April 2024 arXiv papers — page 49

Anthony Correia, Fotis I. Giasemis, Nabil Garroum, Vladimir Vava Gligorov

Over the next decade, increases in instantaneous luminosity and detector granularity will amplify the amount of data that has to be analysed by high-energy physics experiments, whether in real time or offline, by an order of magnitude. The reconstruction of charged particle tracks, which has always been a crucial element of offline data processing pipelines,

Olga Zagovora, Katrin Weller

In this study, we undertake an extensive analysis of YouTube channels that reference research publications in their video descriptions, offering a unique insight into the intersection of digital media and academia. Our investigation focuses on three principal aspects: the background of YouTube channel owners, their thematic focus, and the nature of their ope

Libang Chen, Jun Yang, Lingye Chen, Yuyang Shui

Recording and identifying faint objects through atmospheric scattering media by an optical system are fundamentally interesting and technologically important. In this work, we introduce a comprehensive model that incorporates contributions from target characteristics, atmospheric effects, imaging system, digital processing, and visual perception to assess th

Jingyao Xu, Yuetong Lu, Yandong Li, Siyang Lu

Diffusion models (DMs) embark a new era of generative modeling and offer more opportunities for efficient generating high-quality and realistic data samples. However, their widespread use has also brought forth new challenges in model security, which motivates the creation of more effective adversarial attackers on DMs to understand its vulnerability. We pro

Okko Makkonen

In this paper, we propose a new secure distributed matrix multiplication (SDMM) scheme using the inner product partitioning. We construct a scheme with a minimal number of workers and no redundancy, and another scheme with redundancy against stragglers. Unlike previous constructions in the literature, we do not utilize algebraic methods such as locally repai

Federico Cannerozzi, Giorgio Ferrari

We consider a class of $N$-player games and mean-field games of singular controls with ergodic performance criterion, providing a benchmark case for irreversible investment games featuring mean-field interaction and strategic complementarities. The state of each player follows a geometric Brownian motion, controlled additively through a nondecreasing process

Ben Forrás

We formulate an equivariant version of Greenberg's $p$-adic Artin conjecture for smoothed equivariant $p$-adic Artin $L$-functions in the context of an arbitrary one-dimensional admissible $p$-adic Lie extension of a totally real number field. Using results of the author on the Wedderburn decomposition of the total ring of quotients of the Iwasawa algebra $\

Evan Deddo, James T. Liu, Leopoldo A. Pando Zayas, Robert J. Saskowski

We revisit the existence of monotonic quantities along renormalization group flows using only the Null Energy Condition and the Ryu-Takayanagi formula for the entanglement entropy of field theories with anti-de Sitter gravity duals. In particular, we consider flows within the same dimension and holographically reprove the $c$-, $F$-, and $a$-theorems in dime

Joshua Groen, Salvatore D'Oro, Utku Demir, Leonardo Bonati

The next generation of cellular networks will be characterized by openness, intelligence, virtualization, and distributed computing. The Open Radio Access Network (Open RAN) framework represents a significant leap toward realizing these ideals, with prototype deployments taking place in both academic and industrial domains. While it holds the potential to di

Waner Hou, Xingyu Zhao, Kamran Rehan, Yi Li

Quantum friction, a quantum analog of classical friction, reduces the performance of quantum machines, such as heat engines, and makes them less energy efficient. We here report the experimental realization of an energy efficient quantum engine coupled to a quantum battery that stores the produced work, using a single ion in a linear Paul trap. We first esta

Mounir Bensalem, Admela Jukan

We consider a next generation wireless network incorporating a base station a set of typically low-cost and faulty Reconfigurable Intelligent Surfaces (RISs). The base station needs to select the path including the RIS to provide the maximum signal-to-noise ratio (SNR) to the user. We study the effect of the number of elements, distance and RIS hardware fail

Michael Webster-Clark, Yi Li, Sophie Dell Aniello, Robert W. Platt

Clone-censor-weighting (CCW) is an analytic method for studying treatment regimens that are indistinguishable from one another at baseline without relying on landmark dates or creating immortal person time. One particularly interesting CCW application is estimating outcomes when starting treatment within specific time windows in observational data (e.g., sta

Xander Pope, Kevin Verhaegh, Chris Bowman, Bruce Lipschultz

This paper presents the application, testing and first results of a new adaptive Bayesian inference analysis which utilises conventional and ultrafast spectroscopic measurements made in the divertor chamber to investigate the divertor physics during detachment. Validation of this software is performed prior and during analyses of results, demonstrated by com

