitude modulation sidebands were simultaneously transmitted by the conversion resonator, yielding phase-locked superpositions of up to five transverse modes. Equivalently, an amplit

Kaushik Naskar, Parthasarathi Joarder

Not all activities in living creatures can be explained by classical dynamics. Application of quantum physics in biology helps to study the unexplained phenomena in cells. More detailed research work is needed rather than rejecting the concept of intervention of quantum physics in biology. Here we have used some concepts introduced by Hameroff, Penrose \cite

Bingjie Wang, Joel Leja, V. Ashley Villar, Joshua S. Speagle

Flagship near-future surveys targeting $10^8-10^9$ galaxies across cosmic time will soon reveal the processes of galaxy assembly in unprecedented resolution. This creates an immediate computational challenge on effective analyses of the full data-set. With simulation-based inference (SBI), it is possible to attain complex posterior distributions with the acc

Ömer Faruk Çoban, Unal Ertan

We show that a typical X-ray outburst light curve of Aql X-1 can be reproduced by accretion onto the neutron star in the frame of the disk instability model without invoking partial accretion or propeller effect. The knee and the subsequent sharp decay in the X-ray light curve can be generated naturally by taking into account the weak dependence of the disk

Karl Bringmann, Alejandro Cassis, Nick Fischer

In this work we revisit the fundamental Single-Source Shortest Paths (SSSP) problem with possibly negative edge weights. A recent breakthrough result by Bernstein, Nanongkai and Wulff-Nilsen established a near-linear $O(m \log^8(n) \log(W))$-time algorithm for negative-weight SSSP, where $W$ is an upper bound on the magnitude of the smallest negative-weight

Jamal Elfakir, Brahim Amghar, Abdallah Slaoui, Mohammed Daoud

With the growth of geometric science, including the methods of exploring the world of information by means of modern geometry, there has always been a mysterious and fascinating ambiguous link between geometric, topological and dynamical characteristics with quantum entanglement. Since geometry studies the interrelations between elements such as distance and

Tianyu Li, Li Chen, Huijie Wang, Yang Li

Understanding the road genome is essential to realize autonomous driving. This highly intelligent problem contains two aspects - the connection relationship of lanes, and the assignment relationship between lanes and traffic elements, where a comprehensive topology reasoning method is vacant. On one hand, previous map learning techniques struggle in deriving

Jeremy Yallop, Ningning Xie, Neel Krishnaswami

Lexers and parsers are typically defined separately and connected by a token stream. This separate definition is important for modularity and reduces the potential for parsing ambiguity. However, materializing tokens as data structures and case-switching on tokens comes with a cost. We show how to fuse separately-defined lexers and parsers, drastically impro

Saieed Akbari, Hussah Al Menderj, Miin Huey Ang, Johnny Lim

Let $G_S$ be a graph with loops obtained from a graph $G$ of order $n$ and loops at $S \subseteq V(G)$. In this paper, we establish a neccesary and sufficient condition on the bipartititeness of a connected graph $G$ and the spectrum Spec($G_S$) and Spec($G_{V(G)\backslash S}$). We also prove that for every $S \subseteq V(G)$, $E(G_S) \geq E(G)$ when $G$ is

Shaoyi Liaw, Fan Huang, Fabricio Benevenuto, Haewoon Kwak

Conspiracy theories are widely propagated on social media. Among various social media services, YouTube is one of the most influential sources of news and entertainment. This paper seeks to develop a dataset, YOUNICON, to enable researchers to perform conspiracy theory detection as well as classification of videos with conspiracy theories into different topi

Stefan Müller, Georg Regensburger

We consider positive solutions to parametrized systems of generalized polynomial equations (with real exponents and positive parameters). By a fundamental result obtained in parallel work, polynomial systems are determined by geometric objects, rather than matrices: a polytope $P$ (arising from the coefficient matrix) and two subspaces representing monomial

Marjan Mirahmadi, Jesús Pérez-Ríos

Three-body recombination, or ternary association, is a termolecular reaction in which three particles collide, forming a bound state between two, whereas the third escapes freely. Three-body recombination reactions play a significant role in many systems relevant to physics and chemistry. In particular, they are relevant in cold and ultracold chemistry, quan

