October 2023 arXiv papers — page 44

Ayei E. Ibor, Mark Hooper, Carsten Maple, Gregory Epiphaniou

Foundational identity systems (FIDS) have been used to optimise service delivery and inclusive economic growth in developing countries. As developing nations increasingly seek to use FIDS for the identification and authentication of identity (ID) holders, trustworthy interoperability will help to develop a cross-border dimension of e-Government. Despite this

Toochukwu Aka, Shankar Narayan

Flow across heated parallel channel systems exists in many applications. The performance of such systems experiencing multiphase flow could suffer from the deleterious effects of flow non-uniformity or maldistribution. Modeling the behavior of such systems is challenging due to the inherent non-linearity associated with the multiphase flow and the difficulty

Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li

Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels in computer vision; however, graph models encompass both node features and graph topology as input, and become more susceptible to label noise through message-passing mechanisms.

Xun An

An important dimension of pointer analysis is field-Sensitive, which has been proven to effectively enhance the accuracy of pointer analysis results. A crucial area of research within field-Sensitive is Structure-Sensitive. Structure-Sensitive has been shown to further enhance the precision of pointer analysis. However, existing structure-sensitive methods c

Andrei Benguş-Lasnier, Terence Gaffney, Antoni Rangachev

We study equisingularity of families of reduced curves over smooth parameter spaces of arbitrary positive dimension, using the difference between two analytic invariants of a curve singularity: the multiplicity of its Jacobian ideal and its complete intersection discrepancy. This difference provides a fiberwise multiplicity criterion for Whitney equisingular

Elli Karvonen, Matti Lassas, Pekka Pankka, Samuli Siltanen

A novel reconstruction method is introduced for the severely ill-posed inverse problem of limited-angle tomography. It is well known that, depending on the available measurement, angles specify a subset of the wavefront set of the unknown target, while some oriented singularities remain invisible in the data. Topological Interface recovery for Limited-angle

Peter Raffai, Mária Pálfi, Gergely Dálya, Rachel Gray

We present the first test of coasting cosmological models with gravitational-wave standard sirens observed in the first three observing runs of the LIGO-Virgo-KAGRA detector network. We apply the statistical galaxy catalog method adapted to coasting cosmologies and infer constraints on the $H_0$ Hubble constant for the three fixed values of the curvature par

Hippolyte Charvin, Nicola Catenacci Volpi, Daniel Polani

The presence of symmetries imposes a stringent set of constraints on a system. This constrained structure allows intelligent agents interacting with such a system to drastically improve the efficiency of learning and generalization, through the internalisation of the system's symmetries into their information-processing. In parallel, principled models of com

Jessica Braudo, Noam Soker

We consider the finite velocity of the ejecta of a type Ia supernova (SN Ia) in the double detonation (DDet) scenario with a white dwarf (WD) mass-donor companion, and find that the runaway velocity of the surviving (mass donor) WD is lower than its pre-explosion orbital velocity by about 8-11%. This implies that the fastest runaway WDs in the Galaxy, if com

Svitlana Pastukh, Paweł T. Jochym, Oleksandr Pastukh, Jan Łażewski

Ab initio investigations of structural, electronic, and dynamical properties of the high-temperature $\beta$ phase of copper pyrophosphate were performed using density functional theory. The electronic band structure shows the Mott insulating state due to electron correlations in copper ions. By calculating phonon dispersion relations, the soft mode at the A

Nabil El Malki, Robin Cugny, Olivier Teste, Franck Ravat

Clustering is a data analysis method for extracting knowledge by discovering groups of data called clusters. Among these methods, state-of-the-art density-based clustering methods have proven to be effective for arbitrary-shaped clusters. Despite their encouraging results, they suffer to find low-density clusters, near clusters with similar densities, and hi

Xinghua Jiang, Lucas Rotily, Emmanuel Villermaux, Xiaofei Wang

Bubble bursting on water surfaces is believed to be a main mechanism to produce submicron drops, including sea spray aerosols, which play a critical role in forming cloud and transferring various biological and chemical substances from water to the air. Over the past century, drops production mechanisms from bubble bursting have been extensively studied. The

Beniamin Bogosel

The volume of a Meissner polyhedron is computed in terms of the lengths of its dual edges. This allows to reformulate the Meissner conjecture regarding constant width bodies with minimal volume as a series of explicit finite dimensional problems. A direct consequence is the minimality of the volume of Meissner tetrahedras among Meissner pyramids.

