December 2023 arXiv papers — page 87

Eric L. Wisotzky, Jost Triller, Anna Hilsmann, Peter Eisert

Spectral imaging enables the analysis of optical material properties that are invisible to the human eye. Different spectral capturing setups, e.g., based on filter-wheel, push-broom, line-scanning, or mosaic cameras, have been introduced in the last years to support a wide range of applications in agriculture, medicine, and industrial surveillance. However,

Tiziano Marinaro, Pablo Buiras, Andreas Lindner, Roberto Guanciale

Since the advent of Spectre attacks, researchers and practitioners have developed a range of hardware and software measures to counter transient execution attacks. A prime example of such mitigation is speculative load hardening in LLVM, which protects against leaks by tracking the speculation state and masking values during misspeculation. LLVM relies on st

Oliver D. Street, So Takao

The recent interest in structure preserving stochastic Lagrangian and Hamiltonian systems raises questions regarding how such models are to be understood and the principles through which they are to be derived. By considering a mathematically sound extension of the Hamilton-Pontryagin principle, we derive a stochastic analogue of the Euler-Lagrange equations

Mike Thornton, Danilo Mandic, Tobias Reichenbach

The electroencephalogram (EEG) offers a non-invasive means by which a listener's auditory system may be monitored during continuous speech perception. Reliable auditory-EEG decoders could facilitate the objective diagnosis of hearing disorders, or find applications in cognitively-steered hearing aids. Previously, we developed decoders for the ICASSP Auditory

Yifeng Ma, Shiwei Zhang, Jiayu Wang, Xiang Wang

Emotional talking head generation has attracted growing attention. Previous methods, which are mainly GAN-based, still struggle to consistently produce satisfactory results across diverse emotions and cannot conveniently specify personalized emotions. In this work, we leverage powerful diffusion models to address the issue and propose DreamTalk, a framework

Zi-Yu Khoo, Abel Yang, Jonathan Sze Choong Low, Stéphane Bressan

Can a machine or algorithm discover or learn Kepler's first law from astronomical sightings alone? We emulate Johannes Kepler's discovery of the equation of the orbit of Mars with the Rudolphine tables using AI Feynman, a physics-inspired tool for symbolic regression.

Yundu Zhao, Shan Huang, Shengjun Wu

Uncertainty relations and quantum entanglement are pivotal concepts in quantum theory. Beyond their fundamental significance in shaping our understanding of the quantum world, they also underpin crucial applications in quantum information theory. In this article, we investigate entropic uncertainty relations and entanglement detection with an emphasis on qua

Lev A. Stanislavsky, Ihor N. Bubnov, Aleksander A. Stanislavsky, Philippe Zarka

Context. Cassiopeia A occupies an important place among supernova remnants (SNRs) in low-frequency radio astronomy. The analysis of its continuum spectrum from low frequency observations reveals the evolution of the SNR absorption properties over time and suggests a method for probing unshocked ejecta and the SNR interaction with the circumstellar medium (CS

Piyanat Kittiwisit, Steven G. Murray, Hugh Garsden, Philip Bull

Detection of the faint 21 cm line emission from the Cosmic Dawn and Epoch of Reionisation will require not only exquisite control over instrumental calibration and systematics to achieve the necessary dynamic range of observations but also validation of analysis techniques to demonstrate their statistical properties and signal loss characteristics. A key ing

Harsha Hutridurga, Krishan Kumar, Amiya K. Pani

This paper develops and analyses semi-discrete numerical method for two dimensional Vlasov-Stokes' system with periodic boundary condition. The method is based on coupling of semi-discrete discontinuous Galerkin method for the Vlasov equation with discontinuous Galerkin scheme for the stationary incompressible Stokes' equation. The proposed method is both ma

Hugh G. A. Burton

The ability of quantum computers to overcome the exponential memory scaling of many-body problems is expected to transform quantum chemistry. Quantum algorithms require accurate representations of electronic states on a quantum device, but current approximations struggle to combine chemical accuracy and gate-efficiency while preserving physical symmetries, a

Ao Zhang, Pan Zhou, Kaixun Huang, Yong Zou

Open-vocabulary keyword spotting (KWS), which allows users to customize keywords, has attracted increasingly more interest. However, existing methods based on acoustic models and post-processing train the acoustic model with ASR training criteria to model all phonemes, making the acoustic model under-optimized for the KWS task. To solve this problem, we prop

Peter E. Hydon, John R. King

This paper develops methods for simplifying systems of partial differential equations that have families of conservation laws which depend on functions of the independent or dependent variables. In some cases, such methods can be combined with reduction using families of symmetries, giving a multiple reduction that is analogous to the double reduction of ord

