March 2023 arXiv papers — page 44

Jingzu Yee, Daichi Igarashi, Shun Miyatake, Yoshiyuki Tagawa

The impact of a drop on a solid surface is an important phenomenon that has various implications and applications. However, the multiphase nature of this phenomenon causes complications in the prediction of its morphological evolution, especially when the drop splashes. While most machine-learning-based drop-impact studies have centred around physical parame

Matěj Mezera, Jana Menšíková, Pavel Baláž, Martin Žonda

We utilize neural network quantum states (NQS) to investigate the ground state properties of the Heisenberg model on a Shastry-Sutherland lattice using the variational Monte Carlo method. We show that already relatively simple NQSs can be used to approximate the ground state of this model in its different phases and regimes. We first compare several types of

Graciela B. Gelmini, Jonah Hyman, Anna Simpson, Edoardo Vitagliano

We propose a new scenario for the formation of asteroid-mass primordial black holes (PBHs). Our mechanism is based on the annihilation of the string-wall network associated with the breaking of a $U(1)$ global symmetry into a discrete $Z_N$ symmetry. If the potential has multiple local minima ($N>1$) the network is stable, and the annihilation is guaranteed

Raghda El Shehaby, Matthias Függer, Andreas Steininger

By design, quasi delay-insensitive (QDI) circuits exhibit higher resilience against timing variations as compared to their synchronous counterparts. Since computation in QDI circuits is event-based rather than clock-triggered, spurious events due to transient faults such as radiation-induced glitches, a priori are of higher concern in QDI circuits. In this w

Marie D'haene, Jun-ichi Inoguchi, Joeri Van der Veken

We study hypersurfaces of the four-dimensional Thurston geometry $\text{Sol}^4_0$, which is a Riemannian homogeneous space and a solvable Lie group. In particular, we give a full classification of hypersurfaces whose second fundamental form is a Codazzi tensor, including totally geodesic hypersurfaces and hypersurfaces with parallel second fundamental form,

Sara Simoes, Ana Ribeiro, Carla Ferreira, Nuno Preguica

Application services often support mobile and web applications with REST interfaces, implemented using a set of distributed components that interact with each other. This approach allows services to have high availability and performance at a lower cost than a monolithic system. However, the existence of multiple components makes the development process of t

Yue Wang, Wending Li, Michail Maniatakos, Saif Eddin Jabari

Deep Reinforcement Learning (DRL) enhances the efficiency of Autonomous Vehicles (AV), but also makes them susceptible to backdoor attacks that can result in traffic congestion or collisions. Backdoor functionality is typically incorporated by contaminating training datasets with covert malicious data to maintain high precision on genuine inputs while induci

Andreas Ketterer, Thomas Wellens

Currently available quantum computing hardware based on superconducting transmon architectures realizes networks of hundreds of qubits with the possibility of controlled nearest-neighbor interactions. However, the inherent noise and decoherence effects of such quantum chips considerably alter basic gate operations and lead to imperfect outputs of the targete

Marco Gaido

In the big data era, the key feature that each algorithm needs to have is the possibility of efficiently running in parallel in a distributed environment. The popular Silhouette metric to evaluate the quality of a clustering, unfortunately, does not have this property and has a quadratic computational complexity with respect to the size of the input dataset.

Marta Skreta, Naruki Yoshikawa, Sebastian Arellano-Rubach, Zhi Ji

Generating low-level robot task plans from high-level natural language instructions remains a challenging problem. Although large language models have shown promising results in generating plans, the accuracy of the output remains unverified. Furthermore, the lack of domain-specific language data poses a limitation on the applicability of these models. In th

Emilien Flayac, Karim Dahia, Bruno Hérissé, Frédéric Jean

This paper presents state estimation and stochastic optimal control gathered in one global optimization problem generating dual effect i.e. the control can improve the future estimation. As the optimal policy is impossible to compute, a sub-optimal policy that preserves this coupling is constructed thanks to the Fisher Information Matrix (FIM) and a Particle

Sebastiano Carpi, Luca Tomassini

Let V be a simple unitary vertex operator algebra and U be a (polynomially) energy-bounded unitary subalgebra containing the conformal vector of V. We give two sufficient conditions implying that V is energy-bounded. The first condition is that U is a compact orbifold for some compact group G of unitary automorphisms of V. The second condition is that V is e

