November 2024 arXiv papers — page 136

Huy-Hoang Ngo, Thanh Nguyen Canh, Xiem HoangVan

Intelligent aerial platforms such as Unmanned Aerial Vehicles (UAVs) are expected to revolutionize various fields, including transportation, traffic management, field monitoring, industrial production, and agricultural management. Among these, precise control is a critical task that determines the performance and capabilities of UAV systems. However, current

Thanh Nguyen Canh, Huy-Hoang Ngo, Anh Viet Dang, Xiem HoangVan

Accurate trajectory tracking is an essential characteristic for the safe navigation of a quadrotor in cluttered or disturbed environments. In this paper, we present in detail two state-of-the-art model-based control frameworks for trajectory tracking: the Linear Model Predictive Controller (LMPC) and the Nonlinear Model Predictive Controller (NMPC). Addition

Roger Anderson Hurtado

In this work, we linearize the field equations of $f(R)$ gravity using the Starobinsky model, $R+R^2/(6m^2)$, and examine the modifications to General Relativity. We derive an equation for the trace, $T$, of the energy-momentum tensor, which we then decompose using an auxiliary field. This field satisfies the wave equation with $T$ as its source, while simul

A. L. Wallace, A. R. Casey, A. G. A. Brown, A. Castro-Ginard

Astrometric observations with Gaia are expected to play a valuable role in future exoplanet surveys. With current data from Gaia's third data release (DR3), we are sensitive to periods from less than 1 year to more than 4 years but, unlike radial velocity are not as restricted by the orbital inclination of a potential planet. The presence and potential prope

Efrat Shimron, Shanshan Shan, James Grover, Neha Koonjoo

Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, key barriers to their widespread use include low signal-to-noise ratio (SNR), generally low image quality, and long scan durations. Hence, methods for accelerating acquisition and boosting image quality are critically important to enab

Yanguang Sun, Jian Yang, Lei Luo

Recently, deep learning-based salient object detection (SOD) in optical remote sensing images (ORSIs) have achieved significant breakthroughs. We observe that existing ORSIs-SOD methods consistently center around optimizing pixel features in the spatial domain, progressively distinguishing between backgrounds and objects. However, pixel information represent

Jia Syuen Lim, Yadan Luo, Zhi Chen, Tianqi Wei

In the Detection and Multi-Object Tracking of Sweet Peppers Challenge, we present Track Any Peppers (TAP) - a weakly supervised ensemble technique for sweet peppers tracking. TAP leverages the zero-shot detection capabilities of vision-language foundation models like Grounding DINO to automatically generate pseudo-labels for sweet peppers in video sequences

Márton Trencséni

This paper examines the use of Monte Carlo simulations to understand statistical concepts in A/B testing and Randomized Controlled Trials (RCTs). We discuss the applicability of simulations in understanding false positive rates and estimate statistical power, implementing variance reduction techniques and examining the effects of early stopping. By comparing

Xiaolong Wang, Lei Yu, Yingying Zhang, Jiangwei Lao

Feature matching between image pairs is a fundamental problem in computer vision that drives many applications, such as SLAM. Recently, semi-dense matching approaches have achieved substantial performance enhancements and established a widely-accepted coarse-to-fine paradigm. However, the majority of existing methods focus on improving coarse feature represe

Jifu Lin, Lihua You

Let $k$, $d$ be a positive integer, $G$ be a connected graph of order $n$, $T$ be a tree. The leaf distance of a tree is defined as the minimum distance between any two leaves. For $v\in V(T)$, the leaf degree of $v$ in $T$ is the number of leaves adjacent to $v$, and the leaf degree of $T$ is defined as maximum leaf degree among the vertices of $T$. In this

Yongda Zhu, Tom J. L. C. Bakx, Ryota Ikeda, Hideki Umehata

We report the discovery of a unique quasar-dusty star-forming galaxy (DSFG) system at $z = 5.63$, consisting of the bright quasar J1133+1603 ($M_{\rm UV} = -27.42$) and its compact, dust-obscured companion, J1133c. ALMA observations reveal a prominent [C II] bridge connecting the quasar and DSFG, indicating ongoing interaction at a projected separation of 1.