Mariano Caruso, Cecilia Jarne

Since the 1980s, and particularly with the Hopfield model, recurrent neural networks or RNN became a topic of great interest. The first works of neural networks consisted of simple systems of a few neurons that were commonly simulated through analogue electronic circuits. The passage from the equations to the circuits was carried out directly without justifi

Yash Shukla, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez

Despite advances in Reinforcement Learning, many sequential decision making tasks remain prohibitively expensive and impractical to learn. Recently, approaches that automatically generate reward functions from logical task specifications have been proposed to mitigate this issue; however, they scale poorly on long-horizon tasks (i.e., tasks where the agent n

Naveen Kumar, Deepak, Arpita Chatterjee

We analyze here a nonclassical state produced by an atom-cavity field interaction. The two-level atom is passed through the single-mode electromagnetic cavity field. By deforming the field operators and introducing nonlinearity to the classic Jaynes-Cummings model, we explore the system in respect of a nonlinear Hamiltonian. Assuming that the atom is in an e

Duncan Adamson, Maria Kosche, Tore Koß, Florin Manea

We consider the longest common subsequence problem in the context of subsequences with gap constraints. In particular, following Day et al. 2022, we consider the setting when the distance (i. e., the gap) between two consecutive symbols of the subsequence has to be between a lower and an upper bound (which may depend on the position of those symbols in the s

Qi Wu, Ming-Zhu Liu, Li-Sheng Geng

The exotic states $X(3872)$ and $Z_c(3900)$ have long been conjectured as isoscalar and isovector $\bar{D}^*D$ molecules. In this work, we first propose the triangle diagram mechanism to investigate their productions in $B$ decays as well as their heavy quark spin symmetry partners, $X_2(4013)$ and $Z_c(4020)$. We show that the large isospin breaking of the

Amelie Wührl, Lara Grimminger, Roman Klinger

Existing fact-checking models for biomedical claims are typically trained on synthetic or well-worded data and hardly transfer to social media content. This mismatch can be mitigated by adapting the social media input to mimic the focused nature of common training claims. To do so, Wuehrl & Klinger (2022) propose to extract concise claims based on medical en

Mithun Bairagi

We present a new algorithm by which the Adomian polynomials can be determined for scalar-valued nonlinear polynomial functional in a Hilbert space. This algorithm calculates the Adomian polynomials without the complicated operations such as parametrization, expansion, regrouping, differentiation, etc. The algorithm involves only some matrix operations. Becau

Anthony Genevois

Given a graph $\Gamma$, the right-angled Artin group $A(\Gamma)$ is given by the presentation $\langle u \in V(\Gamma) \mid [u,v]=1, \ \{u,v\} \in E(\Gamma) \rangle$. The Embedding Problem in right-angled Artin groups asks, given two finite graphs $\Phi,\Psi$, how to determine whether or not $A(\Phi)$ is isomorphic to a subgroup of $A(\Psi)$. These are the n

Hiroki Hayashi, Dongwook Go, Yuriy Mokrousov, Kazuya Ando

Harnessing spin and orbital angular momentum is a fundamental concept in condensed matter physics, materials science, and quantum-device applications. In particular, the search for new phenomena that generate a flow of spin angular momentum, a spin current, has led to the development of spintronics, advancing the understanding of angular momentum dynamics at

Jianan Yang, Haobo Wang, Yanming Zhang, Ruixuan Xiao

The recent large-scale generative modeling has attained unprecedented performance especially in producing high-fidelity images driven by text prompts. Text inversion (TI), alongside the text-to-image model backbones, is proposed as an effective technique in personalizing the generation when the prompts contain user-defined, unseen or long-tail concept tokens

Richard A. Harrison, Jackie A. Davies, David Barnes, Christian Möstl

The efficacy of coronal mass ejection (CME) observations as a key input to space weather forecasting is explored by comparing on and off Sun-Earth line observations from the ESA/NASA SOHO and NASA STEREO spacecraft. A comparison is made of CME catalogues based on L1 coronagraph imagery and off Sun-Earth line coronagraph and heliospheric imager (HI) observati

