July 2023 arXiv papers — page 138

Heng Zhu, Hongchao Shi, Zhengguo Tang, Bing Tang

This work is devoted to studying the magnon-magnon interaction effect in a two-dimensional checkerboard ferromagnet with the Dzyaloshinskii-Moriya interaction. By means of the first-order Green function formalism, the influence of magnon-magnon interaction on the magnon band topology is analyzed. In order to verify that the gap-closing phenomenon is a signat

Shiva Omrani Sabbaghi, Robert Wolfe, Aylin Caliskan

Language models are trained on large-scale corpora that embed implicit biases documented in psychology. Valence associations (pleasantness/unpleasantness) of social groups determine the biased attitudes towards groups and concepts in social cognition. Building on this established literature, we quantify how social groups are valenced in English language mode

Ling Chen, Chaodu Song, Xu Wang, Dachao Fu

Anomaly detection based on system logs plays an important role in intelligent operations, which is a challenging task due to the extremely complex log patterns. Existing methods detect anomalies by capturing the sequential dependencies in log sequences, which ignore the interactions of subsequences. To this end, we propose CSCLog, a Component Subsequence Cor

Yiyang Feng, Sen Dai, Sunil A. Bhave

In this work, we have successfully engineered and examined suspended laterally vibrating resonators (LVRs) on a lithium niobate thin film on lithium niobate carrier wafer (LN-on-LN) platform, powered by aluminum interdigital transducers (IDTs). Unlike the lithium niobate-on-silicon system, the LN-on-LN platform delivers a stress-neutral lithium niobate thin

Shuo Li, Sangdon Park, Insup Lee, Osbert Bastani

When applied to open-domain question answering, large language models (LLMs) frequently generate incorrect responses based on made-up facts, which are called $\textit{hallucinations}$. Retrieval augmented generation (RAG) is a promising strategy to avoid hallucinations, but it does not provide guarantees on its correctness. To address this challenge, we prop

Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying

Many machine learning tasks can be formulated as a stochastic compositional optimization (SCO) problem such as reinforcement learning, AUC maximization, and meta-learning, where the objective function involves a nested composition associated with an expectation. While a significant amount of studies has been devoted to studying the convergence behavior of SC

Suprio Bhar, Subhra Sankar Dhar

This article proposes a co-variance operator for Banach valued random elements using the concept of $U$-statistic. We then study the asymptotic distribution of the proposed co-variance operator along with related large sample properties. Moreover, specifically for Hilbert space valued random elements, the asymptotic distribution of the proposed estimator is

Behzad Akbari, Ya-Jun Pan, Shiwei Liu, Tianye Wang

Unmanned Surface Vehicles (USVs) in the ocean environment, considering various spatiotemporal factors such as ocean currents and other energy consumption factors. The paper uses Gaussian Process Motion Planning (GPMP2), a Bayesian optimization method that has shown promising results in continuous and nonlinear motion planning algorithms. The proposed work im

Sara Papi, Peidong Wang, Junkun Chen, Jian Xue

In real-world applications, users often require both translations and transcriptions of speech to enhance their comprehension, particularly in streaming scenarios where incremental generation is necessary. This paper introduces a streaming Transformer-Transducer that jointly generates automatic speech recognition (ASR) and speech translation (ST) outputs usi

Hanqing Ma, James F. Drake, Marc Swisdak

We conduct two-dimensional particle-in-cell simulations to investigate the scattering of electron heat flux by self-generated oblique electromagnetic waves. The heat flux is modeled as a bi-kappa distribution with a T_parallel > T_perp temperature anisotropy maintained by continuous injection at the boundaries. The anisotropic distribution excites oblique wh

Zhonghan Zhao, Wenhao Chai, Shengyu Hao, Wenhao Hu

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical str

Tathagata Karmakar, Abhishek Chakraborty, A. Nick Vamivakas, Andrew N. Jordan

We further develop the concept of supergrowth [Jordan, Quantum Stud.: Math. Found. $\textbf{7}$, 285-292 (2020)], a phenomenon complementary to superoscillation, defined as the local amplitude growth rate of a function being higher than its largest wavenumber. We identify the superoscillating and supergrowing regions of a canonical oscillatory function and f

Fang Xu, Tri Nguyen, Jing Du

Advancements in sensor technology, artificial intelligence (AI), and augmented reality (AR) have unlocked opportunities across various domains. AR and large language models like GPT have witnessed substantial progress and are increasingly being employed in diverse fields. One such promising application is in operations and maintenance (O&M). O&M tasks often