Shuyao Li, Sushrut Karmalkar, Ilias Diakonikolas, Jelena Diakonikolas

We study the problem of learning a single neuron with respect to the $L_2^2$-loss in the presence of adversarial distribution shifts, where the labels can be arbitrary, and the goal is to find a ``best-fit'' function. More precisely, given training samples from a reference distribution $\mathcal{p}_0$, the goal is to approximate the vector $\mathbf{w}^*$ whi

Dil K. Limbu, Nathan London, Md Omar Faruque, Mohammad R. Momeni

Developing efficient path integral (PI) methods for atomistic simulations of vibrational spectra in heterogeneous condensed phases and interfaces has long been a challenging task. Here, we present the h-CMD method, short for hybrid centroid molecular dynamics, that combines the recently introduced fast quasi-CMD (f-QCMD) method with fast CMD (f-CMD). In this

Jerome Gilles

In this paper, we propose to improve image decomposition algorithms in the case of noisy images. In \cite{gilles1,aujoluvw}, the authors propose to separate structures, textures and noise from an image. Unfortunately, the use of separable wavelets shows some artefacts. In this paper, we propose to replace the wavelet transform by the contourlet transform whi

Jérôme Gilles, Stéphane Landeau, Tristan Dagobert, Philippe Chevalier

In this communication, we deal with the question of automatic target detection, recognition and tracking (ATD/R/T) algorithms performance assessment. We propose a complete methodology of evaluation which approaches objective image datasets development and adapted metrics definition for the different tasks (detection, recognition and tracking). We present som

Viktor T. Toth

Direct detection of gravitons in gravitational experiments, including gravitational wave observatories, has been all but ruled out given the weak coupling between the gravitational field and matter. Here we propose an alternative: looking not for the presence but for the absence of graviton shot noise in gravitational wave data. Gravitational wave experiment

Jhanavi Hegde

Satellites are widely used to estimate and monitor ground cover, providing critical information to address the challenges posed by climate change. High-resolution satellite images help to identify smaller features on the ground and classification of ground cover types. Small satellites have become very popular recently due to their cost-effectiveness. Howeve

Guandong Li

Controllable image generation has always been one of the core demands in image generation, aiming to create images that are both creative and logical while satisfying additional specified conditions. In the post-AIGC era, controllable generation relies on diffusion models and is accomplished by maintaining certain components or introducing inference interfer

Tamara A. Tambyah, David Lee, Santiago Badia

In this work, we develop a new compatible finite element formulation of the thermal shallow water equations that conserves energy and mathematical entropies given by buoyancy-related quadratic tracer variances. Our approach relies on restating the governing equations to enable discontinuous approximations of thermodynamic variables and a variational continuo

Dinh-Nguyen Nguyen, Raymond Kai-Yu Tong, Ngoc-Duy Dinh

Droplet-based microfluidic devices have substantial promise as cost-effective alternatives to current assessment tools in biological research. Moreover, machine learning models that leverage tabular data, including input design parameters and their corresponding efficiency outputs, are increasingly utilised to automate the design process of these devices and

Runxin Zhang, Yulin Shao, Yonina C. Eldar

This paper presents a polarization-aware movable antenna (PAMA) framework that integrates polarization effects into the design and optimization of movable antennas (MAs). While MAs have proven effective at boosting wireless communication performance, existing studies primarily focus on phase variations caused by different propagation paths and leverage anten

Weinan Gao, Zhong-Ping Jiang, Tianyou Chai

In this paper, a new framework for the resilient control of continuous-time linear systems under denial-of-service (DoS) attacks and system uncertainty is presented. Integrating techniques from reinforcement learning and output regulation theory, it is shown that resilient optimal controllers can be learned directly from real-time state and input data collec

Isay Katsman, Anna Gilbert

Recent papers in the graph machine learning literature have introduced a number of approaches for hyperbolic representation learning. The asserted benefits are improved performance on a variety of graph tasks, node classification and link prediction included. Claims have also been made about the geometric suitability of particular hierarchical graph datasets

Xianxin Song, Yuan Fang, Feng Wang, Zixiang Ren

This paper presents an overview on intelligent reflecting surface (IRS)-enabled sensing and communication for the forthcoming sixth-generation (6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication (S&C) performance. First, we exploit a single IRS to enable w

Yichun Shi, Peng Wang, Weilin Huang

We introduce SeedEdit, a diffusion model that is able to revise a given image with any text prompt. In our perspective, the key to such a task is to obtain an optimal balance between maintaining the original image, i.e. image reconstruction, and generating a new image, i.e. image re-generation. To this end, we start from a weak generator (text-to-image model