Damin Zhang

As large language models (LLMs) have been used in many downstream tasks, the internal stereotypical representation may affect the fairness of the outputs. In this work, we introduce human knowledge into natural language interventions and study pre-trained language models' (LMs) behaviors within the context of gender bias. Inspired by CheckList behavioral tes

Ziliang Chen, Yongsen Zheng, Zhao-Rong Lai, Quanlong Guan

Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels de-confounded from the environments, advancing the technical roadmap of out-of-distribution (OOD) generalization. Despite spotlights around, recent theoretical results verified that some causal features recovered by IRLs merely pretend domain-invariantl

Ekaterina Chaikovskaya, Inna Minashina, Vladimir Litvinenko, Egor Davydenko

When a gait of a bipedal robot is developed using deep reinforcement learning, reference trajectories may or may not be used. Each approach has its advantages and disadvantages, and the choice of method is up to the control developer. This paper investigates the effect of reference trajectories on locomotion learning and the resulting gaits. We implemented t

M. C. Parisi, R. A. P. Oliveira, M. Angelo, B. Dias

The structure of the Small Magellanic Cloud (SMC) outside of its main body is characterised by tidal branches resulting from its interactions mainly with the Large Magellanic Cloud (LMC). Characterising the stellar populations in these tidal components helps to understand the dynamical history of this galaxy and of the Magellanic system in general. We provid

Siddhartha Chattopadhyay, Carlos Marante, Barry I. Schneider, C. William McCurdy

The pump-probe experiments enabled by X-ray free-electron lasers (XFEL) will allow us to directly observe correlated electronic motion with attosecond time resolution by detecting photoelectron pairs in coincidence. In helium, the transition between the non-sequential and sequential regime in two-photon double ionization (TPDI) is well explained by a virtual

Dennis Hein, Staffan Holmin, Timothy Szczykutowicz, Jonathan S Maltz

Diffusion and Poisson flow models have shown impressive performance in a wide range of generative tasks, including low-dose CT image denoising. However, one limitation in general, and for clinical applications in particular, is slow sampling. Due to their iterative nature, the number of function evaluations (NFE) required is usually on the order of $10-10^3$

Liang He, Hongke Wang, Yongchang Cao, Zhen Wu

Extracting relational facts from multimodal data is a crucial task in the field of multimedia and knowledge graphs that feeds into widespread real-world applications. The emphasis of recent studies centers on recognizing relational facts in which both entities are present in one modality and supplementary information is used from other modalities. However, s

Moritz Hauck, Roland Maier, Axel Målqvist

In this work, we present a multiscale approach for the reliable coarse-scale approximation of spatial network models represented by a linear system of equations with respect to the nodes of a graph. The method is based on the ideas of the Localized Orthogonal Decomposition (LOD) strategy and is constructed in a fully algebraic way. This allows to apply the m

Nan Yin, Mengzhu Wang, Zhenghan Chen, Giulia De Masi

The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in processing the non-Euclidean data represented by graphs. However, as a common problem, dynamic graph representation learning faces challenges such as high complexity and large memory over

Frederic Hecht, Olivier Pironneau

The Dual Characteristic-Galerkin method (DCGM) is conservative, precise and experimentally positive. We present the method and prove convergence and $L^2$-stability in the case of Neumann boundary conditions. In a 2D numerical finite element setting (FEM), the method is compared to Primal Characteristic-Galerkin (PCGM), Streamline upwinding (SUPG), the Dual

Andre Rochow, Max Schwarz, Sven Behnke

Facial animation in virtual reality environments is essential for applications that necessitate clear visibility of the user's face and the ability to convey emotional signals. In our scenario, we animate the face of an operator who controls a robotic Avatar system. The use of facial animation is particularly valuable when the perception of interacting with

François Gelis, Sigtryggur Hauksson

A central question in heavy-ion collisions is how the initial far-from-equilibrium medium evolves and thermalizes while it undergoes a rapid longitudinal expansion. In this work we use the two-particle irreducible (2PI) effective action for the first time to consider this question, focusing on $\phi^4$ scalar theory truncated at three loops. We calculate the

Anahita Baninajjar, Ahmed Rezine, Amir Aminifar

Machine learning techniques often lack formal correctness guarantees, evidenced by the widespread adversarial examples that plague most deep-learning applications. This lack of formal guarantees resulted in several research efforts that aim at verifying Deep Neural Networks (DNNs), with a particular focus on safety-critical applications. However, formal veri