Chen Sun, Shiyao Ma, Ce Zheng, Songtao Wu

User selection has become crucial for decreasing the communication costs of federated learning (FL) over wireless networks. However, centralized user selection causes additional system complexity. This study proposes a network intrinsic approach of distributed user selection that leverages the radio resource competition mechanism in random access. Taking the

Xuejie Liu, Dianyong Chen, Hongxia Huang, Jialun Ping

In this work, we systematically investigate the pentaquark systems with quark contents $qqqq\bar{c}$ with the analyzed total spin and parity quantum numbers of $J^{P}=\frac{1}{2}^{-}$, $J^{P}=\frac{3}{2}^{-}$ and $J^{P}=\frac{5}{2}^{-}$, in the I=0, I=1 and I=2 isospin channels. The effective potentials between baryon and meson clusters are given, and the po

Pedro Ribeiro, Semyon Yakubovich

In his lost notebook, Ramanujan recorded beautiful identities. These include earlier versions of Koshliakov's formula for the divisor function and the transformation formula for the logarithm of Dedekind's $\eta-$function. In this paper we establish some generalizations of these formulas of Ramanujan in a setting that only recently reemerged in the literatur

Nick Alger, Tucker Hartland, Noemi Petra, Omar Ghattas

We present an efficient matrix-free point spread function (PSF) method for approximating operators that have locally supported non-negative integral kernels. The method computes impulse responses at scattered points, and interpolates these impulse responses to approximate integral kernel entries. Impulse responses are computed by applying the operator to Dir

Margaret Meyer, Dmitry Zakharov

We define the Laplacian matrix and the Jacobian group of a finite graph of groups. We prove analogues of the matrix tree theorem and the class number formula for the order of the Jacobian of a graph of groups. Given a group $G$ acting on a graph $X$, we define natural pushforward and pullback maps between the Jacobian groups of $X$ and the quotient graph of

Qing Xu, Min Wu, Xiaoli Li, Kezhi Mao

For many real-world time series tasks, the computational complexity of prevalent deep leaning models often hinders the deployment on resource-limited environments (e.g., smartphones). Moreover, due to the inevitable domain shift between model training (source) and deploying (target) stages, compressing those deep models under cross-domain scenarios becomes m

Einar Bjarki Gunnarsson, Kevin Leder, Xuanming Zhang

The site frequency spectrum (SFS) is a widely used summary statistic of genomic data. Motivated by recent evidence for the role of neutral evolution in cancer, we investigate the SFS of neutral mutations in an exponentially growing population. Using branching process techniques, we establish (first-order) almost sure convergence results for the SFS of a Galt

Pierre-Antoine Bernard, Zachary Mann, Gilles Parez, Luc Vinet

This study investigates the scaling behavior of the ground-state entanglement entropy in a model of free fermions on folded cubes. An analytical expression is derived in the large-diameter limit, revealing a strict adherence to the area law. The absence of the logarithmic enhancement expected for free fermions is explained using a decomposition of folded cub

C. J. Law, K. Sharma, V. Ravi, G. Chen

Fast Radio Bursts (FRBs) are a powerful and mysterious new class of transient that are luminous enough to be detected at cosmological distances. By associating FRBs to host galaxies, we can measure intrinsic and environmental properties that test FRB origin models, in addition to using them as precise probes of distant cosmic gas. The Deep Synoptic Array (DS

Seyong Kim, Jinseok Choi, Namyoon Lee, François Baccelli

The Low Earth Orbit (LEO) satellite industry is undergoing rapid expansion, with operators competitively launching satellites due to the first-come, first-served principle governing orbital rights. This has led to the formation of increasingly large-scale, volumetric constellation where satellites operate across a diverse range of altitudes. To address the n

Shantanu Ghosh, Ke Yu, Forough Arabshahi, Kayhan Batmanghelich

ML model design either starts with an interpretable model or a Blackbox and explains it post hoc. Blackbox models are flexible but difficult to explain, while interpretable models are inherently explainable. Yet, interpretable models require extensive ML knowledge and tend to be less flexible and underperforming than their Blackbox variants. This paper aims

Asraful Haque, Suman Kumar Mandal, Antony Jeyaseelan, Sandeep Vura

Heterogenous integration of complex epitaxial oxides onto Si and other target substrates is recently gaining traction. One of the popular methods involves growing a water-soluble and highly reactive sacrificial buffer layer, such as Sr3Al2O6 (SAO) at the interface, and a functional oxide on top of this. To improve the versatility of layer transfer techniques

