December 2023 arXiv papers — page 128

Ananta Mukherjee, Peeyush Kumar, Boling Yang, Nishanth Chandran

This paper addresses privacy concerns in multi-agent reinforcement learning (MARL), specifically within the context of supply chains where individual strategic data must remain confidential. Organizations within the supply chain are modeled as agents, each seeking to optimize their own objectives while interacting with others. As each organization's strategy

Eduard Emelyanov

We prove that an order continuous Banach lattice E is a KB-space if and only if each positive compact operator on E is a KB operator. We give conditions on quasi-KB (resp., quasi-Levi) operators to be KB (resp., Levi), study norm completeness and domination for these operators, and show that neither KB nor Levi operators are stable under rank one perturbatio

Anna Heggestuen

The ICARUS-T600 detector is a 760-ton Liquid Argon Time Projection Chamber (LArTPC) currently operating at Fermilab as the Far Detector in the Short Baseline Neutrino (SBN) program. The SBN program is composed of three LArTPCs with a central goal of testing the sterile neutrino hypothesis. After operating for 3-years in the Gran Sasso Underground Laboratory,

Joaquin Housset, Joel F. Saavedra, Francisco Tello-Ortiz

This article is devoted to the study of the thermodynamics phase transitions and critical phenomena of an FLRW cosmological model under the so-called Kaniadakis's statistics. The equation of state is derived from the corrected Friedmann field equations and the thermodynamics unified first law. This reveals the existence of non-trivial critical points where a

David Jiang

We will show that a necessary and sufficient condition for a Ferrers board (or Young Diagrams) to be fully tileable with 1x2 dominoes requires the board to be 2-colorable such that no color is adjacent to its own color using both induction and a graph theory approach. We will walk through all prerequisite knowledge and go through the failed attempts we tried

Drew Yarger, Anindya Bhadra

Spatially-indexed multivariate data appear frequently in geostatistics and related fields including oceanography and environmental science. To take full advantage of this data structure, cross-covariance functions are constructed to describe the dependence between any two component variables at different spatial locations. Modeling of multivariate spatial ra

Joel Martin Dalmas, Ambroise van Roekeghem, Natalio Mingo, Stefano Mossa

The question of if silica nanoparticles can enhance the ionic conductivity of a polymer electrolyte above its crystallization temperature has remained unclear for the two decades following the first experiments on these systems. We use Molecular Dynamics simulations to decipher the atomic scale mechanisms affecting the properties of LiTFSI-poly(ethylene oxid

Akankshya Dash, Biswaranjan Panda, Arun K Pati

The Grover search algorithm performs an unstructured search of a marked item in a database quadratically faster than classical algorithms and is shown to be optimal. Here, we show that if the search space is divided into two blocks with the local query operators and the global operators satisfy certain condition, then it is possible to achieve an improvement

Asmaa Eldesoukey, Tryphon T. Georgiou

The problem of reconciling a prior probability law on paths with data was introduced by E. Schr\"odinger in 1931/32. It represents an early formulation of a maximum likelihood problem. This specific formulation can also be seen as the control problem to modify the law of a diffusion process so as to match specifications on marginal distributions at given tim

Eugene Wickett, Matthew Plumlee, Karen Smilowitz, Souly Phanouvong

Ensuring product quality is critical to combating the global challenge of substandard and falsified medical products. Post-marketing surveillance is a central quality-assurance activity in which products from consumer-facing locations are collected and tested. Regulators in low-resource settings use post-marketing surveillance to evaluate product quality acr

Yeming Wen, Swarat Chaudhuri

Low-Rank Adaptation (LoRA) has recently gained attention for fine-tuning foundation models by incorporating trainable low-rank matrices, thereby reducing the number of trainable parameters. While LoRA offers numerous advantages, its applicability for real-time serving to a diverse and global user base is constrained by its incapability to handle multiple tas

Xiaoqi Wei, Guo-Wei Wei

Persistent topological Laplacians constitute a new class of tools in topological data analysis (TDA). They are motivated by the necessity to address challenges encountered in persistent homology when handling complex data. These Laplacians combines multiscale analysis with topological techniques to characterize the topological and geometrical features of fun

Ling Cheung, Lap-Ming Lin, Nicolas Chamel

Torsional oscillations of magnetized neutron stars have been well studied since they may be relevant to the physical interpretation of some of the observed quasiperiodic oscillations in the magnetar giant flares. In the crustal region of a magnetar, the strong magnetic field can alter the equation of state and composition due to the Landau-Rabi quantization

