May 2024 arXiv papers — page 105

Valentin Blomer, Félicien Comtat

We give an asymptotic formula with power saving error term for the twisted first moment of symmetric square L-functions on GL(3) in the level aspect. As applications, we obtain non-vanishing results as well as lower bounds of the expected order of magnitude for all even moments, supporting the random matrix model for a unitary ensemble. Besides the GL(3) Kuz

Jiyang He, Yan Wang

The recent work of Siegelman \& Young (PNAS, vol. 120(44), 2023, pp. e2308018120) revealed two extreme states reached by the evolution of unforced and weakly-damped two-dimensional turbulence above random rough topography, separated by a critical kinetic energy $E_\#$. The low- and high-energy solutions correspond to topographically-locked and roaming vortic

Hao Zhou, Chengming Hu, Ye Yuan, Yufei Cui

Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fields. The advancement of LLM techniques also offers promising opportunities to automate many tasks in the telecommunication (telecom) field. After pre-training and fine-tuning, LLMs

Davide Rucci

Graphs are widely used in various fields of computer science. They have also found application in unrelated areas, leading to a diverse range of problems. These problems can be modeled as relationships between entities in various contexts, such as social networks, protein interactions in cells, and route maps. Therefore it is logical to analyze these data st

Henrique Buglia, Eric Sillekens, Lidia Galdino, Robert I. Killey

Per-channel launch power optimisation in a hybrid-amplified link with optimised pump powers and wavelengths is described. Compared to using the optimum spectrally uniform launch power, an average SNR gain of 0.13 dB is obtained against 0.56 dB for the same system operating with lumped amplifiers only.

Bingbing Dai, Wei Luo, Zhaoyang Yin, Pei Zheng

This paper is concerned with the well-posedness of a time-fractional shallow-water equations, which has received little attention. In the realm of fractional calculus, numerous types of fractional derivatives have been explored in the literature. Among these, one of the most notable and well-structured ones is the conformable fractional derivative. In this p

Samantha J. Fournier, Pierfrancesco Urbani

Generative modeling aims at producing new datapoints whose statistical properties resemble the ones in a training dataset. In recent years, there has been a burst of machine learning techniques and settings that can achieve this goal with remarkable performances. In most of these settings, one uses the training dataset in conjunction with noise, which is add

Valeria Mascolo, Clément Le Priol, Fabio D'Andrea, Freddy Bouchet

In this work, we study and compare the influence of the Atlantic Multidecadal Variability (AMV) and of spring soil moisture in Southern Europe on the duration and intensity of European summer heat waves. We study common heat waves with return times of a few years like in previous studies, but we also propose a new methodological approach, return time maps, t

Philipp Keßler, Laura Garcia-Gassull, Andreas Suter, Thomas Prokscha

Altermagnets are a novel class of magnetic materials besides ferro- and antiferromagnets, where the interplay of lattice and spin symmetries produces a magnetic order that is staggered both in coordinate as well as momentum space. The metallic rutile oxide RuO$_2$, long believed to be a textbook Pauli paramagnet, recently emerged as a workhorse altermagnet w

Dimitris Kechrakos, Mario Carpentieri, Anna Giordano, Riccardo Tomasello

Current-driven magnetic skyrmions show promise as carriers of information bits in racetrack magnetic memory applications. Specifically, the utilization of skyrmions in synthetic antiferromagnetic (SAF) systems is highly attractive due to the potential to suppress the Skyrmion Hall effect, which causes a transverse displacement of driven skyrmions relative to

Jiawei Feng, Mengsi Cai, Fangze Dai, Tianci Bu

In the rapidly evolving automotive industry, Systems-on-Chips (SoCs) are playing an increasingly crucial role in enhancing vehicle intelligence, connectivity, and safety features. For enterprises whose business encompasses automotive SoCs, the sustained and stable provision and receipt of SoC relevant goods or services are essential. Considering the imperati

Azadeh Khaleghi

A more general formulation of the linear bandit problem is considered to allow for dependencies over time. Specifically, it is assumed that there exists an unknown $\mathbb{R}^d$-valued stationary $\varphi$-mixing sequence of parameters $(\theta_t,~t \in \mathbb{N})$ which gives rise to pay-offs. This instance of the problem can be viewed as a generalization

Yiming Pan, Fabio Caruso

We report a theoretical investigation of the ultrafast dynamics of electrons and phonons in strained monolayer WS$_2$ following photoexcitation. We show that strain substantially modifies the phase space for electron-phonon scattering, unlocking new relaxation pathways that are unavailable in the pristine monolayer. In particular, strain triggers a transitio