Szymon Drgas, Lars Bramsløw, Archontis Politis, Gaurav Naithani

This paper proposes neural networks for compensating sensorineural hearing loss. The aim of the hearing loss compensation task is to transform a speech signal to increase speech intelligibility after further processing by a person with a hearing impairment, which is modeled by a hearing loss model. We propose an interpretable model called dynamic processing

Paul Herrmann, Sebastian Klimmer, Till Weickhardt, Anastasios Papavasileiou

In monolayer transition metal dichalcogenides time-reversal symmetry, combined with space-inversion symmetry, defines the spin-valley degree of freedom. As such, engineering and control of time-reversal symmetry by optical or magnetic fields constitutes the foundation of valleytronics. Here, we propose a new approach for the detection of broken time-reversal

Wei Jiang, Hans D. Schotten

The current focus of academia and the telecommunications industry has been shifted to the development of the six-generation (6G) cellular technology, also formally referred to as IMT-2030. Unprecedented applications that 6G aims to accommodate demand extreme communications performance and, in addition, disruptive capabilities such as network sensing. Recentl

Benjamin Chung

The Julia programming language was designed to fill the needs of scientific computing by combining the benefits of productivity and performance languages. Julia allows users to write untyped scripts easily without needing to worry about many implementation details, as do other productivity languages. If one just wants to get the work done-regardless of how e

Bowen Pang, Sicong Liu, Hongli Wang, Bin Guo

With the rapid development of deep learning, recent research on intelligent and interactive mobile applications (e.g., health monitoring, speech recognition) has attracted extensive attention. And these applications necessitate the mobile edge computing scheme, i.e., offloading partial computation from mobile devices to edge devices for inference acceleratio

Taehyun Cho, Seungyub Han, Heesoo Lee, Kyungjae Lee

Distributional reinforcement learning algorithms have attempted to utilize estimated uncertainty for exploration, such as optimism in the face of uncertainty. However, using the estimated variance for optimistic exploration may cause biased data collection and hinder convergence or performance. In this paper, we present a novel distributional reinforcement l

Stephen Cantrell

Suppose that $(M,\mathfrak{g})$ is a compact Riemannian manifold with strictly negative sectional curvatures. A subset of conjugacy classes $E \subset \text{conj}(\pi_1(M))$ is called spectrally rigid if when two negatively curved Riemannian metrics $\mathfrak{g}_1, \mathfrak{g}_2$ on $M$ have the same marked length spectrum on $E$, then their marked length

Alicia Jiayun Law, Ruoyu Hu, Lisa Alazraki, Anandha Gopalan

In this work, we propose a computational framework that leverages existing out-of-language data to create a conversational agent for the delivery of Self-Attachment Technique (SAT) in Mandarin. Our framework does not require large-scale human translations, yet it achieves a comparable performance whilst also maintaining safety and reliability. We propose two

Hanbin Yang, Noah Rhodes, Haoxiang Yang, Line Roald

The frequency of wildfire disasters has surged five-fold in the past 50 years due to climate change. Preemptive de-energization is a potent strategy to mitigate wildfire risks but substantially impacts customers. We propose a multistage stochastic programming model for proactive de-energization planning, aiming to minimize economic loss while accomplishing a

G. A. Bobkov, I. V. Bobkova, A. M. Bobkov

A superconductor/ferromagnet/superconductor Josephson junction with anomalous phase shift (phi_0-S/F/S JJ) is a system, where the anomalous ground state shift phi_0 provides a direct magnetoelectric coupling between a magnetic moment and a phase of the superconducting condensate. If a chain of such phi_0-S/F/S JJs are coupled via superconducting leads, the c

Jules Sanchez, Louis Soum-Fontez, Jean-Emmanuel Deschaud, Francois Goulette

LiDAR is an essential sensor for autonomous driving by collecting precise geometric information regarding a scene. %Exploiting this information for perception is interesting as the amount of available data increases. As the performance of various LiDAR perception tasks has improved, generalizations to new environments and sensors has emerged to test these op

Maximilian E. Merkel, Claude Ederer

We calculate the screened electron-electron interaction for the charge-disproportionated insulator CaFeO$_3$ using the constrained random-phase approximation (cRPA). While in many correlated materials, the formation of a Mott-insulating state is driven by a large local Coulomb repulsion, represented by the Hubbard $U$, several cases have been identified more

