July 2023 arXiv papers — page 155

Subin Kim, Kyungmin Lee, June Suk Choi, Jongheon Jeong

Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting these priors to complex visual modalities, often represented as multiple images (e.g., video), achieving consistency across a set of images is challenging. In this paper, we addres

Ashwin Rao

Videos are increasingly being used for e-learning, and transcripts are vital to enhance the learning experience. The costs and delays of generating transcripts can be alleviated by automatic speech recognition (ASR) systems. In this article, we quantify the transcripts generated by whisper for 25 educational videos and identify some open avenues of research

G. G. L. Nashed

Recently, the mimetic gravitational theory has gained much attention in the frame of cosmology as well as in the domain of astrophysics. In this study, we show that in the frame of mimetic gravitation theory we are not able to derive an isotropic model. As a result, our focus shifts towards combining mimetic gravitational theory with the Lagrangian multiplie

Aysha Thahsin Zahir Ismail, Raj Mani Shukla

Distributed Collaborative Machine Learning (DCML) is a potential alternative to address the privacy concerns associated with centralized machine learning. The Split learning (SL) and Federated Learning (FL) are the two effective learning approaches in DCML. Recently there have been an increased interest on the hybrid of FL and SL known as the SplitFed Learni

Stuart Mirell, Daniel Mirell

Locally real states of electromagnetic radiation derived from the underlying quantum mechanical formalism are shown to provide an alternative basis for definite polarized states of the widely accepted probabilistic interpretation. The locally real states traverse common loop configurations without invoking non-local superposition. Correlated photon pairs rep

Man Fai Wong, Shangxin Guo, Ching Nam Hang, Siu Wai Ho

This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs, augmented with software naturalness, have played a crucial role in

Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos

The advancement in generative AI could be boosted with more accessible mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving, yet their genomics application is limited. This project introduces Math Agents and mathematical embedding as fresh entries to the "Moore's Law of

Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna

Algorithm- and data-dependent generalization bounds are required to explain the generalization behavior of modern machine learning algorithms. In this context, there exists information theoretic generalization bounds that involve (various forms of) mutual information, as well as bounds based on hypothesis set stability. We propose a conceptually related, but

Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye

Document understanding refers to automatically extract, analyze and comprehend information from various types of digital documents, such as a web page. Existing Multi-model Large Language Models (MLLMs), including mPLUG-Owl, have demonstrated promising zero-shot capabilities in shallow OCR-free text recognition, indicating their potential for OCR-free docume

N. S. Kavya, V. Venkatesha, G. Mustafa, P. K. Sahoo

Non-commutativity is a key feature of spacetime geometry. The current article explores the traversable wormhole solutions in the framework of $f(R,L_m)$ gravity within non-commutative geometry. By using the Gaussian and Lorentzian distributions, we construct tideless wormholes for the nonlinear $f(R,L_m)$ model $f(R,L_m)=\dfrac{R}{2}+L_m^α$. For both cases,

Ngo Nghi Truyen Huynh, Pierre-André Garambois, François Colleoni, Benjamin Renard

Tackling the difficult problem of estimating spatially distributed hydrological parameters, especially for floods on ungauged watercourses, this contribution presents a novel seamless regionalization technique for learning complex regional transfer functions designed for high-resolution hydrological models. The transfer functions rely on: (i) a multilayer pe

Nicolas Pinon, Robin Trombetta, Carole Lartizien

We study several methods for detecting anomalies in color images, constructed on patch-based auto-encoders. Wecompare the performance of three types of methods based, first, on the error between the original image and its reconstruction,second, on the support estimation of the normal image distribution in the latent space, and third, on the error between the

Eunju Yang, Gyusang Cho, Chan-Hyun Youn

From a service perspective, Multi-Source Domain Adaptation (MSDA) is a promising scenario to adapt a deployed model to a client's dataset. It can provide adaptation without a target label and support the case where a source dataset is constructed from multiple domains. However, it is impractical, wherein its training heavily relies on prior domain inform