Jongheon Jeong, Sihyun Yu, Hankook Lee, Jinwoo Shin

In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition (i.e., less generalizable), so that one cannot prevent a model from co-adapting on such (so-called) "shortcut" signals: this makes the model fragile in various distribution shifts. To bypass such failure modes, we cons

Ze Shi, Hao Shi, Kailun Yang, Zhe Yin

Visual place recognition has gained significant attention in recent years as a crucial technology in autonomous driving and robotics. Currently, the two main approaches are the perspective view retrieval (P2P) paradigm and the equirectangular image retrieval (E2E) paradigm. However, it is practical and natural to assume that users only have consumer-grade pi

Emilien Flayac, Karim Dahia, Bruno Hérissé, Frédéric Jean

This paper presents a dual receding horizon output feedback controller for a general non linear stochastic system with imperfect information. The novelty of this controller is that stabilization is treated, inside the optimization problem, as a negative drift constraint on the control that is taken from the theory of stability of Markov chains. The dual effe

David F. Anderson, Aidan S. Howells

Stochastic reaction networks, which are usually modeled as continuous-time Markov chains on $\mathbb Z^d_{\ge 0}$, and simulated via a version of the "Gillespie algorithm," have proven to be a useful tool for the understanding of processes, chemical and otherwise, in homogeneous environments. There are multiple avenues for generalizing away from the assumpti

Mingwu Zheng, Haiyu Zhang, Hongyu Yang, Di Huang

Realistic face rendering from multi-view images is beneficial to various computer vision and graphics applications. Due to the complex spatially-varying reflectance properties and geometry characteristics of faces, however, it remains challenging to recover 3D facial representations both faithfully and efficiently in the current studies. This paper presents

Yongyun Qin

Let $\Lambda = \left[\begin{array}{cc} A & 0 \\ M & B \end{array}\right] $ be an Artin algebra and $_BM_A$ a $B$-$A$-bimodule. We prove that there is a triangle equivalence $D_{sg}(\Lambda) \cong D_{sg}(A)\coprod D_{sg}(B)$ between the corresponding singularity categories if $_BM$ is semi-simple and $M_A$ is projective. As a result, we obtain a new method fo

Zhenyu Dai, Ben Moews, Ricardo Vilalta, Romeel Dave

Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmolo

Yaroslav Zharov, Tilo Baumbach, Vincent Heuveline

In Computed Tomography, machine learning is often used for automated data processing. However, increasing model complexity is accompanied by increasingly large volume datasets, which in turn increases the cost of model training. Unlike most work that mitigates this by advancing model architectures and training algorithms, we consider the annotation procedure

Zhexiao Lin, Fang Han

While researchers commonly use the bootstrap for statistical inference, many of us have realized that the standard bootstrap, in general, does not work for Chatterjee's rank correlation. In this paper, we provide proof of this issue under an additional independence assumption, and complement our theory with simulation evidence for general settings. Chatterje

Xiaohao Sun, Hanxiao Jiang, Manolis Savva, Angel Xuan Chang

Openable part detection is the task of detecting the openable parts of an object in a single-view image, and predicting corresponding motion parameters. Prior work investigated the unrealistic setting where all input images only contain a single openable object. We generalize this task to scenes with multiple objects each potentially possessing openable part

Qi-yue Yu, Jiang-xuan Li, Shu Lin

In the past several decades, various techniques have been developed and used for multiple-access (MA) communications. With the new applications for 6G, it is desirable to find new resources, physical or virtual, to confront the fast development of MA communication systems. For binary source transmission, this paper introduces the concept of element-pair (EP)

Patrick Cheridito, Stephan Eckstein

In this paper, we introduce a variant of optimal transport adapted to the causal structure given by an underlying directed graph $G$. Different graph structures lead to different specifications of the optimal transport problem. For instance, a fully connected graph yields standard optimal transport, a linear graph structure corresponds to causal optimal tran

Saeyoung Rho, Rachel Cummings, Vishal Misra

Synthetic control is a causal inference tool used to estimate the treatment effects of an intervention by creating synthetic counterfactual data. This approach combines measurements from other similar observations (i.e., donor pool ) to predict a counterfactual time series of interest (i.e., target unit) by analyzing the relationship between the target and t

Roman Worschech, Bernd Rosenow

Deep neural networks are widely used prediction algorithms whose performance often improves as the number of weights increases, leading to over-parametrization. We consider a two-layered neural network whose first layer is frozen while the last layer is trainable, known as the random feature model. We study over-parametrization in the context of a student-te