Yunming Zhang, Dengpan Ye, Caiyun Xie, Long Tang

The malicious applications of deep forgery, represented by face swapping, have introduced security threats such as misinformation dissemination and identity fraud. While some research has proposed the use of robust watermarking methods to trace the copyright of facial images for post-event traceability, these methods cannot effectively prevent the generation

Yat-Hin Suen

Spectral networks and non-abelianization were introduced by Gaiotto-Moore-Neitzke and they have many applications in mathematics and physics. In a recent work by Nho, he proved that the non-abelianization of an almost flat local system over the spectral curve of a meromorphic quadratic differential is the same as the family Floer construction. Based on the m

Jaemin Shin, Hyungjun Yoon, Seungjoo Lee, Sungjoon Park

Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data privacy, existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via mobile devices. We propose FedTherapist, a mobile mental health monitoring system that utilizes continuous speech and keyboard input in

Kevin Dijkstra, Deepesh Toshniwal

In this paper we propose a local projector for truncated hierarchical B-splines (THB-splines). The local THB-spline projector is an adaptation of the B\'ezier projector proposed by Thomas et al. (Comput Methods Appl Mech Eng 284, 2015) for B-splines and analysis-suitable T-splines (AS T-splines). For THB-splines, there are elements on which the restrictions

Mariana Lima

In this paper, we consider the spin-2 field perturbations of four families of supergravity solutions. These include AdS$_5$ and AdS$_7$ backgrounds of type IIA as well as AdS$_4$ and AdS$_6$ backgrounds of Type IIB. As the main result, we show that, in all the cases, there is a solution given by a combination of the warp factors. We also find the respective

Qingyuan Tian, Hanlun Zhu, Lei Wang, Yang Li

With the help of Chain-of-Thought (CoT) prompting, Large Language Models (LLMs) have achieved remarkable performance on various reasoning tasks. However, most of them have been evaluated under noise-free context and the dilemma for LLMs to produce inaccurate results under the noisy context has not been fully investigated. Existing studies utilize trigger sen

Yang Wu, Shilong Wang, Hao Yang, Tian Zheng

In this paper, we evaluate different abilities of GPT-4V including visual understanding, language understanding, visual puzzle solving, and understanding of other modalities such as depth, thermal, video, and audio. To estimate GPT-4V's performance, we manually construct 656 test instances and carefully evaluate the results of GPT-4V. The highlights of our f

Rami Z. E. Alsaberi, M. D. Filipović, S. Dai, H. Sano

We present new and archival Australia Telescope Compact Array and Atacama Large Millimeter/submillimeter Array data of the Small Magellanic Cloud supernova remnant 1E 0102.2-7219 at 2100, 5500, 9000, and 108000 MHz; as well as Hi data provided by the Australian Square Kilometre Array Pathfinder. The remnant shows a ring-like morphology with a mean radius of

Prajwal Singh, Dwip Dalal, Gautam Vashishtha, Krishna Miyapuram

Decoding the human brain has been a hallmark of neuroscientists and Artificial Intelligence researchers alike. Reconstruction of visual images from brain Electroencephalography (EEG) signals has garnered a lot of interest due to its applications in brain-computer interfacing. This study proposes a two-stage method where the first step is to obtain EEG-derive

Wojciech Jamroga, Damian Kurpiewski

Synthesis of bulletproof strategies in imperfect information scenarios is a notoriously hard problem. In this paper, we suggest that it is sometimes a viable alternative to aim at "reasonably good" strategies instead. This makes sense not only when an ideal strategy cannot be found due to the complexity of the problem, but also when no winning strategy exist

Jaiyoung Park, Donghwan Kim, Jongmin Kim, Sangpyo Kim

Incorporating fully homomorphic encryption (FHE) into the inference process of a convolutional neural network (CNN) draws enormous attention as a viable approach for achieving private inference (PI). FHE allows delegating the entire computation process to the server while ensuring the confidentiality of sensitive client-side data. However, practical FHE impl

Constantin Schuster, Sebastian Kempf

Cryogenic microcalorimeters are key tools for high-resolution X-ray spectroscopy due to their excellent energy resolution and quantum efficiency close to 100%. Multiple types of microcalorimeters exist, some of which have already proven outstanding performance. Nevertheless, they can't yet compete with cutting-edge grating or crystal spectrometers. For this