Arjyama Bordoloi, A. C. Garcia-Castro, Zachary Romestan, Aldo H. Romero

The Rashba spin-orbit coupling effect, primarily arising from structural-inversion asymmetry in periodic crystals, has garnered considerable attention due to its tunability and potential applications in spintronics. Its capability to manipulate electron spin without an external magnetic field opens new avenues for spintronic device design, particularly in se

Noujoud Nader, Patrick Diehl, Marta D'Elia, Christian Glusa

Local-nonlocal coupling approaches combine the computational efficiency of local models and the accuracy of nonlocal models. However, the coupling process is challenging, requiring expertise to identify the interface between local and nonlocal regions. This study introduces a machine learning-based approach to automatically detect the regions in which the lo

Buyun He, Yingguang Yang, Qi Wu, Hao Liu

Detecting social bots has evolved into a pivotal yet intricate task, aimed at combating the dissemination of misinformation and preserving the authenticity of online interactions. While earlier graph-based approaches, which leverage topological structure of social networks, yielded notable outcomes, they overlooked the inherent dynamicity of social networks

Alrik Durand, Yoann Baron, Péter Udvarhelyi, Félix Cache

Among the wealth of single fluorescent defects recently detected in silicon, the G center catches interest for its telecom single-photon emission that could be coupled to a metastable electron spin triplet. The G center is a unique defect where the standard Born-Oppenheimer approximation breaks down as one of its atoms can move between 6 lattice sites under

Ibrahim Ayoub, Martine S. Lenders, Benoît Ampeau, Sandoche Balakrichenan

In this paper, we investigate the domain names of servers on the Internet that are accessed by IoT devices performing machine-to-machine communications. Using machine learning, we classify between them and domain names of servers contacted by other types of devices. By surveying past studies that used testbeds with real-world devices and using lists of top v

Samira Fatemi, Ildar Sadeqi, Fridoun Moradlou

In this paper, we present the concept of subdifferential for fuzzy n-cell number valued functions. Then we state some theorems related to subdifferentiability based on the new definition. Finally, we present some applications emphasized on optimization problems, including the Lagrangian dual problem and minimizing the composite problem.

Haohao Zhu, Xiaokun Zhang, Junyu Lu, Youlin Wu

Textual personality detection aims to identify personality characteristics by analyzing user-generated content on social media platforms. Extensive psychological literature highlights that personality encompasses both long-term stable traits and short-term dynamic states. However, existing studies often concentrate only on either long-term or short-term pers

Andrei Bud, Richard Haburcak

Using limit linear series on chains of curves, we show that closures of certain Brill--Noether loci contain a product of pointed Brill--Noether loci of small codimension. As a result, we obtain new non-containments of Brill--Noether loci, in particular that all dimensionally expected non-containments hold for expected maximal Brill--Noether loci. Using these

Alan Inglis, Andrew Parnell, Natarajan Subramani, Fiona Doohan

Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be su

Tobias Ladner, Michael Eichelbeck, Matthias Althoff

Graph neural networks are becoming increasingly popular in the field of machine learning due to their unique ability to process data structured in graphs. They have also been applied in safety-critical environments where perturbations inherently occur. However, these perturbations require us to formally verify neural networks before their deployment in safet

Harald Appelshäuser

Dielectrons are unique observables in ultra-relativistic heavy-ion collisions. Thanks to their penetrating nature, they carry information from all stages of the collision and can provide knowledge about pre-equilibirium dynamics, QGP temperature and transport coefficients, and chiral symmetry restoration. On the other hand, experimental challenges are enormo

Hai-Liang Wu, Jie Li, Li-Yuan Wang, Chi Hoi Yip

Inspired by the works of L. Carlitz and Z.-W. Sun on cyclotomic matrices, in this paper, we investigate certain cyclotomic matrices involving Gauss sums over finite fields, which can be viewed as finite field analogues of certain matrices related to the Gamma function. For example, let $q=p^n$ be an odd prime power with $p$ prime and $n\in\mathbb{Z}^+$. Let

Carlson Moses Büth, Anastassia Vybornova, Michael Szell

superblockify is a Python package for partitioning an urban street network into Superblock-like neighborhoods and for visualizing and analyzing the partition results. A Superblock is a set of adjacent urban blocks where vehicular through traffic is prevented or pacified, giving priority to people walking and cycling. The Superblock blueprints and descriptive