Andrzej Ruszczyński, Shangzhe Yang

We consider stochastic optimization problems involving an expected value of a nonlinear function of a base random vector and a conditional expectation of another function depending on the base random vector, a dependent random vector, and the decision variables. We call such problems conditional stochastic optimization problems. They arise in many applicatio

Boris Karanov, Chin-Hung Chen, Yan Wu, Alex Young

We developed machine learning approaches for data-driven trellis-based soft symbol detection in coded transmission over intersymbol interference (ISI) channels in presence of bursty impulsive noise (IN), for example encountered in wireless digital broadcasting systems and vehicular communications. This enabled us to obtain optimized detectors based on the Ba

Mengchen Dong, Jean-François Bonnefon, Iyad Rahwan

The deployment of AI systems for welfare benefit allocation allows for accelerated decision-making and faster provision of critical help, but has already led to an increase in unfair benefit denials and false fraud accusations. Collecting data in the US and the UK (N = 2449), we explore the public acceptability of such speed-accuracy trade-offs in population

Chams Gharib Ali Barura, Hajime Kobayashi, Shinji Mukohyama, Naritaka Oshita

We study static tidal Love numbers (TLNs) of a static and spherically symmetric black hole for odd-parity metric perturbations. We describe black hole perturbations using the effective field theory (EFT), formulated on an arbitrary background with a timelike scalar profile in the context of scalar-tensor theories. In particular, we obtain a static solution f

Emma E. Davies, Hannah T. Rüdisser, Ute V. Amerstorfer, Christian Möstl

As both Parker Solar Probe (PSP) and Solar Orbiter (SolO) reach heliocentric distances closer to the Sun, they present an exciting opportunity to study the structure of CMEs in the inner heliosphere. We present an analysis of the global flux rope structure of the 2022 September 5 CME event that impacted PSP at a heliocentric distance of only 0.07 au and SolO

Francesca Aicardi, Jesús Juyumaya, Paolo Papi

Starting from the geometric construction of the framed braid group, we define and study the framization of several Brauer-type monoids and also the set partition monoid, all of which appear in knot theory. We introduce the concept of deframization, which is a procedure to obtain a tied monoid from a given framed monoid. Furthermore, we show in detail how thi

Markus Bayer, Justin Lutz, Christian Reuter

Active learning is designed to minimize annotation efforts by prioritizing instances that most enhance learning. However, many active learning strategies struggle with a `cold-start' problem, needing substantial initial data to be effective. This limitation reduces their utility in the increasingly relevant few-shot scenarios, where the instance selection ha

A. V. Kavokin, S. V. Kavokina, A. A. Varlamov, Yuriy Yerin

The Seebeck effect consists in the induction of a voltage drop due to the temperature difference in a conductor. In the middle of XIXth century, Lord Kelvin has proposed a relation between the Seebeck coefficient and the derivative of the chemical potential over temperature in the broken circuit regime. This relation appears to be nearly universal as it equa

Léonard Polat

The BABAR experiment participates to the global endeavor for a precise prediction of the anomalous magnetic moment of the muon by evaluating the contribution of hadronic processes to the vacuum polarization. After its last result published in 2009 and 2012, BABAR is preparing a new independent measurement of the $e^+e^- \rightarrow \pi^+\pi^-(\gamma)$ cross

Chen-How Huang

We study the two body loss dynamics of fermionic cold atoms near $s$- and $p$-wave Feshbach resonances with a microscopic Keldysh path integral formalism and compare the result to the macroscopic phenomenological loss rate equation. The microscopic loss rate equation is an integral-differential equation of the momentum distribution that depends on the functi

Suman Karan, Nilakshi Senapati, Anand K. Jha

A K-mirror is a device that rotates the wavefront of an incident optical field. It has recently gained prominence over Dove prism, another commonly used wavefront rotator, due to the fact that while a K-mirror has several controls for adjusting the internal reflections, a Dove prism is made of a single glass element with no additional control. Thus, one can

Abdul Rehman, Talha Meraj, Aiman Mahmood Minhas, Ayisha Imran

Earlier diagnosis of Leukemia can save thousands of lives annually. The prognosis of leukemia is challenging without the morphological information of White Blood Cells (WBC) and relies on the accessibility of expensive microscopes and the availability of hematologists to analyze Peripheral Blood Samples (PBS). Deep Learning based methods can be employed to a