Jindřich Helcl, Jindřich Libovický

We present the Charles University system for the MRL~2023 Shared Task on Multi-lingual Multi-task Information Retrieval. The goal of the shared task was to develop systems for named entity recognition and question answering in several under-represented languages. Our solutions to both subtasks rely on the translate-test approach. We first translate the unlab

Tofik Ali, Partha Pratim Roy

This paper introduces a deep learning model tailored for document information analysis, emphasizing document classification, entity relation extraction, and document visual question answering. The proposed model leverages transformer-based models to encode all the information present in a document image, including textual, visual, and layout information. The

Q. Nguyen-Luong, R. Adam, P. Ade, H. Ajeddig

To understand the evolution of dust properties in molecular clouds in the course of the star formation process, we constrain the changes in the dust emissivity index from star-forming filaments to prestellar and protostellar cores to T Tauri stars. Using the NIKA2 continuum camera on the IRAM 30~m telescope, we observed the Taurus B211/B213 filament at 1.2\,

Oleksii Sirotkin

In the process of building (structural learning) a probabilistic graphical model from a set of observed data, the directional, cyclic dependencies between the random variables of the model are often found. Existing graphical models such as Bayesian and Markov networks can reflect such dependencies. However, this requires complicating those models, such as ad

Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

Evaluating the performance of machine learning models on diverse and underrepresented subgroups is essential for ensuring fairness and reliability in real-world applications. However, accurately assessing model performance becomes challenging due to two main issues: (1) a scarcity of test data, especially for small subgroups, and (2) possible distributional

Preethi Lahoti, Nicholas Blumm, Xiao Ma, Raghavendra Kotikalapudi

A crucial challenge for generative large language models (LLMs) is diversity: when a user's prompt is under-specified, models may follow implicit assumptions while generating a response, which may result in homogenization of the responses, as well as certain demographic groups being under-represented or even erased from the generated responses. In this paper

Enrique Álvarez, Jesús Anero, Irene Sánchez-Ruiz

It is well-known that in unimodular gravity the cosmological constant is not sourced by a constant energy density, but rather appears as some sort of integration constant. In this work we try to flesh this out by studying in some detail a couple of examples, one from cosmology and the other from gravitational collapse.

Jaehyun Hong, Aeryeong Seo

Flag domains are open orbits of noncompact real forms of complex semisimple Lie groups acting on flag manifolds. To each flag domain one can associate a compact complex manifold called the base cycle. The ampleness of the normal bundle of the base cycle in a flag domain measures the concavity near the base cycle. In this paper we compute the ampleness of nor

Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a collection. However, current works primarily focus on evaluating graph-level abnormality while failing to provide meaningful explanations for the predictions, which largely limits their reliability and application scope. In this paper

Artur Niewiadomski, Magdalena Kacprzak, Damian Kurpiewski, Michał Knapik

We present MsATL: the first tool for deciding the satisfiability of Alternating-time Temporal Logic (ATL) with imperfect information. MsATL combines SAT Modulo Monotonic Theories solvers with existing ATL model checkers: MCMAS and STV. The tool can deal with various semantics of ATL, including perfect and imperfect information, and can handle additional prac

C. C. Corrigan

In this paper, we use the Weyl-bound for Dirichlet $L$-functions to derive zero-density estimates for $L$-functions associated to families of fixed-order Dirichlet characters. The results improve on previous bounds given by the author when $\sigma$ is sufficiently distanced from the critical line.

Mingfeng Xue, Dayiheng Liu, Kexin Yang, Guanting Dong

The emergence of large language models (LLMs) has revolutionized natural language processing tasks. However, existing instruction-tuning datasets suffer from occupational bias: the majority of data relates to only a few occupations, which hampers the instruction-tuned LLMs to generate helpful responses to professional queries from practitioners in specific f

Ziheng Cheng, Shiyue Zhang, Longlin Yu, Cheng Zhang

Particle-based variational inference methods (ParVIs) such as Stein variational gradient descent (SVGD) update the particles based on the kernelized Wasserstein gradient flow for the Kullback-Leibler (KL) divergence. However, the design of kernels is often non-trivial and can be restrictive for the flexibility of the method. Recent works show that functional

Alireza Khalili Golmankhaneh, Donatella Bongiorno

In this paper, we delve into the fascinating realm of fractal calculus applied to fractal sets and fractal curves. Our study includes an exploration of the method analogues of the separable method and the integrating factor technique for solving $\alpha$-order differential equations. Notably, we extend our analysis to solve Fractal Bernoulli differential equ