Jungang Ge, Ying-Chang Liang, Liao Zhang, Ruizhe Long

To accommodate the explosive wireless traffics, massive multiple-input multiple-output (MIMO) is regarded as one of the key enabling technologies for next-generation communication systems. In massive MIMO cellular networks, coordinated beamforming (CBF), which jointly designs the beamformers of multiple base stations (BSs), is an efficient method to enhance

Lan Jiang, Ye Mao, Xi Chen, Xiangfeng Wang

MRI synthesis promises to mitigate the challenge of missing MRI modality in clinical practice. Diffusion model has emerged as an effective technique for image synthesis by modelling complex and variable data distributions. However, most diffusion-based MRI synthesis models are using a single modality. As they operate in the original image domain, they are me

Paul Hager, Martin J. Menten, Daniel Rueckert

Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images. In practice, clinicians typically have less data, both in terms of diversity and scale, but still wish to deploy deep learning solutions. Combined with increasing medical dataset sizes and expensive annotation costs, the necess

Andrea Giuliani

We will describe the current status of the ASTRI Mini-Array, under construction at the Teide Astronomical Observatory in Tenerife, Spain. The final layout of the array will include nine small Cherenkov telescopes covering an area of about 650 x 270 square meters. The ASTRI telescopes adopt a dual-mirror Schwarzchild-Couder aplanatic optical design. In the fo

Jisoo Jeong, Hong Cai, Risheek Garrepalli, Fatih Porikli

We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models by introducing realistic distractions to the input frames. Based on a mixing ratio, we combine one of the frames in the pair with a distractor image depicting a similar domain, which allows for inducing visual perturbations congruent with natural objects

Lin Li, Michael Spratling

Deep neural networks can be easily fooled into making incorrect predictions through corruption of the input by adversarial perturbations: human-imperceptible artificial noise. So far adversarial training has been the most successful defense against such adversarial attacks. This work focuses on improving adversarial training to boost adversarial robustness.

Daniele Picone, Silvére Gousset, Mauro Dalla-Mura, Yann Ferrec

In recent years, the demand for hyperspectral imaging devices has grown significantly, driven by their ability of capturing high-resolution spectral information. Among the several possible optical designs for acquiring hyperspectral images, there is a growing interest in interferometric spectral imaging systems based on division of aperture. These systems ha

Mingqiang Wei, Qian Sun, Haoran Xie, Dong Liang

Searching by image is popular yet still challenging due to the extensive interference arose from i) data variations (e.g., background, pose, visual angle, brightness) of real-world captured images and ii) similar images in the query dataset. This paper studies a practically meaningful problem of beauty product retrieval (BPR) by neural networks. We broadly e

Francesco Dell'Accio, Alvise Sommariva, Marco Vianello

We prove a.s. (almost sure) unisolvency of interpolation by continuous random sampling with respect to any given density, in spaces of multivariate a.e. (almost everywhere) analytic functions. Examples are given concerning polynomial and RBF approximation.

Tristan Blaineau, Marc Moniez

Studies of gravitational microlensing effects require the estimation of their detection efficiency as soon as one wants to quantify the massive compact objects along the line of sight of source targets. This is particularly important for setting limits on the contribution of massive compact objects to the Galactic halo. These estimates of detection efficienc

Qi Yan, Vitaly Choutko

The Alpha Magnetic Spectrometer (AMS) is a precision particle physics detector operating at an altitude of 410 km aboard the International Space Station. The AMS silicon tracker, together with the permanent magnet, measures the rigidity (momentum/charge) of cosmic rays in the range from 0.5 GV to several TV. In order to have accurate rigidity measurements, t

Yong-Hao Hu, Kenichiro Ito, Ayumi Igarashi

Maintaining real-time communication quality in metaverse has always been a challenge, especially when the number of participants increase. We introduce a proprietary WebRTC SFU service to an open-source web-based VR platform, to realize a more stable and reliable platform suitable for educational communication of audio, video, and avatar transform. We develo

Yunxiang Li, Zihan Li, Kai Zhang, Ruilong Dan

The primary aim of this research was to address the limitations observed in the medical knowledge of prevalent large language models (LLMs) such as ChatGPT, by creating a specialized language model with enhanced accuracy in medical advice. We achieved this by adapting and refining the large language model meta-AI (LLaMA) using a large dataset of 100,000 pati