Ezequiel Lozano, Cecilia Scannapieco, Sebastian E. Nuza

Star formation, together with the associated chemical and energy feedback, is one of the most important processes in galaxy evolution. The star formation activity in galaxies defines and affects many of their fundamental properties, such as stellar mass, morphology and chemical enrichment levels. Simple models for star formation in cosmological hydrodynamica

Chengyuan Zhang, Wenshuo Wang, Lijun Sun

Car-following behavior modeling is critical for understanding traffic flow dynamics and developing high-fidelity microscopic simulation models. Most existing impulse-response car-following models prioritize computational efficiency and interpretability by using a parsimonious nonlinear function based on immediate preceding state observations. However, this a

Hengcan Shi, Munawar Hayat, Jianfei Cai

In recent years, open-vocabulary (OV) object detection has attracted increasing research attention. Unlike traditional detection, which only recognizes fixed-category objects, OV detection aims to detect objects in an open category set. Previous works often leverage vision-language (VL) training data (e.g., referring grounding data) to recognize OV objects.

Takumi Ohashi, Miki Saijo, Kento Suzuki, Shinsuke Arafuka

As global demand for animal products is projected to increase significantly by 2050, driven by population growth and increased incomes, smart livestock technologies are essential for improving efficiency, animal welfare, and environmental sustainability. Conducted within the unique agricultural context of Japan, characterized by small-scale, family-run farms

Tanvir Islam, Peter Washington

Chronic stress can significantly affect physical and mental health. The advent of wearable technology allows for the tracking of physiological signals, potentially leading to innovative stress prediction and intervention methods. However, challenges such as label scarcity and data heterogeneity render stress prediction difficult in practice. To counter these

Yiru Chen, Jeffery Tao, Eugene Wu

Building interactive data interfaces is hard because the design of an interface depends on the data processing needs for the underlying analysis task, yet we do not have a good representation for analysis tasks. To fill this gap, this paper advocates for a Data Interface Grammar (DIG) as an intermediate representation of analysis tasks. We show that DIG is c

Ryota Nozawa, Pierre-Louis Poirion, Akiko Takeda

We propose randomized subspace gradient methods for high-dimensional constrained optimization. While there have been similarly purposed studies on unconstrained optimization problems, there have been few on constrained optimization problems due to the difficulty of handling constraints. Our algorithms project gradient vectors onto a subspace that is a random

C. -C. Joseph Wang, F. Perkkola, I. Salmenperä, A. Meijer-van de Griend

Hybrid variational quantum algorithms (VQAs) are promising for solving practical problems such as combinatorial optimization, quantum chemistry simulation, quantum machine learning, and quantum error correction on noisy quantum computers. However, with typical random ansatz or quantum alternating operator ansatz, derived variational quantum algorithms become

Yuya Yonezu, Kensuke Inaba, Yasuhiro Yamada, Takuya Ikuta

A coherent XY machine (CXYM) is a physical spin simulator that can simulate the XY model by mapping XY spins onto the continuous phases of non-degenerate optical parametric oscillators (NOPOs). Here, we demonstrated a large-scale CXYM with >47,000 spins by generating 10-GHz-clock time-multiplexed NOPO pulses via four-wave mixing in a highly nonlinear fiber i

Peichen Zhong, Fengyu Xie, Luis Barroso-Luque, Liliang Huang

Modern battery materials can contain many elements with substantial site disorder, and their configurational state has been shown to be critical for their performance. The intercalation voltage profile is a critical parameter to evaluate the performance of energy storage. The application of commonly used cluster expansion techniques to model the intercalatio

Santiago Lamata-Otín, Adriana Reyna-Lara, David Soriano-Paños, Vito Latora

Compartmental models are the most widely used framework for modeling infectious diseases. These models have been continuously refined to incorporate all the realistic mechanisms that can shape the course of an epidemic outbreak. Building on a compartmental model that accounts for early detection and isolation of infectious individuals through testing, in thi

Marzieh Dehghani-Madiseh, Milan Hladík

In this work, we investigate the interval generalized Sylvester matrix equation ${\bf{A}}X{\bf{B}}+{\bf{C}}X{\bf{D}}={\bf{F}}$ and develop some techniques for obtaining outer estimations for the so-called united solution set of this interval system. First, we propose a modified variant of the Krawczyk operator which causes reducing computational complexity t