Conrad Borchers, Jiayi Zhang, Ryan S. Baker, Vincent Aleven

Numerous studies demonstrate the importance of self-regulation during learning by problem-solving. Recent work in learning analytics has largely examined students' use of SRL concerning overall learning gains. Limited research has related SRL to in-the-moment performance differences among learners. The present study investigates SRL behaviors in relationship

Kálmán Klapcsik, Bálint Gyires-Tóth, Juan Manuel Rosselló, Ferenc Hegedűs

A control technique is developed via Reinforcement Learning that allows arbitrary controlling of the position of an acoustic cavitation bubble in a dual-frequency standing acoustic wave field. The agent must choose the optimal pressure amplitude values to manipulate the bubble position in the range of $x/\lambda_0\in[0.05, 0.25]$. To train the agent an actor

Rashmi P. Bomiriya, Alina R. Kuvelkar, David R. Hunter, Steffen Triebel

Homophily, the tendency of individuals who are alike to form ties with one another, is an important concept in the study of social networks. Yet accounting for homophily effects is complicated in the context of bipartite networks where ties connect individuals not with one another but rather with a separate set of nodes, which might also be individuals but w

Agustín Sánchez-Lavega, Patrick Irwin, Antonio García Muñoz

This review presents an insight into our current knowledge of the atmospheres of the planets Venus, Mars, Jupiter, Saturn, Uranus and Neptune, the satellite Titan, and those of exoplanets. It deals with the thermal structure, aerosol properties (hazes and clouds, dust in the case of Mars), chemical composition, global winds and selected dynamical phenomena i

Nikhil Narayan, Mrutyunjay Biswal, Pramod Goyal, Abhranta Panigrahi

Social media platforms serve as accessible outlets for individuals to express their thoughts and experiences, resulting in an influx of user-generated data spanning all age groups. While these platforms enable free expression, they also present significant challenges, including the proliferation of hate speech and offensive content. Such objectionable langua

E. D. Kosov, V. N. Temlyakov

In the first part of the paper we study absolute error of sampling discretization of the integral $L_p$-norm for function classes of continuous functions. We use basic approaches from chaining technique to provide general upper bounds for the error of sampling discretization of the $L_p$-norm on a given function class in terms of entropy numbers in the unifo

Ziyi Ye, Xiaohui Xie, Qingyao Ai, Yiqun Liu

The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on the user, extensive studies have explored using pseudo-relevance signals and implicit feedback signals as substitutes. However, such signal

Lucio La Cava, Domenico Mandaglio, Andrea Tagarelli

Centralized social media platforms are currently experiencing a shift in user engagement, drawing attention to alternative paradigms like Decentralized Online Social Networks (DOSNs). The rising popularity of DOSNs finds its root in the accessibility of open-source software, enabling anyone to create a new instance (i.e., server) and participate in a decentr

Ryan Roussel, Auralee L. Edelen, Tobias Boltz, Dylan Kennedy

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these meth

Pontus Johnson, Mathias Ekstedt

This work-in-progress paper introduces a prototype for a novel Graph Neural Network (GNN) based approach to estimate hidden states in cyber attack simulations. Utilizing the Meta Attack Language (MAL) in conjunction with Relational Dynamic Decision Language (RDDL) conformant simulations, our framework aims to map the intricate complexity of cyber attacks wit

CMS Collaboration

Differential cross sections are measured for the standard model Higgs boson produced in association with vector bosons (W, Z) and decaying to a pair of b quarks. Measurements are performed within the framework of the simplified template cross sections. The analysis relies on the leptonic decays of the W and Z bosons, resulting in final states with 0, 1, or 2

Jia-Yi Jhan, Hung-Min Sun

The core component of an Industrial Control System (ICS) is often a Programmable Logic Controller (PLC) combined with various modules. In such systems, the communication between devices is mainly based on the Modbus protocol, which was developed by Modicon (now Schneider Electric) in 1979 as an application-level communication protocol and has become a de fac

Heng Yu, Joel Julin, Zoltán Á. Milacski, Koichiro Niinuma

Capturing and re-animating the 3D structure of articulated objects present significant barriers. On one hand, methods requiring extensively calibrated multi-view setups are prohibitively complex and resource-intensive, limiting their practical applicability. On the other hand, while single-camera Neural Radiance Fields (NeRFs) offer a more streamlined approa