Li Yu, Zhihui Li, Jimin Xiao, Moncef Gabbouj

Neural Representations for Videos (NeRV) have simplified the video codec process and achieved swift decoding speeds by encoding video content into a neural network, presenting a promising solution for video compression. However, existing work overlooks the crucial issue that videos reconstructed by these methods lack high-frequency details. To address this p

Nazmush Sakib, Xin Chen

The infrastructure development of electric vehicle charging stations (EVCS) is critical to the integration of electrical vehicles (EVs) into transportation systems, which requires significant investment and has long-term impact on the adoption of EVs. In this paper, a mathematical model is developed to identify the optimal placement of EVCS by utilizing a no

Simon J. Murphy

Asteroseismology has become an indispensable method for measuring stellar ages and radii, while binary systems remain the most prevalent tool for determining stellar masses. The synergy of the two, namely pulsating stars in binary systems, offer even more than the sum of their parts. The sometimes-overwhelming number of pulsation models to be examined for as

Yiding Wang, Hanbo Sun, Chao Wu, Weixi Zhang

Compared to the ferromagnetic materials that realize the anomalous valley Hall effect by breaking time-reversal symmetry and spin-orbit coupling, the antiferromagnetic materials with the joint spatial inversion and time-reversal (PT) symmetry are rarely reported that achieve the anomalous valley Hall effect. Here, we predict that the antiferromagnetic monola

Nan Xue, Yaping Sun, Zhiyong Chen, Meixia Tao

Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but the role of wireless networks in supporting LLMs has not been thoroughly explored. In this paper, we propose a wireless distributed Mixture of Experts (WDMoE) architecture to enable collaborative deployment of LLMs across edge servers at the base

Xiangyu Zhang, Yu Zhou, Guang Yang, Harald C. Gall

The development of large language models (LLMs) has revolutionized automated code generation. However, their high demand of computation resources has hindered a broader deployment and raised environmental concerns. A common strategy for diminishing computational demands is to cache Key-Value (KV) states from the attention mechanism which is adopted predomina

Cheng-Jun Xia, Yu-Ting Rong, Ting-Ting Sun

We propose a new theoretical framework to investigate the properties of finite nuclei based on an extended Nambu-Jona-Lasinio (eNJL) model, where the Dirac sea, the spontaneous chiral symmetry breaking, and the quark degrees of freedom are considered by extending the SU(3) NJL model and treating baryons as clusters of quarks. The eNJL model can then be readi

Sachin Vaidya, Martin Kruczenski

In this article, we study the dynamics of a Bose-Einstein condensate (BEC) with the idea of finding solutions that could possibly correspond to a so-called acoustic (or Unruh) black/white holes. Those are flows with horizons where the speed of the flow goes from sub-sonic to super-sonic. This is because sound cannot go back from the supersonic to the subsoni

Yifan Zhu, Runze Liu, Ailun Yi, Xudong Wang

The ability to control nonclassical light emission from a single quantum emitter by an integrated cavity may unleash new perspectives for integrated photonic quantum applications. However, coupling a single quantum emitter to cavity within photonic circuitry towards creation of the Purcell-enhanced single-photon emission is elusive due to the complexity of i

Shakila Behzadi, Jacquelin Ho, Zainab Tanvir, Gal Haspel

Expansion Microscopy is a super-resolution technique in which physically enlarging samples in an isotropic manner increases inter-molecular distances such that nano-scale structures can be resolved using light microscopy. This is particularly useful in neuroscience as many important structures are smaller than the diffraction limit. Since its invention in 20

Edith Vargas-GarcÍa, Andreas Wachtel

In this note we give a very short introduction to Formal Concept Analysis, accompanied by an example in order to build concept lattices from a context. We build the lattice using the Java-based software Concept Explorer (ConExp) in a recent version of Linux. Installing an appropriate Java version is necessary, because ConExp was developed some time ago using

D. W. Crews, E. T. Meier, U. Shumlak

Adiabatic compression of a self-magnetizing current filament (a Z pinch) is analyzed via the adiabatic invariants of its constituent cyclotron and betatron motions. Chew-Goldberger-Low (CGL) models are recovered for both trajectories but with distinct anisotropy axes, about the magnetic field for cyclotron fluid and about the electric current for betatron fl

Tomoya Sano, Takumi Sato, Akihiro Sasaki, Satoshi Ikegaya

We study theoretically the thermoelectric effect in a superconducting state having the Bogoliubov-Fermi surfaces which stays in a thin superconducting layer between a conventional superconductor and an insulator. The thermoelectric coefficients calculated based on the linear response theory show the remarkable anisotropy in real space, which are explained we

