arXiv papers from August 2024

Md Sadman Sakib Rahman, Tianyi Gan, Mona Jarrahi, Aydogan Ozcan

Snell's law dictates the phenomenon of light refraction at the interface between two media. Here, we demonstrate arbitrary programming of light refraction through an engineered material where the direction of the output wave can be set independently for different directions of the input wave, covering arbitrarily selected permutations of light refraction bet

Rajasekhar Anguluri, Anamitra Pal

Localizing sources of troublesome oscillations, particularly forced oscillations (FOs), in power systems has received considerable attention over the last few years. This is driven in part by the massive deployment of phasor measurement units (PMUs) that capture these oscillations when they occur; and in part by the increasing incidents of FOs due to malfunc

Yangfan Deng, Hamad Albidah, Ahmed Dallal, Jijun Yin

Sleep is crucial for human health, and EEG signals play a significant role in sleep research. Due to the high-dimensional nature of EEG signal data sequences, data visualization and clustering of different sleep stages have been challenges. To address these issues, we propose a two-stage hierarchical and explainable feature selection framework by incorporati

P. Curvo, D. R. Ferreira, R. Jorge

The design of fusion devices is typically based on computationally expensive simulations. This can be alleviated using high aspect ratio models that employ a reduced number of free parameters, especially in the case of stellarator optimization where non-axisymmetric magnetic fields with a large parameter space are optimized to satisfy certain performance cri

Emadeldeen Hamdan, Hongyi Pan, Ahmet Enis Cetin

Structured state space models' (SSMs) development in recent studies, such as Mamba and Mamba2, outperformed and solved the computational inefficiency of transformers and large language models at small to medium scale. In this work, we introduce the concept of controllability and observability to the original Mamba SSM's architecture in our Sparse-Mamba (S-Ma

Aref Farhadipour, Masoumeh Chapariniya, Teodora Vukovic, Volker Dellwo

Multimodal learning involves integrating information from various modalities to enhance learning and comprehension. We compare three modality fusion strategies in person identification and verification by processing two modalities: voice and face. In this paper, a one-dimensional convolutional neural network is employed for x-vector extraction from voice, wh

Lin Ge, Yang Xu, Jianing Chu, David Cramer

Today's top advertisers typically manage hundreds of campaigns simultaneously and consistently launch new ones throughout the year. A crucial challenge for marketing managers is determining the optimal allocation of limited budgets across various ad lines in each campaign to maximize cumulative returns, especially given the huge uncertainty in return outcome

Ivan Arraut, Enrique Arrieta-Diaz

The origins of neutrino masses is one of the biggest mysteries in modern physics since they are beyond the realm of the Standard Model. As massive particles, neutrinos undergo flavor oscillations throughout their propagation. In this paper we show that when a neutrino oscillates from a flavor state {\alpha} to a flavor state \b{eta}, it follows three possibl

Tomer Ezra

We study a variant of the single-choice prophet inequality problem where the decision-maker does not know the underlying distribution and has only access to a set of samples from the distributions. Rubinstein et al. [2020] showed that the optimal competitive-ratio of $\frac{1}{2}$ can surprisingly be obtained by observing a set of $n$ samples, one from each

Hanxin Zhu, Tianyu He, Anni Tang, Junliang Guo

Significant progress has been made in text-to-video generation through the use of powerful generative models and large-scale internet data. However, substantial challenges remain in precisely controlling individual concepts within the generated video, such as the motion and appearance of specific characters and the movement of viewpoints. In this work, we pr

Wenxuan Wang, Juluan Shi, Zixuan Ling, Yuk-Kit Chan

Equipped with the capability to call functions, modern large language models (LLMs) can leverage external tools for addressing a range of tasks unattainable through language skills alone. However, the effective execution of these tools relies heavily not just on the advanced capabilities of LLMs but also on precise user instructions, which often cannot be en

Yuanwei Li, Elizaveta Ivanova, Martins Bruveris

Automatic image anomaly detection is important for quality inspection in the manufacturing industry. The usual unsupervised anomaly detection approach is to train a model for each object class using a dataset of normal samples. However, a more realistic problem is zero-/few-shot anomaly detection where zero or only a few normal samples are available. This ma

Philippe Spalart, Kenneth Jansen, Gary Coleman

A pair of Direct Numerical Simulations is used to investigate curvature and pressure effects. One has a Gaussian test bump and a straight opposite wall, while the other has a straight test wall and a blowing/suction distribution on an opposite porous boundary, adjusted to produce the same pressure distribution. The calculation of the transpiration distributi