Ziqian Wang, Xinfa Zhu, Zihan Zhang, YuanJun Lv

Language models (LMs) have shown superior performances in various speech generation tasks recently, demonstrating their powerful ability for semantic context modeling. Given the intrinsic similarity between speech generation and speech enhancement, harnessing semantic information holds potential advantages for speech enhancement tasks. In light of this, we p

Bingshen Mu, Pengcheng Guo, Dake Guo, Pan Zhou

Automatic Speech Recognition (ASR) has shown remarkable progress, yet it still faces challenges in real-world distant scenarios across various array topologies each with multiple recording devices. The focal point of the CHiME-7 Distant ASR task is to devise a unified system capable of generalizing various array topologies that have multiple recording device

Lukas Postler, Friederike Butt, Ivan Pogorelov, Christian D. Marciniak

Encoding information redundantly using quantum error-correcting (QEC) codes allows one to overcome the inherent sensitivity to noise in quantum computers to ultimately achieve large-scale quantum computation. The Steane QEC method involves preparing an auxiliary logical qubit of the same QEC code used for the data register. The data and auxiliary registers a

A. Y. Timashkov, I. N. Abrosimov, V. M. Yaltonsky

This paper presents the results of a study on the perception of illness and adaptation parameters in patients with type 2 diabetes. The study involved 173 patients diagnosed with "Type 2 Diabetes" (ICD-11 code 5 A 11). The average age of the patients was 55.21+/-13.47 and the average duration of the disease was 11.79+/-8.16. Two profiles of illness perceptio

Guangxuan Song, Dongmei Fu, Zhongwei Qiu, Zijiang Yang

Using machine learning (ML) techniques to predict material properties is a crucial research topic. These properties depend on numerical data and semantic factors. Due to the limitations of small-sample datasets, existing methods typically adopt ML algorithms to regress numerical properties or transfer other pre-trained knowledge graphs (KGs) to the material.

Qi-Yuan Feng, Hao-Xiang Chen, Qun-Ce Xu, Tai-Jiang Mu

Neural radiance field (NeRF) has achieved great success in novel view synthesis and 3D representation for static scenarios. Existing dynamic NeRFs usually exploit a locally dense grid to fit the deformation field; however, they fail to capture the global dynamics and concomitantly yield models of heavy parameters. We observe that the 4D space is inherently s

Maria Vittoria Gurrieri, Emil V. Denning, Kristian Seegert, Philip T. Kristensen

We present a comprehensive investigation of the light-matter interaction dynamics in two-dimensional materials coupled with a spectrally isolated cavity mode in the strong coupling regime. The interaction between light and matter breaks the translational symmetry of excitons in the two-dimensional lattice and results in the emergence of a localized polariton

Wenjun Zhou, Artem Polyvyanyy, James Bailey

Process mining, a data-driven approach for analyzing, visualizing, and improving business processes using event logs, has emerged as a powerful technique in the field of business process management. Process forecasting is a sub-field of process mining that studies how to predict future processes and process models. In this paper, we introduce and motivate th

Micol Spitale, Minja Axelsson, Hatice Gunes

Recently, several works have explored if and how robotic coaches can promote and maintain mental well-being in different settings. However, findings from these studies revealed that these robotic coaches are not ready to be used and deployed in real-world settings due to several limitations that span from technological challenges to coaching success. To over

Rafael Granero-Belinchón, Martina Magliocca

In this paper we establish three global in time results for two fourth order nonlinear parabolic equations. The first of such equations involves the Hessian and appears in epitaxial growth. For such equation we give conditions ensuring the global existence of solution. For certain regime of the parameters, our size condition involves the norm in a critical s

Dingning Liu, Xiaomeng Dong, Renrui Zhang, Xu Luo

In this work, we present a new visual prompting method called 3DAxiesPrompts (3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks. Our investigation reveals that while GPT-4V exhibits proficiency in discerning the position and interrelations of 2D entities through current visual prompting techniques, its abilities in handling 3D spatia

Ariel Caputo, Massimo Zancanaro, Andrea Giachetti

This work compares three locomotion techniques for an immersive VR environment: two different types of teleporting (with and without animation) and a manual (joystick-based) technique. We tested the effect of these techniques on visual motion sickness, spatial awareness, presence, subjective pleasantness, and perceived difficulty of operating the navigation.