Andrew Litteken, Lennart Maximilian Seifert, Jason D. Chadwick, Natalia Nottingham

Superconducting quantum devices are a leading technology for quantum computation, but they suffer from several challenges. Gate errors, coherence errors and a lack of connectivity all contribute to low fidelity results. In particular, connectivity restrictions enforce a gate set that requires three-qubit gates to be decomposed into one- or two-qubit gates. T

Md Asif Bin Syed, Imtiaz Ahmed

In marine surveillance, distinguishing between normal and anomalous vessel movement patterns is critical for identifying potential threats in a timely manner. Once detected, it is important to monitor and track these vessels until a necessary intervention occurs. To achieve this, track association algorithms are used, which take sequential observations compr

Cameron Kisailus, Daksh Narang, Matthew Shannon, Odest Chadwicke Jenkins

Recent advances in robotic mobile manipulation have spurred the expansion of the operating environment for robots from constrained workspaces to large-scale, human environments. In order to effectively complete tasks in these spaces, robots must be able to perceive, reason, and execute over a diversity of affordances, well beyond simple pick-and-place. We po

Parikshit Moitra, Xuewu Xu, Rasna Maruthiyodan Veetil, Xinan Liang

All-dielectric optical metasurfaces can locally control the amplitude and phase of light at the nanoscale, enabling arbitrary wavefront shaping. However, lack of post-fabrication tunability has limited the true potential of metasurfaces for many applications. Here, we utilize a thin liquid crystal (LC) layer as a tunable medium surrounding the metasurface to

Paolo Rissone, Isabel Pastor, Felix Ritort

We review the basic concepts and tools for mechanically unzipping RNA hairpins using force spectroscopy. By pulling apart the ends of an RNA molecule using optical tweezers, it is possible to measure the folding free energy at varying experimental conditions. Energy measurements permit us to characterize the thermodynamics of RNA hybridization (base pairing

Henrique Gomes, Carlo Rovelli

We give a conceptual exposition of aspects of gravitational radiation, especially in relation to energy. Our motive for doing so is that the strong analogies with electromagnetic radiation seem not to be widely enough appreciated. In particular, we reply to some recent papers in the philosophy of physics literature that seem to deny that gravitational waves

Thomas Podgorski

In a recent paper published in Physics of Fluids, Sanal Kumar et al. present a model of transonic compressible flows based on ideal gas theory that is irrelevant to biofluid flow and there are flaws in the general reasoning. In addition, the experimental attempts do not show any evidence of supersonic flow and do not provide any support for the flawed theory

Anirban Roy Chowdhury, Ashis Saha, Sunandan Gangopadhyay

In this work, we show that the two proposals associated to the mutual information of matter fields can be given for an eternal Schwarzschild black hole in de-Sitter spacetime. These proposals also depicts the status of associated entanglement wedges and their roleplay in obtaining the correct Page curve of radiation. The first proposal has been give for the

Leo Ardon, Daniel Furelos-Blanco, Alessandra Russo

This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) that combines cooperative task decomposition with the learning of reward machines (RMs) encoding the structure of the sub-tasks. The proposed method helps deal with the non-Markovian nature of the rewards in partially observable environments and improves the interpretability of

Henrique Gomes, Jeremy Butterfield

In this two-part paper we review, and then develop, the assessment of the hole argument for general relativity. The review (in Part I) discussed how to compare points in isomorphic spacetimes, i.e. models of the theory. This second Part proposes a framework for making comparisons of {\em non}-isomorphic spacetimes. It combines two ideas we discussed in Part

Luca Capizzi, Carlo Vanoni, Pasquale Calabrese, Andrea Gambassi

When a free Fermi gas on a lattice is subject to the action of a linear potential it does not drift away, as one would naively expect, but it remains spatially localized. Here we revisit this phenomenon, known as Stark localization, within the recently proposed framework of generalized hydrodynamics. In particular, we consider the dynamics of an initial stat

Otmar Scherzer, Bernd Hofmann, Zuhair Nashed

Neural networks functions are supposed to be able to encode the desired solution of an inverse problem very efficiently. In this paper, we consider the problem of solving linear inverse problems with neural network coders. First we establish some correspondences of this formulation with existing concepts in regularization theory, in particular with state spa