Richard Tattersall, Andrew Baggaley, Thomas Billam

When a two-dimensional system undergoes a rapid quench from a disordered to an ordered phase, it does not order instantly but instead relaxes towards equilibrium over time. During this relaxation, the dynamical scaling hypothesis predicts that the length scale of ordered regions increases, with later patterns statistically similar to earlier ones except for

Mohamed F. Hagag, Thomas R. Jones, Karim Seddik, Dimitrios Peroulis

Nonreciprocity in space-time modulated photonic crystals has been investigated in the context of nonreciprocal propagation and polarization. Here, we investigate a reconfigurable nonreciprocal wave growth in space-time modulated crystals. Imposing an adaptable progressive phase shift between successive time-modulated cells results in blue and red shifts of t

Kaituo Zhang, Tianyao Zhou, Bing Xu, Qihong Huang

In this paper, using the latest Pantheon+ sample of Type Ia supernovae (SNe Ia), Baryon Acoustic Oscillation (BAO) measurements, and observational Hubble data (OHD), we carry out a joint constraint on the Hubble constant $H_0$, the spatial curvature $\Omega_{\rm K}$, and the sound horizon at the end of drag epoch $r_{\rm d}$. To be model-independent, four co

C. C. Corrigan

The main purpose of this paper is to establish bounds on the second moment of $L\big(\tfrac{1}{2}+it,\chi\big)$, averaged over families of fixed order characters. A discrete version of the main result is also stated, from which zero-density estimates pertaining to fixed order characters are derived.

Juan Mera Menéndez, Martin Bartlett

Despite its already widespread popularity, it continues to gain adoption. More and more developers and architects continue to adopt and apply the FaaS (Function as a Service) model in cloud solutions. The most extensively used FaaS service is AWS Lambda, provided by Amazon Web Services. Moreover, despite the new trends in programming languages, Java still ma

Aniello Mennella, Kam Arnold, Susanna Azzoni, Carlo Baccigalupi

In this paper we present the European Low Frequency Survey (ELFS), a project that will enable foregrounds-free measurements of primordial $B$-mode polarization to a level 10$^{-3}$ by measuring the Galactic and extra-Galactic emissions in the 5--120\,GHz frequency window. Indeed, the main difficulty in measuring the B-mode polarization comes not just from it

Janez Komelj

The Owen's T function is presented in four new ways, one of them as a series similar to the Euler's arctangent series divided by $2\pi$, which is its majorant series. All possibilities enable numerically stable and fast convergent computation of the bivariate normal integral with simple recursion. When tested $\Phi_\varrho^2(x,y)$ computation on a random sam

Shaul Zemel

We develop the theory of Hermitian Jacobi forms of lattice index, for both definite and indefinite Hermitian lattices. We also prove a theta decomposition theorem for vector-valued Jacobi forms (both in the orthogonal and Hermitian settings), with enhanced periodicity properties. This allows us to give a good definition of orthogonal and Hermitian Jacobi for

Wafa Labidi, Christian Deppe, Holger Boche

Numerous applications in the field of molecular communications (MC) such as healthcare systems are often event-driven. The conventional Shannon capacity may not be the appropriate metric for assessing performance in such cases. We propose the identification (ID) capacity as an alternative metric. Particularly, we consider randomized identification (RI) over

Madeleine Waller, Odinaldo Rodrigues, Oana Cocarascu

As algorithmic decision-making systems become more prevalent in society, ensuring the fairness of these systems is becoming increasingly important. Whilst there has been substantial research in building fair algorithmic decision-making systems, the majority of these methods require access to the training data, including personal characteristics, and are not

C. Beaufort, M. Bastero-Gil, A. Catalano, D-S. Erfani-Harami

This paper presents Dandelion, a new dish antenna experiment searching for dark photons (DPs) with masses around the meV that will start acquiring data by the end of 2023. A spherical mirror acts as a conversion surface between DPs and standard photons that converge to a matrix of 418 Kinetic Inductance Detectors cooled down to 150 mK. A tilt of the mirror a

Elena Berardini, Alessio Caminata, Alberto Ravagnani

We investigate CSS and CSS-T quantum error-correcting codes from the point of view of their existence, rarity, and performance. We give a lower bound on the number of pairs of linear codes that give rise to a CSS code with good correction capability, showing that such pairs are easy to produce with a randomized construction. We then prove that CSS-T codes ex

