May 2023 arXiv papers — page 172

Alister W. Graham

One of the most protracted problems in astronomy has been understanding the evolution of galaxy morphology. Much discussion has surrounded how lenticular galaxies may form a bridging population between elliptical and spiral galaxies. However, with recourse to a galaxy's central black hole mass, accretion-built spiral galaxies have emerged as the bridging pop

Qian Xu, Victor Li, Crews Darren S

Hardware-aware Neural Architecture Search (NAS) technologies have been proposed to automate and speed up model design to meet both quality and inference efficiency requirements on a given hardware. Prior arts have shown the capability of NAS on hardware specific network design. In this whitepaper, we further extend the use of NAS to Intel Movidius VPU (Visio

Henry Ehrhard

We introduce a new basis for the polynomial ring which lifts the complete homogeneous symmetric polynomials while retaining representation theoretic significance. Using a specialized RSK algorithm we give an explicit nonnegative expansion into key polynomials and, generalizing the special rim hook tabloids of E\u{g}ecio\u{g}lu and Remmel, give an explicit si

Xing Lyu, Travis Gagie, Meng He, Yakov Nekrich

It is not difficult to think of applications that can be modelled as graph problems in which placing some facility or commodity at a vertex has some positive or negative effect on the values of all the vertices out to some distance, and we want to be able to calculate quickly the cumulative effect on any vertex's value at any time or the list of the most ben

Yuanxing Liu, Weinan Zhang, Baohua Dong, Yan Fan

Conversational recommender systems (CRSs) aim to understand the information needs and preferences expressed in a dialogue to recommend suitable items to the user. Most of the existing conversational recommendation datasets are synthesized or simulated with crowdsourcing, which has a large gap with real-world scenarios. To bridge the gap, previous work contri

Xiao-Chen Sun, Jia-Bao Wang, Cheng He, Yan-Feng Chen

Non-Abelian topological phases (NATPs) are highly sought-after candidate states for quantum computing and communication while lacking straightforward configuration and manipulation, especially for classical waves. In this work, we exploit novel braid-type couplings among a pair of triple-component acoustic dipoles, which act as functional elements with effec

H M Dipu Kabir

Multitask learning is a popular approach to training high-performing neural networks with improved generalization. In this paper, we propose a background class to achieve improved generalization at a lower computation compared to multitask learning to help researchers and organizations with limited computation power. We also present a methodology for selecti

Hao Lang, Yinhe Zheng, Binyuan Hui, Fei Huang

Out-of-Domain (OOD) intent detection is vital for practical dialogue systems, and it usually requires considering multi-turn dialogue contexts. However, most previous OOD intent detection approaches are limited to single dialogue turns. In this paper, we introduce a context-aware OOD intent detection (Caro) framework to model multi-turn contexts in OOD inten

Hao Lang, Yinhe Zheng, Yixuan Li, Jian Sun

Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world. Great progress has been made over the past years. This paper presents the first review of recent advances in OOD detection with a particular focus on natural language processing approaches. First, we provide a formal definition

A N M Nafiul Islam, Kezhou Yang, Amit K. Shukla, Pravin Khanal

Despite the promise of superior efficiency and scalability, real-world deployment of emerging nanoelectronic platforms for brain-inspired computing have been limited thus far, primarily because of inter-device variations and intrinsic non-idealities. In this work, we demonstrate mitigating these issues by performing learning directly on practical devices thr

David Chester, Xerxes D. Arsiwalla, Louis Kauffman, Michel Planat

We generalize Koopman-von Neumann classical mechanics to poly-symplectic fields and recover De Donder-Weyl theory. Comparing with Dirac's Hamiltonian density inspires a new Hamiltonian formulation with a canonical momentum field that is Lorentz covariant with symplectic geometry. We provide commutation relations for the classical and quantum fields that gene

Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial

The ability to faithfully represent real social networks is critical from the perspective of testing various what-if scenarios which are not feasible to be implemented in a real system as the system's state would be irreversibly changed. High fidelity simulators allow one to investigate the consequences of different actions before introducing them to the rea

Martin-Isbjörn Trappe, Jun Hao Hue, Jonah Huang Zi Chao, Mikołaj Paraniak

We introduce 'single-particle-exact density functional theory' (1pEx-DFT), a novel density functional approach that represents all single-particle contributions to the energy with exact functionals. Here, we parameterize interaction energy functionals by utilizing two new schemes for constructing density matrices from 'participation numbers' of the single-pa

Xian Yeow Lee, Aman Kumar, Lasitha Vidyaratne, Aniruddha Rajendra Rao

This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig. Various traditional machine learning and deep learning methods have been proposed for multivariate time-series classification, including distance-based, functional data-oriented, feature-driven, and con

Kobe Knowles, Joshua Bensemann, Diana Benavides-Prado, Vithya Yogarajan

We introduce a novel architecture, the Neuromodulation Gated Transformer (NGT), which is a simple implementation of neuromodulation in transformers via a multiplicative effect. We compare it to baselines and show that it results in the best average performance on the SuperGLUE benchmark validation sets.