Kailun Chen

We consider the second class particle in half-line open TASEP under two different initial conditions with shock discontinuities. The exact formulas for the distribution of the second class particle can be derived by using the color-position symmetry theorem of colored half-space TASEP. We study the asymptotic distribution of second class particles under the

Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang

The post-processing approaches are becoming prominent techniques to enhance machine learning models' fairness because of their intuitiveness, low computational cost, and excellent scalability. However, most existing post-processing methods are designed for task-specific fairness measures and are limited to single-output models. In this paper, we introduce a

William Bjorndahl, Jack Easton, Austin Modoff, Eric C. Larson

Spiking neural networks (SNNs) are the third generation of neural networks that are biologically inspired to process data in a fashion that emulates the exchange of signals in the brain. Within the Computer Vision community SNNs have garnered significant attention due in large part to the availability of event-based sensors that produce a spatially resolved

Wenxuan Wang

Large language models (LLMs), such as ChatGPT, have rapidly penetrated into people's work and daily lives over the past few years, due to their extraordinary conversational skills and intelligence. ChatGPT has become the fastest-growing software in terms of user numbers in human history and become an important foundational model for the next generation of ar

S. Qi, H. Moore, N. Hogade, D. Milojicic

Serverless computing is an emerging cloud computing paradigm that can reduce costs for cloud providers and their customers. However, serverless cloud platforms have stringent performance requirements (due to the need to execute short duration functions in a timely manner) and a growing carbon footprint. Traditional carbon-reducing techniques such as shutting

Pablo Dorta-González, Alexis Jorge López-Puig, María Isabel Dorta-González, Sara M. González-Betancor

The integration of generative artificial intelligence technology into research environments has become increasingly common in recent years, representing a significant shift in the way researchers approach their work. This paper seeks to explore the factors underlying the frequency of use of generative AI amongst researchers in their professional environments

Mehdi Bouzid, Cesar Valencia Gallardo, Magdalena Kopec, Lara Koehler

Branched actin networks exert pushing forces in eukaryotic cells, and adapt their stiffness to their environment. The physical basis for their mechanics and adaptability is however not understood. Indeed, here we show that their high density and low connectivity place them outside the scope of standard elastic network models for actin. We combine high-precis

Lynnyngs K. Arruda, Nikolai V. Chemetov, Fernanda Cipriano

This article studies the Stochastic Degasperis-Procesi (SDP) equation on $\mathbb{R}$ with an additive noise. Applying the kinetic theory, and considering the initial conditions in $L^2(\mathbb{R})\cap L^{2+\delta}(\mathbb{R})$, for arbitrary small $\delta>0$, we establish the existence of a global pathwise solution. Restricting to the particular case of zer

Fazle Rahat, M Shifat Hossain, Md Rubel Ahmed, Sumit Kumar Jha

Scaling laws dictate that the performance of AI models is proportional to the amount of available data. Data augmentation is a promising solution to expanding the dataset size. Traditional approaches focused on augmentation using rotation, translation, and resizing. Recent approaches use generative AI models to improve dataset diversity. However, the generat

Noah Apthorpe, Boen Beavers, Yan Shvartzshnaider, Brett Frischmann

Technical standards are a longstanding method of communicating best practice recommendations based on expert consensus. Cybersecurity standards are particularly important for informing policies that protect critical systems and sensitive data. Measuring standards compliance is therefore essential to identify vulnerabilities arising from outdated policies and

Synim Selimi, Blerim Rexha, Kamer Vishi

The rapid evolution of the Internet, particularly the emergence of Web3, has transformed the ways people interact and share data. Web3, although still not well defined, is thought to be a return to the decentralization of corporations' power over user data. Despite the obsolescence of the idea of building systems to detect and prevent cyber intrusions, this

Burak Kaya, Mahmut Kuzucuoğlu, Patrizia Longobardi, Mercede Maj

The structure of automorphism groups of $\kappa$-existentially closed groups are studied by Kaya-Kuzucuo\u{g}lu in 2022. It was proved that Aut(G) is the union of subgroups of level preserving automorphisms and $|Aut(G)|=2^\kappa$ whenever $\kappa$ is an inaccessible cardinal and $G$ is the unique $\kappa$-existentially closed group of cardinality $\kappa$.