Mohamed Elhamdadi, Manpreet Singh

A biquandle is a solution to the set-theoretical Yang-Baxter equation, which yields invariants for virtual knots such as the coloring number and the state-sum invariant. A virtual biquandle enriches the structure of a biquandle by incorporating an invertible unary map. This unary operator plays a crucial role in defining the action of virtual crossings on th

Gustavo Gonçalves, Emma Strubell

Large Language Models (LLMs) trained with self-supervision on vast corpora of web text fit to the social biases of that text. Without intervention, these social biases persist in the model's predictions in downstream tasks, leading to representational harm. Many strategies have been proposed to mitigate the effects of inappropriate social biases learned duri

Vivin Vinod, Ulrich Kleinekathöfer, Peter Zaspel

Machine learning (ML) provides access to fast and accurate quantum chemistry (QC) calculations for various properties of interest such as excitation energies. It is often the case that high accuracy in prediction using an ML model, demands a large and costly training set. Various solutions and procedures have been presented to reduce this cost. These include

Dylon Chow

Over a global field (number field or function field of a curve over a finite field), theorems for the Galois cohomology of algebraic groups have long been known. For $F$ the function field of a curve over the formal series field $\mathbb{C}((t))$, under an additional assumption on the curve, we establish an explicit description of the localization map in Gal

Ashwinkumar Badanidiyuru, Badih Ghazi, Pritish Kamath, Ravi Kumar

We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP). In particular, we leverage the trade-offs between bias and variance to construct better label randomizers depending on a privately estimated prior distribution over the labels. We demonstrate that these randomizers achieve stat

William M. Farmer, Dennis Y. Zvigelsky

Alonzo is a practice-oriented classical higher-order version of predicate logic that extends first-order logic and that admits undefined expressions. Named in honor of Alonzo Church, Alonzo is based on Church's type theory, Church's formulation of simple type theory. The little theories method is a method for formalizing mathematical knowledge as a theory gr

Shukai Duan, Nikos Kanakaris, Xiongye Xiao, Heng Ping

Code optimization is a challenging task requiring a substantial level of expertise from developers. Nonetheless, this level of human capacity is not sufficient considering the rapid evolution of new hardware architectures and software environments. In light of this, recent research proposes adopting machine learning and artificial intelligence techniques to

Vipin Vijayan, L. Chotorlishvili, A. Ernst, M. I. Katsnelson

The primary obstacle in the field of quantum thermodynamics revolves around the development and practical implementation of quantum heat engines operating at the nanoscale. One of the key challenges associated with quantum working bodies is the occurrence of "quantum friction," which refers to irreversible wasted work resulting from quantum inter-level trans

Marcin Pitera, Thorsten Schmidt, Łukasz Stettner

The assessment of risk based on historical data faces many challenges, in particular due to the limited amount of available data, lack of stationarity, and heavy tails. While estimation on a short-term horizon for less extreme percentiles tends to be reasonably accurate, extending it to longer time horizons or extreme percentiles poses significant difficulti

Emanuele Zappala

Neural integral equations are deep learning models based on the theory of integral equations, where the model consists of an integral operator and the corresponding equation (of the second kind) which is learned through an optimization procedure. This approach allows to leverage the nonlocal properties of integral operators in machine learning, but it is com

Rumeng Li, Xun Wang, Hong Yu

Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text mining, a domain with scarce, sensitive, and imbalanced medical data, is underexplored. We investigate whether LLMs can augment clinical data for detecting Alzheimer's Disease (AD)-related signs and symptoms from electronic he

Madhur Mangalam, Aaron D Likens, Damian G Kelty-Stephen

Multifractal formalisms provide an apt framework to study random cascades in which multifractal spectrum width $\Delta\alpha$ fluctuates depending on the number of estimable power-law relationships. Then again, multifractality without surrogate comparison can be ambiguous: the original measurement series' multifractal spectrum width $\Delta\alpha_\mathrm{Ori

Jianxin Chen, Dawei Ding, Weiyuan Gong, Cupjin Huang

The design and architecture of a quantum instruction set are paramount to the performance of a quantum computer. This work introduces a gate scheme for qubits with $XX+YY$ coupling that directly and efficiently realizes any two-qubit gate up to single-qubit gates. First, this scheme enables high-fidelity execution of quantum operations and achieves minimum p