Tao Liu, Tianyu Zhang, Yongxue Chen, Yuming Huang

We introduce a novel neural network-based computational pipeline as a representation-agnostic slicer for multi-axis 3D printing. This advanced slicer can work on models with diverse representations and intricate topology. The approach involves employing neural networks to establish a deformation mapping, defining a scalar field in the space surrounding an in

Yiqiao Zhang, Karl Oskar Ekvall, Aaron J. Molstad

We show that confidence intervals in a variance component model, with asymptotically correct uniform coverage probability, can be obtained by inverting certain test-statistics based on the score for the restricted likelihood. The results apply in settings where the variance is near or at the boundary of the parameter set. Simulations indicate the proposed te

Raphael Koster, Miruna Pîslar, Andrea Tacchetti, Jan Balaguer

A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves. What resource allocation mechanisms will encourage levels of reciprocation that sustain the commons? Here, in an iterated multiplayer trust game, we use deep reinforcement learnin

Seliem El-Sayed, Canfer Akbulut, Amanda McCroskery, Geoff Keeling

Recent generative AI systems have demonstrated more advanced persuasive capabilities and are increasingly permeating areas of life where they can influence decision-making. Generative AI presents a new risk profile of persuasion due the opportunity for reciprocal exchange and prolonged interactions. This has led to growing concerns about harms from AI persua

Zhensong Hu, Meicun Hou, Zhiyuan Li

We present a systematic search for extraplanar X-ray point sources around 19 late-type, highly inclined disk galaxies residing in the Virgo cluster, based on archival Chandra observations reaching a source detection sensitivity of $L\rm(0.5- 8~keV)\sim10^{38}\rm~erg~s^{-1}$. Based on the cumulative source surface density distribution as a function of project

Pongpisit Thanasutives, Ken-ichi Fukui

The uncertainty-penalized information criterion (UBIC) has been proposed as a new model-selection criterion for data-driven partial differential equation (PDE) discovery. In this paper, we show that using the UBIC is equivalent to employing the conventional BIC to a set of overparameterized models derived from the potential regression models of different com

Hao Chen, Robin Karlsson, Alexander Zhiboedov

Energy correlations characterize the energy flux through detectors at infinity produced in a collision event. Remarkably, in holographic conformal field theories, they probe high-energy gravitational scattering in the dual anti-de Sitter geometry. We use known properties of high-energy gravitational scattering and its unitarization to explore the leading qua

A. Barnaveli, R. van Roij

In this paper, building upon the discovery of asymmetric rectified electric fields (AREF) in recent experiments [S.H. Hashemi et al., Physical Review Letters 121, 185504 (2018)], we explore the generation of AREF by applying a sawtooth-like voltage to 1:1 electrolytes with equal diffusion coefficients confined between two planar blocking electrodes. This dif

Xilun Li, Shengxuan Zhou

For any integers $m\geqslant n\geqslant 3$, we construct a Ricci limit space $X_{m,n}$ such that for a fixed point, some tangent cones are $\mathbb{R}^m$ and some are $\mathbb{R}^n$. This is an improvement of Menguy's example. Moreover, we show that for any finite collection of closed Riemannian manifolds $(M_i^{n_i},g_i)$ with $\mathrm{Ric}_{g_i}\geqslant(n

Gemma De les Coves, Joshua Graf, Andreas Klingler, Tim Netzer

We investigate the generalized moment membership problem for matrices, a formulation equivalent to Skolem's problem for linear recurrence sequences. We show decidability for orthogonal, unitary, and real eigenvalue matrices, and undecidability for matrices over certain commutative and non-commutative polynomial rings. As consequences, we deduce that positivi

Frank Drewes, Berthold Hoffmann, Mark Minas

Engelfriet and Vereijken have shown that linear graph grammars based on hyperedge replacement generate graph languages that can be considered as interpretations of regular string languages over typed symbols. In this paper we show that finite automata can be lifted from strings to graphs within the same framework. For the efficient recognition of graphs with

Anton Rodomanov

In this paper, we present a global complexity analysis of the classical BFGS method with inexact line search, as applied to minimizing a strongly convex function with Lipschitz continuous gradient and Hessian. We consider a variety of standard line search strategies including the backtracking line search based on the Armijo condition, Armijo-Goldstein and Wo