Mateusz Gabor, Rafał Zdunek

Convolutional neural networks (CNNs) are among the most widely used machine learning models for computer vision tasks, such as image classification. To improve the efficiency of CNNs, many CNNs compressing approaches have been developed. Low-rank methods approximate the original convolutional kernel with a sequence of smaller convolutional kernels, which lea

Chris Barrett, Daniel Castle, Willem Heijltjes

This paper presents the Relational Machine Calculus (RMC): a simple, foundational model of first-order relational programming. The RMC originates from the Functional Machine Calculus (FMC), which generalizes the lambda-calculus and its standard call-by-name stack machine in two directions. One, "locations", introduces multiple stacks, which enable effect ope

Zheng Dong, Renhe Jiang, Haotian Gao, Hangchen Liu

Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and leveraging spatiotemporal heterogeneity remains a fundamental challenge. Therefore, we propose a novel Heterogeneity-Informed Meta-Parameter Learning scheme. Specifically, our approach

Lennart Heim, Leonie Koessler

Regulators in the US and EU are using thresholds based on training compute--the number of computational operations used in training--to identify general-purpose artificial intelligence (GPAI) models that may pose risks of large-scale societal harm. We argue that training compute currently is the most suitable metric to identify GPAI models that deserve regul

Jorge P. Rodríguez, Lluís Arola-Fernández

Professional sports enhance interaction among athletes through training groups, sponsored events and competitions. Among these, the Olympic Games represent the largest competition with a global impact, providing the participants with a unique opportunity for interaction. We studied the following patterns among highly successful athletes to understand the str

Jann H. Ungerer, Alessia Pally, Stefano Bosco, Artem Kononov

Qubits require a compromise between operation speed and coherence. Here, we demonstrate a compromise-free singlet-triplet (ST) qubit, where the qubit couples maximally to the driving field while simultaneously coupling minimally to the dominant noise sources. The qubit is implemented in a crystal-phase defined double-quantum dot in an InAs nanowire. Using a

Enrico Bernardi, Alberto Lanconelli, Christopher S. A. Lauria, Berk Tan Perçin

We examine a mean-reverting Ornstein-Uhlenbeck process that perturbs an unknown Lipschitz-continuous drift and aim to estimate the drift's value at a predetermined time horizon by sampling the path of the process. Due to the time varying nature of the drift we propose an estimation procedure that involves an online, time-varying optimization scheme implement

Petr A. Igoshev, Igor A. Nekrasov

A thermodynamic theory based on Landau grand potential expansion for ferromagnetic-paramagnetic phase transitions is developed for an electronic phase-separated state. It is rigorously shown that ferromagnetic phase involved in the phase-separated state exhibits negative magnetic susceptibility in the vicinity of~tricritical point. Thus, an entropy of the ma

Gang Wang, Chaoran Zhu, Qian Xu, Tongzhou Zhang

Place recognition is a fundamental task for robotic application, allowing robots to perform loop closure detection within simultaneous localization and mapping (SLAM), and achieve relocalization on prior maps. Current range image-based networks use single-column convolution to maintain feature invariance to shifts in image columns caused by LiDAR viewpoint c

Rupert Coy, Jean Kimus, Michel H. G. Tytgat

We study a scenario in which the expansion of the Early Universe is driven by a hot hidden sector (HS) with an initial temperature $T'$ that is significantly higher than that of the visible sector (VS), $T' \gg T$. The latter is assumed to be made of Standard Model (SM) particles and our main focus is on the possibility that dark matter (DM) is part of the d

Yu-Jia Wei, Yuan-Pei Yang, Da-Ming Wei, Zi-Gao Dai

Magnetars are young, highly magnetized neutron stars that are associated with magnetar short bursts (MSBs), magnetar giant flares (MGFs), and at least some fast radio bursts (FRBs). In this work, we consider a magnetar and a main sequence star in a binary system and analyze the properties of the electromagnetic signals generated by the interaction between th

A. Dolliou, S. Parenti, K. Bocchialini

Context: A large number of the small and the short-lived EUV brightenings have been detected in the quiet Sun (QS) over the past three years, by the High Resolution Imager of the Extreme Ultraviolet Imager (HRIEUV) on board Solar Orbiter. It is still uncertain whether these events reach coronal temperatures, and thus if they directly participate to coronal h