Zizhuo Zhang, Bang Wang

Some recent \textit{news recommendation} (NR) methods introduce a Pre-trained Language Model (PLM) to encode news representation by following the vanilla pre-train and fine-tune paradigm with carefully-designed recommendation-specific neural networks and objective functions. Due to the inconsistent task objective with that of PLM, we argue that their modelin

Deepak, Arpita Chatterjee

In this paper, we investigate the nonclassicality and non-Gaussianity of a coherent superposed quantum state (CSQS) which is obtained by applying a coherent superposition of field annihilation ($a$) and creation ($a^\dagger$) operators, $N(ta+ra^\dagger)$ to a classical coherent state $|\alpha\rangle$, where $t$ and $r$ are scalars with $t^2+r^2=1$. Such an

Claudio Turchetti, Laura Falaschetti

The aim of this paper is to present a mathematical framework for tensor PCA. The proposed approach is able to overcome the limitations of previous methods that extract a low dimensional subspace by iteratively solving an optimization problem. The core of the proposed approach is the derivation of a basis in tensor space from a real self-adjoint tensor operat

Rodolfo Carobene, Stefano Barison, Andrea Giachero

We propose an extension of the Variational Quantum Eigensolver (VQE) that leads to more accurate energy estimations and can be used to study excited states. The method is based on the introduction of a sequence of increasing penalties in the cost function. This approach does not require circuit modifications and thus can be applied with no additional depth c

Sanat K. Sarkar

This paper revisits the following open question in simultaneous testing of multivariate normal means against two-sided alternatives: Can the method of Benjamini and Hochberg (BH, 1995) control the false discovery rate (FDR) without imposing any dependence structure on the correlations? The answer to this question is generally believed to be yes, and is conje

Gwen Legate, Lucas Caccia, Eugene Belilovsky

In Federated Learning, a global model is learned by aggregating model updates computed at a set of independent client nodes, to reduce communication costs multiple gradient steps are performed at each node prior to aggregation. A key challenge in this setting is data heterogeneity across clients resulting in differing local objectives which can lead clients

Tierui Gong, Li Wei, Chongwen Huang, Zhijia Yang

Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and achieving fundamental wireless limits. Empowered by the nearly continuous, large and energy-efficient surfaces with powerful electromagnetic (EM)

J. T. Lázaro, A. Albó, T. Alarcón, S. F. Elena

Almost all viruses, regardless of their genomic material, produce defective viral genomes (DVG) as an unavoidable byproduct of their error-prone replication. Defective interfering (DI) elements are a subgroup of DVGs that have been shown to interfere with the replication of the wild-type (WT) virus. Along with DIs, other genetic elements known as satellite R

Tong Zhou

In this paper, we present a transformer architecture for predicting student performance on standardized tests. Specifically, we leverage students historical data, including their past test scores, study habits, and other relevant information, to create a personalized model for each student. We then use these models to predict their future performance on a gi

Dunkan Martínez, Álvaro Díaz-Fernández, Pedro A Orellana, Francisco Domínguez-Adame

Topological superconductors are foreseen as good candidates for the search of Majorana zero modes, where they appear as edge states and can be used for quantum computation. In this context, it becomes necessary to study the robustness and behavior of electron states in topological superconductors when a magnetic or non-magnetic impurity is present. We focus

Chenshen Wu, Joost van de Weijer

We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A na\"ive approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a n

Philipp Lazen, Pedro Lima Cardoso, Sukrit Sharma, Cornelius Cadrien

This paper investigates the correlation between magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance fingerprinting (MRF) in glioma patients by comparing neuro-oncological markers obtained from MRSI to T1/T2 maps from MRF. Data from 12 consenting patients with gliomas were analyzed by defining hotspots for T1, T2 and various metabolic ratio

Ekaterina Svikhnushina, Pearl Pu

As conversational models become increasingly available to the general public, users are engaging with this technology in social interactions. Such unprecedented interaction experiences may pose considerable social and psychological risks to the users unless the technology is properly controlled. This highlights the need for scalable and robust evaluation met