Shahrooz Pouryousef, Nitish K. Panigrahy, Monimoy Deb Purkayastha, Sabyasachi Mukhopadhyay

In this study, we develop a resource management framework for a quantum virtual private network (qVPN), which involves the sharing of an underlying public quantum network by multiple organizations for quantum entanglement distribution. Our approach involves resolving the issue of link entanglement resource allocation in a qVPN by utilizing a centralized opti

Weijun Luo, Benjamin Lawrie, Alexander Puretzky, Qishuo Tan

Nanoscale strain control of exciton funneling is an increasingly critical tool for the scalable production of single photon emitters (SPEs) in two-dimensional materials. However, conventional far-field optical microscopies remain constrained in spatial resolution by the diffraction limit and thus can only provide a limited description of nanoscale strain loc

Nader Masmoudi, Yuxi Wang, Di Wu, Zhifei Zhang

The Tollmien-Schlichting (T-S) waves play a key role in the early stages of boundary layer transition. In a breakthrough work, Grenier, Guo and Nguyen gave the first rigorous construction of the T-S waves of temporal mode for the incompressible fluid. Yang and Zhang recently made an important contribution by constructing the compressible T-S waves of tempora

Brittany Lu, Keith Wernsing, Sergio Carbajo

We report on the design and numerical simulation of a tunable, near-infrared (NIR) femtosecond noncollinear optical parametric amplifier (OPA) seeded by a nonlinear fiber amplifier operating in the parabolic and gain-managed regime. With microjoule-level pump pulses in the visible, we achieved amplification bandwidths of signal and idler pulses between 1010-

Dorota Glazowska, Paolo Leonetti, Janusz Matkowski, Salvatore Tringali

Let $(X, \mathscr{L}, \lambda)$ and $(Y, \mathscr{M}, \mu)$ be finite measure spaces for which there exist $A \in \mathscr{L}$ and $B \in \mathscr{M}$ with either $0 < \lambda(A) < 1 < \lambda(X)$ and $0 < \mu(B) < \mu(Y)$, or the other way around. In addition, let $I \subseteq \mathbb{R}$ be a non-empty open interval, and suppose that $f,g\colon I \to \math

R. Wes Baldwin, Vijayan Asari, Keigo Hirakawa

We present a novel Fourier camera, an in-hardware optical compression of high-speed frames employing pixel-level sign-coded exposure where pixel intensities temporally modulated as positive and negative exposure are combined to yield Hadamard coefficients. The orthogonality of Walsh functions ensures that the noise is not amplified during high-speed frame re

Jiliang Jing, Weike Deng, Sheng Long, Jieci Wang

This paper extends the research on the self-consistent effective-one-body theory of a real spinless two-body system based on the post-Minkowskian approximation (Science China, 65, 100411, (2022)) to the case of a binary system for the spinning black holes. An effective rotating metric and an improved Hamiltonian for the spinning black hole binaries were cons

Tianqi Pang, Kehui Tan, Chenyou Fan

Carbon futures has recently emerged as a novel financial asset in the trading markets such as the European Union and China. Monitoring the trend of the carbon price has become critical for both national policy-making as well as industrial manufacturing planning. However, various geopolitical, social, and economic factors can impose substantial influence on t

Adrian Arnaiz-Rodriguez, Georgina Curto, Nuria Oliver

Social networks contribute to the distribution of social capital, defined as the relationships, norms of trust and reciprocity within a community or society that facilitate cooperation and collective action. Therefore, better positioned members in a social network benefit from faster access to diverse information and higher influence on information dissemina

Ila Gokarn, Hemanth Sabella, Yigong Hu, Tarek Abdelzaher

Sustaining high fidelity and high throughput of perception tasks over vision sensor streams on edge devices remains a formidable challenge, especially given the continuing increase in image sizes (e.g., generated by 4K cameras) and complexity of DNN models. One promising approach involves criticality-aware processing, where the computation is directed select