Pierre Guetschel, Sara Ahmadi, Michael Tangermann

In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest. This review synthesizes empirical findings from a collection of articles using deep representation learning techniques for BCI decoding, to provide a comprehensive analysi

Endre Boros, Vladimir Gurvich, Martin Milanič, Dmitry Tikhanovsky

Given a hypergraph $H$, the dual hypergraph of $H$ is the hypergraph of all minimal transversals of $H$. A hypergraph is conformal if it is the family of maximal cliques of a graph. In a recent work, Boros, Gurvich, Milani\v{c}, and Uno (Journal of Graph Theory, 2025) studied conformality of dual hypergraphs and proved several results related to this propert

Ivan Čilić, Valentin Jukanović, Ivana Podnar Žarko, Pantelis Frangoudis

While various service orchestration aspects within Computing Continuum (CC) systems have been extensively addressed, including service placement, replication, and scheduling, an open challenge lies in ensuring uninterrupted data delivery from IoT devices to running service instances in this dynamic environment, while adhering to specific Quality of Service (

Daniel Aguilar, Minor Acuña, Breyner Chacón

The stability of the inflation rate is a necessary condition for the proper functioning of any capitalist economy. In an economic environment with volatile inflation, the growth of the economy and its distribution among the agents of society is compromised. For this reason, and because in Costa Rica, as in most capitalist nations, the monetary authority is i

Subhyal Bin Iqbal, Behnam Khodapanah, Philipp Schulz, Gerhard P. Fettweis

Achieving connectivity reliability is one of the significant challenges for 5G and beyond 5G cellular networks. The present understanding of reliability in the context of mobile communication does not adequately cover the stochastic temporal aspects of the network, such as the duration and spread of packet errors that an outage session may cause. Rather, it

Jixun Yao, Qing Wang, Pengcheng Guo, Ziqian Ning

Speaker anonymization is an effective privacy protection solution that aims to conceal the speaker's identity while preserving the naturalness and distinctiveness of the original speech. Mainstream approaches use an utterance-level vector from a pre-trained automatic speaker verification (ASV) model to represent speaker identity, which is then averaged or mo

CMS Collaboration

We review key measurements performed by CMS in the context of its heavy ion physics program, using event samples collected in 2010-2018 with several collision systems and energies. These studies provide detailed macroscopic and microscopic probes of the quark-gluon plasma (QGP) created at the LHC energies, a medium characterized by the highest temperature an

Aryaman Mishra

The correlation function in Ads/CFT are correlation of the operator insertions on the boundary (at CFT) through the complete geometry of bulk. These are represented by Witten diagrams which at tree level doesn't have any quantum corrections. Generally, correlation functions are of low scaling (or conformal) dimension, $\Delta$, which is related to the mass o

Dogancan Karabas, Sangjin Lee

Plumbing spaces have drawn significant attention among symplectic topologists due to their natural occurrence as examples of Weinstein manifolds. In our paper, we provide a general formula for the wrapped Fukaya category of plumbings (with arbitrary grading structure) of cotangent bundles along any quiver. Our approach relies on "local-to-global" computation

Guillaume F. Nataf, Sebastian Volz, Jose Ordonez-Miranda, Jorge Íñiguez-González

One of the most innovative possibilities offered by oxides is the use of heat currents for computational purposes. Towards this goal, phase-change oxides, including ferroelectrics, ferromagnets and related materials, could reproduce sources, logic units and memories used in current and future computing schemes.

Eeshan G. Dandamudi

Traditional methods of controlling prosthetics frequently encounter difficulties regarding flexibility and responsiveness, which can substantially impact people with varying cognitive and physical abilities. Advancements in computational neuroscience and machine learning (ML) have recently led to the development of highly advanced brain-computer interface (B

Philippe Gris, Humna Awan, Matthew R. Becker, Huan Lin

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will image billions of astronomical objects in the wide-fast-deep primary survey and in a set of minisurveys including intensive observations of a group of deep drilling fields (DDFs). The DDFs are a critical piece of three key aspects of the LSST Dark Energy Science Collaboration (DESC) co

Max D. Champneys, Gerben I. Beintema, Roland Tóth, Maarten Schoukens

Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting improvements or extensions to existing methods. It is natural, therefore, to consider how one might choose between these competing models. Benchmark datasets provide one clear way to

Filippos Dakis, Evangelia Takou, Edwin Barnes, Sophia E. Economou

Quantum memories play a key role in facilitating tasks within quantum networks and quantum information processing, including secure communications, advanced quantum sensing, and distributed quantum computing. Progress in characterizing large nuclear spin registers coupled to defect electronic spins has been significant, but selecting memory qubits remains ch