Hong Meng Yam, Nathan J Paek

We explore the impact of pre-training data composition on the performance of small language models in a sample-efficient setting. Using datasets limited to 10 million words, we evaluate several dataset sources, including child-directed speech (CHILDES), classic books (Gutenberg), synthetic data (TinyStories), and a mix of these (Mix) across different model s

Ryota Ono, Ravi Kaushik, Sergey Artyukhin, Martin Jansen

Alkali superoxides differ from conventional transition metal magnets, exhibit magnetism from partially occupied oxygen molecular $\pi^*$-orbitals. Among them, CsO$_2$ stands out for its potential to exhibit novel quantum collective phenomena, such as an orbital order induced Tomonaga-Luttinger liquid state. Using ab-initio Hubbard models, superexchange theor

Jhixon Macías, Reyes Ortiz

In this paper, we study some properties of the closure operator in the Mac\'ias topology on infinite integral domains. Moreover, under certain conditions, we present topological proofs of the infiniteness of maximal ideals and non-associated irreducible elements, taking advantage of the hyperconnectedness of the Mac\'ias topology. Additionally, some problems

D. W. Crews, E. T. Meier, U. Shumlak

The azimuthal self-magnetic field of the ideal Z pinch contains a central magnetic null. Trajectories around this null govern transport in the core. Particles follow cyclotron orbits when the guiding-center approximation holds. Approaching the field null, where the ordinary guiding-center regime breaks down, particles exhibit trajectories called, in some his

Yihui Jiang, Suyang Sun, Hanyi Zhang, Xiaozhe Wang

Chalcogenide phase-change materials (PCMs) are one of the most mature candidates for next-generation memory technology. Recently, CrGeTe3 (CrGT) emerged as a promising PCM due to its enhanced amorphous stability and fast crystallization for embedded memory applications. The amorphous stability of CrGT was attributed to the complex layered structure of the cr

Shu-Tong Niu, Jun Du, Ruo-Yu Wang, Gao-Bin Yang

We propose a single-channel Deep Cascade Fusion of Diarization and Separation (DCF-DS) framework for back-end automatic speech recognition (ASR), combining neural speaker diarization (NSD) and speech separation (SS). First, we sequentially integrate the NSD and SS modules within a joint training framework, enabling the separation module to leverage speaker t

Xiaowei Long, Jie Lin, Xiangyuan Yang

Adversarial detection is designed to identify and reject maliciously crafted adversarial examples(AEs) which are generated to disrupt the classification of target models. Presently, various input transformation-based methods have been developed on adversarial example detection, which typically rely on empirical experience and lead to unreliability against ne

Xinyang Huang, Chuang Zhu, Bowen Zhang, Shanghang Zhang

Semi-supervised domain adaptation (SSDA) has been widely studied due to its ability to utilize a few labeled target data to improve the generalization ability of the model. However, existing methods only consider designing certain strategies for target samples to adapt, ignoring the exploration of customized learning for different target samples. When the mo

T. Nakamura, Y. Fujisawa, B. R. M. Smith, N. Tomoda

Rare earth tri-tellurides (RTe3) are van der Waals (vdW) coupled semimetals ideal for exploring exotic electronic phases. LaTe3 is especially important for understanding the fundamental Fermiology of the RTe3 family because it is non-magnetic and has a simpler charge density wave structure. In this study, we used spectroscopic-imaging scanning tunneling micr

Weike Zhao, Xiaolin Yi, Jieshan Huang, Ruoran Liu

Structured light carrying angular momentum, such as spin angular momentum (SAM) and orbital angular momentum (OAM), has been at the core of new science and applications, driving the need for compact on-chip sources. While many static on-chip solutions have been demonstrated, as well as on-chip sources of free-space modes, no architecture that is fully reconf

B. R. M. Smith, Y. Fujisawa, P. Wu, T. Nakamura

The charge density wave (CDW) state is a widespread phenomenon in low-dimensional metals/semimetals. The spectral weight of the associated folded bands (shadow bands) can be an intriguing trigger leading to additional Fermi surface instability and unexplored phase transitions. The rare earth tri-telluride CeTe3 exhibits a single CDW stabilized below ~400 K a

Jhixon Macías

In this paper, we study some properties of self-homeomorphisms on the Mac\'ias topology over $\mathbb{N}$, and we demonstrate that this space is not topologically rigid.