Pratip Nandi, Atasi Deb Ray, Sudip Kumar Acharyya

In this paper, replacing `equality' by 'equality almost everywhere' we modify several terms associated with the ring of measurable functions defined on a measure space $(X, \mathcal{A}, μ)$ and thereby study the graph theoretic features of the modified comaximal graph, annihilator graph and the weakly zero-divisor graph of the said ring. The stud

Seung-eon Lee, Sang-Chul Lee

The exploration of few-shot tabular learning becomes imperative. Tabular data is a versatile representation that captures diverse information, yet it is not exempt from limitations, property of data and model size. Labeling extensive tabular data can be challenging, and it may not be feasible to capture every important feature. Few-shot tabular learning, how

Eduardo Chiumiento, Pedro Massey

Let $\mathcal{H}$ be a separable infinite-dimensional complex Hilbert space and let $\mathcal{J}$ be a two-sided ideal of the algebra of bounded operators $\mathcal{B}(\mathcal{H})$. The groups $\mathcal{G} \ell_\mathcal{J}$ and $\mathcal{U}_{\mathcal{J}}$ consist of all the invertible operators and unitary operators of the form $I + \mathcal{J}$, respective

Takuma Hattori, Yuto Watanabe, Terukazu Nishizaki, Koki Hiraoka

We explored quinary body-centered cubic (bcc) high-entropy alloy (HEA) superconductors with valence electron concentrations (VECs) ranging from 4.6 to 5.0, a domain that has received limited attention in prior research. Our search has led to the discovery of new bcc Ti-Hf-Nb-Ta-Re superconducting alloys, which exhibit an interesting phenomenon of phase segre

Kun Han, Shanlin Sun, Xiaohui Xie

Deep Implicit Functions (DIFs) have gained popularity in 3D computer vision due to their compactness and continuous representation capabilities. However, addressing dense correspondences and semantic relationships across DIF-encoded shapes remains a critical challenge, limiting their applications in texture transfer and shape analysis. Moreover, recent endea

Ehsan Latif, Ramviyas Parasuraman

Localizing mobile robotic nodes in indoor and GPS-denied environments is a complex problem, particularly in dynamic, unstructured scenarios where traditional cameras and LIDAR-based sensing and localization modalities may fail. Alternatively, wireless signal-based localization has been extensively studied in the literature yet primarily focuses on fingerprin

Pierre Houdouin, Matthieu Jonkcheere, Frederic Pascal

Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing maximum likelihood estimate when dealing with Gaussian Mixture Model (GMM). When the sample size is smaller than the data dimension, this could lead to a singular or poorly conditioned covariance matrix and, thus, to performance reduction. This paper presents a regular

Pierre Houdouin, Matthieu Jonckheere, Frederic Pascal

Linear and Quadratic Discriminant Analysis (LDA and QDA) are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the underlying Gaussian assumption that is not robust. This paper studies the robustness to scale changes in the data of a new discriminant analysis technique where ea

Lukman Ismaila, Pejman Rasti, Jean-Michel Lemée, David Rousseau

We refer to a machine learning situation where models based on classical convolutional neural networks have shown good performance. We are investigating different encoding techniques in the form of supervoxels, then graphs to reduce the complexity of the model while tracking the loss of performance. This approach is illustrated on a recognition task of resti

Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann

We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We design multiple novel conditioning sche

Nafis Sadik, Mohammad Rasoul Narimani

Power systems often include a specific set of lines that are crucial for the regular operations of the grid. Identifying the reasons behind the criticality of these lines is an important challenge in power system studies. When a line fails, the line outage distribution factor (LODF) quantifies the changes in power flow on the remaining lines. This paper prop

Brianna D. Ball, Roland Kothes, Erik Rosolowsky, Jennifer West

We use data from the pilot observations of the EMU/POSSUM surveys to study the "missing supernova remnant (SNR) problem", the discrepancy between the number of Galactic SNRs that have been observed and the number that are estimated to exist. The Evolutionary Map of the Universe (EMU) and the Polarization Sky Survey of the Universe's Magnetism (PO

Jia-Hong Huang, Chao-Han Huck Yang, Pin-Yu Chen, Andrew Brown

Recently, video summarization has been proposed as a method to help video exploration. However, traditional video summarization models only generate a fixed video summary which is usually independent of user-specific needs and hence limits the effectiveness of video exploration. Multi-modal video summarization is one of the approaches utilized to address thi