Jiacong Mi, Yi Zu, Zhuoyuan Wang, Jieyue He

Medication recommendation using Electronic Health Records (EHR) is challenging due to complex medical data. Current approaches extract longitudinal information from patient EHR to personalize recommendations. However, existing models often lack sufficient patient representation and overlook the importance of considering the similarity between a patient's med

Cédric Josz, Lexiao Lai, Xiaopeng Li

We propose a new length formula that governs the iterates of the momentum method when minimizing differentiable semialgebraic functions with locally Lipschitz gradients. It enables us to establish local convergence, global convergence, and convergence to local minimizers without assuming global Lipschitz continuity of the gradient, coercivity, and a global g

Talha Mushtaq, Peter Seiler, Maziar S. Hemati

Computing a stabilizing static output-feedback (SOF) controller is an NP-hard problem, in general. Yet, these controllers have amassed popularity in recent years because of their practical use in feedback control applications, such as fluid flow control and sensor/actuator selection. The inherent difficulty of synthesizing SOF controllers is rooted in solvin

Huai-Yu Wang

In this paper, a theory of dark energy is proposed that matches dark matter. The relativistic quantum mechanics equations reveal that free particles can have negative energies. We think that the negative energy is the dark energy which behaviors as dark photons with negative energies. In this work, the photon number states are extended to the cases where the

Na Zhang

MOBIO is a bi-modal database that was captured almost exclusively on mobile phones. It aims to improve research into deploying biometric techniques to mobile devices. Research has been shown that face and speaker recognition can be performed in a mobile environment. Facial landmark localization aims at finding the coordinates of a set of pre-defined key poin

Munkhjargal Lkhagvadorj

Interplanetary shocks are one of the crucial dynamic processes in the Heliosphere. They accelerate particles into a high energy, generate plasma waves, and could potentially trigger geomagnetic storms in the terrestrial magnetosphere disturbing significantly our technological infrastructures. In this study, two IP shock events are selected to study the tempo

Huai-Yu Wang

According to relativistic quantum mechanics, particles can be of negative kinetic energies (NKE). The author asserts in his previous works that the NKE substances are dark matters. Some NKE particles, say a pair of NKE electrons, can constitute a stable system by means of the repulsive interaction between them. In the present work, two simplest three-particl

Rajib Bhattacharjea, Nathan West

This work presents the first applications of self-supervised learning applied to data from digital antenna arrays. Encoder-decoder networks are pretrained on digital array data to perform a self-supervised noisy-reconstruction task called channel in-painting, in which the network infers the contents of array data that has been masked with zeros. The self-sup

Varvara Guljajeva, Mar Canet Sola, Isaac Joseph Clarke

Artificial Intelligence is present in the generation and distribution of culture. How do artists exploit neural networks? What impact do these algorithms have on artistic practice? Through a practice-based research methodology, this paper explores the potentials and limits of current AI technology, more precisely deep neural networks, in the context of image

Sergii Parchenko, Kevin Hofhuis, Agne Ciuciulkaite, Vassilios Kapaklis

The relationship between magnetization and light has been the subject of intensive research for the past century, focusing on the impact of magnetic moments on light polarization. Conversely, the manipulation of magnetism through polarized light is being investigated to achieve all-optical control of magnetism in spintronics. While remarkable discoveries suc

Eric Vin, Shun Kashiwa, Matthew Rhea, Daniel J. Fremont

We present a major new version of Scenic, a probabilistic programming language for writing formal models of the environments of cyber-physical systems. Scenic has been successfully used for the design and analysis of CPS in a variety of domains, but earlier versions are limited to environments which are essentially two-dimensional. In this paper, we extend S

Teresa W. Lo, Han Kyou James Choi, Dean Huang, Paul A. Wiggins

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical de

Diane Tuyizere, Remy Ihabwikuzo

This research proposes a machine learning-based attack detection model for power systems, specifically targeting smart grids. By utilizing data and logs collected from Phasor Measuring Devices (PMUs), the model aims to learn system behaviors and effectively identify potential security boundaries. The proposed approach involves crucial stages including datase

Abhirut Gupta, Ananya B. Sai, Richard Sproat, Yuri Vasilevski

A large number of people are forced to use the Web in a language they have low literacy in due to technology asymmetries. Written text in the second language (L2) from such users often contains a large number of errors that are influenced by their native language (L1). We propose a method to mine phoneme confusions (sounds in L2 that an L1 speaker is likely