Qiao Qiao, Yuepei Li, Qing Wang, Kang Zhou

Knowledge graph completion (KGC) is a task of inferring missing triples based on existing Knowledge Graphs (KGs). Both structural and semantic information are vital for successful KGC. However, existing methods only use either the structural knowledge from the KG embeddings or the semantic information from pre-trained language models (PLMs), leading to subop

Dong Li, Aijia Zhang, Junqi Gao, Biqing Qi

Incremental graph learning has gained significant attention for its ability to address the catastrophic forgetting problem in graph representation learning. However, traditional methods often rely on a large number of labels for node classification, which is impractical in real-world applications. This makes few-shot incremental learning on graphs a pressing

Yoon-Soo Jang, Dong Il Lee, Jaime Flor Flores, Wenting Wang

Acoustic detection has many applications across science and technology, from medical to imaging and communications. However, most acoustic sensors have a common limitation in that the detection must be near the acoustic source. Alternatively laser interferometry with picometer-scale motional displacement detection can rapidly and precisely measure sound indu

Clayton Fields, Casey Kennington

In the past several years there has been an explosion of available models for vision-language (VL) tasks. Unfortunately, the literature still leaves open a number of questions related to best practices in designing and training such models. Additionally, the limited programming tools available for modeling make conducting VL research more difficult than nece

Zhen Guo, Xin Li

Suppose $k\geqslant3$ is an integer. Let $\tau_k(n)$ be the number of ways $n$ can be written as a product of $k$ fixed factors. For any fixed integer $r\geqslant2$, we have the asymptotic formula \begin{equation*} \sum_{n_1,\cdots,n_r\leqslant x}\tau_k(n_1 \cdots n_r)=x^r\sum_{\ell=0}^{r(k-1)}d_{r,k,\ell}(\log x)^{\ell}+O(x^{r-1+\alpha_k+\varepsilon}), \end

Shu Wang, Lei Ji, Renxi Wang, Wenxiao Zhao

Reasoning is a central capability of human intelligence. In recent years, with the advent of large-scale datasets, pretrained large language models have emerged with new capabilities, including reasoning. However, these models still struggle with long-term, complex reasoning tasks, such as playing chess. Based on the observation that expert chess players emp

Tarmizi Adam

Optimization on the Stiefel manifold or with orthogonality constraints is an important problem in many signal processing and data analysis applications such as Sparse Principal Component Analysis (SPCA). Algorithms such as the Riemannian proximal gradient algorithms addressing this problem usually involve an intricate subproblem requiring a semi-smooth Newto

Farnoush Baghestani, Youngsun Kong, Ki H. Chon

In recent years, skin sympathetic nerve activity (SKNA) extracted from electrocardiogram has gained attention as a novel noninvasive measure of the sympathetic nervous system (SNS), while electrodermal activity (EDA) has long served this purpose. SparsEDA is a sparse deconvolution technique originally developed for EDA to extract phasic drivers indicating th

Haruka Tsuchiya, Zen Somei, Yasutoshi Makino, Hiroyuki Shinoda

The airborne ultrasound tactile display can present tactile information without direct contact. Using this technology, we developed two methods for simulating the tactile sensation of tapping an object with a finger: the Amplitude Modulation Method and the Lateral Modulation Method. The first method, Amplitude Modulation, simulates the tactile sensation of t

Zhengyi Liu, Longzhen Wang, Xianyong Fang, Zhengzheng Tu

A light field camera can reconstruct 3D scenes using captured multi-focus images that contain rich spatial geometric information, enhancing applications in stereoscopic photography, virtual reality, and robotic vision. In this work, a state-of-the-art salient object detection model for multi-focus light field images, called LFSamba, is introduced to emphasiz

Rafael Orozco, Huseyin Tuna Erdinc, Yunlin Zeng, Mathias Louboutin

Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration projects. Traditional velocity model building methods such as Full-Waveform Inversion (FWI) are powerful but often struggle with the inherent complexities of the inverse problem, includin

David M. Bossens, Kishor Bharti, Jayne Thompson

Parametrised quantum circuits offer expressive and data-efficient representations for machine learning. Due to quantum states residing in a high-dimensional Hilbert space, parametrised quantum circuits have a natural interpretation in terms of kernel methods. The representation of quantum circuits in terms of quantum kernels has been studied widely in quantu

Kedi Zheng, Qixin Chen, Yi Wang, Chongqing Kang

The two-way flow of information and energy is an important feature of the Energy Internet. Data analytics is a powerful tool in the information flow that aims to solve practical problems using data mining techniques. As the problem of electricity thefts via tampering with smart meters continues to increase, the abnormal behaviors of thefts become more divers