Jia-Hong Huang, Luka Murn, Marta Mrak, Marcel Worring

Existing datasets for manually labelled query-based video summarization are costly and thus small, limiting the performance of supervised deep video summarization models. Self-supervision can address the data sparsity challenge by using a pretext task and defining a method to acquire extra data with pseudo labels to pre-train a supervised deep model. In this

Eric Lei, Yiğit Berkay Uslu, Hamed Hassani, Shirin Saeedi Bidokhti

Recent advances in text-to-image generative models provide the ability to generate high-quality images from short text descriptions. These foundation models, when pre-trained on billion-scale datasets, are effective for various downstream tasks with little or no further training. A natural question to ask is how such models may be adapted for image compressi

Ehsan Yousefi, Mo Chen, Inna Sharf

In this work, we propose a novel shared autonomy framework to operate articulated robots. We provide strategies to design both the task-oriented hierarchical planning and policy shaping algorithms for efficient human-robot interactions in context-aware operation of articulated robots. Our framework for interplay between the human and the autonomy, as the par

Hao Yan, Keith Levin

Latent space models play an important role in the modeling and analysis of network data. Under these models, each node has an associated latent point in some (typically low-dimensional) geometric space, and network formation is driven by this unobserved geometric structure. The random dot product graph (RDPG) and its generalization (GRDPG) are latent space m

Saeed Sanati, Maher Azzouz, Ahmed Awad

Over-Current (OC) protection is one of the pervasive protections in solar-based DC microgrids. Fast operation is a key advantage of its popularity. On the other hand, utilizing OC in DC microgrids has some challenges that are not in AC grids. Some of these challenges are related to the grounding approach of the DC microgrid, and others are related to the hig

Austin Luchsinger, David Doty, David Soloveichik

Discrete chemical reaction networks formalize the interactions of molecular species in a well-mixed solution as stochastic events. Given their basic mathematical and physical role, the computational power of chemical reaction networks has been widely studied in the molecular programming and distributed computing communities. While for Turing-universal system

Daniele Reda, Jungdam Won, Yuting Ye, Michiel van de Panne

Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an avatar might have a different ske

Jonghyuk Baek, Jiun-Shyan Chen

Numerical modeling of localizations is a challenging task due to the evolving rough solution in which the localization paths are not predefined. Despite decades of efforts, there is a need for innovative discretization-independent computational methods to predict the evolution of localizations. In this work, an improved version of the neural network-enhanced

Jodin Morey

In the cosmos, any two bodies share a gravitational attraction. When in proximity to one another in empty space, their motions can be modeled by Newtonian gravity. Newton found their orbits when the two bodies are infinitely small, the so-called two-body problem. The general situation in which the bodies have varying shapes and sizes, called the full two-bod

Ines Kath, Margarita Kraus

We determine the spectrum of Kostant's cubic Dirac operator $D^{1/3}$ on locally symmetric Lorentzian manifolds of the form $Γ\backslash {\rm Osc}_1$, where ${\rm Osc}_1$ is the four-dimensional oscillator group and $Γ\subset {\rm Osc}_1$ is a (cocompact) lattice. Moreover, we give an explicit decomposition of the regular representation of ${\rm Osc}_1$

Zijie Huang, Daheng Wang, Binxuan Huang, Chenwei Zhang

Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact many KGs contain two fundamentally different views: high-level ontology-view concepts and fine-grained instance-view entities. They usually embed all nodes as vectors in one latent s

Nafis Sadik, Mohammad Rasoul Narimani

AC optimal power flow (AC OPF) is a fundamental problem in power system operation and control. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem that results in significant computational challenges. To search for global optima, recent research has developed a variety of convex relaxations to

Sara Shoouri, Shayan Jalili, Jiahong Xu, Isabelle Gallagher

At smaller airports without an instrument approach or advanced equipment, automatic landing of aircraft is a safety-critical task that requires the use of sensors present on the aircraft. In this paper, we study falsification of an automatic landing system for fixed-wing aircraft using a camera as its main sensor. We first present an architecture for vision-