Zachary P. Bradshaw, Margarite L. LaBorde

We assign an arbitrary density matrix to a weighted graph and associate to it a graph zeta function that is both a generalization of the Ihara zeta function and a special case of the edge zeta function. We show that a recently developed bipartite pure state separability algorithm based on the symmetric group is equivalent to the condition that the coefficien

Grigoris Maravelias, Stephan de Wit, Alceste Z. Bonanos, Frank Tramper

Mass loss is one of the key parameters that determine stellar evolution. Despite the progress we have achieved over the last decades we still cannot match the observational derived values with theoretical predictions. Even worse, there are certain phases, such as the B[e] supergiants (B[e]SGs) and the Luminous Blue Variables (LBVs), where significant mass is

Roni Rabin, Alexandre Djerbetian, Roee Engelberg, Lidan Hackmon

Human communication often involves information gaps between the interlocutors. For example, in an educational dialogue, a student often provides an answer that is incomplete, and there is a gap between this answer and the perfect one expected by the teacher. Successful dialogue then hinges on the teacher asking about this gap in an effective manner, thus cre

Linh Anh Nguyen, Ivana Micić, Stefan Stanimirović

Simulations and bisimulations are well-established notions in crisp/fuzzy automata theory and are widely used to compare the behaviors of automata. Their main drawback is that they compare the behaviors of fuzzy automata in a crisp manner. Recently, fuzzy simulations and fuzzy bisimulations have been defined for fuzzy automata as a kind of approximate simula

Daeyoung Ham, Adam J. Rothman

We propose a penalized least-squares method to fit the linear regression model with fitted values that are invariant to invertible linear transformations of the design matrix. This invariance is important, for example, when practitioners have categorical predictors and interactions. Our method has the same computational cost as ridge-penalized least squares,

Haijun Li

Operator regular variation reveals general power-law distribution tail decay phenomena using operator scaling, that includes multivariate regular variation with scalar scaling as a special case. In this paper, we show that a multivariate Liouville distribution is operator regularly varying if its driving function is univariate regularly varying. Our method f

Bing Xue, Ahmed Sameh Said, Ziqi Xu, Hanyang Liu

Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting modality for COVID-19 patients who are refractory to conventional therapies. However, the proper treatment decision has been the subject of significant debate and it remains controversial about who benefits from this scarcely available and technically complex treatment option. To supp

A. V. Parafilo, V. M. Kovalev, I. G. Savenko

We predict a photoinduced Hall effect in an isotropic conventional two-dimensional superconductor with a built-in supercurrent exposed to a circularly-polarized light. This second-order with respect to the electromagnetic field amplitude effect occurs when the frequency of the field exceeds the double value of the superconducting gap. It reveals itself in th

Siddharth Khincha, Chelsi Jain, Vivek Gupta, Tushar Kataria

Information Synchronization of semi-structured data across languages is challenging. For instance, Wikipedia tables in one language should be synchronized across languages. To address this problem, we introduce a new dataset InfoSyncC and a two-step method for tabular synchronization. InfoSync contains 100K entity-centric tables (Wikipedia Infoboxes) across

Maarten V. de Hoop, Joonas Ilmavirta, Matti Lassas, Anthony Várilly-Alvarado

We study inverse problems in anisotropic elasticity using tools from algebraic geometry. The singularities of solutions to the elastic wave equation in dimension $n$ with an anisotropic stiffness tensor have propagation kinematics captured by so-called slowness surfaces, which are hypersurfaces in the cotangent bundle of $\mathbb{R}^n$ that turn out to be al

Janis Keuper

The mathematical representations of data in the Spherical Harmonic (SH) domain has recently regained increasing interest in the machine learning community. This technical report gives an in-depth introduction to the theoretical foundation and practical implementation of SH representations, summarizing works on rotation invariant and equivariant features, as

Victor Wei, Alev Orfi, Felix Fehse, W. A. Coish

We study the dynamics of the Gaudin magnet ("central-spin model") using machine-learning methods. This model is of practical importance, e.g., for studying non-Markovian decoherence dynamics of a central spin interacting with a large bath of environmental spins and for studies of nonequilibrium superconductivity. The Gaudin magnet is also integrable, admitti

Weijie Xu, Jay Desai, Srinivasan Sengamedu, Xiaoyu Jiang

Language model based methods are powerful techniques for text classification. However, the models have several shortcomings. (1) It is difficult to integrate human knowledge such as keywords. (2) It needs a lot of resources to train the models. (3) It relied on large text data to pretrain. In this paper, we propose Semi-Supervised vMF Neural Topic Modeling (