Wantao Wang, Shuo Liu, Jiaqiang Li, Shi-Xin Zhang

Measurement-induced phase transitions (MIPT), characterizing abrupt changes in entanglement properties in quantum many-body systems subjected to unitary evolution with interspersed projective measurements, have garnered increasing interest. In this work, we generalize the Kibble-Zurek (KZ) driven critical dynamics that has achieved great success in tradition

A. Gallenne, N. R. Evans, P. Kervella, J. D. Monnier

Cepheid masses are particularly necessary to help solving the mass discrepancy, while independent distance determinations provide crucial test of the period-luminosity relation and Gaia parallaxes. We used CHARA/MIRC to measure the astrometric positions of the high-contrast companion orbiting the Cepheid SU Cygni. We also present new radial velocity measurem

Alex Havrilla, Wenjing Liao

When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the data size. Perhaps the best known example of such scaling laws are for transformer-based large language models, where networks with billions of parameters are trained on trillions of tokens of text. Yet,

Xingyu Zhou, Wenbin Chen, Mingyu Xu

The optimal execution problem has always been a continuously focused research issue, and many reinforcement learning (RL) algorithms have been studied. In this article, we consider the execution problem of targeting the volume weighted average price (VWAP) and propose a relaxed stochastic optimization problem with an entropy regularizer to encourage more exp

S. J. Frank, J. Viola, Yu. V. Petrov, J. K. Anderson

This paper presents Hammir tandem mirror confinement performance analysis based on Realta Fusion's first-of-a-kind model for axisymmetric magnetic mirror fusion performance. This model uses an integrated end plug simulation model including, heating, equilibrium, and transport combined with a new formulation of the plasma operation contours (POPCONs) techniqu

Jacob S. Izraelevitz, Siddharth Krishnamoorthy, Ashish Goel, Caleb Turner

This paper details a significant milestone towards maturing a buoyant aerial robotic platform, or aerobot, for flight in the Venus clouds. We describe two flights of our subscale altitude-controlled aerobot, fabricated from the materials necessary to survive Venus conditions. During these flights over the Nevada Black Rock desert, the prototype flew at the i

Shanpu Shen, Kai-Kit Wong, Ross Murch

Pixel antennas, based on discretizing a continuous radiation surface into small elements called pixels, are a flexible reconfigurable antenna technology. By controlling the connections between pixels via switches, the characteristics of pixel antennas can be adjusted to enhance the wireless channel. Inspired by this, we propose a novel technique denoted ante

Kai Jiang, Shifeng Li, Xiangcheng Zheng

This work proposes and analyzes an efficient numerical method for solving the nonlinear Schr\"odinger equation with quasiperiodic potential, where the projection method is applied in space to account for the quasiperiodic structure and the Strang splitting method is used in time.While the transfer between spaces of low-dimensional quasiperiodic and high-dime

Hengxin Cui, Ken Seng Tan, Fan Yang

In this paper, we study large losses arising from defaults of a credit portfolio. We assume that the portfolio dependence structure is modelled by the Archimedean copula family as opposed to the widely used Gaussian copula. The resulting model is new, and it has the capability of capturing extremal dependence among obligors. We first derive sharp asymptotics

Adam Zebrowski, Haithem Afli

Country instability is a global issue, with unpredictably high levels of instability thwarting socio-economic growth and possibly causing a slew of negative consequences. As a result, uncertainty prediction models for a country are becoming increasingly important in the real world, and they are expanding to provide more input from 'big data' collections, as

Xiaopeng Li, Shangwen Wang, Shasha Li, Jun Ma

Large Language Models for Code (LLMs4Code) have been found to exhibit outstanding performance in the software engineering domain, especially the remarkable performance in coding tasks. However, even the most advanced LLMs4Code can inevitably contain incorrect or outdated code knowledge. Due to the high cost of training LLMs4Code, it is impractical to re-trai

Genessa Benton, Diogo Boito, Maarten Golterman, Alexander Keshavarzi

A key issue affecting the attempt to reduce the uncertainty on the Standard Model prediction for the muon anomalous magnetic moment is the current discrepancy between lattice-QCD and data-driven results for the hadronic vacuum polarization. Progress on this issue benefits from precise data-driven determinations of the isospin-limit light-quark-connected (lqc