Harsh, Sk Jahanur Hoque, Sitender Pratap Kashyap, Amitabh Virmani

We present de Sitter Teukolsky waves -- linearised quadrupolar gravitational waves in the transverse-traceless gauge in de Sitter spacetime. In the cosmological constant $\Lambda$ going to zero limit, our solutions match to Teukolsky solutions. For non-zero $\Lambda$, we compare our solutions to the wider literature, where different authors have constructed

David Kipping, Xian-Yu Wang

One of the most basic quantities relevant to planning observations and assessing detection bias is the signal-to-noise ratio (SNR). Remarkably, the SNR of an idealised radial velocity (RV) signal has not been previously derived beyond scaling behaviours and ignoring orbital eccentricity. In this work, we derive the RV SNR for three relevant cases to observer

Yu-Jia Wei, Jia Ren, Hao-Ning He, Yuan-Pei Yang

Gamma-ray bursts (GRBs) have long been considered potential sources of ultra-high-energy cosmic rays (UHECRs; with energy $\gtrsim 10^{18} {\rm~eV}$). In this work, we propose a novel model generating MeV emission lines in GRB, which can constrain the properties of heavy nuclei that potentially exist in GRB jets. Specifically, we find that relativistic hydro

Demian Banakh, Marcin Kozik

We present a template for the Promise Constraint Satisfaction Problem (PCSP) which is NP-hard but does not satisfy the current state-of-the-art hardness condition [ACMTCT'21]. We introduce a new "injective" condition based on the smooth version of the layered PCP Theorem and use this new condition to confirm that the problem is indeed NP-hard. In the second

Zehao Su, Helene C. W. Rytgaard, Henrik Ravn, Frank Eriksson

We consider the problem of indirect comparison, where a treatment arm of interest is absent by design in one randomized controlled trial but available in the other. The former is the target trial, and the latter is the source trial. The identifiability of the target population average treatment effect often relies on conditional transportability assumptions.

Terry Generet

In this talk, I presented some of the results of the first calculation of open bottom production at hadron colliders at NNLO+NNLL, i.e. a next-to-next-to-leading-order calculation that resums collinear logarithms at next-to-next-to-leading-logarithmic accuracy. This new computation achieves significantly reduced theory errors compared to previous calculation

Rirong Yuan

We study fully nonlinear elliptic equations on Hermitian manifolds through blow-up argument and partial uniform ellipticity. We apply our results to draw geometric conclusions on finding conformal Hermitian metrics with prescribed Chern-Ricci curvature functions. By some obstruction from geometric function theory, our assumptions are almost sharp.

Hiroshi Ando, Yuki Miyamoto, Narutaka Ozawa

The Paszkiewicz conjecture about a product of positive contractions asserts that given a decreasing sequence $T_1\ge T_2\ge \dots$ of positive contractions on a separable infinite-dimensional Hilbert space, the product $S_n=T_n\dots T_1$ converges strongly. Recently, the first named author verified the conjecture for certain classes of sequences. In this pap

Zehao Su, Helene Charlotte Rytgaard, Henrik Ravn, Frank Eriksson

We consider estimation of the target population average treatment effect (TATE) when outcome information is unavailable. Instead, we observe the outcome in multiple source populations and wish to combine the treatment effects therein to make inference on the TATE. In contrast to existing works that assume transportability on the conditional distribution of p

Alyzia-Maria Konsta, Alberto Lluch Lafuente, Christoph Matheja

Partially observable Markov Decision Processes (POMDPs) are a standard model for agents making decisions in uncertain environments. Most work on POMDPs focuses on synthesizing strategies based on the available capabilities. However, system designers can often control an agent's observation capabilities, e.g. by placing or selecting sensors. This raises the q

Xiaotian Lu, Jiyi Li, Zhen Wan, Xiaofeng Lin

Deep learning models have performed well on many NLP tasks. However, their internal mechanisms are typically difficult for humans to understand. The development of methods to explain models has become a key issue in the reliability of deep learning models in many important applications. Various saliency explanation methods, which give each feature of input a

C. Augier, A. S. Barabash, F. Bellini, G. Benato

The current experiments searching for neutrinoless double-$\beta$ ($0\nu\beta\beta$) decay also collect large statistics of Standard Model allowed two-neutrino double-$\beta$ ($2\nu\beta\beta$) decay events. These can be used to search for Beyond Standard Model (BSM) physics via $2\nu\beta\beta$ decay spectral distortions. $^{100}$Mo has a natural advantage