Jiaju Zhang, Arash Jafarizadeh, M. A. Rajabpour

In this paper, we employ the bootstrap method, a technique that relies on consistency relations instead of direct diagonalization, to determine the expectation values in quantum many-body systems. We then use these values to assess the entanglement content of the system. Our work extends the bootstrap approach to quantum many-body systems, rather than single

Jeremy J. Drake, David Cohen, Michael Corcoran, Maurice Leutenegger

We present some of the salient aspects of the scientific motivation for high resolution soft X-ray spectroscopy of early-type stars with the Line Emission Mapper X-ray Probe. The major strength of {\it LEM} for hot star physics is its large effective area, aided by the inherent energy resolution of its microcalorimeter that readily achieves resolving powers

Christoph Schultheiss, Peter Bühlmann

We propose a method to detect model misspecifications in nonlinear causal additive and potentially heteroscedastic noise models. We aim to identify predictor variables for which we can infer the causal effect even in cases of such misspecification. We develop a general framework based on knowledge of the multivariate observational data distribution. We then

Cong Li, Tianjiao Feng, Xiudeng Zheng, Sabin Lessard

In order to better understand the impact of environmental stochastic fluctuations on the evolution of animal behavior, we introduce the concept of a stochastic Nash equilibrium (SNE) that extends the classical concept of a Nash equilibrium (NE). Based on a stochastic stability analysis of a linear evolutionary game with temporally varying payoffs, we address

Lang Liu, You Wu, Zu-Cheng Chen

Recently, several major pulsar timing array (PTA) collaborations have assembled strong evidence for the existence of a gravitational-wave background at frequencies around the nanohertz regime. Assuming that the PTA signal is attributed to scalar-induced gravitational waves, we jointly employ the PTA data from the NANOGrav 15-year data set, PPTA DR3, and EPTA

Daniel John Mannion

Sophisticated machine learning struggles to transition onto battery-operated devices due to the high-power consumption of neural networks. Researchers have turned to neuromorphic engineering, inspired by biological neural networks, for more efficient solutions. While previous research focused on artificial neurons and synapses, an essential component has bee

Ou Wu, Rujing Yao

Large-scale, high-quality data are considered an essential factor for the successful application of many deep learning techniques. Meanwhile, numerous real-world deep learning tasks still have to contend with the lack of sufficient amounts of high-quality data. Additionally, issues such as model robustness, fairness, and trustworthiness are also closely rela

Christian Malacaria, Daniela Huppenkothen, Oliver J. Roberts, Lorenzo Ducci

RX J0440.9+4431 is an accreting X-ray pulsar (XRP) that remained relatively unexplored until recently, when major X-ray outburst activity enabled more in-depth studies. Here, we report on the discovery of ${\sim}0.2$ Hz quasi-periodic oscillations (QPOs) from this source observed with $Fermi$-GBM. The appearance of QPOs in RX J0440.9+4431 is thricely transie

ATLAS Collaboration

Clusters of topologically connected calorimeter cells around cells with large absolute signal-to-noise ratio (topo-clusters) are the basis for calorimeter signal reconstruction in the ATLAS experiment. Topological cell clustering has proven performant in LHC Runs 1 and 2. It is, however, susceptible to out-of-time pile-up of signals from soft collisions outs

Ioannis Nasios, Konstantinos Vogklis, Avleen Malhi, Anastasia Vayona

In the present era of sustainable innovation, the circular economy paradigm dictates the optimal use and exploitation of existing finite resources. At the same time, the transition to smart infrastructures requires considerable investment in capital, resources and people. In this work, we present a general machine learning approach for offering indoor locati

Z. F. Wu, P. Z. Sun, O. J. Wahab, Y. -T. Tao

Two-dimensional (2D) materials offer a prospect of membranes that combine negligible gas permeability with high proton conductivity and could outperform the existing proton exchange membranes used in various applications including fuel cells. Graphene oxide (GO), a well-known 2D material, facilitates rapid proton transport along its basal plane but proton co

Sebastian Koch, Pedro Hermosilla, Narunas Vaskevicius, Mirco Colosi

D scene graphs are an emerging 3D scene representation, that models both the objects present in the scene as well as their relationships. However, learning 3D scene graphs is a challenging task because it requires not only object labels but also relationship annotations, which are very scarce in datasets. While it is widely accepted that pre-training is an e