Congzao Dong, Alexander Marynych, Ilya Molchanov

We study vantage-point trees constructed using an independent sample from the uniform distribution on a fixed convex body $K$ in $(\mathbb{R}^d,\|\cdot\|)$, where $\|\cdot\|$ is an arbitrary norm on $\mathbb{R}^d$. We prove that a sequence of sets, associated with the left boundary of a vantage-point tree, forms a recurrent Harris chain on the space of conve

Tom Meyerovitch

Krieger's embedding theorem provides necessary and sufficient conditions for an arbitrary subshift to embed in a given topologically mixing $\mathbb{Z}$-subshift of finite type. For some $\mathbb{Z}^d$-subshifts of finite type, Lightwood characterized the \emph{aperiodic} subsystems. In the current paper we prove a new embedding theorem for a class of subshi

A. P. Morozov, P. A. Gostishchev, A. Zharkova, A. A. Vasilev

In this study, we present a complex investigation for miniaturizing of perovskite photodiodes (PPDs) in various geometries with use of ultraviolet laser scribing (UV-LS). Employing a 355 nm (3.5 eV) pulsed laser at 30 kHz, we successfully manufactured PPDs with pixel configurations of 70x130 um2, 520x580 um2, and 2000x2000 um2. The utilization of UV-LS has a

Matthew Markowitz, Kevin Zelaya, Mohammad-Ali Miri

Programmable photonic integrated circuits represent an emerging technology that amalgamates photonics and electronics, paving the way for light-based information processing at high speeds and low power consumption. Considering their wide range of applications as one of the most fundamental mathematical operations there has been a particular interest in progr

Pedro Pessoa, Max Schweiger, Steve Presse

Exact methods for exponentiation of matrices of dimension $N$ can be computationally expensive in terms of execution time ($N^{3}$) and memory requirements ($N^{2}$) not to mention numerical precision issues. A type of matrix often exponentiated in the sciences is the rate matrix. Here we explore five methods to exponentiate rate matrices some of which apply

E. A. Ianovich

In this work the general results about asymptotics of eigenvalues of unbounded operators are obtained. We consider here different cases of compact, relatively compact, selfadjoint or nonselfadjoint perturbations. In particular we prove a generalization of Janas-Naboko lemma about eigenvalues asymptotics of unbounded operators at compact perturbation. A gener

Alireza F. Pour, Hassan Ashtiani, Shahab Asoodeh

We study the problem of hypothesis selection under the constraint of local differential privacy. Given a class $\mathcal{F}$ of $k$ distributions and a set of i.i.d. samples from an unknown distribution $h$, the goal of hypothesis selection is to pick a distribution $\hat{f}$ whose total variation distance to $h$ is comparable with the best distribution in $

Xu You, Xinping Yan, Jialun Liu, Shijie Li

Designing an autonomous precise controller for ships requires accurate and reliable ship models, including the ship dynamic model and actuator model. However, selecting a suitable model for controller design and determining its parameters pose a significant challenge, considering factors such as ship actuation, input constraints, environmental disturbances,

Chuhan Zhang, Wei Pan, Cosimo Della Santina

Motor imagery, an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations. Traditional deep learning algorithms, despite their effectiveness, are characterized by significant computational demands accompanied by high energ

Seyed Mahmoud Sajjadi Mohammadabadi, Syed Zawad, Feng Yan, Lei Yang

Federated learning (FL) enables collaboratively training a model while keeping the training data decentralized and private. However, one significant impediment to training a model using FL, especially large models, is the resource constraints of devices with heterogeneous computation and communication capacities as well as varying task sizes. Such heterogene

Ivan Galinskiy, Georg Enzian, Michał Parniak, Eugene Polzik

We demonstrate non-classical correlations between phonons and photons created using opto-mechanical spontaneous parametric down-conversion in a system based on a soft-clamped ultracoherent membrane oscillator inside of a Fabry-P\'erot optical resonator. Non-Gaussian quantum features are demonstrated for the center-of-mass motion of a sub-millimeter nanogram-

Sumedha Rai, Tong Li, Bella Lyu

Speech recognition has become an important task in the development of machine learning and artificial intelligence. In this study, we explore the important task of keyword spotting using speech recognition machine learning and deep learning techniques. We implement feature engineering by converting raw waveforms to Mel Frequency Cepstral Coefficients (MFCCs)