David Galvin, John Engbers, Clifford Smyth

The reciprocal of $e^{-x}$ has a power series about $0$ in which all coefficients are non-negative. Gessel [Reciprocals of exponential polynomials and permutation enumeration, Australas. J. Combin., 74, 2019] considered truncates of the power series of $e^{-x}$, i.e. polynomials of the form $\sum_{n=0}^r (-1)^n\frac{x^n}{n!}$, and established combinatorially

Vladislav Popkov, Xin Zhang, Frank Göhmann, Andreas Klümper

We propose a qubit basis composed of transverse spin helices with kinks. Unlike the usual computational basis, this chiral basis is well suited for describing quantum states with nontrivial topology. Choosing appropriate parameters the operators of the transverse spin components, $\sigma_n^x$ and $\sigma_n^y$, become diagonal in the chiral basis, which facil

Emilyann Nault, Ronnie Smith, Lynne Baillie

In the field of Human Robot Interaction (HRI), socially assistive robots are being investigated to see if they can help combat challenges that can come with aging by providing different forms of support to older adults. As a result, it is imperative that the HRI community are aware of the potential pitfalls that can occur such as over-attachment, over-relian

Carl Bettosi, Kefan Chen, Ryan Shah, Lynne Baillie

Socially Assistive Robots (SARs) have shown promising potential in therapeutic scenarios as decision-making instructors or motivational companions. In human-human therapy, experts often communicate the thought process behind the decisions they make to promote transparency and build trust. As research aims to incorporate more complex decision-making models in

William A. Wood, Ian E. Jacobs, Leszek J. Spalek, Yuxuan Huang

Hall effect measurements in doped polymer semiconductors are widely reported, but are difficult to interpret due to screening of Hall voltages by carriers undergoing incoherent transport. Here, we propose a refined analysis for such Hall measurements, based on measuring the Hall coefficient as a function of temperature, and modelling carriers as existing in

Jingchen Sun, Jiayu Qin, Zihao Lin, Changyou Chen

Large pre-trained vision-language (VL) models have shown significant promise in adapting to various downstream tasks. However, fine-tuning the entire network is challenging due to the massive number of model parameters. To address this issue, efficient adaptation methods such as prompt tuning have been proposed. We explore the idea of prompt tuning with mult

Henrique Gomes, Jeremy Butterfield

In this two-part paper, we review, and then develop, the assessment of the hole argument for general relativity. This first Part reviews the literature hitherto, focussing on the philosophical aspects. It also introduces two main ideas we will need in Part II: which will propose a framework for making comparisons of non-isomorphic spacetimes. In Section 1 of

Xiaolan Yu, Xingting Wang

In this paper, we prove that the quantum groups $G(A,B)$ introduced by Mrozinski and their Hopf-Galois objects are twisted Calabi-Yau algebras, and give their Nakayama automorphisms explicitly.

Ze Zhong, Yang Guo, Thomas Wiegelmann, Mingde Ding

Two major mechanisms have been proposed to drive the solar eruptions: the ideal magnetohydrodynamic instability and the resistive magnetic reconnection. Due to the close coupling and synchronicity of the two mechanisms, it is difficult to identify their respective contribution to solar eruptions, especially to the critical rapid acceleration phase. Here, to

Tobias Fritz, Fabio Gadducci, Paolo Perrone, Davide Trotta

Introduced in the 1990s in the context of the algebraic approach to graph rewriting, gs-monoidal categories are symmetric monoidal categories where each object is equipped with the structure of a commutative comonoid. They arise for example as Kleisli categories of commutative monads on cartesian categories, and as such they provide a general framework for e

Arman Ferdowsi, Matthias Függer, Thomas Nowak, Ulrich Schmid

Thresholded mode-switched ODEs are restricted dynamical systems that switch ODEs depending on digital input signals only, and produce a digital output signal by thresholding some internal signal. Such systems arise in recent digital circuit delay models, where the analog signals within a gate are governed by ODEs that change depending on the digital inputs.

Jarno Laakso, Lauri Himanen, Henrietta Homm, Eiaki V. Morooka

We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives

Patrick J. Burchell

Ramanujan derived the well known divergent-sum of integers in more than one way. We generalise the informal method to higher powers of the Riemann zeta function through a study of the Eulerian numbers in particular. Within the context of additive combinatorics a heuristic approach that unifies generating series and difference matrices is presented.