Mayukh Sarkar, Jitesh Pradhan, Anil Kumar Singh, Hathiram Nenavath

With IoT systems' increasing scale and complexity, maintenance of a large number of nodes using stationary devices is becoming increasingly difficult. Hence, mobile devices are being employed that can traverse through a set of target locations and provide the necessary services. In order to reduce energy consumption and time requirements, the devices are req

Benno van den Berg

We introduce a new version of arithmetic in all finite types which extends the usual versions with primitive notions of extensionality and extensional equality. This new hybrid version allows us to formulate a strong form of extensionality, which we call converse extensionality. Inspired by Brouwer's notion of apartness, we show that converse extensionality

Sangha Park, Jisoo Mok, Dahuin Jung, Saehyung Lee

Successful detection of Out-of-Distribution (OoD) data is becoming increasingly important to ensure safe deployment of neural networks. One of the main challenges in OoD detection is that neural networks output overconfident predictions on OoD data, make it difficult to determine OoD-ness of data solely based on their predictions. Outlier exposure addresses

Wenbo Cao, Weiwei Zhang

Deep neural networks (DNNs), especially physics-informed neural networks (PINNs), have recently become a new popular method for solving forward and inverse problems governed by partial differential equations (PDEs). However, these methods still face challenges in achieving stable training and obtaining correct results in many problems, since minimizing PDE r

Alkis Blanz

In this paper, we analyze the long-term distributive impact of climate change through rising food prices. We use a standard incomplete markets model and account for non-linear Engel curves for food consumption. For the calibration of our model, we rely on household data from 92 developing countries, representing 4.5 billion people. The results indicate that

Matteo Framba, Veronica Vinciotti, Ernst C. Wit

Various processes can be modelled as quasi-reaction systems of stochastic differential equations, such as cell differentiation and disease spreading. Since the underlying data of particle interactions, such as reactions between proteins or contacts between people, are typically unobserved, statistical inference of the parameters driving these systems is deve

Guy Bouchitté, Rajesh Mahadevan

In models of $N$ interacting particles in $\R^d$ as in Density Functional Theory or crowd motion, the repulsive cost is usually described by a two-point function $c_\e(x,y) =\ell\Big(\frac{|x-y|}{\e}\Big)$ where $\ell: \R_+ \to [0,\infty]$ is decreasing to zero at infinity and parameter $\e>0$ scales the interaction distance. In this paper we identify the me

Florian Felten, Daniel Gareev, El-Ghazali Talbi, Grégoire Danoy

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex problems. The recent introduction of multi-objective reinforcement learning (MORL) has further expanded the scope of RL by enabling agents to make trade-offs among multiple objectives. This advancement not only has broadened the range of problems that can be tackled but also

V. Plastovets, A. Buzdin

In this letter we discuss physical mechanisms for delocalization of the magnetic field of Abrikosov vortex in superconductors in the presence of planar defects of various electronic transparency. The resulting strong perturbation of the supercurrent can significantly affect the local flux measurements and mimic the vortices that carried only part of a flux q

Menouar Azib, Benjamin Renard, Philippe Garnier, Vincent Génot

Event detection in time series data is crucial in various domains, including finance, healthcare, cybersecurity, and science. Accurately identifying events in time series data is vital for making informed decisions, detecting anomalies, and predicting future trends. Despite extensive research exploring diverse methods for event detection in time series, with

Mario Picerno, Lucas Koch, Kevin Badalian, Marius Wegener

The process of developing control functions for embedded systems is resource-, time-, and data-intensive, often resulting in sub-optimal cost and solutions approaches. Reinforcement Learning (RL) has great potential for autonomously training agents to perform complex control tasks with minimal human intervention. Due to costly data generation and safety cons

Max Müller-Eberstein, Rob van der Goot, Barbara Plank, Ivan Titov

Representational spaces learned via language modeling are fundamental to Natural Language Processing (NLP), however there has been limited understanding regarding how and when during training various types of linguistic information emerge and interact. Leveraging a novel information theoretic probing suite, which enables direct comparisons of not just task p

Jongbin Ryu, Dongyoon Han, Jongwoo Lim

We introduce a novel architecture design that enhances expressiveness by incorporating multiple head classifiers (\ie, classification heads) instead of relying on channel expansion or additional building blocks. Our approach employs attention-based aggregation, utilizing pairwise feature similarity to enhance multiple lightweight heads with minimal resource