Gulcin Baykal, Halil Faruk Karagoz, Taha Binhuraib, Gozde Unal

Diffusion models are generative models that have shown significant advantages compared to other generative models in terms of higher generation quality and more stable training. However, the computational need for training diffusion models is considerably increased. In this work, we incorporate prototype learning into diffusion models to achieve high generat

Dominic Gold, Koray Karabina, Francis C. Motta

Topological Data Analysis (TDA) offers a suite of computational tools that provide quantified shape features in high dimensional data that can be used by modern statistical and predictive machine learning (ML) models. In particular, persistent homology (PH) takes in data (e.g., point clouds, images, time series) and derives compact representations of latent

Sara Shoouri, Mingyu Yang, Gordy Carichner, Yuyang Li

A new GPS-less, daily localization method is proposed with deep learning sensor fusion that uses daylight intensity and temperature sensor data for Monarch butterfly tracking. Prior methods suffer from the location-independent day length during the equinox, resulting in high localization errors around that date. This work proposes a new Siamese learning-base

Bobur Turimov, Shuhrat Hayitov

According to the Banados-Silk-West (BSW) process, rotating black holes can act as particle colliders capable of achieving arbitrarily high center-of-mass energy (CME), provided that a specific angular momentum of one of the particles is present. In this discussion, we demonstrate that both Kerr black holes and Schwarzschild black holes could serve as potenti

Matthew Morgan, Seng Loo, Tod Boyd, Miho Hunter

We report on the development of millimeter-wave, lumped-element reflectionless filters using an advanced thin-film fabrication process. Based on previously demonstrated circuit topologies capable of achieving 50Ω impedance match at all frequencies, these circuits have been implemented at higher frequencies than ever before by leveraging a thin-film process w

Tung Nguyen, Jason Jewik, Hritik Bansal, Prakhar Sharma

Modeling weather and climate is an essential endeavor to understand the near- and long-term impacts of climate change, as well as inform technology and policymaking for adaptation and mitigation efforts. In recent years, there has been a surging interest in applying data-driven methods based on machine learning for solving core problems such as weather forec

A. Frej, C. S. Davies, A. Kirilyuk, A. Stupakiewicz

The developing field of strain-induced magnetization dynamics offers a promising path toward efficiently controlling spins and phase transitions. Understanding the underlying mechanisms is crucial in finding the optimal parameters supporting the phononic switching of magnetization. Here, we present an experimental and numerical study of time-resolved magneti

Sina Labbaf, Mahyar Abbasian, Iman Azimi, Nikil Dutt

The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHealth) appl

Andrew Haberlandt, Harrison Green, Marijn J. H. Heule

Extended resolution shows that auxiliary variables are very powerful in theory. However, attempts to exploit this potential in practice have had limited success. One reasonably effective method in this regard is bounded variable addition (BVA), which automatically reencodes formulas by introducing new variables and eliminating clauses, often significantly re

Oliver Limoyo, Filip Marić, Matthew Giamou, Petra Alexson

Quickly and reliably finding accurate inverse kinematics (IK) solutions remains a challenging problem for robotic manipulation. Existing numerical solvers typically produce a single solution only and rely on local search techniques to minimize a highly nonconvex objective function. Recently, learning-based approaches that approximate the entire feasible set

Vidya Ganapati, Daniel Tchon, Aaron S. Brewster, Nicholas K. Sauter

The Computational Crystallography Toolbox (CCTBX) is open-source software that allows for processing of crystallographic data, including from serial femtosecond crystallography (SFX), for macromolecular structure determination. We aim to use the modules in CCTBX to determine the oxidation state of individual metal atoms in a macromolecule. Changes in oxidati

Isar Nejadgholi, Svetlana Kiritchenko, Kathleen C. Fraser, Esma Balkır

Classifiers tend to learn a false causal relationship between an over-represented concept and a label, which can result in over-reliance on the concept and compromised classification accuracy. It is imperative to have methods in place that can compare different models and identify over-reliances on specific concepts. We consider three well-known abusive lang

David Auger, Pierre Coucheney, Loric Duhazé, Kossi Roland Etse

Rotor walks are cellular automata that determine deterministic traversals of particles in a directed multigraph using simple local rules, yet they can generate complex behaviors. Furthermore, these trajectories exhibit statistical properties similar to random walks. In this study, we investigate a generalized version of the reachability problem known as ARRI