Qiang Fu, Thomas B. Rolinger, H. Howie Huang

Achieving high performance for Sparse MatrixMatrix Multiplication (SpMM) has received increasing research attention, especially on multi-core CPUs, due to the large input data size in applications such as graph neural networks (GNNs). Most existing solutions for SpMM computation follow the aheadof-time (AOT) compilation approach, which compiles a program ent

Helaman R. Flores, Samuel R. Layton, Dirk Englund, Ryan M. Camacho

We propose a scalable design for a spin-photon interface to a color center in a diamond microdisk. The design consists of a silicon oxynitride hexagonal lattice overlaid on a diamond microdisk to enable vertical emission from the microdisk into low-numerical aperture modes, with quantum efficiencies as high as 45\% for a tin vacancy (SnV) center. Our design

Tatiana Komarova, Thomas Zinn, Theyencheri Narayanan, Andrei V. Petukhov

Concentric microtubes of $\beta$-cyclodextrin and SDS grow from the outside in and melt from the inside out, we observe using in situ small angle X-ray scattering. We find that the conformation of the crystalline bilayer affects the saturation concentration, providing an example of a phenomenon we call conformational freezing point depression. We propose a m

Shivendra Kumar Gupta, Nikhilesh Singh, Saurabh Kumar Sen, Poorva Singh

Dirac semimetals (DSMs), characterized by linear dispersion relations in their electronic band structure, have gained prominence due to their unique topological features and potential applications in electronic devices. Through systematic calculation, we explore the electronic structure evolution of KCdP under varying negative pressure conditions. Our findin

Sabir Ahammed, Molla Basir Ahamed

In this paper, we study Bohr's inequality and refined versions of Bohr-Rogosinski inequalities involving Schwarz functions. Moreover, we establish a version of multidimensional analogue of Bohr inequality and Bohr-Rogosinski inequalities involving Schwarz functions. Finally, we establish a multidimensional analogue of the refined version of Bohr inequalities

Quoc-Huy Trinh, Nhat-Tan Bui, Dinh-Hieu Hoang, Phuoc-Thao Vo Thi

Person Re-Identification (Re-ID) task seeks to enhance the tracking of multiple individuals by surveillance cameras. It supports multimodal tasks, including text-based person retrieval and human matching. One of the most significant challenges faced in Re-ID is clothes-changing, where the same person may appear in different outfits. While previous methods ha

Richeng Piao

This study investigates the implications of algorithmic pricing in digital marketplaces, focusing on Airbnb's pricing dynamics. With the advent of Airbnb's new pricing tool, this research explores how digital tools influence hosts' pricing strategies, potentially leading to market dynamics that straddle the line between efficiency and collusion. Utilizing a

Muhammad Osama Zeeshan, Muhammad Haseeb Aslam, Soufiane Belharbi, Alessandro Lameiras Koerich

Adapting a deep learning model to a specific target individual is a challenging facial expression recognition (FER) task that may be achieved using unsupervised domain adaptation (UDA) methods. Although several UDA methods have been proposed to adapt deep FER models across source and target data sets, multiple subject-specific source domains are needed to ac

Baharin Aliashrafi Jodat, Abhishek Chandar, Shiva Nejati, Mehrdad Sabetzadeh

Test inputs fail not only when the system under test is faulty but also when the inputs are invalid or unrealistic. Failures resulting from invalid or unrealistic test inputs are spurious. Avoiding spurious failures improves the effectiveness of testing in exercising the main functions of a system, particularly for compute-intensive (CI) systems where a sing

B. F. Oliveira, A. V. M Oliveira

This paper describes an econometric model of the Brazilian domestic carrier Azul Airlines' network construction. We employed a discrete-choice framework of airline route entry to examine the effects of the merger of another regional carrier, Trip Airlines, with Azul in 2012, especially on its entry decisions. We contrasted the estimated entry determinants be

Babak Rahimi Ardabili, Shanle Yao, Armin Danesh Pazho, Lauren Bourque

The pervasive deployment of surveillance cameras produces a massive volume of data, requiring nuanced interpretation. This study thoroughly examines data representation and visualization techniques tailored for AI surveillance data within current infrastructures. It delves into essential data metrics, methods for situational awareness, and various visualizat

Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha, Clinton J. V. Campbell

Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of their generalizability. The aim of this systematic review was to assess the performance of externally validated ML models based on histopathology images for diagnosis, classificatio