Oleg M. Sotnikov, Ilia A. Iakovlev, Evgeniy O. Kiktenko, Aleksey K. Fedorov

Manipulating entanglement, which reflects non-local correlations in a quantum system and defines the complexity of describing its wave function, represents the extremely tough challenge in the fields of quantum computing, quantum information, and condensed matter physics. In this work, by the example of the well-structured Dicke states we demonstrate that th

Zachary Brennan

Probabilistic zero forcing is a graph coloring process in which blue vertices "infect" (color blue) white vertices with a probability proportional to the number of neighboring blue vertices. We introduce reversion probabilistic zero forcing (RPZF), which shares the same infection dynamics but also allows for blue vertices to revert to being white in each rou

V. Akshay, Ar. Melnikov, A. Termanova, M. R. Perelshtein

In optimization, one of the well-known classical algorithms is power iterations. Simply stated, the algorithm recovers the dominant eigenvector of some diagonalizable matrix. Since numerous optimization problems can be formulated as an eigenvalue/eigenvector search, this algorithm features wide applicability. Operationally, power iterations consist of perfor

Chuan-Qiang Song, Hao Sun, Jiang-Hao Yu

Chiral perturbation theory describes the low energy dynamics of mesons and baryons in terms of the nonlinear Goldstone boson and fermion degrees of freedom. Through the Young tensor technique, we construct the on-shell operator bases for the meson-baryon system up to $p^5$-order, using the chiral dimension power counting and heavy baryon expansion. For the L

Alfons Van Daele

Let $A$ be an algebra with identity and $\Delta:A\to A\otimes A$ a coproduct that admits a counit. If there exist a faithful left integral and a faithful right integral, one can construct an antipode and $(A,\Delta)$ is a Hopf algebra. This is the Larson-Sweedler theorem. There are generalizations of this result for multiplier Hopf algebras, weak Hopf algebr

Xun Wu, Shaohan Huang, Wenhui Wang, Furu Wei

Sparse Mixtures of Experts (SMoE) scales model capacity without significant increases in training and inference costs, but exhibits the following two issues: (1) Low expert activation, where only a small subset of experts are activated for optimization. (2) Lacking fine-grained analytical capabilities for multiple semantic concepts within individual tokens.

Geovane G. A. de Souza, Hugo Natal da Luz, Marco Bregant

In this work a set of simulations that aim at the optimization of gaseous detectors for applications in X-ray fluorescence imaging in the energy range of 3 -- 30keV is presented. By studying the statistical distribution of the radiation interactions with gases, the energy resolution limits after charge multiplication for 6keV X-ray photons in Ar/CO$_2$(70/30

Jie Lei, Enrique S. Quintana-Ortí

This paper investigates the design of parallel general matrix multiplication (GEMM) for a Versal Adaptive Compute Accelerated Platform (ACAP) equipped with a VC1902 system-on-chip and multiple Artificial Intelligence Engines (AIEs). Our efforts aim to port standard optimization techniques applied in the high-performance realization of GEMM on CPUs to the Ver

Kai Li, Xin Yuan, Jingjing Zheng, Wei Ni

This paper puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the benign local models overheard without any access to the training data of FL. Such an advancement leads to the VGAE-MP at

Fan Zhang, Zhi-Qi Cheng, Jian Zhao, Xiaojiang Peng

Semi-supervised learning has emerged as a promising approach to tackle the challenge of label scarcity in facial expression recognition (FER) task. However, current state-of-the-art methods primarily focus on one side of the coin, i.e., generating high-quality pseudo-labels, while overlooking the other side: enhancing expression-relevant representations. In

Edith Hübner

In this note, we study an integral analogue of animated $\delta$-rings: Animated $\lambda$-rings. We define animated $\lambda$-rings in terms of animated rings equipped with a structure of coherently compatible Frobenius lifts and show that the resulting $\infty$-category is obtained from animating the classical notion of a $\lambda$-ring. Our results build

J. -B. Bru, W. de Siqueira Pedra, A. Ramer dos Santos

Quantum interactions exchanging different types of particles play a pivotal r\^{o}le in quantum many-body theory, but they are not sufficiently investigated from a mathematical perspective. Here, we consider a system made of two fermions and one boson, in order to study the effect of such an off-diagonal interaction term, having in mind the physics of cuprat

Pooja Bhattacharjee, Amit Dutta Banik

Continuous gravitational waves (CGWs) from various astrophysical sources are one of the many future probes of upcoming gravitational wave (GW) search missions. Neutron stars (NSs) with deformity are one of the leading sources of CGW emissions. In this work, for the first time, a novel attempt to estimate the dark matter (DM) capture rate is performed using C