D. Stello, S. Sharma

The asteroseismic scaling relation, dnu~rho^{0.5}, linking a star's large frequency separation, dnu, and its mean density, rho, is not exact. Yet, it provides a very useful way to obtain fundamental stellar properties. Common ways to make the relation more accurate is to apply correction factors to it. Because the corrections depend on stellar properties, su

Alejandro Vargas

We develop a combinatorial framework to study certain polyhedral maps which are higher-dimensional analogues of tropical covers between metric graphs. Under a mild combinatorial assumption, we show that a map satisfies the so-called balancing condition if and only if it is an indexed branched cover, i.e.~locally over connected sets the count with multiplicit

Alex Youssef, Michael Pencina, Anshul Thakur, Tingting Zhu

External validation is often recommended to ensure the generalizability of ML models. However, it neither guarantees generalizability nor equates to a model's clinical usefulness (the ultimate goal of any clinical decision-support tool). External validation is misaligned with current healthcare ML needs. First, patient data changes across time, geography, an

Jinan A. Jasim, Alan Jalal Abdulqader, Emad A. Kuffi

In this paper, the double fuzzy complex EE transform was applied to get the solution to partial Volterra fuzzy integro-differential equations with convolution kernel under H-differentiability. This work presents important results to this transform for double fuzzy convolution and fuzzy partial derivatives of the n-th order that help us to solve previously me

Jie Zheng, Aile Sun, Jie Zhang

Understanding the relationship between micromechanics and macroscopic plastic deformation is vital for elucidating the deformation mechanism of amorphous solids, such as granular materials. In this study, we directly measure T1 events, which are topological rearrangements of particles, and the associated microscopic stresses in dense packings of photoelastic

Tingyu Gou, Rui Liu, Astrid M. Veronig, Bin Zhuang

Solar coronal mass ejections are the most energetic events in the Solar System. In their standard formation model, a magnetic flux rope builds up into a coronal mass ejection through magnetic reconnection that continually converts overlying, untwisted magnetic flux into twisted flux enveloping the pre-existing rope. However, only a minority of coronal mass e

Hyojoon Park, Sangeetha Grama Srinivasan, Matthew Cong, Doyub Kim

We present a neural network-based simulation super-resolution framework that can efficiently and realistically enhance a facial performance produced by a low-cost, realtime physics-based simulation to a level of detail that closely approximates that of a reference-quality off-line simulator with much higher resolution (26x element count in our examples) and

Victor-Emmanuel Brunel

We study the asymptotic properties of geodesically convex $M$-estimation on non-linear spaces. Namely, we prove that under very minimal assumptions besides geodesic convexity of the cost function, one can obtain consistency and asymptotic normality, which are fundamental properties in statistical inference. Our results extend the Euclidean theory of convex $

Lindley R. Slipetz, Jeremy W. Eberle, Cheri A. Levinson, Ami Falk

Ecological momentary assessment (EMA) data have a broad base of application in the study of time trends and relations. In EMA studies, there are a number of design considerations which influence the analysis of the data. One general modeling framework is particularly well-suited for these analyses: state-space modeling. Here, we present the state-space model

Taegyu Kim

In this paper, we consider the following nonlinear Schrödinger equation with derivative: \begin{align*} i\partial_tu+\partial_{xx}u+i|u|^{2}\partial_xu+b|u|^4u=0, \quad (t,x) \in \mathbb{R}\times\mathbb{R}, \quad b\geq 0. \end{align*} For the case $b=0$, the original DNLS, Kwon and Wu \cite{KwonWu2018} proved the conditional orbital stability of degenerate s

J. Peltonen, Erik Rosolowsky, L. Clifton Johnson, Anil C. Seth

We use young clusters and giant molecular clouds (GMCs) in the galaxies M33 and M31 to constrain temporal and spatial scales in the star formation process. In M33, we compare the PHATTER catalogue of 1214 clusters with ages measured via colour-magnitude diagram (CMD) fitting to 444 GMCs identified from a new 35 pc resolution ALMA $^{12}$CO(2-1) survey. In M3

Eric Jankowski

In this paper we describe the notion of a toric supervariety, generalizing that of a toric variety from the classical setting. We give a combinatorial interpretation of the category of quasinormal toric supervarieties with one odd dimension using decorated polyhedral fans. We then use this interpretation to calculate some invariants of these supervarieties a

Shervin Ardeshir

Trained on a vast amount of data, Large Language models (LLMs) have achieved unprecedented success and generalization in modeling fairly complex textual inputs in the abstract space, making them powerful tools for zero-shot learning. Such capability is extended to other modalities such as the visual domain using cross-modal foundation models such as CLIP, an