Christian M. Pluchar, Aman R. Agrawal, Dalziel J. Wilson

The pursuit of room temperature quantum optomechanics with tethered nanomechanical resonators faces stringent challenges owing to extraneous mechanical degrees of freedom. An important example is thermal intermodulation noise (TIN), a form of excess optical noise produced by mixing of thermal noise peaks. While TIN can be decoupled from the phase of the opti

Grace Sun, Sandip Patel

Breast cancer is the leading type of cancer in women. About 10-15% of breast cancers are triple-negative breast cancer (TNBC), a subtype with the worst prognosis. Due to the lack of estrogen, progesterone and HER2 receptor expression, chemotherapies have been the standard of care for decades. Immunotherapy has emerged as promising for TNBC treatment. In 2020

Caleb Ju, Serif Yesil, Mengyuan Sun, Chandra Chekuri

Positive linear programs (LPs) model many graph and operations research problems. One can solve for a $(1+\epsilon)$-approximation for positive LPs, for any selected $\epsilon$, in polylogarithmic depth and near-linear work via variations of the multiplicative weight update (MWU) method. Despite extensive theoretical work on these algorithms through the deca

Avijit Ghosh, Pablo Kvitca, Christo Wilson

The operationalization of algorithmic fairness comes with several practical challenges, not the least of which is the availability or reliability of protected attributes in datasets. In real-world contexts, practical and legal impediments may prevent the collection and use of demographic data, making it difficult to ensure algorithmic fairness. While initial

Miguel Lerma, Mirtha Lucas

We discuss a vulnerability involving a category of attribution methods used to provide explanations for the outputs of convolutional neural networks working as classifiers. It is known that this type of networks are vulnerable to adversarial attacks, in which imperceptible perturbations of the input may alter the outputs of the model. In contrast, here we fo

Mehmet Dogan, James R. Chelikowsky, Marvin L. Cohen

Elucidating the phase diagram of solid hydrogen is a key objective in condensed matter physics. Several decades ago, it was proposed that at low temperatures and high pressures, solid hydrogen would be a metal with a high superconducting transition temperature. This transition to a metallic state can happen through the closing of the energy gap in the molecu

Erin McCarthy, Ojan Damavandi, Raj Kumar Manna, M. Lisa Manning

Spontaneous phase separation, or demixing, is important in biological phenomena such as cell sorting. In particle-based models, an open question is whether differences in diffusivity can drive such demixing. While differential-diffusivity-induced phase separation occurs in mixtures with a packing fraction up to $0.7$ [Weber et al. Phys Rev Lett 2016], here w

Rakvi

Let $E$ be an elliptic curve defined over $\mathbb{Q}$/ Associated to $E$, there is an adelic Galois representation $\rho_E \colon {\rm Gal}(\bar{\mathbb{Q}}/\mathbb{Q}) \to {\rm GL}_2(\hat{\mathbb{Z}})$. In this article, we give possibilities for groups generated by $\rho_E({\rm Gal}(\bar{\mathbb{Q}}/\mathbb{Q}))$ and $-I$ as $E$ varies over non CM elliptic

Pengyu Le

In this paper, we prove an isoperimetric inequality for the domain of dependence of a finite lightcone in the Minkowski spacetime of dimension greater than or equal to 3. The inequality involves two quantities: the volume of the domain of dependence, and the perimeter of the finite lightcone. It states that among all finite lightcones with the same perimeter

Clément Stahl, Yohan Dubois, Benoit Famaey, Oliver Hahn

Collisionless simulations of structure formation with significant local primordial non-Gaussianities at Mpc scales have shown that a non-Gaussian tail favouring underdensities, with a negative $f_{\rm NL}$ parameter, can significantly change the merging history of galaxy-sized dark matter halos, which then typically assemble later than in vanilla $\Lambda$CD

Juan Daniel Torres Luna, Sathish R. Kuppuswamy, Anton R. Akhmerov

Braiding of Majorana states demonstrates their non-Abelian exchange statistics. One implementation of braiding requires control of the pairwise couplings between all Majorana states in a trijunction device. To have adiabaticity, a trijunction device requires the desired pair coupling to be sufficiently large and the undesired couplings to vanish. In this wor

Amirreza Hashemi, Yuemeng Feng, Arman Rahmim, Hamid Sabet

This work investigates use of equivariant neural networks as efficient and high-performance frameworks for image reconstruction and denoising in nuclear medicine. Our work aims to tackle limitations of conventional Convolutional Neural Networks (CNNs), which require significant training. We investigated equivariant networks, aiming to reduce CNN's dependency