Niels van der Weide

Internal language theorems are fundamental in categorical logic, since they express an equivalence between syntax and semantics. One such theorem was proven by Clairambault and Dybjer, who corrected the result originally by Seely. More specifically, they constructed a biequivalence between the bicategory of locally Cartesian closed categories and the bicateg

Aixa X. Andrade, Son Nguyen, Austin Marckx, Albert Montillo

Single-cell RNA sequencing enables high-resolution analysis of cellular heterogeneity, yet disentangling biological signal from batch effects remains a major challenge. Existing batch-correction algorithms suppress or discard batch-related variation rather than modeling it. We propose scMEDAL, single-cell Mixed Effects Deep Autoencoder Learning, a framework

Yayong Li, Peyman Moghadam, Can Peng, Nan Ye

Node classification with Graph Neural Networks (GNN) under a fixed set of labels is well known in contrast to Graph Few-Shot Class Incremental Learning (GFSCIL), which involves learning a GNN classifier as graph nodes and classes growing over time sporadically. We introduce inductive GFSCIL that continually learns novel classes with newly emerging nodes whil

James East, Matthias Fresacher, P. A. Azeef Muhammed, Timothy Stokes

DRC-semigroups model associative systems with domain and range operations, and contain many important classes, such as inverse, restriction, Ehresmann, regular $*$-, and $*$-regular semigroups. In this paper we show that the category of DRC-semigroups is isomorphic to a category of certain biordered categories whose object sets are projection algebras in the

Maddalena Boscaro, Federico Mason, Federico Chiariotti, Andrea Zanella

Artificial Intelligence (AI) is a key component of 6G networks, as it enables communication and computing services to adapt to end users' requirements and demand patterns. The management of Mobile Edge Computing (MEC) is a meaningful example of AI application: computational resources available at the network edge need to be carefully allocated to users,

Maria Azhar, Sandra C. Shaju, Ross Knapman, Alessandro Pignedoli

Knots and links play a crucial role in understanding topology and discreteness in nature. In magnetic systems, twisted, knotted and braided vortex tubes manifest as Skyrmions, Hopfions, or screw dislocations. These complex textures are characterized by topologically non-trivial quantities, such as a Skyrmion number, a Hopf index $H$, a Burgers vector (quanti

M. Olshanetsky

We define the families of Kuramoto models (KM) related to bounded symmetric domains. The families include the Lohe unitary model and the spherical models as special cases. Our approach is based on the construction proposed by Watanabe and Strogats WS. We replace the Poincare disc and its $S^1$ boundary in the WS construction on the bounded symmetric domains

Deegan Atha, Xianmei Lei, Shehryar Khattak, Anna Sabel

Off-road environments pose significant perception challenges for high-speed autonomous navigation due to unstructured terrain, degraded sensing conditions, and domain-shifts among biomes. Learning semantic information across these conditions and biomes can be challenging when a large amount of ground truth data is required. In this work, we propose an approa

Kianté Fernandez, Dominique Makowski, Christopher Fisher

Sequential sampling models (SSMs) are a widely used framework describing decision-making as a stochastic, dynamic process of evidence accumulation. SSMs popularity across cognitive science has driven the development of various software packages that lower the barrier for simulating, estimating, and comparing existing SSMs. Here, we present a software tool, S

Abhinav Kala, David Sharp, Minho Choi, Arnab Manna

Nonlinear interactions between single quantum particles are at the heart of any quantum information system, including analog quantum simulation and fault-tolerant quantum computing. This remains a particularly difficult problem for photonic qubits, as photons do not interact with each other. While engineering light-matter interaction can effectively create p

Dougal Stewart, Nathan Lee, Kenneth Duru

Robust and convergent high-order numerical methods for solving partial differential equations are highly attractive due to their efficiency on modern and next-generation hardware architectures. However, designing such methods for nonlinear hyperbolic conservation laws remains a significant challenge. In this work, we introduce a framework based on dual-pairi

Rui-Bo Wang, Shi-Jie Ma, Jian-Bo Deng, Xian-Ru Hu

In this paper, we study four classical tests of Schwarzschild space-time with Lorentzian distribution in non-commutative geometry. We performed detailed calculations of the first-order corrections induced by the non-commutative parameter on planetary orbital precession, light deflection, radar wave delay, and gravitational redshift. The study showed that the

Daniel Larby, Fulvio Forni

Passivity-based control is a cornerstone of control theory and an established design approach in robotics. Its strength is based on the passivity theorem, which provides a powerful interconnection framework for robotics. However, the design of passivity-based controllers and their optimal tuning remain challenging. We propose here an intuitive design approac