Heather M. Gray

The field of experimental particle physics studies the fundamental particles and forces that constitute matter and radiation. Frequently the experimental tools used to enable this study are accelerators and detectors. The Large Hadron Collider (LHC) is the highest energy proton-proton accelerator currently operating and where the ATLAS and CMS collaboration

Abbas Türkoğlu, Erdem Akagündüz

Tracking objects can be a difficult task in computer vision, especially when faced with challenges such as occlusion, changes in lighting, and motion blur. Recent advances in deep learning have shown promise in challenging these conditions. However, most deep learning-based object trackers only use visible band (RGB) images. Thermal infrared electromagnetic

V. S. Beskin, F. A. Kniazev, K. Chatterjee

The present work is devoted to the analysis of the internal structure of relativistic jets under the condition that the velocity of the plasma flow at the jet axis vanishes. It is shown that in spite of the seemingly fundamental difference in the formulation of the problem at the axis, the key properties of the internal structure of such relativistic jets re

J. W. Bruce, F. Tari

We study quotients of quadratic forms and associated polar lines in the projective plane. Our results, applied pointwise to quadratic differential forms, shed some light on classical binary differential equations (BDEs) associated to congruences of lines in Euclidean 3-space and allows us to introduce a new one. The new BDE yields a new singular surface in t

Ye-Ji Mun, Zhe Huang, Haonan Chen, Yilong Niu

Collaborative robots are being increasingly utilized in industrial production lines due to their efficiency and accuracy. However, the close proximity between humans and robots can pose safety risks due to the robot's high-speed movements and powerful forces. To address this, we developed a vision-based safety monitoring system that creates a 3D reconstr

James Siene

The Bach tensor is classically defined in dimension 4, and work from J. Bergman \cite{bergman:2004} and others shows that $B = \frac{1}{2}U + \frac{1}{6}V$ where $U$ and $V$ are more basic 2-tensors, which are symmetric, divergence-free, algebraically independent, and quadratic in the Riemann tensor. In this paper, we extend H.-D. Cao and Q. Chen's resul

Siwon Kim, Sangdoo Yun, Hwaran Lee, Martin Gubri

The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data. This paper presents ProPILE, a novel probing t

Gabriel Favre

Inner amenability is a bridge between amenability of an object and amenability of its operator algebras. It is an open problem of Ananantharman-Delaroche to decide whether all étale groupoids are inner amenable. Approximate lattices and their dynamics have recently attracted increased attention and have been studied using groupoid methods. In this note, we p

Tal Wagner, Yonatan Naamad, Nina Mishra

We study efficient mechanisms for differentially private kernel density estimation (DP-KDE). Prior work for the Gaussian kernel described algorithms that run in time exponential in the number of dimensions $d$. This paper breaks the exponential barrier, and shows how the KDE can privately be approximated in time linear in $d$, making it feasible for high-dim

Rasul Abdusalamov, Julius Kaplunov, Mikhail Itskov

Recently, symbolic regression (SR) has demonstrated its efficiency for discovering basic governing relations in physical systems. A major impact can be potentially achieved by coupling symbolic regression with asymptotic methodology. The main advantage of asymptotic approach involves the robust approximation to the sought for solution bringing a clear idea o

Tamas Madl, Weijie Xu, Olivia Choudhury, Matthew Howard

The availability of large amounts of informative data is crucial for successful machine learning. However, in domains with sensitive information, the release of high-utility data which protects the privacy of individuals has proven challenging. Despite progress in differential privacy and generative modeling for privacy-preserving data release in the literat

G. J. M. Luna

I analyze the K2 and TESS data taken in 2016, 2019 and 2021 of the symbiotic X-ray binaries GX 1+4 and IGR J16194-2810. GX 1+4 consists of a pulsar accreting from a red giant companion in a 1160 days orbit. Since 1984, the pulsar has shown a continuous spin-down rate of $\dot{P}$=-0.1177(3) mHZ/yr. I report the detection of the spin period at an average valu