Jacqueline Lammert, Nicole Pfarr, Leonid Kuligin, Sonja Mathes

Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective therapies by tailoring treatment based on biomarkers, beyond cancer type. Unstructured data that requires manual

Sicheng Wang, Che Liu, Rossella Arcucci

Recent advancements in medical vision-language pre-training (MedVLP) have significantly enhanced zero-shot medical vision tasks such as image classification by leveraging large-scale medical image-text pair pre-training. However, the performance of these tasks can be heavily influenced by the variability in textual prompts describing the categories, necessit

Shih-Yu Chang

This paper presents significant advancements in tensor analysis and the study of random walks on manifolds. It introduces new tensor inequalities derived using the Mond-Pecaric method, which enriches the existing mathematical tools for tensor analysis. This method, developed by mathematicians Mond and Pecaric, is a powerful technique for establishing inequal

Maximilian Fels, Lisa Hartung, Oren Louidor

This is the first in a series of two works which study the discrete Gaussian free field on the binary tree when all leaves are conditioned to be positive. In this work, we obtain sharp asymptotics for the probability of this "hard-wall constraint" event, and identify the repulsion profile followed by the field in order to achieve it. We also provide estimate

Jong-Seo Kim, Hendrik Mueller, Aleksei S. Nikonov, Ru-Sen Lu

The galaxy M87 is one of the prime targets for high resolution radio imaging to investigate the supermassive black hole, accretion flow, and relativistic jet. However, it remains challenging to observe them jointly. In 2018, GMVA+ALMA observations at 86 GHz enabled the simultaneous reconstruction of a ring structure and the extended jet emission. In order to

Stefan Ivanov, Ivan Minchev, Marina Tchomakova

We consider certain fiber bundles over a paraquaternionic contact manifolds, called twistor and reflector spaces, and show that these carry an intrinsic geometric structure that is always integrable.

Md. Rayid Hasan Mojumder, Naruttam Kumar Roy

A Proportional- Integral- Derivative (PID) controller is required to bring a system back to the stable operating region as soon as possible following a disturbance or discrepancy. For successful operation of the PID controller, it is necessary to design the controller parameters in a manner that will render low optimization complexity, less memory for operat

Adilson Almeida, Nikolai V. Chemetov, Fernanda Cipriano

We study an optimal control problem with a quadratic cost functional for non-Newtonian fluids of differential type. More precisely, we consider the system governing the evolution of a second grade fluid filling a two-dimensional bounded domain, supplemented with a Navier slip boundary condition, and under certain assumptions on the size of the initial data a

Lars Lindemann, Yiqi Zhao, Xinyi Yu, George J. Pappas

We present recent advances in formal verification and control for autonomous systems with practical safety guarantees enabled by conformal prediction (CP), a statistical tool for uncertainty quantification. This survey is particularly motivated by learning-enabled autonomous systems (LEASs), where the complexity of learning-enabled components (LECs) poses a

Michael K. -H. Kiessling, Boris L. Altshuler, Emil A. Yuzbashyan

The dispersionless limit of the standard Eliashberg theory of superconductivity is studied. The effective electron-electron interactions are mediated by Einstein phonons of frequency $\Omega>0$, equipped with electron-phonon coupling strength $\lambda$. This allows for a detailed evaluation of the general results on $T_c$ for phonons with non-trivial dispers

Jaehyun Kim, Hyungbin Park

This study proposes a BSDE approach to the long-term decomposition of pricing kernels under the G-expectation framework. We establish the existence, uniqueness, and regularity of solutions to three types of quadratic G-BSDEs: finite-horizon G-BSDEs, infinite-horizon G-BSDEs, and ergodic G-BSDEs. Moreover, we explore the Feynman--Kac formula associated with t

D. V. V. Narayana, D. Mattiolo, Kalyani Gohokar, Nishad Kothari

A connected r-regular graph, where $r \geq 3$, is an r-graph if each odd cut has at least r edges. Every r-graph is matching covered - a connected graph whose each edge participates in some perfect matching. We set out to: (i) characterize solitary edges - those edges that participate in only one perfect matching, and (ii) upper bound the number of such edge

Michael K. -H. Kiessling, Boris L. Altshuler, Emil A. Yuzbashyan

Using the recent reformulation for the Eliashberg theory of superconductivity in terms of a classical interacting Bloch spin chain model, rigorous upper and lower bounds on the critical temperature $T_c$ are obtained for the $\gamma$ model -- a version of Eliashberg theory in which the effective electron-electron interaction is proportional to $(g/|\omega_n-