Ekaterina S. Ivshina

Based on the data of 12-17-crossing knots, we establish three new conjectures about the hyperbolic volume and knot cohomology: (1) There exists a constant $a \in R_{>0}$ such that the percentage of knots for which the following inequality holds converges to 1 as the crossing number $c \to \infty$: $\log r(K) < a \cdot Vol(K)$ for a knot $K$ where $r(K)$ is t

Aref Farhadipour, Hadi Veisi

Dysarthria is a disability that causes a disturbance in the human speech system and reduces the quality and intelligibility of a person's speech. Because of this effect, the normal speech processing systems can not work properly on impaired speech. This disability is usually associated with physical disabilities. Therefore, designing a system that can perfor

Jacqueline E. McCleary, Spencer W. Everett, Mohamed M. Shaaban, Ajay S. Gill

The Superpressure Balloon-borne Imaging Telescope (SuperBIT) is a diffraction-limited, wide-field, 0.5 m, near-infrared to near-ultraviolet observatory designed to exploit the stratosphere's space-like conditions. SuperBIT's 2023 science flight will deliver deep, blue imaging of galaxy clusters for gravitational lensing analysis. In preparation, we have deve

Allan P. Fordy, Qing Huang

In this paper we continue our analysis of the stationary flows of $M$ component, coupled KdV (cKdV) hierarchies and their modifications. We describe the general structure of the $t_1$ and $t_2$ flows, using the case $M=3$ as our main example. One of our stationary reductions gives $N$ degrees of freedom, superintegrable systems. When $N=1$ (for $t_1$) and $N

Nicolás Gaggion, Candelaria Mosquera, Lucas Mansilla, Julia Mariel Saidman

The development of successful artificial intelligence models for chest X-ray analysis relies on large, diverse datasets with high-quality annotations. While several databases of chest X-ray images have been released, most include disease diagnosis labels but lack detailed pixel-level anatomical segmentation labels. To address this gap, we introduce an extens

Andrea Delgado, Francisco Rios, Kathleen E. Hamilton

In machine learning, overparameterization is associated with qualitative changes in the empirical risk landscape, which can lead to more efficient training dynamics. For many parameterized models used in statistical learning, there exists a critical number of parameters, or model size, above which the model is constructed and trained in the overparameterized

Salem AlJanah, Ning Zhang, Siok Wah Tay

Authentication is the first defence mechanism in many electronic systems, including Internet of Things (IoT) applications, as it is essential for other security services such as intrusion detection. As existing authentication solutions proposed for IoT environments do not provide multi-level authentication assurance, particularly for device-to-device authent

Oyku Berfin Mercan, Sercan Cepni, Davut Emre Tasar, Sukru Ozan

In this study, the performances of the Whisper-Small and Wav2Vec2-XLS-R-300M models which are two pre-trained multilingual models for speech to text were examined for the Turkish language. Mozilla Common Voice version 11.0 which is prepared in Turkish language and is an open-source data set, was used in the study. The multilingual models, Whisper- Small and

Andreas Karatzas, Iraklis Anagnostopoulos

Modern Deep Neural Networks (DNNs) exhibit profound efficiency and accuracy properties. This has introduced application workloads that comprise of multiple DNN applications, raising new challenges regarding workload distribution. Equipped with a diverse set of accelerators, newer embedded system present architectural heterogeneity, which current run-time con

Dibyajyoti Nayak, Anirudh Jonnalagadda, Uma Balakrishnan, Hemanth Kolla

For turbulent reacting flows, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the computational cost of device-scale simulations. Principal component analysis (PCA), and its variants, is a widely employed class of methods. Recently, an alternative technique that focu

Qiuyi Zhang

Scalarization is a general, parallizable technique that can be deployed in any multiobjective setting to reduce multiple objectives into one, yet some have dismissed this versatile approach because linear scalarizations cannot explore concave regions of the Pareto frontier. To that end, we aim to find simple non-linear scalarizations that provably explore a

Ryan M. McGranaghan

This review examines complexity science in Heliophysics, describing it not as a discipline, but as a paradigm. In the context of Heliophysics, complexity science is the study of a star, interplanetary environment, magnetosphere, upper and terrestrial atmospheres, and planetary surface as interacting subsystems. Complexity science studies entities in a system