Sunjae Yoon, Dahyun Kim, Eunseop Yoon, Hee Suk Yoon

Video-grounded Dialogue (VGD) aims to answer questions regarding a given multi-modal input comprising video, audio, and dialogue history. Although there have been numerous efforts in developing VGD systems to improve the quality of their responses, existing systems are competent only to incorporate the information in the video and text and tend to struggle i

Yuuka Kanakubo

I overview the recent progress of phenomenological studies exploring collective dynamics in relativistic nuclear collisions to understand various QCD properties. Originally, collectivity was interpreted as a manifestation of the hydrodynamic behaviour of the QGP as a response to the initial collision geometry. Over the past decade, however, particularly foll

Torbjørn Smith, Olav Egeland

This paper presents a method for learning Hamiltonian dynamics from a limited set of data points. The Hamiltonian vector field is found by regularized optimization over a reproducing kernel Hilbert space of vector fields that are inherently Hamiltonian, and where the vector field is required to be odd or even. This is done with a symplectic kernel, and it is

Ryan P. Brady, Sergei N. Yurchenko, Jonathan Tennyson, Gap-Sue Kim

A semi-empirical IR/Vis line list, SOLIS, for the sulphur monoxide molecule $^{32}$S$^{16}$O is presented. SOLIS includes accurate empirical rovibrational energy levels, uncertainties, lifetimes, quantum number assignments, and transition probabilities in the form of Einstein $A$ coefficients covering the $X\,{}^{3}\Sigma^{-}$, $a\,{}^{1}\Delta^{ }$, $b\,{}^

Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski

Understanding and identifying the causes behind developers' emotions (e.g., Frustration caused by `delays in merging pull requests') can be crucial towards finding solutions to problems and fostering collaboration in open-source communities. Effectively identifying such information in the high volume of communications across the different project channels, s

Marios Krestenitis, Emmanuel K. Raptis, Athanasios Ch. Kapoutsis, Konstantinos Ioannidis

This paper deals with the problem of informative path planning for a UAV deployed for precision agriculture applications. First, we observe that the ``fear of missing out'' data lead to uniform, conservative scanning policies over the whole agricultural field. Consequently, employing a non-uniform scanning approach can mitigate the expenditure of time in are

Konstantin Semeniuk, Meike Pfeiffer, Javier F. Landaeta, Michael Nicklas

Odd-parity superconductivity is a fundamentally interesting but rare state of matter with a potential for applications in topological quantum computing. Crystals with staggered locally noncentrosymmetric structures have been proposed as platforms where a magnetic field can induce a transition between even- and odd-parity superconducting (SC) states. The stro

Meike Pfeiffer, Konstantin Semeniuk, Javier F. Landaeta, Robert Borth

The unconventional superconductor CeRh$_2$As$_2$ (critical temperature $T_{\mathrm{c}}\approx0.4\,\mathrm{K}$) displays an exceptionally rare magnetic-field-induced transition between two distinct superconducting (SC) phases, proposed to be states of even and odd parity of the SC order parameter, which are enabled by a locally noncentrosymmetric structure. T

Hendrik Laux, Emil Mededovic, Ahmed Hallawa, Lukas Martin

This paper proposes a novel, resource-efficient approach to Visual Speech Recognition (VSR) leveraging speech representations produced by any trained Automatic Speech Recognition (ASR) model. Moving away from the resource-intensive trends prevalent in recent literature, our method distills knowledge from a trained Conformer-based ASR model, achieving competi

M. I. García de Soria, P. Maynar, David Guéry-Odelin, Emmanuel Trizac

Boltzmann showed that in spite of momentum and energy redistribution through collisions, a rarefied gas confined in a isotropic harmonic trapping potential does not reach equilibrium; it evolves instead into a breathing mode where density, velocity and temperature oscillate. This counter-intuitive prediction is upheld by cold atoms experiments. Yet, are the

Rafael Holanda, Cleto B. Miranda-Neto

We provide a duality theorem between Ext and Tor modules over a Cohen-Macaulay local ring possessing a canonical module, and use it to prove some freeness criteria for finite modules. The applications include a characterization of codimension three complete intersection ideals and progress on a long-held multi-conjecture of Vasconcelos. By a similar techniqu

V. V. Zaitsev, V. E. Shaposhnikov, M. L. Khodachenko, M. S. Rumenskikh

On the example of the exoplanet HD 189733 an influence of stellar activity on the efficiency of the plasma mechanism of radio emission generation of the exoplanet and the properties of this emission are considered. The plasma generation mechanism can be effectively implemented in the plasmasphere of exoplanets with a weak magnetic field and a relatively high