David Angeli, Davide Martini, Giacomo Innocenti, Alberto Tesi

This paper introduces small-gain sufficient conditions for $2$-contraction of feedback interconnected systems, on the basis of individual gains of suitable subsystems arising from a modular decomposition of the second additive compound equation. The condition applies even to cases when individual subsystems might fail to be contractive (due to the extra marg

Catherine Yeh, Yida Chen, Aoyu Wu, Cynthia Chen

Transformer models are revolutionizing machine learning, but their inner workings remain mysterious. In this work, we present a new visualization technique designed to help researchers understand the self-attention mechanism in transformers that allows these models to learn rich, contextual relationships between elements of a sequence. The main idea behind o

Yuri Cacchio', Amirali Hannani, Gigliola Staffilani

We consider the 2d $\beta$-plane stochastic Navier-Stokes equations in a periodic channel. We prove the well-posedness and existence of the stationary measure, as well as certain regularity estimates concerning the support of the stationary measure. The mentioned estimates are crucial for the rigorous study of the cascade phenomena in this equation [8]. To t

Adrian S. Lewis, Tonghua Tian

A central tool for understanding first-order optimization algorithms is the Kurdyka-Lojasiewicz inequality. Standard approaches to such methods rely crucially on this inequality to leverage sufficient decrease conditions involving gradients or subgradients. However, the KL property fundamentally concerns not subgradients but rather "slope", a purely metric n

Sonal Sannigrahi, Rachel Bawden

Multilingual language models have shown impressive cross-lingual transfer ability across a diverse set of languages and tasks. To improve the cross-lingual ability of these models, some strategies include transliteration and finer-grained segmentation into characters as opposed to subwords. In this work, we investigate lexical sharing in multilingual machine

Luis F. Alcerro

Recent results on inclusive jet production and production of a W boson in association with a charm quark by the CMS Collaboration are presented in this proceeding. The impact of these measurements on proton PDFs are also discussed.

Duncan Ermini Leaf

Statistical inferential results generally come with a measure of reliability for decision-making purposes. For a policy implementer, the value of implementing published policy research depends critically upon this reliability. For a policy researcher, the value of policy implementation may depend weakly or not at all upon the policy's outcome. Some researche

Xilun Chen, Lili Yu, Wenhan Xiong, Barlas Oğuz

We propose a new two-stage pre-training framework for video-to-text generation tasks such as video captioning and video question answering: A generative encoder-decoder model is first jointly pre-trained on massive image-text data to learn fundamental vision-language concepts, and then adapted to video data in an intermediate video-text pre-training stage to

MohammadTaghi Hajiaghayi, Keivan Rezaei, Suho Shin

We consider a multi-agent delegation mechanism without money. In our model, given a set of agents, each agent has a fixed number of solutions which is exogenous to the mechanism, and privately sends a signal, e.g., a subset of solutions, to the principal. Then, the principal selects a final solution based on the agents' signals. In stark contrast to single-a

Jiaheng Hu, Peter Stone, Roberto Martín-Martín

Developing the next generation of household robot helpers requires combining locomotion and interaction capabilities, which is generally referred to as mobile manipulation (MoMa). MoMa tasks are difficult due to the large action space of the robot and the common multi-objective nature of the task, e.g., efficiently reaching a goal while avoiding obstacles. C

Muhammad Shoufie Ukhtary, Ahmad R. T. Nugraha, Adam B. Cahaya, Andrivo Rusydi

We propose and investigate the performance of a hybrid quantum battery, the so-called Kerr quantum battery, which consists of two interacting quantum oscillators, i.e., the charger is a harmonic oscillator and the battery is an anharmonic oscillator involving the Kerr nonlinearity. Such a setup creates nonuniform spacing between energy levels of the quantum

Mursal Dawodi, Jawid Ahmad Baktash

Text classification has become a crucial task in various fields, leading to a significant amount of research on developing automated text classification systems for national and international languages. However, there is a growing need for automated text classification systems that can handle local languages. This study aims to establish an automated classif

Jawid Ahmad Baktash, Mursal Dawodi

This paper is an extension of our previous conference paper. In recent years, there has been a growing interest among researchers in developing and improving speech recognition systems to facilitate and enhance human-computer interaction. Today, Automatic Speech Recognition (ASR) systems have become ubiquitous, used in everything from games to translation sy