Sina Tayebati, Kyu Taek Cho

During the past decade, metal additive manufacturing (MAM) has experienced significant developments and gained much attention due to its ability to fabricate complex parts, manufacture products with functionally graded materials, minimize waste, and enable low-cost customization. Despite these advantages, predicting the impact of processing parameters on the

G. A. Vasquez, Jilberto Zamora-Saa

In this work, we study the lepton number violating Bc meson decays via one intermediate on-shell heavy neutrino. The specific studied process is $B_{c}^{+} \to μ^{+} \ N \to μ^{+} μ^{+} τ^{-} ν$ which could allow distinguishing the nature of the heavy neutrino nature (Dirac or Majorana) by studying the tau lepton energy spectrum in the LHCb experiment. The r

Riccardo Orlando, Simone Conia, Roberto Navigli

Although we have witnessed impressive progress in Semantic Role Labeling (SRL), most of the research in the area is carried out assuming that the majority of predicates are verbs. Conversely, predicates can also be expressed using other parts of speech, e.g., nouns and adjectives. However, non-verbal predicates appear in the benchmarks we commonly use to mea

Danica Jakubíková-Studenovská, Reinhard Pöschel, Sándor Radeleczki

Equivalence relations or, more general, quasiorders (i.e., reflexive and transitive binary relations) $ρ$ have the property that an $n$-ary operation $f$ preserves $ρ$, i.e., $f$ is a polymorphism of $ρ$, if and only if each translation (i.e., unary polynomial function obtained from $f$ by substituting constants) preserves $ρ$, i.e., it is an endomorphism of

Grzegorz Rzadkowski

In the present paper, we define the Gompertz wavelets and show their basic properties. In particular, we prove that the admissibility condition holds for them. We also compute the normalizing factors in the space of square intergrable functions $L^{2}(\mathbb{R})$ and present an explicit formula for them in terms of the Bernoulli numbers. Then, after impleme

Santiago de Leon-Martinez

Carousels (also-known as multilists) have become the standard user interface for e-commerce platforms replacing the ranked list, the previous standard for recommender systems. While the research community has begun to focus on carousels, there are many unanswered questions and undeveloped areas when compared to the literature for ranked lists, which includes

Kate Hartman, William E. Harris, John P. Blakeslee, Chung-Pei Ma

We present Hubble Space Telescope photometry in optical (F475X) and near-infrared (F110W) bands of the globular cluster (GC) systems of the inner halos of a sample of 15 massive elliptical galaxies. The targets are selected from the volume-limited MASSIVE survey, and chosen to sample a range of environments from sparsely populated groups to BCGs in dense clu

Jack Garbus, Jordan Pollack

For decades, the evolution of cooperation has piqued the interest of numerous academic disciplines such as game theory, economics, biology, and computer science. In this work, we demonstrate the emergence of a novel and effective resource exchange protocol formed by dropping and picking up resources in a foraging environment. This form of cooperation is made

Yi Zhuang, Chandrashekhar Gaikwad, Daria Kowsari, Kater Murch

We present a unified approach for designing a diverse range of superconducting non-reciprocal components, including circulators, isolators, and uni-directional amplifiers, based on temporally-modulated coupled resonator networks. Our method leverages standard SQUID-based resonators as building blocks, arranged in various configurations such as series-coupled

Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun

Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models. Repeating this process creates an autophagous (self-consuming) loop whose properties are poorly understood. We conduct a thorough analytical and empirical analysis using state-of-the-art generat

Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu

Equipping embodied agents with commonsense is important for robots to successfully complete complex human instructions in general environments. Recent large language models (LLM) can embed rich semantic knowledge for agents in plan generation of complex tasks, while they lack the information about the realistic world and usually yield infeasible action seque

Korcan Uyanik, S. Faegheh Yeganli, Ivan V. Bajić

Collaborative intelligence (CI) involves dividing an artificial intelligence (AI) model into two parts: front-end, to be deployed on an edge device, and back-end, to be deployed in the cloud. The deep feature tensors produced by the front-end are transmitted to the cloud through a communication channel, which may be subject to packet loss. To address this is