Leon Tolksdorf, Christian Birkner, Arturo Tejada, Nathan van de Wouw

Many state-of-the-art methods for safety assessment and motion planning for automated driving require estimation of the probability of collision (POC). To estimate the POC, a shape approximation of the colliding actors and probability density functions of the associated uncertain kinematic variables are required. Even with such information available, the der

Chiara Gavioli, Pavel Krejčí

Hysteresis in the pressure-saturation relation in unsaturated porous media, which is due to surface tension on the liquid-gas interface, exhibits strong degeneracy in the resulting mass balance equation. Solutions to such degenerate equations have been recently constructed by the method of convexification even if the permeability coefficient depends on the h

Rayan Harfouche, Giovanni Piccioli, Lenka Zdeborová

Path optimization is a fundamental concern across various real-world scenarios, ranging from traffic congestion issues to efficient data routing over the internet. The Traffic Assignment Problem (TAP) is a classic continuous optimization problem in this field. This study considers the Integer Traffic Assignment Problem (ITAP), a discrete variant of TAP. ITAP

Yu Cheng, Qin Yang, Liyang Wang, Ao Xiang

In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropagation (BP) neural network, to pioneer a novel model for preem

Kirsten Fischer, Javed Lindner, David Dahmen, Zohar Ringel

A key property of neural networks driving their success is their ability to learn features from data. Understanding feature learning from a theoretical viewpoint is an emerging field with many open questions. In this work we capture finite-width effects with a systematic theory of network kernels in deep non-linear neural networks. We show that the Bayesian

Kamila Barylska, Anna Gogolińska

Diabetes is a chronic condition, considered one of the civilization diseases, that is characterized by sustained high blood sugar levels. There is no doubt that more and more people is going to suffer from diabetes, hence it is crucial to understand better its biological foundations. The essential processes related to the control of glucose levels in the blo

Benjamin Fleury, Murthy S. Gudipati, Isabelle Couturier-Tamburelli

A large fraction of the organic species produced photochemically in the atmosphere of Titan can condense to form ice particles in the stratosphere and in the troposphere. According to various studies, diacetylene (C$_4$H$_2$) condenses below 100 km where it can be exposed to ultraviolet radiation. We studied experimentally the photochemistry of diacetylene i

Ronghui Quan, Zhiying Song, Zhigui Liu

Surface charging phenomenon of asteroids, mainly resulting from solar wind plasma and solar radiation, has been studied extensively. However, the influence of asteroid's rotation on surface charging has yet to be fully understood. Here neural network is established to replace numerical integration, improving the efficiency of dynamic three-dimensional simula

Wangchenlu Huang, Shenao Wang, Yanjie Zhao, Guosheng Xu

In the digital era, Online Social Networks (OSNs) play a crucial role in information dissemination, with sharing cards for link previews emerging as a key feature. These cards offer snapshots of shared content, including titles, descriptions, and images. In this study, we investigate the construction and dissemination mechanisms of these cards, focusing on t

Zhiwei Zhang, Minhua Lin, Enyan Dai, Suhang Wang

Graph Neural Networks (GNNs) have shown remarkable performance in various tasks. However, recent works reveal that GNNs are vulnerable to backdoor attacks. Generally, backdoor attack poisons the graph by attaching backdoor triggers and the target class label to a set of nodes in the training graph. A GNN trained on the poisoned graph will then be misled to p

Bertille Granet, Felix Joos, Jonathan Schrodt

Fix $k\geq 2$, choose $\frac{\log n}{n^{(k-1)/k}}\leq p\leq 1-\Omega(\frac{\log^4 n}{n})$, and consider $G\sim G(n,p)$. For any pair of vertices $v,w\in V(G)$, we give a simple and precise formula for the expected number of steps that a random walk on $G$ starting at $w$ needs to first arrive at $v$. The formula only depends on basic structural properties of

Eyasu Tadesse Muda, Tewodros Eyob Ada, Kenate Nemera Nigussa, Cecil N. M. Ouma

A GW calculation based on a truncated Coulomb interaction with an added small q limit was applied to 2D van der Waals heterolayered structures, and the Kane dispersion model was used to determine the accurate band gap edge. All ab initio calculations were performed with the gpaw package. Our findings show that layering the same or different types of atoms wi