Michael K. -H. Kiessling, Boris L. Altshuler, Emil A. Yuzbashyan

The standard Eliashberg theory of superconductivity is studied, in which the effective electron-electron interactions are mediated by generally dispersive phonons, with Eliashberg spectral function $\alpha^2 F(\omega)\geq 0$ that is $\propto\omega^2$ for small $\omega>0$ and vanishes for large $\omega$. The Eliashberg function also defines the electron-phono

Kristin Kliemt

This work provides a review of crystal growth, crystal structure, compositional details, magnetism, thermodynamic, and transport behavior in the family of the trigonal intermetallic systems Eu$T_2Pn_2$ ($T=$ Cd, Zn; $Pn=$ P, As, Sb; space group $P\overline{3}m1$, No.164). The physical properties observed in these materials, and how these change depending on

Sayan Rakshit, Hmrishav Bandyopadhyay, Nibaran Das, Biplab Banerjee

Catastrophic forgetting makes neural network models unstable when learning visual domains consecutively. The neural network model drifts to catastrophic forgetting-induced low performance of previously learnt domains when training with new domains. We illuminate this current neural network model weakness and develop a forgetting-resistant incremental learnin

Ryo Wakizaka, Yasunari Suzuki, Atsushi Igarashi

Fault-tolerant quantum computation using lattice surgery can be abstracted as operations on graphs, wherein each logical qubit corresponds to a vertex of the graph, and multi-qubit measurements are accomplished by connecting the vertices with paths between them. Operations attempting to connect vertices without a valid path will result in abnormal terminatio

Robert Lasarzik, Elisabetta Rocca, Riccarda Rossi

In this paper we investigate the existence of solutions and their weak-strong uniqueness property for a PDE system modelling damage in viscoelastic materials. In fact, we address two solution concepts, weak and strong solutions. For the former, we obtain a global-in-time existence result, but the highly nonlinear character of the system prevents us from prov

Angel Beshirov, Milena Dobreva, Dimitar Dimitrov, Momchil Hardalov

The digitization of historical documents is crucial for preserving the cultural heritage of the society. An important step in this process is converting scanned images to text using Optical Character Recognition (OCR), which can enable further search, information extraction, etc. Unfortunately, this is a hard problem as standard OCR tools are not tailored to

Romain Puech, Vincent Gouldieff

We consider the frequency estimation of periodic signals using noisy time-of-arrival (TOA) information with missing (sparse) data contaminated with outliers. We tackle the problem from a mathematical optimization standpoint, formulating it as a linear regression with an unknown increasing integer independent variable and outliers. Assuming an upper bound on

Ibrahim Alshehri, Adnan Alshehri, Abdulrahman Almalki, Majed Bamardouf

The increasing complexity and scale of modern digital environments have exposed significant gaps in traditional cybersecurity penetration testing methods, which are often time-consuming, labor-intensive, and unable to rapidly adapt to emerging threats. There is a critical need for an automated solution that can efficiently identify and exploit vulnerabilitie

Tumpa Biswas, Dibakar Dhar, Azharuddin Ahmed, Prabir Kumar Haldar

The fractal and phase transitional properties of each type of pions (i.e. $\pi^{\pm,0}$) through one-dimensional $\eta-$space, at an energy of $\sqrt{s}=13~$TeV, have been studied with the help of the Scaled Factorial Moment (SFM) framework. To generate simulated data sets for $pp$ collisions under the minimum bias (MB) condition at $\sqrt{s}=13~$TeV, we hav

Yuga Iguchi, Toshihiro Yamada

We propose a straightforward and effective method for discretizing multi-dimensional diffusion processes as an extension of Milstein scheme. The new scheme is explicitly given and can be simulated using Gaussian variates, requiring the same number of random variables as Euler-Maruyama (EM) scheme. We show that the proposed scheme has a weak convergence rate

Tobias M. Schuett, Sophia A. Henneberg

The new class of compact quasi-axisymmetric stellarators with a wide range of field periods offers the unique potential to combine the advantages of the two leading magnetic confinement fusion devices, tokamaks and stellarators. Here we present the first numerical optimization of this class which has so far only been obtained analytically. Our approach finds

Alper Canberk, Maksym Bondarenko, Ege Ozguroglu, Ruoshi Liu

Creative processes such as painting often involve creating different components of an image one by one. Can we build a computational model to perform this task? Prior works often fail by making global changes to the image, inserting objects in unrealistic spatial locations, and generating inaccurate lighting details. We observe that while state-of-the-art mo