Salvador Lopez-Joya, Jose A. Diaz-Garcia, M. Dolores Ruiz, Maria J. Martin-Bautista

The importance of social media in our daily lives has unfortunately led to an increase in the spread of misinformation, political messages and malicious links. One of the most popular ways of carrying out those activities is using automated accounts, also known as bots, which makes the detection of such accounts a necessity. This paper addresses that problem

Daniel Alpay, Ilwoo Cho, Mihaela Vajiac

We develop the theory of minimal realizations and factorizations of rational functions where the coefficient space is a ring of the type introduced in our previous work, the scaled quaternions, which includes as special cases the quaternions and the split quaternions. The methods involved are not a direct generalization of the complex or quaternionic setting

Caleb J. S. Barr, Olivia Erdelyi, Paul D. Docherty, Randolph C. Grace

Recent regulatory proposals for artificial intelligence emphasize fairness requirements for machine learning models. However, precisely defining the appropriate measure of fairness is challenging due to philosophical, cultural and political contexts. Biases can infiltrate machine learning models in complex ways depending on the model's context, rendering a s

Eviatar Bach, Dan Crisan, Michael Ghil

There is a history of simple forecast error growth models designed to capture the key properties of error growth in operational numerical weather prediction (NWP) models. We propose here such a scalar model that relies on the previous ones and incorporates multiplicative noise in a nonlinear stochastic differential equation (SDE). We analyze the properties o

Matthew G. Baring, Hoa Dinh Thi, George A. Younes, Kun Hu

The intense magnetic fields of neutron stars naturally lead to strong anisotropy and polarization of radiation emanating from their surfaces, both being sensitive to the hot spot position on the surface. Accordingly, pulse phase-resolved intensities and polarizations depend on the angle between the magnetic and spin axes and the observer's viewing direction.

Chongjun Ouyang, Zhaolin Wang, Xingqi Zhang, Yuanwei Liu

The performance of linear receive beamforming in continuous-aperture array (CAPA)-based uplink communications is analyzed. Three continuous beamforming techniques are proposed under the criteria of maximum-ratio combining (MRC), zero-forcing (ZF), and minimum mean-squared error (MMSE). \romannumeral1) For \emph{MRC beamforming}, a closed-form expression for

Jiadu Lin, Qing-Dong Jiang

We study the ponderomotive potential of a bosonic Josephson junction periodically modulated by a high-frequency electromagnetic field. Within the small population difference approximation, the ponderomotive drive induces the well-known Kapitza pendulum effect, stabilizing a $\pi$-phase mode. We discuss the parameter dependence of the dynamical transition fro

Yongsheng Mei, Liangqi Yuan, Dong-Jun Han, Kevin S. Chan

Federated learning (FL) has become a cornerstone in decentralized learning, where, in many scenarios, the incoming data distribution will change dynamically over time, introducing continuous learning (CL) problems. This continual federated learning (CFL) task presents unique challenges, particularly regarding catastrophic forgetting and non-IID input data. E

Melissa L. Graham, Midori Rollins, Robert A. Knop, Suhail Dhawan

In this second paper on the DECam deep drilling field (DDF) program we release 2,020 optical gri-band light curves for transients and variables in the extragalactic COSMOS and ELAIS fields based on time series observations with a 3-day cadence from semester 2021A through 2023A. In order to demonstrate the wide variety of time domain events detected by the pr

Benjamin Iyoya Irving, Annika Marie Schoene

The stock market provides a rich well of information that can be split across modalities, making it an ideal candidate for multimodal evaluation. Multimodal data plays an increasingly important role in the development of machine learning and has shown to positively impact performance. But information can do more than exist across modes -- it can exist across

Ciera McFarland, Ankush Dhawan, Riya Kumari, Chad Council

Soft, growing vine robots are well-suited for exploring cluttered, unknown environments, and are theorized to be performant during structural collapse incidents caused by earthquakes, fires, explosions, and material flaws. These vine robots grow from the tip, enabling them to navigate rubble-filled passageways easily. State-of-the-art vine robots have been t

Stefan Steinerberger

The Kaczmarz method is a way to iteratively solve a linear system of equations $Ax = b$. One interprets the solution $x$ as the point where hyperplanes intersect and then iteratively projects an approximate solution onto these hyperplanes to get better and better approximations. We note a somewhat related idea: one could take two random hyperplanes and proje