J. Menezes, E. Rangel

We investigate a tritrophic system in which organisms' energy depletion, resulting from failed selection attempts, leads to a partial loss of capacity to win the cyclic spatial game. The energy required to maintain optimal organism fitness may be impacted by changes in environmental conditions, increasing the death risk due to accelerated deterioration of he

Jawid Ahmad Baktash, Mursal Dawodi, Mohammad Zarif Joya, Nematullah Hassanzada

Today text classification becomes critical task for concerned individuals for numerous purposes. Hence, several researches have been conducted to develop automatic text classification for national and international languages. However, the need for an automatic text categorization system for local languages is felt. The main aim of this study is to establish

Jun-Sik Yoo, Tanmoy Bhattacharya, Rajan Gupta, Santanu Mondal

One of the sensitive probes of physics beyond the standard model is the test of the unitarity of the Cabbibo-Kobyashi-Maskawa (CKM) matrix. Current analysis of the first row is based on $|V_{ud}|$ from fourteen superallowed $0^+ \to 0^+$ nuclear $\beta$ decays and $|V_{ud}|$ from the kaon semileptonic decay, $K \to \pi \ell \nu_\ell$. Modeling the nuclear ef

E. O. Oladimeji, T. T. Ibrahim, A. N. Ikot, J. D. Koffa

This study presents an analysis of a quantum mechanical formulation of the Carnot like cycle using diatomic molecules, i.e., the Morse oscillator, as the working substance. The generalized model with an arbitrary one dimensional potential is used to obtain the important performance parameters such as the efficiency, the power output, and the optimal region o

Zexin Sun, John Baillieul

Building on our recent research on neural heuristic quantization systems, results on learning quantized motions and resilience to channel dropouts are reported. We propose a general emulation problem consistent with the neuromimetic paradigm. This optimal quantization problem can be solved by model predictive control (MPC), but because the optimization step

Jawid Ahmad Baktash, Mursal Dawodi

Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation language model in the GPT series, developed by OpenAI, which promises significant advancements in the field of natural language processing (NLP). In this research article, we have discussed the features of GPT-4, its potential applications, and the challenges that it might face. We have al

Hadley Black, Eric Blais, Nathaniel Harms

We study the problems of testing and learning high-dimensional discrete convex sets. The simplest high-dimensional discrete domain where convexity is a non-trivial property is the ternary hypercube, $\{-1,0,1\}^n$. The goal of this work is to understand structural combinatorial properties of convex sets in this domain and to determine the complexity of the t

Saptam Ganguly, David Pesquera, Daniel Moreno Garcia, Umair Saeed

Complex oxides offer a wide range of functional properties, and recent advances in fabrication of freestanding membranes of these oxides are adding new mechanical degrees of freedom to this already rich functional ecosystem. Here, we demonstrate photoactuation in freestanding thin film resonators of ferroelectric Barium Titanate (BaTiO3) and paraelectric Str

Victoria Clerico, Jorge Gonzalez-Lopez, Gady Agam, Jesus Grajal

Although radar and communications signal classification are usually treated separately, they share similar characteristics, and methods applied in one domain can be potentially applied in the other. We propose a simple and unified scheme for the classification of radar and communications signals using Long Short-Term Memory (LSTM) neural networks. This propo

Chungjae Lee, Wirattawut Boonbandansook, Vahid Eghbal Akhlaghi, Kevin Dalmeijer

The Autonomous Transfer Hub Network (ATHN) is one of the most promising ways to adapt self-driving trucks for the freight industry. These networks use autonomous trucks for the middle mile, while human drivers perform the first and last miles. This paper extends previous work on optimizing ATHN operations by including transfer hub capacities, which are cruci

Ryusuke Hamazaki

How multiple observables mutually influence their dynamics has been a crucial issue in statistical mechanics. We introduce a new concept, "quantum velocity limits," to establish a quantitative and rigorous theory for non-equilibrium quantum dynamics for multiple observables. Quantum velocity limits are universal inequalities for a vector the describes veloci

Rafael Puerta, Mahdieh Joharifar, Mengyao Han, Anders Djupsjöbacka

The sixth-generation (6G) mobile networks must increase coverage and improve spectral efficiency, especially for cell-edge users. Distributed multiple-input multiple-output (D-MIMO) networks can fulfill these requirements provided that transmission/reception points (TRxPs) of the network can be synchronized with sub nanosecond precision, however, synchroniza

Alen Adamyan, Erik Harutyunyan

There are various optimization techniques in the realm of 3D, including point cloud-based approaches that use mesh, texture, and voxels which optimize how you store, and how do calculate in 3D. These techniques employ methods such as feed-forward networks, 3D convolutions, graph neural networks, transformers, and sparse tensors. However, the field of 3D is o