Ronan Sicre, Hanwei Zhang, Julien Dejasmin, Chiheb Daaloul

This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module. This system learns and detects parts in the images that are discriminative among categories, without the need for fine-tuning the C

B. M. Unikewicz, A. M. Pincot, T. Cohen

Soft material research has seen significant growth in recent years, with emerging applications in robotics, electronics, and healthcare diagnostics where understanding material mechanical response is crucial for precision design. Traditional methods for measuring nonlinear mechanical properties of soft materials require specially sized samples that are extra

Richard Hladík, Jakub Tětek

Devising mechanisms with good beyond-worst-case input-dependent performance has been an important focus of differential privacy, with techniques such as smooth sensitivity, propose-test-release, or inverse sensitivity mechanism being developed to achieve this goal. This makes it very natural to use the notion of universal optimality in differential privacy.

Hao Miao, Senzhang Wang, Meiyue Zhang, Diansheng Guo

Accurately forecasting traffic flows is critically important to many real applications including public safety and intelligent transportation systems. The challenges of this problem include both the dynamic mobility patterns of the people and the complex spatial-temporal correlations of the urban traffic data. Meanwhile, most existing models ignore the diver

Jinfan Liu, Yichao Yan, Junjie Li, Weiming Zhao

Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes. In industrial scenes, there are often a variety of unpredictable anomalies, and the VAD method can play a significant role in these scenarios. However, there is a

Pablo del Mazo-Sevillano, Alfredo Aguado, Javier R. Goicoechea, Octavio Roncero

CH$_3^+$, a cornerstone intermediate in interstellar chemistry, has recently been detected for the first time by the James Webb Space Telescope. The photodissociation of this ion is studied here. Accurate explicitly correlated multi-reference configuration interaction {\it ab initio} calculations are done, and full dimensional potential energy surfaces are d

Kshitij S. Bane, Indrajit V. Barve, G. V. S. Gireesh, C. Kathiravan

Recently we had reported commissioning of a prototype for pulsar observations at low radio frequencies (<100 MHz) using log-periodic dipole antennas (LPDAs) in the Gauribidanur Radio Observatory near Bangalore in India. The aforementioned system (GAuribidanur Pulsar System, GAPS) is currently being augmented to directly digitize the radio frequency signals f

David Brecht, Nils Gehrke, Tobias Kerbl, Niklas Krauss

Teleoperation is a popular solution to remotely support highly automated vehicles through a human remote operator whenever a disengagement of the automated driving system is present. The remote operator wirelessly connects to the vehicle and solves the disengagement through support or substitution of automated driving functions and therefore enables the vehi

Ana Letícia Garcez Vicente, Roseval Donisete Malaquias Junior, Roseli A. F. Romero

Myocardial Infarction is a main cause of mortality globally, and accurate risk prediction is crucial for improving patient outcomes. Machine Learning techniques have shown promise in identifying high-risk patients and predicting outcomes. However, patient data often contain vast amounts of information and missing values, posing challenges for feature selecti

Hao Li, Han Liu, Dewei Hu, Jiacheng Wang

In this paper, we present PRISM, a Promptable and Robust Interactive Segmentation Model, aiming for precise segmentation of 3D medical images. PRISM accepts various visual inputs, including points, boxes, and scribbles as sparse prompts, as well as masks as dense prompts. Specifically, PRISM is designed with four principles to achieve robustness: (1) Iterati

Kyohei Kawaguchi, Nanae Domoto, Sho Fujibayashi, Hamid Hamidani

We study long-term evolution of the matter ejected in a black-hole neutron-star (BH-NS) merger employing the results of a long-term numerical-relativity simulation and nucleosynthesis calculation, in which both dynamical and post-merger ejecta formation is consistently followed. In particular, we employ the results for the merger of a $1.35\,M_\odot$ NS and

Benjamin Brück

Church-Farb-Putman formulated stability and vanishing conjectures for the high-dimensional cohomology of $\operatorname{SL}_n(\mathbb{Z})$, surface mapping class groups and automorphism groups of free groups. This is a survey on the current status of these conjectures and their generalisations.