Lulu Fang, Carlos Gustavo Moreira, Yiwei Zhang

In 1928, Jarn\'{\i}k \cite{Jar} obtained that the set of continued fractions with bounded coefficients has Hausdorff dimension one. Good \cite{Goo} observed a dimension drop phenomenon by proving that the Hausdorff dimension of the set of continued fractions whose coefficients tend to infinity is one-half. For the set of continued fractions whose coefficient

M. A. Reyes, C. Dalfó, M. A. Fiol

The chordal ring (CR) graphs are a well-known family of graphs used to model some interconnection networks for computer systems in which all nodes are in a cycle. Generalizing the CR graphs, in this paper, we introduce the families of chordal multi-ring (CMR), chordal ring mixed (CRM), and chordal multi-ring mixed (CMRM) graphs. In the case of mixed graphs,

Mohameden Ahmedou, Thomas Bartsch, Zhengni Hu

We study the following Neumann boundary problem related to the stationary solutions of the Keller-Segel system, a basic model of chemotaxis phenomena: \[ \left\{\begin{array}{ll} -\Delta_g u +\beta u =\lambda\left(\frac{Ve^u}{\int_{\Sigma} Ve^u d v_g}-1\right), &\text { in } \mathring\Sigma\\ \partial_{ \nu_g} u=0, &\text { on } \partial \Sigma \end{array} \

Baki Uzun, Shivam Pande, Gwendal Cachin-Bernard, Minh-Tan Pham

Regular patterns of vegetation are considered widespread landscapes, although their global extent has never been estimated. Among them, spotted landscapes are of particular interest in the context of climate change. Indeed, regularly spaced vegetation spots in semi-arid shrublands result from extreme resource depletion and prefigure catastrophic shift of the

Chen Chen, Emil Björnson, Carlo Fischione

Over-the-air computation (AirComp) is considered as a communication-efficient solution for data aggregation and distributed learning by exploiting the superposition properties of wireless multi-access channels. However, AirComp is significantly affected by the uneven signal attenuation experienced by different wireless devices. Recently, Cell-free Massive MI

C. Dalfó, M. A. Fiol

In this note, we give an infinite family of optimal graphs called $G^+(d,c)$. They are optimal in the sense that they have the maximum possible number of vertices for given a diameter $d$ and the so-called `outer multiset dimension' $c$. We provide their spectra, which have the property that their Laplacian eigenvalues are all different and integral. Finally

Aleksandr Bashkatov, Florian Bürkle, Çayan Demirkır, Wei Ding

Electrolytically generated gas bubbles can significantly hamper the overall electrolysis efficiency. Therefore it is crucial to understand their dynamics in order to optimise water electrolyzer systems. Here we demonstrate a distinct transport mechanism where coalescence with microbubbles drives electrolyte droplets, resulting from the fragmentation of the W

Oscar Nierstrasz, Andrei Chiş, Tudor Gîrba

Software systems should be explainable, that is, they should help us to answer questions while exploring, developing or using them. Textual documentation is a very weak form of explanation, since it is not causally connected to the code, so easily gets out of date. Tests, on the other hand, are causally connected to code, but they are also a weak form of exp

Shivam Pande, Baki Uzun, Florent Guiotte, Thomas Corpetti

In this study, we tackle the challenge of identifying plant species from ultra high resolution (UHR) remote sensing images. Our approach involves introducing an RGB remote sensing dataset, characterized by millimeter-level spatial resolution, meticulously curated through several field expeditions across a mountainous region in France covering various landsca

Dushyantha A Basnayaka

This paper, based on recent research, articulates the opportunities and challenges posed by an emerging area of study known as ``mediumband wireless communication'', which refers to digital radio-frequency (RF) wireless communication through mediumband channels. This class of channels that falls in the transitional region between the narrowband and broadband

William Heyden, Habib Ullah, M. Salman Siddiqui, Fadi Al Machot

In Generalized Zero-Shot Learning (GZSL), we aim to recognize both seen and unseen categories using a model trained only on seen categories. In computer vision, this translates into a classification problem, where knowledge from seen categories is transferred to unseen categories by exploiting the relationships between visual features and available semantic