Jean-Jacques Godeme, Jalal Fadili, Claude Amra, Myriam Zerrad

In this paper, we aim to reconstruct an n-dimensional real vector from m phaseless measurements corrupted by an additive noise. We extend the noiseless framework developed in [15], based on mirror descent (or Bregman gradient descent), to deal with noisy measurements and prove that the procedure is stable to (small enough) additive noise. In the deterministi

Bin Fang, Riccardo Tomasello, Yuxuan Wu, Aitian Chen

The growing market and massive use of Internet of Things nodes is placing unprecedented demands of energy efficient hardware for edge computing and microwave devices. In particular, magnetic tunnel junctions (MTJs), as main building blocks of spintronic microwave technology, can offer a path for the development of compact and high-performance microwave detec

Samuele Ronchini, Suman Bala, Joshua Wood, James Delaunay

GW230529 is the first compact binary coalescence detected by the LIGO-Virgo-KAGRA collaboration with at least one component mass confidently in the lower mass-gap, corresponding to the range 3-5$M_{\odot}$. If interpreted as a neutron star-black hole merger, this event has the most symmetric mass ratio detected so far and therefore has a relatively high prob

Judita Runcziková, Jan Chleboun, Chiara Gavioli, Pavel Krejčí

The contribution deals with the mathematical modelling of fluid flow in porous media, in particular water flow in soils, with the aim of describing the competition between transport and diffusion. The analysis is based on a mathematical model developed by B. Detmann, C. Gavioli, and P. Krej\v{c}\'i, in which the effects of gravity are included in a novel way

Jianzong Pi, Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Abhishek Gupta

Efficient parameter identification of electrochemical models is crucial for accurate monitoring and control of lithium-ion cells. This process becomes challenging when applied to complex models that rely on a considerable number of interdependent parameters that affect the output response. Gradient-based and metaheuristic optimization techniques, although pr

Yoon Huh, Hyowoon Seo, Wan Choi

From the perspective of joint source-channel coding (JSCC), there has been significant research on utilizing semantic communication, which inherently possesses analog characteristics, within digital device environments. However, a single-model approach that operates modulation-agnostically across various digital modulation orders has not yet been established

Ravi Kashyap

We have designed an innovative portfolio rebalancing mechanism termed the Cascading Waterfall Round Robin Mechanism. This algorithmic approach recommends an ideal size and number of trades for each asset during the periodic rebalancing process, factoring in the gas fee and slippage. The essence of the model we have created gives indications regarding whether

Hanyu Chen, Zhixiu Hao, Liying Xiao

Diffusion models have become a successful approach for solving various image inverse problems by providing a powerful diffusion prior. Many studies tried to combine the measurement into diffusion by score function replacement, matrix decomposition, or optimization algorithms, but it is hard to balance the data consistency and realness. The slow sampling spee

Iva Kodrnja, Helena Koncul

In this paper we find the number of homogeneous polynomials of degree d such that they vanish on cuspidal modular forms of even weight $m\geq 2$ that form a basis for $S_m(\Gamma_0(N))$. We use these cuspidal forms to embedd $X_0(N)$ to projective space and we find the Hilbert polynomial of the graded ideal of the projective curve that is the image of this e

Yicheng Qi, Ang Li

Artificially sweetened beverages like Diet Coke are often considered better alternatives to sugary drinks, but the debate over their impact on health, particularly in relation to obesity, continues. Previous research has predominantly used association-based methods with observational or Randomized Controlled Trial (RCT) data, which may not accurately capture

Albert Sawczyn, Jakub Binkowski, Piotr Bielak, Tomasz Kajdanowicz

Knowledge-intensive tasks pose a significant challenge for Machine Learning (ML) techniques. Commonly adopted methods, such as Large Language Models (LLMs), often exhibit limitations when applied to such tasks. Nevertheless, there have been notable endeavours to mitigate these challenges, with a significant emphasis on augmenting LLMs through Knowledge Graph

Ronghui Quan, Zhigui Liu, Zhiying Song

The charged dust on the surface of airless celestial bodies, such as the moon and asteroids, is a threat to space missions. Further research on the charged dust will contribute to the success of space missions. In this paper, we study the charging and dynamics of dust particles with different work functions. By integrating the photoelectron energy distributi

Liang Zhao, Yingyu Wang, Shoudong Huang

In this paper, we propose an optimization based SLAM approach to simultaneously optimize the robot trajectory and the occupancy map using 2D laser scans (and odometry) information. The key novelty is that the robot poses and the occupancy map are optimized together, which is significantly different from existing occupancy mapping strategies where the robot p