Suyi Jiang, Haimin Luo, Haoran Jiang, Ziyu Wang

Recent months have witnessed rapid progress in 3D generation based on diffusion models. Most advances require fine-tuning existing 2D Stable Diffsuions into multi-view settings or tedious distilling operations and hence fall short of 3D human generation due to the lack of diverse 3D human datasets. We present an alternative scheme named MVHuman to generate h

Alicja Dota, Leszek Skrzypczak

Let $ R_\gamma B^{s}_{p,q}(\mathbb{R}d)$ be a subspace of the Besov space $B^{s}_{p,q}(\mathbb{R}^d)$ that consists of block-radial (multi-radial) functions. We study an asymptotic behaviour of approximation numbers of compact embeddings $id: R_\gamma B^{s_1}_{p_1,q_1}(\mathbb{R}^d) \rightarrow R_\gamma B^{s_2}_{p_2,q_2}(\mathbb{R}^d)$. Moreover we find the

Matteo Dunnhofer, Luca Sordi, Niki Martinel, Christian Micheloni

Skiing is a popular winter sport discipline with a long history of competitive events. In this domain, computer vision has the potential to enhance the understanding of athletes' performance, but its application lags behind other sports due to limited studies and datasets. This paper makes a step forward in filling such gaps. A thorough investigation is perf

Isaac M. Mutie, David Williams-Baldwin, Robert J. Beswick, Emmanuel K. Bempong-Manful

We present new high-sensitivity e-MERLIN and VLA radio images of the prototypical Seyfert 2 galaxy NGC 1068 at 5, 10 and 21 GHz. We image the radio jet, from the compact components NE, C, S1 and S2 to the faint double-lobed jet structure of the NE and SW jet lobes. Furthermore, we map the jet between by combining e-MERLIN and VLA data for the first time. Com

L. Toribio San Cipriano, J. De Vicente, I. Sevilla-Noarbe, W. G. Hartley

Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches are machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples s

Jere T. Mäkinen, Samuli Autti, Vladimir B. Eltsov

Under suitable experimental conditions collective spin-wave excitations, magnons, form a Bose-Einstein condensate (BEC) where the spins precess with a globally coherent phase. Bose-Einstein condensation of magnons has been reported in a few systems, including superfluid phases of $^3$He, solid state systems such as Yttrium-iron-garnet (YIG) films, and cold a

Moustafa Rahal, Benoit Denis, Musa Furkan Keskin, Bernard Uguen

In the context of single-base station (BS) non-line-of-sight (NLoS) single-epoch localization with the aid of a reflective reconfigurable intelligent surface (RIS), this paper introduces a novel three-step algorithm that jointly estimates the position and velocity of a mobile user equipment (UE), while compensating for the Doppler effects observed in near-fi

Alexander Kurz, Hendrik A. Mehrtens, Tabea-Clara Bucher, Titus J. Brinker

Deep Neural Networks have shown promising classification performance when predicting certain biomarkers from Whole Slide Images in digital pathology. However, the calibration of the networks' output probabilities is often not evaluated. Communicating uncertainty by providing reliable confidence scores is of high relevance in the medical context. In this work

Daichi Haraguchi, Kiyoaki Shirai, Naoya Inoue, Natthawut Kertkeidkachorn

Shortcut reasoning is an irrational process of inference, which degrades the robustness of an NLP model. While a number of previous work has tackled the identification of shortcut reasoning, there are still two major limitations: (i) a method for quantifying the severity of the discovered shortcut reasoning is not provided; (ii) certain types of shortcut rea

Kunio Kaneta, Hye-Sung Lee, Jiheon Lee, Jaeok Yi

We investigate non-gravitational signals of dark energy within the framework of gauge symmetry in the dark energy sector. Traditionally, dark energy has been primarily studied through gravitational effects within general relativity or its extensions. On the other hand, the gauge principles have played a central role in the standard model sector and dark matt

Zhe Ma, Jianfeng Dong, Shouling Ji, Zhenguang Liu

Visual retrieval aims to search for the most relevant visual items, e.g., images and videos, from a candidate gallery with a given query item. Accuracy and efficiency are two competing objectives in retrieval tasks. Instead of crafting a new method pursuing further improvement on accuracy, in this paper we propose a multi-teacher distillation framework White

Honghao Li, Lei Sang, Yi Zhang, Xuyun Zhang

Click-through rate (CTR) Prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search. Most existing CTR Prediction models utilize explicit feature interactions to overcome the performance bottleneck of implicit feature interactions. Hence, deep CTR models based on parallel st

Kiryl Asheichyk, Matthias Krüger

We study heat radiation and radiative heat transfer for nanoparticles in the presence of an infinitely long cylinder in different geometrical configurations, based on its electromagnetic Green's tensor. The heat radiation of a single particle can be enhanced by placing it close to a nanowire, and this enhancement can be much larger as compared to placing it

Cuipo Jiang, Ching Hung Lam, Hiroshi Yamauchi

We classify vertex operator algebras (VOAs) of OZ-type generated by Ising vectors of $\sigma$-type. As a consequence of the classification, we also prove that such VOAs are simple, rational, $C_2$-cofinite and unitary, that is, they have compact real forms generated by Ising vectors of $\sigma$-type over the real numbers.