Pengfei Liu, Wenjin Deng, Hengda Li, Jintai Wang

It is still an interesting and challenging problem to synthesize a vivid and realistic singing face driven by music signal. In this paper, we present a method for this task with natural motions of the lip, facial expression, head pose, and eye states. Due to the coupling of the mixed information of human voice and background music in common signals of music

Paul Pollack

Let $\lambda(n)$ denote the exponent of the multiplicative group modulo $n$. We show that when $q$ is odd, each coprime residue class modulo $q$ is hit equally often by $\lambda(n)$ as $n$ varies. Under the stronger assumption that $\gcd(q,6)=1$, we prove that equidistribution persists throughout a Siegel--Walfisz-type range of uniformity. By similar methods

Zilin Luo, Yaoyao Liu, Bernt Schiele, Qianru Sun

Exemplar-based class-incremental learning (CIL) finetunes the model with all samples of new classes but few-shot exemplars of old classes in each incremental phase, where the "few-shot" abides by the limited memory budget. In this paper, we break this "few-shot" limit based on a simple yet surprisingly effective idea: compressing exemplars by downsampling no

Tobias Weber

We present the free and open source software TAS-Paths, a novel system which calculates optimal, collision-free paths for the movement of triple-axis spectrometers. The software features an easy to use graphical user interface, but can also be scripted and used as a library. It allows the user to plan and visualise the motion of the instrument before the exp

Olympio Hacquard, Vadim Lebovici

In this article, we study Euler characteristic techniques in topological data analysis. Pointwise computing the Euler characteristic of a family of simplicial complexes built from data gives rise to the so-called Euler characteristic profile. We show that this simple descriptor achieve state-of-the-art performance in supervised tasks at a very low computatio

Anders Martinsson

The burning number of a graph $G$ is the smallest positive integer $k$ such that the vertex set of $G$ can be covered with balls of radii $0, 1, \dots, k-1$. A well-known conjecture by Bonato, Janssen and Roshabin states that any connected graph on $n$ vertices has burning number at most $\lceil \sqrt{n} \rceil$. It was recently shown by Norin and Turcotte t

Teng Wang, Yixiao Ge, Feng Zheng, Ran Cheng

The state of the arts in vision-language pretraining (VLP) achieves exemplary performance but suffers from high training costs resulting from slow convergence and long training time, especially on large-scale web datasets. An essential obstacle to training efficiency lies in the entangled prediction rate (percentage of tokens for reconstruction) and corrupti

Nicolás Andruskiewitsch, Sonia Natale, Blas Torrecillas

We present a rich source of Hopf algebras starting from a cofinite central extension of a Noetherian Hopf algebra and a subgroup of the algebraic group of characters of the central Hopf subalgebra. The construction is transparent from a Tannakian perspective. We determine when the new Hopf algebras are co-Frobenius, or cosemisimple, or Noetherian, or regular

Mathias Nikolai Arnesen, Mats Ehrnstrom, Atanas G. Stefanov

Recently, two different proofs for large and intermediate-size solitary waves of the nonlocally dispersive Whitham equation have been presented, using either global bifurcation theory or the limit of waves of large period. We give here a different approach by maximising directly the dispersive part of the energy functional, while keeping the remaining nonlin

Markus Fidler, Jaya Champati, Joerg Widmer, Mahsa Noroozi

This paper contributes tail bounds of the age-of-information of a general class of parallel systems and explores their potential. Parallel systems arise in relevant cases, such as in multi-band mobile networks, multi-technology wireless access, or multi-path protocols, just to name a few. Typically, control over each communication channel is limited and rand

Yang Sun, Feng Zhang, Shunqing Wu, Vladimir Antropov

The report on the near-ambient superconductivity in a nitrogen-doped lutetium hydride has stimulated great interest in this material (Dasenbrock-Gammon et al. 2023). While its superconductivity is still a subject of debate, the structure of the claimed cubic phase remains uncertain. In this work, we study the effect of nitrogen doping and pressure on the ene

Denys Y. Kononenko, Ulrich K. Rößler, Jeroen van den Brink, Oleg Janson

Undoped cuprates are an abundant class of magnetic insulators, in which the synergy of rich chemistry and sizable quantum fluctuations leads to a variety of magnetic behaviors. Understanding the magnetism of these materials is impossible without the knowledge of the underlying spin model. The typically dominant antiferromagnetic superexchanges can be accurat