Lemeng Zhao, Junjie Hu, Jianchao Bi, Yanbing Bai

In recent years, unmanned aerial vehicles (UAVs) have played an increasingly crucial role in supporting disaster emergency response efforts by analyzing aerial images. While current deep-learning models focus on improving accuracy, they often overlook the limited computing resources of UAVs. This study recognizes the imperative for real-time data processing

Ritwik Raj Saxena

Traditional medical basic sciences educational approaches follow a one-size-fits-all model, neglecting the diverse learning styles of individual students. I propose an intelligent AI companion which will fill this gap by providing on-the-fly solutions to students' questions in the context of not only USMLE Step 1 but also other similar examinations in other

Zhiyuan Hu, Yuliang Liu, Jinman Zhao, Suyuchen Wang

Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences. Meanwhile, extending the context window in LLMs through post-pretraining is highly resource-intensive. To address this, we intro

Iván M. Zerón, Jesús Algaba, José Manuel Míguez, Bruno Mendiboure

Averaged local bond order parameters based on spherical harmonics, also known as Lechner and Dellago order parameters, are routinely used to determine crystal structures in molecular simulations. Among different options, the combination of the $\overline{q}_{4}$ and $\overline{q}_{6}$ parameters is one of the best choices in the literature since allows one t

Moreshwar Tayde, P. K. Sahoo

This paper discusses the possibility of traversable wormholes in the galactic region supported by dark matter (DM) models and global monopole charge in the context of $f(Q)$ gravity. To understand the features of the wormholes, we comprehensively studied wormhole solutions with various redshift functions under different $f(Q)$ models. We obtained wormhole sh

Tao Fu, Yue Wang

A significant proportion of exoplanets have been detected with highly tilted or even polar orbits relative to their host stars' equatorial planes. These unusual orbital configurations are often linked to post-disk secular interactions among multiple bodies. However, many aspects remain elusive. In this study, we investigate the role of disk-induced spin-orbi

Yuji Hashimoto, Koji Nuida

It is known that any isogeny graph consisting of ordinary elliptic curves over $\mathbb{F}_q$ with $q = p$ or $p^2$ has a special structure, called a volcano graph. We have a bound $h < \log_2 \sqrt{4q}$ of a height $h$ of the $2$-volcano graph. In this paper, we improve the bound on a height of $2$-volcano graphs over $\mathbb{F}_q$. In case $q = p^2$, we s

Stephen E. Derenzo

The recent discovery that GaAs(Si,B) is a bright cryogenic scintillator with no apparent afterglow offers new opportunities for detecting rare, low-energy, electronic excitations from interacting dark matter. This paper presents Monte Carlo calculations of the scintillation photon detection efficiencies of optical cavities using three current cryogenic photo

Hendrik Bernd Zarucha, Peter Jung

It is known that sparse recovery is possible if the number of measurements is in the order of the sparsity, but the corresponding decoders either lack polynomial decoding time or robustness to noise. Commonly, decoders that rely on a null space property are being used. These achieve polynomial time decoding and are robust to additive noise but pay the price

Evan Berkowitz, Seth Buesing, Shi Chen, Aleksey Cherman

The BKT transition in low-dimensional systems with a $U(1)$ global symmetry separates a gapless conformal phase from a trivially gapped, disordered phase, and is driven by vortex proliferation. Recent developments in modified Villain discretizations provide a class of lattice models which have a $\mathbb{Z}_W$ global symmetry that counts vortices mod W, mixe

Onel L. A. López, Zhu Han, Ashutosh Sabharwal

Accurate orientation estimation of objects can aid in scene understanding in many applications. In this paper, we consider use cases where passive tags could be deployed to assist radar systems in estimating object orientation. Towards that end, we propose the concept of passive iridescent reflective tags that selectively reflect different wavelengths in dif

Yike Zhang, Jack Noble

Cochlear Implant (CI) procedures involve performing an invasive mastoidectomy to insert an electrode array into the cochlea. In this paper, we introduce a novel pipeline that is capable of generating synthetic multi-view videos from a single CI microscope image. In our approach, we use a patient's pre-operative CT scan to predict the post-mastoidectomy surfa

Haoze He, Daniel Kressner, Bor Plestenjak

It is well known that a family of $n\times n$ commuting matrices can be simultaneously triangularized by a unitary similarity transformation. The diagonal entries of the triangular matrices define the $n$ joint eigenvalues of the family. In this work, we consider the task of numerically computing approximations to such joint eigenvalues for a family of (near