Zikang Leng, Hyeokhyen Kwon, Thomas Plötz

The development of robust, generalized models in human activity recognition (HAR) has been hindered by the scarcity of large-scale, labeled data sets. Recent work has shown that virtual IMU data extracted from videos using computer vision techniques can lead to substantial performance improvements when training HAR models combined with small portions of real

Yirong Yang

Nevo, Santos, and Wilson constructed $2^{\Omega(N^d)}$ combinatorially distinct simplicial $(2d-1)$-spheres with $N$ vertices. We prove that all spheres produced by one of their methods are shellable. Combining this with prior results of Kalai, Lee, and Benedetti and Ziegler, we conclude that for all $D \ge 3$, there are $2^{\Theta(N^{\lceil D/2 \rceil})}$ s

Lindsey Daniels, Smita Sahu, Kevin J. Sanders, Gillian R. Goward

Most mathematical models of the transport of charged species in battery electrodes require a constitutive relation describing intercalation of Lithium, which is a reversible process taking place on the interface between the electrolyte and active particle. The most commonly used model is the Butler-Volmer relation, which gives the current density as a produc

Minglang Yin, Zongren Zou, Enrui Zhang, Cristina Cavinato

Quantifying biomechanical properties of the human vasculature could deepen our understanding of cardiovascular diseases. Standard nonlinear regression in constitutive modeling requires considerable high-quality data and an explicit form of the constitutive model as prior knowledge. By contrast, we propose a novel approach that combines generative deep learni

Simon B. Jäger, Ralf Betzholz

Recently, an effective Lindblad master equation for quantum systems whose dynamics are coupled to dissipative bosonic modes has been introduced [Phys. Rev. Lett. 129, 063601 (2022)]. In this approach, the bosonic modes are adiabatically eliminated and one can effectively describe the dynamics of the quantum systems. Here, we demonstrate that this effective m

Joao Faria Martins, Frank W Nijhoff, Daniel Riccombeni

In a previous paper by one of the authors, a Lagrangian 3-form structure was established for a generalised Darboux system, originally describing orthogonal curvilinear coordinate systems, which encodes the Kadomtsev-Petviashvili (KP) hierarchy. Here a hierarchy of Lagrangian multiforms is established for the same system, viewed as a hierarchy of Chern-Simons

Roberto Insabella, Martin Gonzalez, Lucas Riobo, Klaus Hass

In this work we present the first application of software-defined optoelectronics (SDO) for bidimensional optoacoustic tomography (OAT). The SDO concept refers to optoelectronic systems where the functionality associated with the conditioning and processing of optical and electrical signals are digitally implemented and controlled by software. This paradigm

Peyman Afshani, Pingan Cheng, Aniket Basu Roy, Zhewei Wei

We study the query version of the approximate heavy hitter and quantile problems. In the former problem, the input is a parameter $\varepsilon$ and a set $P$ of $n$ points in $\mathbb{R}^d$ where each point is assigned a color from a set $C$, and we want to build a structure s.t. given any geometric range $\gamma$, we can efficiently find a list of approxima

Mahmood Khalsan, Mu Mu, Eman Salih Al-Shamery, Lee Machado

Machine learning (ML) approaches have been used to develop highly accurate and efficient applications in many fields including bio-medical science. However, even with advanced ML techniques, cancer classification using gene expression data is still complicated because of the high dimensionality of the datasets employed. We developed a new fuzzy gene selectio

Alexandru Macridin, Andy C. Y. Li, Panagiotis Spentzouris

Transferring quantum information between different types of quantum hardware is crucial for integrated quantum technology. In particular, converting information between continuous-variable (CV) and discrete-variable (DV) devices enables many applications in quantum networking, quantum sensing, quantum machine learning, and quantum computing. This paper addre

Pedro Martin, António Rodrigues, João Ascenso, Maria Paula Queluz

This short paper proposes a new database - NeRF-QA - containing 48 videos synthesized with seven NeRF based methods, along with their perceived quality scores, resulting from subjective assessment tests; for the videos selection, both real and synthetic, 360 degrees scenes were considered. This database will allow to evaluate the suitability, to NeRF based s

Jingxiao Liu, Siyuan Yuan, Yiwen Dong, Biondo Biondi

We introduce the TelecomTM system that uses pre-existing telecommunication fiber-optic cables as virtual strain sensors to sense vehicle-induced ground vibrations for fine-grained and ubiquitous traffic monitoring and characterization. Here we call it a virtual sensor because it is a software-based representation of a physical sensor. Due to the extensively