Wilson S. Martins, Federico Carollo, Weibin Li, Kay Brandner

Trapped ions provide a platform for quantum technologies that offers long coherence times and high degrees of scalability and controllability. Here, we use this platform to develop a realistic model of a thermal device consisting of two laser-driven, strongly coupled Rydberg ions in a harmonic trap. We show that the translational degrees of freedom of this s

Sabrina Aufiero, Giordano De Marzo, Angelica Sbardella, Andrea Zaccaria

We use algorithmic and network-based tools to build and analyze the bipartite network connecting jobs with the skills they require. We quantify and represent the relatedness between jobs and skills by using statistically validated networks. Using the fitness and complexity algorithm, we compute a skill-based complexity of jobs. This quantity is positively co

S. T. Wang, V. Khachatryan, P. Nishikawa

The predominant source of nonlinearity in most existing accelerators are sextupoles, which introduce nonlinear resonances. When the horizontal tune of an accelerator is near such a resonance line ($n\nu_x$), stable fixed points (SFPs) may appear in the horizontal phase space to form a second closed orbit different from the "zero" closed orbit. The stable isl

Ahana Ghoshal, Swati Choudhary, Ujjwal Sen

We find that the m-separability and k-partite entanglement of a multipartite quantum system is correlated with quantum coherence of the same with respect to complete orthonormal bases, distinguishable under local operations and classical communication in certain partitions. In particular, we show that the geometric measure of m-inseparable entanglement of a

S. M. Tschopp, H. D. Vuijk, J. M. Brader

Superadiabatic dynamical density functional theory (superadiabatic-DDFT), a first-principles approach based on the inhomogeneous two-body correlation functions, is employed to investigate the response of interacting Brownian particles to time-dependent external driving. Predictions for the superadiabatic dynamics of the one-body density are made directly fro

Matthew D. Blair, Xiaoqi Huang, Christopher D. Sogge

We obtain improved Strichartz estimates for solutions of the Schr\"odinger equation on negatively curved compact manifolds which improve the classical universal results results of Burq, G\'erard and Tzvetkov [11] in this geometry. In the case where the spatial manifold is a hyperbolic surface we are able to obtain no-loss $L^{q_c}_{t,x}$-estimates on interva

Zhiping Bai

Attitude is omnipresent in almost every type of text. There has yet to be any relevant research on attitudinal shifts in self-translation. The Chinese version of Between Tears and Laughter is a rare case of self-translation and co-translation in that the first 11 chapters are self-translated by Lin Yutang, and the last 12 chapters by Xu Chengbin. The current

W. Jonas, A. Abraham, L. Dreyfus-Schmidt

Despite the vast body of literature on Active Learning (AL), there is no comprehensive and open benchmark allowing for efficient and simple comparison of proposed samplers. Additionally, the variability in experimental settings across the literature makes it difficult to choose a sampling strategy, which is critical due to the one-off nature of AL experiment

Andrew Clarke, Carl Tipler

We consider a sufficiently smooth semi-stable holomorphic vector bundle over a compact K\"ahler manifold. Assuming the automorphism group of its graded object to be abelian, we provide a semialgebraic decomposition of a neighbourhood of the polarisation in the K\"ahler cone into chambers characterising (in)stability. For a path in a stable chamber converging

Lancelot Fitzgerald James, Juho Lee, Abhinav Pandey

Bayesian nonparametric hierarchical priors are highly effective in providing flexible models for latent data structures exhibiting sharing of information within and across groups. In this work, we focus on latent feature allocation models, where the data structures correspond to multi-sets or unbounded sparse matrices, which we refer to as generalized hierar

Klaudia Bałazy, Łukasz Struski, Marek Śmieja, Jacek Tabor

Nowadays artificial neural network models achieve remarkable results in many disciplines. Functions mapping the representation provided by the model to the probability distribution are the inseparable aspect of deep learning solutions. Although softmax is a commonly accepted probability mapping function in the machine learning community, it cannot return spa

Ali Baouan, Sébastien Coustou, Mathieu Lacome, Sergio Pulido

We introduce an innovative methodology to identify football players at the origin of threatening actions in a team. In our framework, a threat is defined as entering the opposing team's danger area. We investigate the timing of threat events and ball touches of players, and capture their correlation using Hawkes processes. Our model-based approach allows us