Haonan Chang, Kowndinya Boyalakuntla, Yuhan Liu, Xinyu Zhang

Solving storage problem: where objects must be accurately placed into containers with precise orientations and positions, presents a distinct challenge that extends beyond traditional rearrangement tasks. These challenges are primarily due to the need for fine-grained 6D manipulation and the inherent multi-modality of solution spaces, where multiple viable g

Wanyu Bian

This paper introduces an optimal control framework to address the inverse problem using a learned regularizer, with applications in image reconstruction. We build upon the concept of Learnable Optimization Algorithms (LOA), which combine deep learning with traditional optimization schemes to improve convergence and stability in image reconstruction tasks suc

Zehao Zhou, Peng Huang, Tao Wang, Guihua Zeng

Modulators based on the Mach-Zehnder interferometer (MZI) structure are widely used in continuous-variable quantum key distribution (CVQKD) systems. MZI-based variable optical attenuator (VOA) and amplitude modulator can reshape the waveform and control the intensity of coherent state signal to realize secret key information modulation in CVQKD system. Howev

Helen S. Ansell, Chengling Li, Daniel M. Sussman

Observations of glassy dynamics in experiments on confluent cellular tissue have inspired a wealth of computational and theoretical research to model their emergent collective behavior. Initial studies of the physical properties of several geometric cell models, including vertex-type models, have highlighted anomalous sub-Arrhenius, or "ultra-strong," scalin

Maeesha Binte Hashem, Benjamin Parpillon, Divake Kumar, Dinithi Jayasuria

In this work, we propose "TimeFloats," an efficient train-in-memory architecture that performs 8-bit floating-point scalar product operations in the time domain. While building on the compute-in-memory paradigm's integrated storage and inferential computations, TimeFloats additionally enables floating-point computations, thus facilitating DNN training within

Haowen Xu, Jinghui Yuan, Anye Zhou, Guanhao Xu

Leveraging recent advances in generative AI, multi-agent systems are increasingly being developed to enhance the functionality and efficiency of smart city applications. This paper explores the transformative potential of large language models (LLMs) and emerging Retrieval-Augmented Generation (RAG) technologies in Intelligent Transportation Systems (ITS), p

Amir Noori, Babak Tavassoli, Alireza Fereidunian

Renewable prosumers face the complex challenge of balancing self-sufficiency with seamless grid and market integration. This paper introduces a novel prosumers network framework aimed at mitigating peak loads in Iran, particularly under the uncertainties inherent in renewable energy generation and demand. A cost-oriented integrated prediction and optimizatio

Vage Egiazarian, Denis Kuznedelev, Anton Voronov, Ruslan Svirschevski

Text-to-image diffusion models have emerged as a powerful framework for high-quality image generation given textual prompts. Their success has driven the rapid development of production-grade diffusion models that consistently increase in size and already contain billions of parameters. As a result, state-of-the-art text-to-image models are becoming less acc

M. R. Formica, E. Ostrovsky, L. Sirota

We offer in this short report the so-called adaptive functional smoothness estimation in the Hilbert space norm sense in the three classical problems of non-parametrical statistic: regression, density and spectral (density) function measurement (estimation).

David Futer, Rose Kaplan-Kelly

The family of right-angled tiling links consists of links built from regular 4-valent tilings of constant-curvature surfaces that contain one or two types of tiles. The complements of these links admit complete hyperbolic structures and contain two totally geodesic checkerboard surfaces that meet at right angles. In this paper, we give a complete characteriz

Chia-Yu Hsu, Wenwen Li, Sizhe Wang

Research on geospatial foundation models (GFMs) has become a trending topic in geospatial artificial intelligence (AI) research due to their potential for achieving high generalizability and domain adaptability, reducing model training costs for individual researchers. Unlike large language models, such as ChatGPT, constructing visual foundation models for i

Yair Stolero, Itzik Klein

Low-cost gyroscope calibration is essential for ensuring the accuracy and reliability of gyroscope measurements. Stationary calibration estimates the deterministic parts of measurement errors. To this end, a common practice is to average the gyroscope readings during a predefined period and estimate the gyroscope bias. Calibration duration plays a crucial ro

Bin Hu, Run Luo, Zelin Liu, Cheng Wang

Temporal motion modeling has always been a key component in multiple object tracking (MOT) which can ensure smooth trajectory movement and provide accurate positional information to enhance association precision. However, current motion models struggle to be both efficient and effective across different application scenarios. To this end, we propose TrackSSM