A. Baudry, K. T. Wong, S. Etoka, A. M. S. Richards

Water and the hydroxyl radical are major constituents of the envelope of O-rich late-type stars. Transitions involving energy levels that are highly excited have been observed in both H$_2$O and OH. These and more recently discovered transitions can now be observed at a high sensitivity and angular resolution with the ALMA Array. Spectra and maps of H$_2$O a

L. Alpoge, N. M. Katz, G. Navarro, E. A. O'Brien

We study geometric monodromy groups $G_{\geo,\sF_q}$ of the local systems $\sF_q$ on the affine line over $\F_2$ of rank $D=\sqrt{q}(q-1)$, $q=2^{2n+1}$, constructed in \cite{Ka-ERS}. The main result of the paper shows that $G_{\geo,\sF_q}$ is either the Suzuki simple group $\tw2 B_2(q)$, or the special linear group $\SL_D$. We also show that $\sF_8$ has geo

K. S. Babu, Shreyashi Chakdar, Nandini Das, Dilip Kumar Ghosh

We investigate the phenomenology of a non-thermal dark matter (DM) candidate in the context of flavor models that explain the hierarchy in the masses and mixings of quarks and leptons via the Froggatt-Nielsen (FN) mechanism. A flavor-dependent $U(1)_{\rm FN}$ symmetry explains the fermion mass and mixing hierarchy, and also provides a mechanism for suppresse

M. H. Alqahtani

An open (resp., closed) subset A of a topological space (X, T ) is called C-open (resp., C-closed) set if cl(A) \ A (resp., A \ int(A)) is a countable set. This paper aims to present the concept of C-open and C-closed sets. We first investigate their basic properties. Then, we found some operators such as interior, closure, limit, border, and frontier using

Walid A. Hanafy, Limin Wang, Hyunseok Chang, Sarit Mukherjee

It is commonly assumed that the end-to-end networking performance of edge offloading is purely dictated by that of the network connectivity between end devices and edge computing facilities, where ongoing innovation in 5G/6G networking can help. However, with the growing complexity of edge-offloaded computation and dynamic load balancing requirements, an off

Zhihao Jiang, Jinho Lim, Yi Li, Wolfgang Pfaff

Magnons, the quanta of collective spin excitations in magnetically ordered materials, have distinct properties that make them uniquely appealing for quantum information applications. They can have ultra-small wavelengths down to the nanometer scale even at microwave frequencies. They can provide coupling to a diverse set of other quantum excitations, and the

Christian Bargetz, Adam Bartoš, Wiesław Kubiś, Franz Luggin

We study homogeneity aspects of metric spaces in which all triples of distinct points admit pairwise different distances; such spaces are called isosceles-free. In particular, we characterize all homogeneous isosceles-free spaces up to isometry as vector spaces over the two-element field, endowed with an injective norm. Using isosceles-free decompositions, w

James M. McCaw, Michael J. Plank

Mathematical modelling has been used to support the response to the COVID-19 pandemic in countries around the world including Australia and New Zealand. Both these countries have followed similar pandemic response strategies, using a combination of strict border measures and community interventions to minimise infection rates until high vaccine coverage was

Moaathe Belhaj Ahmed, Wan Cong, David Kubiznak, Robert B. Mann

Employing the novel exact dictionary between the laws of extended black hole thermodynamics and the laws of the dual CFT, we study the extended thermodynamics for CFT states that are dual to neutral singly-spinning asymptotically AdS black holes in $d$ bulk spacetime dimensions. On the field theory side we include two independent pairs of thermodynamic conju

Christopher J. Ryu, Dong-Yeop Na, Weng C. Chew, Erhan Kudeki

We present a novel generalization of the chain mapping technique that applies to multi-atom, multimode systems by making use of coupling matrix transformations. This is extremely useful for tensor network simulations of multimode Dicke model and multi-spin-boson model because their coupling structures are altered from the star form to the chain form with nea

Iretomiwa Esho, Austin J. Minnich

The semiconductor BAs has drawn significant interest due to experimental reports of simultaneous high thermal conductivity and ambipolar charge mobility. The \textit{ab~initio} prediction of high electron and hole mobility assumed the dominance of charge carrier scattering by one phonon. Recently, higher-order electron-phonon scattering processes in polar an