Sinya Aoki, Yoshimasa Hidaka, Kiyoharu Kawana, Kengo Shimada

We provide an improved definition of new conserved quantities derived from the energy-momentum tensor in curved spacetime by introducing an additional scalar function. We find that the conserved current and the associated conserved charge become geometric under a certain initial condition of the scalar function, and show that such a conserved geometric curre

Daniel Philip Venmani, Fares Zerradi, Fatiha Hamma, Bruno Jahan

Traditionally, the production efficiency of a factory floor is evaluated using non-real time objective functions. These are based on scheduling punctuality criteria such as 'earliness (a measure of finishing operations ahead of schedule)' and 'tardiness (a measure of delay in executing certain operations)'. With process automation becoming more and more inev

Antoine Caradot, Cuipo Jiang, Zongzhu Lin

This is the continuation of the study of differential graded (dg) vertex algebras previously defined by the authors. The goal of this paper is to construct a functor from the category of dg vertex Lie algebras to the category of dg vertex algebras which is left adjoint to the forgetful functor. This functor not only provides an abundant number of examples of

Jingcai Guo, Qihua Zhou, Ruibing Li, Xiaocheng Lu

This paper provides a novel parsimonious yet efficient design for zero-shot learning (ZSL), dubbed ParsNets, where we are interested in learning a composition of on-device friendly linear networks, each with orthogonality and low-rankness properties, to achieve equivalent or even better performance against existing deep models. Concretely, we first refactor

Tianhao Peng, Wenjun Wu, Haitao Yuan, Zhifeng Bao

Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on heterophilic graphs, where the linked nodes have dissimilar features and different class labels, and the semantically related nodes might be multi-hop away. To address this limitation, t

Francesco Cesarone, Rosella Giacometti, Manuel Luis Martino, Fabio Tardella

In this paper, we propose a general bi-objective model for portfolio selection, aiming to maximize both a diversification measure and the portfolio expected return. Within this general framework, we focus on maximizing a diversification measure recently proposed by Choueifaty and Coignard for the case of volatility as a risk measure. We first show that the m

Nurlan Abiev

We study the behavior of a three-dimensional dynamical system with respect to some set $S$ given in 3-dimensional euclidian space. Geometrically such a system arises from the normalized Ricci flow on some class of generalized Wallach spaces that can be described by a real parameter $a\in (0,1/2)$, as for~$S$ it represents the set of invariant Riemannian metr

Jianhui Zhu

The study of heavy-flavour mesons and baryons in hadronic collisions provides unique access to the properties of heavy-quark hadronisation in the presence of large partonic densities, where new mechanisms of hadron formation beyond in-vacuum fragmentation can emerge. Performing these measurements in intervals of charged-particle multiplicities across differe

M. E. Bal, E. Cheah, Z. Lei, R. Schott

We have characterized the electronic properties of a high-mobility two-dimensional electron system in modulation doped InAsSb quantum wells and compare them to InSb quantum wells grown in a similar fashion. Using temperature-dependent Shubnikov-de Haas experiments as well as FIR transmission we find an effective mass of $m^{\ast} \approx$ 0.022$m_{e}$, which

Arun K Pujari, Sowmini Devi Veeramachaneni

Particle Swarm Optimization (PSO) has emerged as a powerful metaheuristic global optimization approach over the past three decades. Its appeal lies in its ability to tackle complex multidimensional problems that defy conventional algorithms. However, PSO faces challenges, such as premature stagnation in single-objective scenarios and the need to strike a bal

C. M. J. Marques, C. J. A. P. Martins, B. Gilabert López

The possibility of watching the Universe expand in real time and in a model-independent way, first envisaged by Allan Sandage more than 60 years ago and known as the redshift drift, is within reach of forthcoming astrophysical facilities, particularly the Extremely Large Telescope (ELT) and the Square Kilometre Array Observatory (SKAO). The latter, probing l