Ivo V. Stoepker

We consider outbreak detection settings of endemic diseases where the population under study consists of various subpopulations available for stratified surveillance. These subpopulations can for example be based on age cohorts, but may also correspond to other subgroups of the population under study such as international travellers. Rather than sampling uni

Marco Gaido, Sara Papi, Matteo Negri, Mauro Cettolo

Subtitling plays a crucial role in enhancing the accessibility of audiovisual content and encompasses three primary subtasks: translating spoken dialogue, segmenting translations into concise textual units, and estimating timestamps that govern their on-screen duration. Past attempts to automate this process rely, to varying degrees, on automatic transcripts

Rupak Timalsina, Bharat Giri, Haohan Wang, Adam Erickson

Rare-earth iron garnets have distinctive spin-wave (SW) properties such as low magnetic damping and long SW coherence length making them ideal candidates for magnonics. Among them, thulium iron garnet (TmIG) is a ferrimagnetic insulator with unique magnetic properties including perpendicular magnetic anisotropy (PMA) and topological hall effect at room tempe

Yizhang Jin, Jian Li, Yexin Liu, Tianjun Gu

In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning. However, the extensive model size and high training and inference costs have hindered the widespread application of MLLMs in academia and industry. Thus, studying efficient and lig

Jiahao Li, Quan Wang, Licheng Zhang, Guoqing Jin

In-context learning (ICL), which promotes inference with several demonstrations, has become a widespread paradigm to stimulate LLM capabilities for downstream tasks. Due to context length constraints, it cannot be further improved in spite of more training data, and general features directly from LLMs in ICL are not adaptive to the specific downstream task.

Stefan Haller, Cornelia Vizman

We use cotangent bundles of spaces of smooth embeddings to construct symplectic dual pairs involving the group of volume preserving diffeomorphisms. Via symplectic reduction we obtain descriptions of coadjoint orbits of this group in terms of nonlinear Grassmannians of augmented submanifolds. For codimension one embeddings these submanifolds are further cons

Motahhare Mirzaei, Mohammad Javad Pirhadi, Sauleh Eetemadi

In recent years, people have increasingly used AI to help them with their problems by asking questions on different topics. One of these topics can be software-related and programming questions. In this work, we focus on the questions which need the understanding of images in addition to the question itself. We introduce the StackOverflowVQA dataset, which i

Zeinab Alizadeh, Erfan Yazdandoost Hamedani, Afrooz Jalilzadeh

In this paper, we address variational inequalities (VI) with a finite-sum structure. We introduce a novel single-loop stochastic variance-reduced algorithm, incorporating the Bregman distance function, and establish an optimal convergence guarantee under a monotone setting. Additionally, we explore a structured class of non-monotone problems that exhibit wea

Karl-Theodor Sturm

We study spectral properties and geometric functional inequalities on Riemannian manifolds of dimension $\ge3$ with (finite or countably many) conical singularities $\{z_i\}_{i\in\mathfrak I}$ in the neighborhood of which the largest lower bound for the Ricci curvature is \begin{equation}\label{d2} k(x)\simeq K_i-\frac{s_i}{d^2(z_i,x)}. \end{equation} Thus n

Eva Noskovicova, Martin Koys, Monika Jerigova, Dusan Velic

Converting a THz signal into the optical domain is of great interest for THz sensing and spectroscopy. Here intense broadband THz pulses with a central frequency of $\Omega$$_{THz}$ are mixed with an optical pump at $\omega$$_p$ and a signal is observed at a wavelength of $\omega$$_s$ = 2($\omega$$_p$ - $\Delta\omega$$_p$) - $\Omega_{THz}$ with the detuning

Scott Armstrong, Tuomo Kuusi

We prove a quantitative estimate for the homogenization length scale in terms of the ellipticity ratio $\Lambda/\lambda$ of the coefficient field. This upper bound applies to high-contrast elliptic equations exhibiting near-critical behavior. Specifically, we show, assuming a suitable decay of correlations, the length scale at which homogenization occurs is

Aleksandr Nesterenok

The model is constructed of the propagation of gamma-ray burst radiation through a dense molecular cloud. The main processes of the interaction of the radiation with the interstellar gas are taken into account in the simulations: the ionization of H and He atoms, the ionization of metal ions and the emission of Auger electrons, the photoionization and the ph