Jyotishka Datta, Nicholas G. Polson

In Bayesian inference, the approximation of integrals of the form $\psi = \mathbb{E}_{F}{l(X)} = \int_{\chi} l(\mathbf{x}) d F(\mathbf{x})$ is a fundamental challenge. Such integrals are crucial for evidence estimation, which is important for various purposes, including model selection and numerical analysis. The existing strategies for evidence estimation a

Ira M. Gessel

In the film Good Will Hunting, the main character, a janitor at MIT named Will Hunting, attacks the problem of drawing all the homeomorphically irreducible trees with 10 vertices. Although the film suggests that this is a difficult problem, it is in fact quite easy. A much more interesting problem is counting homeomorphically irreducible trees with $n$ verti

Mingyu Kang, Hanggai Nuomin, Sutirtha N. Chowdhury, Jonathon L. Yuly

Simulating the quantum dynamics of molecules in the condensed phase represents a longstanding challenge in chemistry. Trapped-ion quantum systems may serve as a platform for the analog-quantum simulation of chemical dynamics that is beyond the reach of current classical-digital simulation. To identify a 'quantum advantage' for these simulations, performance

Gabriel Abellán, Nelson Bolívar, Ivaylo Vasilev

In this work we study the influence of isotropic and anisotropic fluids on the spherically symmetric warp metric. We evaluate the energy conditions and the influence of including a cosmological constant type term. We find that, considering this term, there is a trade-off between the weak and strong energy conditions. The obtained solutions are numerical and

Marcos Ranieri, Elaine Sampaio, Feliciano Vitório

We show some area estimates for stable CMC hypersurfaces immersed in Riemannian manifolds with scalar and sectional curvature bounded from below. In particular, we focus on immersions in three-dimensional Riemannian manifolds. As an application, we derive some upper estimates for the bottom spectrum of these hypersurfaces. This paper generalizes and builds o

Richard H. Bamler, Eric Chen

We develop a new degree theory for 4-dimensional, asymptotically conical gradient expanding solitons. Our theory implies the existence of gradient expanding solitons that are asymptotic to any given cone over $S^3$ with non-negative scalar curvature. We also obtain a similar existence result for cones whose link is diffeomorphic to $S^3/\Gamma$ if we allow t

Kevin Zhang, Vipul Mann, Venkat Venkatasubramanian

Various template-based and template-free approaches have been proposed for single-step retrosynthesis prediction in recent years. While these approaches demonstrate strong performance from a data-driven metrics standpoint, many model architectures do not incorporate underlying chemistry principles. Here, we propose a novel chemistry-aware retrosynthesis pred

Tim Kaler, Alexandros-Stavros Iliopoulos, Philip Murzynowski, Tao B. Schardl

Training and inference with graph neural networks (GNNs) on massive graphs has been actively studied since the inception of GNNs, owing to the widespread use and success of GNNs in applications such as recommendation systems and financial forensics. This paper is concerned with minibatch training and inference with GNNs that employ node-wise sampling in dist

William M. Jacobs

Biomolecular condensates constitute a newly recognized form of spatial organization in living cells. Although many condensates are believed to form as a result of phase separation, the physicochemical properties that determine the phase behavior of heterogeneous biomolecular mixtures are only beginning to be explored. Theory and simulation provide invaluable

Ling Chen, Yuqi Gu

Grade of Membership (GoM) models are popular individual-level mixture models for multivariate categorical data. GoM allows each subject to have mixed memberships in multiple extreme latent profiles. Therefore GoM models have a richer modeling capacity than latent class models that restrict each subject to belong to a single profile. The flexibility of GoM co

Sai Qian Zhang, Thierry Tambe, Nestor Cuevas, Gu-Yeon Wei

On-device learning allows AI models to adapt to user data, thereby enhancing service quality on edge platforms. However, training AI on resource-limited devices poses significant challenges due to the demanding computing workload and the substantial memory consumption and data access required by deep neural networks (DNNs). To address these issues, we propos

Alberto Lastra, Cruz Prisuelos-Arribas

A generalized exponential matrix based on the construction of kernel operators for generalized summability is defined and analyzing its main properties, generalizing the classical exponential matrix and fractional exponential matrix. This object serves as a practical tool to express the solutions of linear systems of moment differential equations in a compac

Anindya De, Shivam Nadimpalli, Rocco A. Servedio

We study the basic statistical problem of testing whether normally distributed $n$-dimensional data has been truncated, i.e. altered by only retaining points that lie in some unknown truncation set $S \subseteq \mathbb{R}^n$. As our main algorithmic results, (1) We give a computationally efficient $O(n)$-sample algorithm that can distinguish the standard nor