Indra Yudhistira, Ramal Afrose, Shaffique Adam

Electrons in quantum matter behave like a fluid when the quantum-mechanical carrier-carrier scattering dominates over other relaxation mechanisms. By combining a microscopic treatment of electron-electron interactions within the random phase approximation with a phenomenological Navier-Stokes like equation, we predict that in the limit of high temperature an

R S Mrinaleni, E P Amaladass, A T Sathyanarayana, Jegadeesan P

The Nd0.6Sr0.4MnO3 manganite system exhibits a phase transition from paramagnetic insulating (PMI) to ferromagnetic metallic (FMM) state around its Curie temperature TC = 270 K (bulk). The morphology-driven changes in the kinetically arrested magnetic phases in NSMO thin films with granular and a crossed-nano-rod-type morphology are studied. At low temperatu

Nicolas Troquard, Martina De Sanctis, Paola Inverardi, Patrizio Pelliccione

The rise of AI-based and autonomous systems is raising concerns and apprehension due to potential negative repercussions stemming from their behavior or decisions. These systems must be designed to comply with the human contexts in which they will operate. To this extent, Townsend et al. (2022) introduce the concept of SLEEC (social, legal, ethical, empathet

Jaeho Moon, Juan Luis Gonzalez Bello, Byeongjun Kwon, Munchurl Kim

Self-supervised monocular depth estimation (DE) is an approach to learning depth without costly depth ground truths. However, it often struggles with moving objects that violate the static scene assumption during training. To address this issue, we introduce a coarse-to-fine training strategy leveraging the ground contacting prior based on the observation th

Connor Gascoigne, Theresa Smith, Andrea Riebler

Age-Period-Cohort (APC) models are well used in the context of modelling health and demographic data to produce smooth estimates of each time trend. When smoothing in the context of APC models, there are two main schools, frequentist using penalised smoothing splines, and Bayesian using random processes with little crossover between them. In this article, we

Boris Grimm, Rowan Hoogervorst, Ralf Borndörfer

A major step in the planning process of passenger railway operators is the assignment of rolling stock, i.e., train units, to the trips of the timetable. A wide variety of mathematical optimization models have been proposed to support this task, which we discuss and argue to be justified in order to deal with operational differences between railway operators

Bartłomiej Bąk

It is shown that Maxwell equations for electromagnetic fields generated by the uniformly accelerated charge could be reduced to the Laplace equation (in {\L}obaczewski geometry) for a single scalar potential. The full solution of this equation is presented.

Javier Rodríguez Rodríguez, Enrique Díez Alonso, Santiago Iglesias Álvarez, Saúl Pérez Fernández

We present 24 new dense lightcurves of the near-Earth asteroids (3103) Eger, (161989) Cacus, (2100) Ra-Shalom and (12711) Tukmit, obtained with the Instituto Astrof\'isico Canarias 80 and Telescopio Abierto Remoto 2 telescopes at the Teide Observatory (Tenerife, Spain) during 2021 and 2022, in the framework of projects visible NEAs observations survey and NE

Dapeng Zhi, Peixin Wang, Cheng Chen, Min Zhang

Deep Reinforcement Learning (DRL) has gained prominence as an effective approach for control systems. However, its practical deployment is impeded by state perturbations that can severely impact system performance. Addressing this critical challenge requires robustness verification about system performance, which involves tackling two quantitative questions:

Ping Tang, Gerrit E. W. Bauer

Non-collinear antiferromagnets (nAFMs) with a small net magnetic moment offer new opportunities for ultrafast spintronic devices, owing to unique physical properties. While in ferromagnets and collinear AFMs the spin current polarization is locked to the magnetization $\hat{\mathbf{m}}$ and N\'eel vector $\hat{\mathbf{n}}$ directions, we predict that magnon

Han Wang, Nirmalendu Prakash, Nguyen Khoi Hoang, Ming Shan Hee

Topic modeling is a widely used technique for revealing underlying thematic structures within textual data. However, existing models have certain limitations, particularly when dealing with short text datasets that lack co-occurring words. Moreover, these models often neglect sentence-level semantics, focusing primarily on token-level semantics. In this pape

Valeria Giunta, Thomas Hillen, Mark Lewis, Jonathan Potts

In this paper we study a broad class of non-local advection-diffusion models describing the behaviour of an arbitrary number of interacting species, each moving in response to the non-local presence of others. Our model allows for different non-local interaction kernels for each species and arbitrarily many spatial dimensions. We prove the global existence o