Hailin Li, Raghavendra Ramachandra

The rapid evolution of high-end smartphones with advanced high-resolution cameras has resulted in contactless capture of fingerprint biometrics that are more reliable and suitable for verification. Similar to other biometric systems, contactless fingerprint-verification systems are vulnerable to presentation attacks. In this paper, we present a comparative s

Hoang M. Ngo, Tamer Kahveci, My T. Thai

Quantum annealing (QA) has emerged as a powerful technique to solve optimization problems by taking advantages of quantum physics. In QA process, a bottleneck that may prevent QA to scale up is minor embedding step in which we embed optimization problems represented by a graph, called logical graph, to Quantum Processing Unit (QPU) topology of quantum comput

Yan Ge, Yuan Xu

We prove several new families of Bernstein inequalities of two types on the simplex. The first type consists of inequalities in $L^2$ norm for the Jacobi weight, some of which are sharp, and they are established via the spectral operator that has orthogonal polynomials as eigenfunctions. The second type consists of inequalities in $L^p$ norm for doubling wei

Salvatore Simone Perrotta

This work is concerned with the theoretical study of nuclear reactions between light charged ions at incident energies around and below the reactants Coulomb barrier, with a focus on the energy range of astrophysical interest for Big Bang and quiescent stellar processes. The main goal of the analysis is to investigate the sensitivity of nuclear reaction dyna

K. S. Kravtsov

The paper analyzes security aspects of practical entanglement-based quantum key distribution (QKD), namely, BBM92 or entanglement-based BB84 protocol. Similar to prepare-and-measure QKD protocols, practical implementations of the entanglement-based QKD have to rely upon non-ideal photon sources. A typical solution for entanglement generation is the spontaneo

Jan Dreier, Daniel Mock, Peter Rossmanith

It is known that first-order logic with some counting extensions can be efficiently evaluated on graph classes with bounded expansion, where depth-$r$ minors have constant density. More precisely, the formulas are $\exists x_1 ... x_k \#y φ(x_1,...,x_k, y)>N$, where $φ$ is an FO-formula. If $φ$ is quantifier-free, we can extend this result to nowhere dense g

Shentong Mo, Enze Xie, Ruihang Chu, Lewei Yao

Recent Diffusion Transformers (e.g., DiT) have demonstrated their powerful effectiveness in generating high-quality 2D images. However, it is still being determined whether the Transformer architecture performs equally well in 3D shape generation, as previous 3D diffusion methods mostly adopted the U-Net architecture. To bridge this gap, we propose a novel D

K. Bučar Bricman, S. van Velzen, M. Nicholl, A. Gomboc

Tidal Disruption Events (TDEs) are rare transients, which are considered as promising tools in probing supermassive black holes in quiescent galaxies. The majority of $\approx 60$ known TDEs has been discovered with time-domain surveys in the last two decades. Currently, $\approx 10$ TDEs are discovered per year, and this number will increase with the Legacy

Robert Haslhofer, Daniel Ketover

In this paper, we prove that every strictly convex 3-ball with nonnegative Ricci-curvature contains at least 3 embedded free-boundary minimal 2-disks for any generic metric, and at least 2 solutions even without genericity assumption. Our approach combines ideas from mean curvature flow, min-max theory and degree theory. We also establish the existence of sm

Marian Boktor, James E. D. Tweel, Benjamin R. Ecclestone, Jennifer Ai Ye

Accurate and fast histological staining is crucial in histopathology, impacting diagnostic precision and reliability. Traditional staining methods are time-consuming and subjective, causing delays in diagnosis. Digital pathology plays a vital role in advancing and optimizing histology processes to improve efficiency and reduce turnaround times. This study in

Jorge Antonio Cruz Chapital, Alejandro Darío Rojas Sánchez, Ángel Tamariz Mascarúa, Humberto Villegas Rodríguez

For a topological space X, let (RX)s := (RX,Ts) be the cartesian product of |X| copies of the real line R with the topology of the uniform convergence on separable subsets of X. In this article we analyze the subspace C(X) of (RX)s of all real-valued continuous functions on X, denoted by Cs(X). We determine when Cs(X) is dense and when is closed in (RX)s, an