Manaswin Oddiraju, Divyang Amin, Michael Piedmonte, Souma Chowdhury

Complex optimal design and control processes often require repeated evaluations of expensive objective functions and consist of large design spaces. Data-driven surrogates such as neural networks and Gaussian processes provide an attractive alternative to simulations and are utilized frequently to represent these objective functions in optimization. However,

Erika Bérczi-Kovács, András Frank

Clar number and Fries number are two thoroughly investigated parameters of plane graphs emerging from mathematical chemistry to measure stability of organic molecules. We consider first a common generalization of these two concepts for bipartite plane graphs, and then extend it to a framework on general (not necessarily planar) directed graphs. The correspon

Omar Kchit

In this paper, for any nonic number field $K$ generated by a root $\alpha$ of a monic irreducible trinomial $F(x)=x^9+ax+b \in \mathbb{Z}[x]$ and for every rational prime $p$, we characterize when $p$ divides the index of $K$. We also describe the prime power decomposition of the index $i(K)$. In such a way we give a partial answer of Problem $22$ of Narkiew

Nouédyn Baspin, Venkatesan Guruswami, Anirudh Krishna, Ray Li

For quantum error-correcting codes to be realizable, it is important that the qubits subject to the code constraints exhibit some form of limited connectivity. The works of Bravyi & Terhal (BT) and Bravyi, Poulin & Terhal (BPT) established that geometric locality constrains code properties -- for instance $[[n,k,d]]$ quantum codes defined by local checks on

Nicoló Drago, Sonia Mazzucchi, Valter Moretti

In this work we consider a suitable generalization of the Feynman path integral on a specific class of Riemannian manifolds consisting of compact Lie groups with bi-invariant Riemannian metrics. The main tools we use are the Cartan development map, the notion of oscillatory integral, and the Chernoff approximation theorem. We prove that, for a class of funct

Sayantan Bhattacharya, Dimitris M. Christodoulou, Andre-Nicolas Chene, Silas G. T. Laycock

We present a statistical analysis of the He ii 4686 emission line in the spectra of the black hole and Wolf-Rayet (WR) star of the high-mass X-ray binary IC10 X-1. This line is visibly skewed, and the third moment (skewness) varies with the binary's orbital phase. We describe a new method of extracting such weak/faint features lying barely above a noisy cont

Boris M. Varbanov, Marc Serra-Peralta, David Byfield, Barbara M. Terhal

Neural-network decoders can achieve a lower logical error rate compared to conventional decoders, like minimum-weight perfect matching, when decoding the surface code. Furthermore, these decoders require no prior information about the physical error rates, making them highly adaptable. In this study, we investigate the performance of such a decoder using bot

Christian Birchler, Cyrill Rohrbach, Hyeongkyun Kim, Alessio Gambi

Software systems for safety-critical systems like self-driving cars (SDCs) need to be tested rigorously. Especially electronic control units (ECUs) of SDCs should be tested with realistic input data. In this context, a communication protocol called Controller Area Network (CAN) is typically used to transfer sensor data to the SDC control units. A challenge f

Enrico Maria Fenoaltea, Alejandro Lage-Castellanos

With social media, the flow of uncertified information is constantly increasing, with the risk that more people will trust low-credible information sources. To design effective strategies against this phenomenon, it is of paramount importance to understand how people end up believing one source rather than another. To this end, we propose a realistic and cog

Hans Walter Behrens, Nicolò Vallarano, Claudio J. Tessone

The proceedings of ChainScience 2023 epitomize the integration of various scientific disciplines with the dynamic world of blockchain and AI. This collection, encapsulating both full and short papers, as well as posters, delves into areas such as cryptoeconomics, machine learning, and analysis of blockchain networks, under the guiding principle of this year'

S. Isaac Geronimo Anderson, Keita Teranishi, Daniel M. Dunlavy, Jee Choi

We employ pressure point analysis and roofline modeling to identify performance bottlenecks and determine an upper bound on the performance of the Canonical Polyadic Alternating Poisson Regression Multiplicative Update (CP-APR MU) algorithm in the SparTen software library. Our analyses reveal that a particular matrix computation, $\Phi^{(n)}$, is the critica

Tushar Kataria, Beatrice Knudsen, Shireen Elhabian

Annotating medical imaging datasets is costly, so fine-tuning (or transfer learning) is the most effective method for digital pathology vision applications such as disease classification and semantic segmentation. However, due to texture bias in models trained on real-world images, transfer learning for histopathology applications might result in underperfor