Minsu Kim, Seong-Hyeon Hwang, Steven Euijong Whang

Continuous machine learning pipelines are common in industrial settings where models are periodically trained on data streams. Unfortunately, concept drifts may occur in data streams where the joint distribution of the data X and label y, P(X, y), changes over time and possibly degrade model accuracy. Existing concept drift adaptation approaches mostly focus

Jingmeng Cui, Anna Lichtwarck-Aschoff, Fred Hasselman

The article under discussion, titled "Climbing Escher's stairs: A way to approximate stability landscapes in multidimensional systems" (doi: 10.1371/journal.pcbi.1007788), has captured our attention due to important methodological limitations that we believe warrant discussion. Our aim in writing this Formal Comment is to bring to the attention of potential

Mauricio Hippert, Joaquin Grefa, T. Andrew Manning, Jorge Noronha

We present results for a Bayesian analysis of the location of the QCD critical point constrained by first-principles lattice QCD results at zero baryon density. We employ a holographic Einstein-Maxwell-dilaton model of the QCD equation of state, capable of reproducing the latest lattice QCD results at zero and finite baryon chemical potential. Our analysis i

Mousa Sondoqah, Fehmi Ben Abdesslem, Kristina Popova, Moira McGregor

We report on a three-day challenge during which five teams each programmed a nanodrone to be piloted through an obstacle course using bodily movement, in a 3D transposition of the '80s video-game Pacman. Using a bricolage approach to analyse interviews, field notes, video recordings, and inspection of each team's code revealed how participants were shaping a

Ilaria Colazzo, Eric Jespers, Łukasz Kubat, Arne Van Antwerpen

We study simple set-theoretic solutions of the Yang-Baxter equation that are finite and non-degenerate. Such retractable solutions are fully described and to investigate the irretracble solutions we give a new algebraic method. Our approach includes and extends the work of Joyce for quandles and Castelli for involutive solutions, demonstrating that the simpl

Wei Tan, Lan Du, Wray Buntine

The effectiveness of active learning largely depends on the sampling efficiency of the acquisition function. Expected Loss Reduction (ELR) focuses on a Bayesian estimate of the reduction in classification error, and more general costs fit in the same framework. We propose Bayesian Estimate of Mean Proper Scores (BEMPS) to estimate the increase in strictly pr

Supanat Kamtue, Shiping Liu, Florentin Münch, Norbert Peyerimhoff

In this paper, we propose a generalization of Bakry-\'Emery's calculus which allows us to formulate both Bakry-\'Emery and entropic curvature simultaneously. This formulation represents both curvatures as an integral of the Bochner formula against some measure. This leads to a natural optimality criterion of measures, which we investigate in the Bakry-\'Emer

Jiashuo Zhang, Jiachi Chen, Zhiyuan Wan, Ting Chen

To empower smart contracts with the promising capabilities of cryptography, Ethereum officially introduced a set of cryptographic APIs that facilitate basic cryptographic operations within smart contracts, such as elliptic curve operations. However, since developers are not necessarily cryptography experts, requiring them to directly interact with these basi

Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme

Sequential recommendation models are crucial for next-item recommendations in online platforms, capturing complex patterns in user interactions. However, many focus on a single behavior, overlooking valuable implicit interactions like clicks and favorites. Existing multi-behavioral models often fail to simultaneously capture sequential patterns. We propose C

Roel Lambers, Rudi Pendavingh, Frits Spieksma, Céline M. F. Swennenhuis

We consider a 1-machine scheduling problem where the temperature of a job rises during processing, and cools down when not being processed according to given linear heating and cooling rates. No job's temperature is allowed to rise above a given threshold, and no job's temperature can cool below 0. Another crucial property of our problem is that jobs can be

Selcuk Anil Karatopak, Deniz Sen

Many species face the threat of extinction. It's important to study these species and gather information about them as much as possible to preserve biodiversity. Due to the rarity of endangered species, there is a limited amount of data available, making it difficult to apply data requiring generative AI methods to this domain. We aim to study the feasibilit

Zechen Li, Weiming Huang, Kai Zhao, Min Yang

Recently, learning urban region representations utilizing multi-modal data (information views) has become increasingly popular, for deep understanding of the distributions of various socioeconomic features in cities. However, previous methods usually blend multi-view information in a posteriors stage, falling short in learning coherent and consistent represe

Q. Mazouni, H. Spieker, A. Gotlieb, M. Acher

In sequential decision making, neural networks (NNs) are nowadays commonly used to represent and learn the agent's policy. This area of application has implied new software quality assessment challenges that traditional validation and verification practises are not able to handle. Subsequently, novel approaches have emerged to adapt those techniques to NN-ba