Nana Cabo Bizet

We describe non-Abelian T-dualities for $\mathcal{N} = 2$ two dimensional gauged linear sigma model (GLSM). We start with the case of left and right $(2, 2)$ supersymmetry (SUSY), $U(1)$ gauge group, and global non-Abelian symmetries. Our analysis applies to the specialization of the GLSM with the global group $SU(2)\times SU(2)$, whose original model is the

Felipe Torres Figueroa, Hanwei Zhang, Ronan Sicre, Yannis Avrithis

This paper studies interpretability of convolutional networks by means of saliency maps. Most approaches based on Class Activation Maps (CAM) combine information from fully connected layers and gradient through variants of backpropagation. However, it is well understood that gradients are noisy and alternatives like guided backpropagation have been proposed

Liyuan Lin, Ruodu Wang, Ruixun Zhang, Chaoyi Zhao

We study the problem of choosing the copula when the marginal distributions of a random vector are not all continuous. Inspired by four motivating examples including simulation from copulas, stress scenarios, co-risk measures, and dependence measures, we propose to use the checkerboard copula, that is, intuitively, the unique copula with a distribution that

Yihao Li, Mostafa El Habib Daho, Pierre-Henri Conze, Rachid Zeghlache

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it combines information from various imaging modalities to provide a more comprehensive understanding of the underlying pathology. Recently, deep learning-based multimodal fusion techniques have emerged as powerful tools for improving medical image classification. This rev

Lia Šibav, Žiga Gosar, Tilen Knaflič, Zvonko Jagličić

Doping of quantum antiferromagnets is an established approach to investigate the robustness of their ground state against the competing phases. Predictions of doping effects on the ground state of the Shastry-Sutherland dimer model are here verified experimentally on Mg-doped SrCu2(BO3)2. A partial incorporation of Mg2+ on the Cu2+-site in the SrCu2(BO3)2 st

Gyula Fodor, Péter Forgács, Muneeb Mushtaq

The computation of the amplitude, $\alpha$, of asymptotic standing wave tails of weakly delocalized, stationary solutions in a fifth-order Korteweg-de Vries equation is revisited. Assuming the coefficient of the fifth order derivative term, $\epsilon^2\ll1$, a new derivation of the ``beyond all orders in $\epsilon$'' amplitude, $\alpha$, is presented. It is

Fabio Guglietta, Francesca Pelusi

The steady-state behavior of a single drop under shear flow has been extensively investigated in the limit of small deformation and negligible inertia effects. In this work, we combine the calculations proposed by Flumerfelt [R. W. Flumerfelt, J. Colloid Interface Sci. 76, 330 (1980)] for unconfined drops with interface viscosity, with those by Shapira & Hab

Jef Jonkers, Glenn Van Wallendael, Luc Duchateau, Sofie Van Hoecke

Conformal Predictive Systems (CPS) offer a versatile framework for constructing predictive distributions, allowing for calibrated inference and informative decision-making. However, their applicability has been limited to scenarios adhering to the Independent and Identically Distributed (IID) model assumption. This paper extends CPS to accommodate scenarios

Asher Spector, Rina Foygel Barber, Trevor Hastie, Ronald N. Kahn

Financial firms often rely on fundamental factor models to explain correlations among asset returns and manage risk. Yet after major events, e.g., COVID-19, analysts may reassess whether existing risk models continue to fit well: specifically, after accounting for a set of known factor exposures, are the residuals of the asset returns independent? With this

Joel Fine, Weiyong He, Chengjian Yao

A hypersymplectic structure on a 4-manifold is a triple $\omega_1, \omega_2, \omega_3$ of 2-forms for which every non-trivial linear combination $a^1\omega_1 + a^2 \omega_2 + a^3 \omega_3$ is a symplectic form. Donaldson has conjectured that when the underlying manifold is compact, any such structure is isotopic in its cohomolgy class to a hyperk\"ahler trip

Nahid Binandeh Dehaghani, A. Pedro Aguiar, Rafal Wisniewski

This paper proposes an integrated quantum-classical approach that merges quantum computational dynamics with classical computing methodologies tailored to address control problems based on Pontryagin's minimum principle within a Physics-Informed Neural Network (PINN) framework. By leveraging a dynamic quantum circuit that combines Gaussian and non-Gaussian g

Guoqing Wang, Zhongdao Wang, Pin Tang, Jilai Zheng

Existing solutions for 3D semantic occupancy prediction typically treat the task as a one-shot 3D voxel-wise segmentation perception problem. These discriminative methods focus on learning the mapping between the inputs and occupancy map in a single step, lacking the ability to gradually refine the occupancy map and the reasonable scene imaginative capacity