Johannes J. van de Wetering, Simon M. Hooker, Roman Walczak

We explore the regime of operation of the modulator stage of a recently proposed laser-plasma accelerator scheme [Phys. Rev. Lett. 127, 184801 (2021)], dubbed the Plasma-Modulated Plasma Accelerator (P-MoPA). The P-MoPA scheme offers a potential route to high-repetition-rate, GeV-scale plasma accelerators driven by picosecond-duration laser pulses from, for

Tanima Duary, Narayan Banerjee, Ananda Dasgupta

Using the Hayward-Kodama temperature for the apparent horizon, it is found that matter content in the Universe is not thermodynamically stable, and the entry to the late accelerated expansion is actually a second order phase transition. The cosmological model used for the purpose is one that imitates the $\Lambda$CDM model, the favoured model for the present

Nitish Kumar Chandra, Rajiuddin Sk, Prasanta K. Panigrahi

We analyze the dynamics of quantum correlations by obtaining the exact expression of Bures distance entanglement, trace distance discord, and local quantum uncertainty of two two-level atoms. Here, the atoms undergo two-photon transitions mediated through an intermediate virtual state where each atom is separately coupled to a dissipative reservoir at zero t

Murali Sridharan, Leevi Rantala, Mika Mäntylä

Most Self-Admitted Technical Debt (SATD) research utilizes explicit SATD features such as 'TODO' and 'FIXME' for SATD detection. A closer look reveals several SATD research uses simple SATD ('Easy to Find') code comments without the contextual data (preceding and succeeding source code context). This work addresses this gap through PENTACET (or 5C dataset) d

Kanika Dheman, Stefan Walser, Philipp Mayer, Manuel Eggimann

Urine output is a vital parameter to gauge kidney health. Current monitoring methods include manually written records, invasive urinary catheterization or ultrasound measurements performed by highly skilled personnel. Catheterization bears high risks of infection while intermittent ultrasound measures and manual recording are time consuming and might miss ea

Max van Spengler, Erwin Berkhout, Pascal Mettes

This paper introduces an end-to-end residual network that operates entirely on the Poincar\'e ball model of hyperbolic space. Hyperbolic learning has recently shown great potential for visual understanding, but is currently only performed in the penultimate layer(s) of deep networks. All visual representations are still learned through standard Euclidean net

David Benrimoh, Akiva Kleinerman, Toshi A. Furukawa, Charles F. Reynolds

Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological substrates could be associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning has shown promise in predicting treatment response in MDD, but one limitation has been th

M. Deal, M. -J. Goupil, M. S. Cunha, M. J. P. F. G. Monteiro

The transition between convective and radiative stellar regions is still not fully understood. The sharp variations in sound speed located in these transition regions give rise to a signature in specific seismic indicators, opening the possibility to constrain the physics of convection to radiation transition. Among those seismic indicators, the ratios of th

Giulia Cesana

In this paper we employ the Circle Method to give exact formulae for Fourier coefficients of an infinite family of weight zero mixed false modular forms using and extending the techniques of Bringmann and Nazaroglu as well as Rademacher. To do so we additionally provide a bound on a Kloosterman sum of modulus $k$.

Annika Bach, Matthias Ruf

We prove a stochastic homogenization result for integral functionals defined on finite partitions assuming the surface tension to be stationary and possibly ergodic. We also consider the convergence of boundary value problems when we impose a boundary value just on part of the boundary. As a consequence, we show that if the homogenized surface tension is iso

Jiahao Dai, Jing-Wen Yi, Li Chai

This paper utilizes the agent's memory in accelerated consensus for second-order multi-agent systems (MASs). In the case of one-tap memory, explicit formulas for the optimal consensus convergence rate and control parameters are derived by applying the Jury stability criterion. It is proved that the optimal consensus convergence rate with one-tap memory is fa

Ewa Bednarczuk, Giovanni Bruccola, Gabriele Scrivanti, The Hung Tran

We investigate the convergence properties of exact and inexact forward-backward algorithms to minimise the sum of two weakly convex functions defined on a Hilbert space, where one has a Lipschitz-continuous gradient. We show that the exact forward-backward algorithm converges strongly to a global solution, provided that the objective function satisfies a sha

Marina Gomtsyan, Céline Lévy-Leduc, Sarah Ouadah, Laure Sansonnet

In this work, we consider an estimation method in sparse Poisson models inspired by [1] and provide novel sign consistency results under mild conditions.