Albert Escrivà, Chul-Moon Yoo

We consider the formation of primordial black holes (PBHs), during the radiation-dominated Universe, generated from the collapse of super-horizon curvature fluctuations that are overlapped with others on larger scales. Using a set of different curvature profiles, we show that the threshold for PBH formation (defined as the critical peak of the compaction fun

Mohammad Reza Hasanabadi

Object Based Audio (OBA) provides a new kind of audio experience, delivered to the audience to personalize and customize their experience of listening and to give them choice of what and how to hear their audio content. OBA can be applied to different platforms such as broadcasting, streaming and cinema sound. This paper presents a novel approach for creatin

Miikka S. Väisälä, Hsien Shang, Daniele Galli, Susana Lizano

Pseudodisks are dense structures formed perpendicular to the direction of the magnetic field during the gravitational collapse of a molecular cloud core. Numerical simulations of the formation of pseudodisks are usually computationally expensive with conventional CPU codes. To demonstrate the proof-of-concept of a fast computing method for this numerically c

Adil El Abdouni

In this paper we propose some Harris-like criteria in order to study the long time behavior of general positive and periodic semiflows. These criteria allow us to obtain new existence results of principal eigenelements, and their exponential attractiveness. We present applications to two biological models in a space-time varying environment: a non local sele

Romain Xu-Darme, Jenny Benois-Pineau, Romain Giot, Georges Quénot

In the field of Explainable AI, multiples evaluation metrics have been proposed in order to assess the quality of explanation methods w.r.t. a set of desired properties. In this work, we study the articulation between the stability, correctness and plausibility of explanations based on feature importance for image classifiers. We show that the existing metri

Cátia Campos, Flavio Tiago Couto, Jean-Baptiste Filippi, Roberta Baggio

The present study contributes to an increased understanding of pyro-convection phenomena by using a fire-atmosphere coupled simulation, and investigates in detail the large-scale meteorological conditions affecting Portugal during the occurrence of multiple mega-fires events on 15 October 2017. Two numerical simulations were performed using the MesoNH atmosp

Jianbo Jiao, Mohammad Alsharid, Lior Drukker, Aris T. Papageorghiou

Auditory and visual signals usually present together and correlate with each other, not only in natural environments but also in clinical settings. However, the audio-visual modelling in the latter case can be more challenging, due to the different sources of audio/video signals and the noise (both signal-level and semantic-level) in auditory signals -- usua

Alireza Khalili Golmankhaneh, Stergios Pellis, Massimiliano Zingales

This paper delves into the world of fractal calculus, investigating its implications for fractal sets. It introduces the Fractal Schr\"{o}dinger Equation and provides insights into its consequences. The study presents a General Solution for the Time-Dependent Schr\"{o}dinger Equation, unveiling its core aspects. Exploring quantum mechanics in the context of

Joackim Bernier, Nicolas Camps, Benoît Grébert, Zhiqiang Wang

We prove an exponential stability result for the small solutions of the Schr{\"o}dinger-Poisson equation on the circle without exterior parameters in Gevrey class. More precisely we prove that for most of the initial data of Gevrey-norm smaller than $\varepsilon$ small enough, the solution of the Schr{\"o}dinger-Poisson equation remains smaller than $2\varep

Jiong Lou, Zhiqing Tang, Shijing Yuan, Jie Li

Serverless computing is a promising approach for edge computing since its inherent features, e.g., lightweight virtualization, rapid scalability, and economic efficiency. However, previous studies have not studied well the issues of significant cold start latency and highly dynamic workloads in serverless function scheduling, which are exacerbated at the res

Zhen Sun, Yao Zhang, Zezhou Li, Xuanxuan Du

Icosahedral nanoparticles composed of fivefold twinned tetrahedra have broad applications. The strain relief mechanism and angular deficiency in icosahedral multiply twinned particles are poorly understood in three dimensions. Here, we resolved the three-dimensional atomic structures of Janus icosahedral nanoparticles using atomic resolution electron tomogra

Matthieu Jonckheere, Chiara Mignacco, Gilles Stoltz

We revisit the reduction of learning in adversarial Markov decision processes [MDPs] to adversarial learning based on $Q$--values; this reduction has been considered in a number of recent articles as one building block to perform policy optimization. Namely, we first consider and extend this reduction in an ideal setting where an oracle provides value functi