Hui Li, Ting Gao, Fengli Yan

In this paper, we investigate how to quantify the quantum states of $n$-particles from the point of $(k+1)$-partite entanglement $(1\leq k\leq n-1)$, which plays an instrumental role in quantum nonlocality and quantum metrology. We put forward two families of entanglement measures termed $q$-$(k+1)$-PE concurrence $(q>1)$ and $\alpha$-$(k+1)$-PE concurrence

Xingrui Peng, Denis Martynov, Zonghong Zhu, Teng Zhang

The quantum noise in gravitational-wave detectors can be suppressed in a broadband by frequency-dependent squeezing. It usually requires one large scale filter cavity and even two, for example in the low frequency detector of Einstein Telescope, which is a detuned dual recycling Fabry-Perot Michelson interferometer. In this paper, we study the feasibility of

Dinesh Wagle, Daniel Stoeffler, Loic Temdie, Mojtaba Taghipour Kaffash

A caustic is a mathematical concept describing the beam formation when the beam envelope is reflected or refracted by a manifold. While caustics are common in a wide range of physical systems, caustics typically exhibit a reciprocal wave propagation and are challenging to control. Here, we utilize the highly anisotropic dispersion and inherent non-reciprocit

Shuofeng Sun, Yongming Rao, Jiwen Lu, Haibin Yan

Numerous prior studies predominantly emphasize constructing relation vectors for individual neighborhood points and generating dynamic kernels for each vector and embedding these into high-dimensional spaces to capture implicit local structures. However, we contend that such implicit high-dimensional structure modeling approch inadequately represents the loc

Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu, Deep Gandhi

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-

Wen Liang, Peipei Ran, Mengchao Bai, Xiao Liu

Salient object detection (SOD) aims at finding the most salient objects in images and outputs pixel-level binary masks. Transformer-based methods achieve promising performance due to their global semantic understanding, crucial for identifying salient objects. However, these models tend to be large and require numerous training parameters. To better harness

Ningning Jia, Zhao Yang, Jiangtao Cai, Zhiheng Lv

Exotic electronic bands, such as flat bands, linear crossing bands, spontaneously valley- or spin-polarized bands, in two-dimensional materials have been the hot topics in condensed matter physics. Herein, we first propose a general dispersion model for possible hat-like electronic bands, and then identify an intriguing single-spin \emph{waved-brim flat-top

A. Barnaveli, T. M. Kamsma, W. Q. Boon, R. van Roij

A hitherto unexploited characteristic feature of emerging iontronic devices for information processing is the intrinsic mobility of the medium (water) of dissolved ions in aqueous electrolytes, which therefore not only respond to voltage but also to pressure. Here we study a microfluidic memristor, in the form of a conical channel, exposed to simultaneously

Walter Gubinelli, Pietro Simeoni, Ryan Tetro, Luca Colombo

This work reports on the modeling, fabrication, and experimental characterization of a 13 GHz 30% Scandium-doped Aluminum Nitride (ScAlN) Overmoded Bulk Acoustic Resonator (OBAR) for high-frequency Radio Frequency (RF) applications, notably in 5G technology and beyond. The Finite Element Analysis (FEA) optimization process targets the top and bottom metal el

Derek Powell, Walter Gerych, Thomas Hartvigsen

Humans rarely learn one fact in isolation. Instead, learning a new fact induces knowledge of other facts about the world. For example, in learning a korat is a type of cat, you also infer it is a mammal and has claws, ensuring your model of the world is consistent. Knowledge editing aims to inject new facts into language models to improve their factuality, b

Adolfo Ortiz, Jianhua Yang, Mattia Coccolo, Jesús M. Seoane

The main purpose of this paper is to study both the underdamped and the overdamped dynamics of the nonlinear Helmholtz oscillator with a fractional order damping. For that purpose, we use the Grunwald-Letnikov fractional derivative algorithm in order to get the numerical simulations. Here, we investigate the effect of taking the fractional derivative in the

Aleksei Dorkin, Kairit Sirts

This study evaluates three different lemmatization approaches to Estonian -- Generative character-level models, Pattern-based word-level classification models, and rule-based morphological analysis. According to our experiments, a significantly smaller Generative model consistently outperforms the Pattern-based classification model based on EstBERT. Addition

Antonios Makris, Theodoros Theodoropoulos, Evangelos Psomakelis, Emanuele Carlini