Jiangbei Yue, Dinesh Manocha, He Wang

Human trajectory forecasting helps to understand and predict human behaviors, enabling applications from social robots to self-driving cars, and therefore has been heavily investigated. Most existing methods can be divided into model-free and model-based methods. Model-free methods offer superior prediction accuracy but lack explainability, while model-based

Zhou Fang, Haiqing Xu

The over-the-counter (OTC) market is characterized by a unique feature that allows market makers to adjust bid-ask spreads based on order size. However, this flexibility introduces complexity, transforming the market-making problem into a high-dimensional stochastic control problem that presents significant challenges. To address this, this paper proposes an

Yu Tian, Renaud Lambiotte

Complex numbers define the relationship between entities in many situations. A canonical example would be the off-diagonal terms in a Hamiltonian matrix in quantum physics. Recent years have seen an increasing interest to extend the tools of network science when the weight of edges are complex numbers. Here, we focus on the case when the weight matrix is Her

Jonas Koehler, Wei Li, Johannes Faber, Georg Ruempker

Reliable earthquake forecasting methods have long been sought after, and so the rise of modern data science techniques raises a new question: does deep learning have the potential to learn this pattern? In this study, we leverage the large amount of earthquakes reported via good seismic station coverage in the subduction zone of Japan. We pose earthquake for

Isaac M. Sonin

We present a Defense/Attack resource allocation model, where Defender has some number of ``locks" to protect $n$ vulnerable boxes (sites), and Attacker is trying to destroy these boxes, having $m$ ``bombs" that can be placed into the boxes. Similar models were studied in game theory - (Colonel) Blotto games, but our model has a feature absent in the

Zheyuan Zhou, Jiachen Lu, Yihan Zeng, Hang Xu

3D object detection from LiDAR point cloud is of critical importance for autonomous driving and robotics. While sequential point cloud has the potential to enhance 3D perception through temporal information, utilizing these temporal features effectively and efficiently remains a challenging problem. Based on the observation that the foreground information is

Afshin Khadangi

In this paper, we propose a novel learning paradigm called "DeepFlorist" for flower classification using ensemble learning as a meta-classifier. DeepFlorist combines the power of deep learning with the robustness of ensemble methods to achieve accurate and reliable flower classification results. The proposed network architecture leverages a combinati

Jiangce Chen, Wenzhuo Xu, Martha Baldwin, Björn Nijhuis

High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, the complexities of part geometries make it challenging for current models to maintain high acc

Volker Dohm

Recently a unified hypothesis of multiparameter universality for the critical behavior of bulk and confined anisotropic systems has been formulated [V. Dohm, Phys. Rev. E {\bf 97}, 062128 (2018)]. We prove the validity of this hypothesis on the basis of the principle of two-scale-factor universality for isotropic systems. We introduce an angular-dependent co

Xiaojiao Xiao, Qinmin Hu, Guanghui Wang

Simultaneous multi-index quantification, segmentation, and uncertainty estimation of liver tumors on multi-modality non-contrast magnetic resonance imaging (NCMRI) are crucial for accurate diagnosis. However, existing methods lack an effective mechanism for multi-modality NCMRI fusion and accurate boundary information capture, making these tasks challenging.

Robert Marskar

This paper contains the foundation for a new Particle-In-Cell model for gas discharges, based on Ito diffusion and Kinetic Monte Carlo (KMC). In the new model the electrons are described with a microscopic drift-diffusion model rather than a macroscopic one. We discuss the connection of the Ito-KMC model to the equations of fluctuating hydrodynamics and the

Ali Shoker

Digital Sovereignty must be on the agenda of every modern nation. Digital technology is becoming part of our life details, from the vital essentials, like food and water management, to transcendence in the Metaverse and Space. Protecting these digital assets will, therefore, be inevitable for a modern country to live, excel and lead. Digital Sovereignty is a

Anusha Kalyaan, Paola Pinilla, Sebastiaan Krijt, Andrea Banzatti

Substructures in protoplanetary disks can act as dust traps that shape the radial distribution of pebbles. By blocking the passage of pebbles, the presence of gaps in disks may have a profound effect on pebble delivery into the inner disk, crucial for the formation of inner planets via pebble accretion. This process can also affect the delivery of volatiles