Eren Kurshan, Paul Franzon

3D integration offers key advantages in improving system performance and efficiency for the End-of-Scaling era. It enables the incorporation of heterogeneous system components and disparate technologies, eliminates off-chip communication constraints, reduces on-chip latency and total power dissipation. Moreover, AIs demand for increased computational power,

Shentong Mo, Haofan Wang

Visual sound localization is a typical and challenging problem that predicts the location of objects corresponding to the sound source in a video. Previous methods mainly used the audio-visual association between global audio and one-scale visual features to localize sounding objects in each image. Despite their promising performance, they omitted multi-scal

Vikram Sudarshan, Warren D. Seider

Previously, using forward-flux sampling (FFS) and machine learning (ML), we developed multivariate alarm systems to counter rare un-postulated abnormal events. Our alarm systems utilized ML-based predictive models to quantify committer probabilities as functions of key process variables (e.g., temperature, concentrations, and the like), with these data obtai

P. Swaathi, Sanjit Das, N. Nirmal Thyagu

The dynamics of inertial particles in fluid flows have been the focus of extensive research due to their relevance in a wide range of industrial and environmental processes. Earlier studies have examined the dynamics of aerosols and bubbles using the Maxey-Riley equation in some standard systems but their dynamics within the traveling wave flow remain unexpl

Tomasz Stanisz, Stanisław Drożdż, Jarosław Kwapień

As the recent studies indicate, the structure imposed onto written texts by the presence of punctuation develops patterns which reveal certain characteristics of universality. In particular, based on a large collection of classic literary works, it has been evidenced that the distances between consecutive punctuation marks, measured in terms of the number of

Francescantonio Oliva, Francesco Petitta

In this survey we provide an overview of nonlinear elliptic homogeneous boundary value problems featuring singular zero-order terms with respect to the unknown variable whose prototype equation is $$ -\Delta u = {u^{-\gamma}} \ \text{in}\ \Omega $$ where $\Omega$ is a bounded subset of $\mathbb{R}^N$ ($N\geq 2$), and $\gamma>0$. We start by outlining the bas

Xinyu Wang, Haotian Jiang, Haolin Huang, Yu Fang

Speech recognition is the technology that enables machines to interpret and process human speech, converting spoken language into text or commands. This technology is essential for applications such as virtual assistants, transcription services, and communication tools. The Audio-Visual Speech Recognition (AVSR) model enhances traditional speech recognition,

Mohit Apte, Yashodhara Haribhakta

In the rapidly evolving field of financial forecasting, the application of neural networks presents a compelling advancement over traditional statistical models. This research paper explores the effectiveness of two specific neural forecasting models, N-HiTS and N-BEATS, in predicting financial market trends. Through a systematic comparison with conventional

Nikolai Chemetov, Fernanda Cipriano

We consider a velocity tracking problem for stochastic Navier-Stokes equations in a 2D-bounded domain. The control acts on the boundary through an injection-suction device with uncertainty, which acts in accordance with the non-homogeneous Navier-slip boundary conditions. After establishing a suitable stability result for the solution of the stochastic state

Daniel Witschard, Ilir Jusufi, Andreas Kerren

Embeddings are powerful tools for transforming complex and unstructured data into numeric formats suitable for computational analysis tasks. In this work, we use multiple embeddings for similarity calculations to be applied in bibliometrics and scientometrics. We build a multivariate network (MVN) from a large set of scientific publications and explore an as

Jianli Zhang, YiQing Guo

"PeVatrons" refer to astrophysical sources capable of accelerating particles to energies around $10^{15}$ electron volts and higher, potentially contributing to the cosmic ray spectrum in the knee region. Recently, LHAASO has discovered a large number of PeVatrons, allowing us to investigate in greater depth the contributions of these sources to cosmic rays

Reggie C. Pantig

In this paper, an analytic generalization of the weak field deflection angle (WDA) is derived by utilizing the current non-asymptotically flat generalization of the Gauss-Bonnet theorem. The derived formula is valid for any Schwarzschild-like spacetime, which deviates from the classical Schwarzschild case through some constant parameters. This work provided

Wenqiang Li, Haohuang Wen, Zhiqiang Lin

In modern mobile devices, baseband is an integral component running on top of cellular processors to handle crucial radio communications. However, recent research reveals significant vulnerabilities in these basebands, posing serious security risks like remote code execution. Yet, effectively scrutinizing basebands remains a daunting task, as they run closed