The shift from Cloud Computing to a Cloud-Edge continuum presents new opportunities and challenges for data-intensive and interactive applications. Edge computing has garnered a lot of attention from both industry and academia in recent years, emerging as a key enabler for meeting the increasingly strict demands of Next Generation applications. In Edge compu

Nikolaos Petropoulos, Xutong Wu, Andrii Sokolov, Panagiotis Giounanlis

We present an in-depth analysis of a single-electron box (SEB) biased through a floating node technique that is common in charge-coupled devices (CCDs). The device is analyzed and characterized in the context of single-electron charge-sensing techniques for integrated silicon quantum dots (QD). The unique aspect of our SEB design is the incorporation of a me

Elle Miller, Maximilian Durner, Matthias Humt, Gabriel Quere

We propose a novel pipeline for unknown object grasping in shared robotic autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are typically learning-based approaches optimised for a specific end-effector, that generate grasp poses directly from sensor input. In the domain of assistive robotics, we seek instead to utilise the user's co

Xiping Sun, Jing Chen, Kun He, Zhixiang He

Receiving calls is one of the most universal functions of smartphones, involving sensitive information and critical operations. Unfortunately, to prioritize convenience, the current call receiving process bypasses smartphone authentication mechanisms (e.g., passwords, fingerprint recognition, and face recognition), leaving a significant security gap. To addr

Hao Miao, Yan Zhao, Chenjuan Guo, Bin Yang

The widespread deployment of wireless and mobile devices results in a proliferation of spatio-temporal data that is used in applications, e.g., traffic prediction, human mobility mining, and air quality prediction, where spatio-temporal prediction is often essential to enable safety, predictability, or reliability. Many recent proposals that target deep lear

Constantin Bachas, Zhongwu Chen

We revisit the problem of defining an invariant notion of tension in gravity. For spacetimes whose asymptotics are those of a Defect CFT we propose two independent definitions : Gravitational tension given by the one-point function of the dilatation current, and inertial tension, or stiffness, given by the norm of the displacement operator. We show that both

Marta Niedostatek, Anthony Baptista, Jun Yamamoto, Jurgen Kurths

Complex systems often involve higher-order interactions which require us to go beyond their description in terms of pairwise networks. Triadic interactions are a fundamental type of higher-order interaction that occurs when one node regulates the interaction between two other nodes. Triadic interactions are found in a large variety of biological systems, fro

Felipe Torres, Hanwei Zhang, Ronan Sicre, Stéphane Ayache

Explanations obtained from transformer-based architectures in the form of raw attention, can be seen as a class-agnostic saliency map. Additionally, attention-based pooling serves as a form of masking the in feature space. Motivated by this observation, we design an attention-based pooling mechanism intended to replace Global Average Pooling (GAP) at inferen

S. Agarwal, J. A. Aguilar, S. Ali, P. Allison

The Radio Neutrino Observatory - Greenland (RNO-G) seeks discovery of ultra-high energy neutrinos from the cosmos through their interactions in ice. The science program extends beyond particle astrophysics to include radioglaciology and, as we show herein, solar observations, as well. Currently seven of 35 planned radio-receiver stations (24 antennas/station

Anej Svete, Ryan Cotterell

Existing work has analyzed the representational capacity of the transformer architecture by means of formal models of computation. However, the focus so far has been on analyzing the architecture in terms of language \emph{acceptance}. We contend that this is an ill-suited problem in the study of \emph{language models} (LMs), which are definitionally \emph{p

Aurélien Stcherbinine, Michael J. Wolff, Christopher S. Edwards, Oleg Korablev

Retrieving the optical depth of the Martian clouds ($\tau_\mathrm{cld}$) is a powerful way to monitor their spatial and temporal evolution. However, such retrievals from nadir imagery rely on several assumptions, including the vertical structure of the clouds in the atmosphere. Here we compare the results of cloud optical depth retrievals at 320 nm from the

J. Walkowiak, M. Hoppe, I. Ekmark, A. Jardin

The disruption and runaway electron analysis model code was extended to include tungsten impurities in disruption simulations with the aim of studying the runaway electron (RE) generation. This study investigates RE current sensitivity on the following plasma parameters and modelling choices: tungsten concentration, magnetic perturbation strength, electron m

Rick Du, Huilong An, Keyu Wang, Weidong Liu

Ontologies provide formal representation of knowledge shared within Semantic Web applications. Ontology learning involves the construction of ontologies from a given corpus. In the past years, ontology learning has traversed through shallow learning and deep learning methodologies, each offering distinct advantages and limitations in the quest for knowledge