Ravindra Pawar, Tarkeshwar Singh

Let $G = (V,E)$ be a finite simple undirected graph without isolated vertices. A bijective map $f: V \cup E \rightarrow \{1,2, \dots, |V|+ |E| \}$ is called total local antimagic labeling if for each edge $uv \in E, w(u) \ne w(v)$, where $w(v)$ is a weight of a vertex $v$ defined by $w(v) = \sum_{x \in NT(v)} f(x)$, where $NT(u) = N(u) \cup \{uv: uv\in E\}$

Jon-Richard Sommernes, Alfred Millett-Sikking, Florian Ströhl

Oblique plane microscopy (OPM) offers 3D optically sectioned imaging with high spatial- and temporal-resolution while enabling conventional sample mounting. The technique uses a concatenation of three microscopes, two for remote focusing and a tilted tertiary microscope, often including an immersion objective, to image an oblique sample plane. This design in

Chi Wang, Min Zhou, Tiezheng Ge, Yuning Jiang

Content and style disentanglement is an effective way to achieve few-shot font generation. It allows to transfer the style of the font image in a source domain to the style defined with a few reference images in a target domain. However, the content feature extracted using a representative font might not be optimal. In light of this, we propose a content fus

Rok Vogrinčič, Janez Kos, Tomaž Zwitter, Gregor Traven

We use more than 872,000 mid-to-high resolution (R $\sim$ 20,000) spectra of stars from the GALAH survey to discern the spectra of diffuse interstellar bands (DIBs). We use four windows with the wavelength range from 4718 to 4903, 5649 to 5873, 6481 to 6739, and 7590 to 7890 \AA, giving a total coverage of 967 \AA. We produce $\sim$400,000 spectra of interst

Hao Yin

In this paper, we study the blow-up of a sequence of Yang-Mills connection with bounded energy on a four manifold. We prove a set of equations relating the geometry of the bubble connection at the infinity with the geometry of the limit connection at the energy concentration point. These equations exclude certain scenarios from happening, for example, there

Pengwei Liang, Junjun Jiang, Xianming Liu, Jiayi Ma

Image deblurring continues to achieve impressive performance with the development of generative models. Nonetheless, there still remains a displeasing problem if one wants to improve perceptual quality and quantitative scores of recovered image at the same time. In this study, drawing inspiration from the research of transformer properties, we introduce the

Lena M. Saure, Niklas Kohlmann, Haoyi Qiu, Shwetha Shetty

Conversion of light into heat is essential for a broad range of technologies such as solar thermal heating, catalysis and desalination. Three-dimensional (3D) carbon nanomaterial-based aerogels have shown to hold great promise as photothermal transducer materials. However, till now, their light-to-heat conversion is limited by surface-near absorption, result

Thierry Combot

Consider a hyperelliptic integral $I=\int P/(Q\sqrt{S}) dx$, $P,Q,S\in\mathbb{K}[x]$, with $[\mathbb{K}:\mathbb{Q}]<\infty$. When $S$ is of degree $\leq 4$, such integral can be calculated in terms of elementary functions and elliptic integrals of three kinds $\mathcal{F},\mathcal{E},\Pi$. When $S$ is of higher degree, it is typically non elementary, but it

Tran Nguyen Le, Fares J. Abu-Dakka, Ville Kyrki

Planning robotic manipulation tasks, especially those that involve interaction between deformable and rigid objects, is challenging due to the complexity in predicting such interactions. We introduce SPONGE, a sequence planning pipeline powered by a deep learning-based contact prediction model for contacts between deformable and rigid bodies under interactio

Yi-Syuan Chen, Yun-Zhu Song, Hong-Han Shuai

Neural abstractive summarization has been widely studied and achieved great success with large-scale corpora. However, the considerable cost of annotating data motivates the need for learning strategies under low-resource settings. In this paper, we investigate the problems of learning summarizers with only few examples and propose corresponding methods for

Masaki Unno, Tomoyuki Hanawa, Shinsuke Takasao

Recent observations suggest late accretion, which is generally nonaxisymmetric, onto protoplanetary disks. We investigated nonaxisymmetric late accretion considering the effects of magnetic fields. Our model assumes a cloudlet encounter event at a few hundred au scale, where a magnetized gas clump (cloudlet) encounters a protoplanetary disk. We studied how t