Ethan Simpson Lee, Paweł Nosal

In this paper, we establish new bounds for classical prime-counting functions. All of our bounds are explicit and assume the Riemann Hypothesis. First, we prove that $|\psi(x) - x|$ and $|\vartheta(x) - x|$ are bounded from above by $$\frac{\sqrt{x}\log{x}(\log{x} - \log\log{x})}{8\pi}$$ for all $x\geq 101$ and $x \geq 2\,657$ respectively, where $\psi(x)$ a

Ibtisam Shaikh, Priya Hasan, S. N. Hasan

In this paper we study the location and stability of the collinear Lagrangian points for the RTBP for the case in which one of the primary bodies is radiating and the other is oblate. We consider the effect of Poynting-Roberson drag and investigate how the location and stability of the Lagrangian points change with changes in the radiation parameter $\beta$

Somnath Banerjee, Avik Dutta, Sayan Layek, Amruit Sahoo

In this paper, we delve into the advancement of domain-specific Large Language Models (LLMs) with a focus on their application in software development. We introduce DevAssistLlama, a model developed through instruction tuning, to assist developers in processing software-related natural language queries. This model, a variant of instruction tuned LLM, is part

Oleg Morzhin, Alexander Pechen

In this work, we consider a general form of the dynamics of open quantum systems determined by the Gorini-Kossakowsky-Sudarchhan-Lindblad type master equation with simultaneous coherent and incoherent controls with three particular forms of the two-qubit Hamiltonians. Coherent control enters in the Hamiltonian and incoherent control enters in both the Hamilt

Samuel Pate, Bowen Chen, Bing Shen, Kezhen Li

TaCo$_2$Te$_2$ is recently reported to be an air-stable, high mobility Van der Waals material with probable magnetic order. Here we investigate the scaling behavior of its magnetoresistance. We measured both the longitudinal ($\rho_{xx}$) and Hall ($\rho_{xy}$) magnetoresistivities of TaCo$_2$Te$_2$ crystals in magnetic fields parallel to the c-axis and foun

Yunzhe Xue, Olanrewaju Eletta, Justin W. Ady, Nell M. Patel

As part of their training all medical students and residents have to pass basic surgical tasks such as knot tying, needle-passing, and suturing. Their assessment is typically performed in the operating room by surgical faculty where mistakes and failure by the student increases the operation time and cost. This evaluation is quantitative and has a low margin

Mohammad Taha Shah, Ankit Kumar, Gourab Ghatak, Shobha Sundar Ram

Prior works have analyzed the performance of millimeter wave automotive radars in the presence of diverse clutter and interference scenarios using stochastic geometry tools instead of more time-consuming measurement studies or system-level simulations. In these works, the distributions of radars or discrete clutter scatterers were modeled as Poisson point pr

Mohamed Farhat, Younes Achaoui, Julio A. Iglesias Martinez, Mahmoud Addouche

The confinement of waves in open systems represents a fundamental phenomenon extensively explored across various branches of wave physics. Recently, significant attention has been directed towards bound states in the continuum (BIC), a class of modes that are trapped but do not decay in an otherwise unbounded continuum. Here, we theoretically investigate and

Vida Ranjbar, Robbert Beerten, Marc Moonen, Sofie Pollin

Cell-free massive multiple-input multiple-output (MIMO) is an emerging technology that will reshape the architecture of next-generation networks. This paper considers the sequential fronthaul, whereby the access points (APs) are connected in a daisy chain topology with multiple sequential processing stages. With this sequential processing in the uplink, each

Zhenting Qi, Xiaoyu Tan, Shaojie Shi, Chao Qu

Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of tasks. Nonetheless, this technique often necessitates substantial computational resources, making it impractical for deployment by individuals or small-scale entities. Recently, Low-Rank Adaptation (LoRA) has become a promising alternative, offering

György Kiss

We present simple, geometric constructions for small regular graphs of girth 7 from the incidence graphs of some generalized quadrangles. We obtain infinite families of (q-1)-regular, q-regular and (q + 1)-regular graphs of girth 7, for q a prime power. Some of them have the smallest order known so far.

Ai-Jun Ma, Wen-Fei Wang

We study the contributions for $K\eta^{\prime}$ pair originating from the scalar intermediate state $K_0^{*}(1430)$ in the three-body decays $B\to K\eta^{\prime} h$ ($h=\pi, K$) within the perturbative QCD approach. The contribution of $K^*_0(1430)\to K\eta^{\prime}$ is described by the Flatt${\rm \acute{e}}$ formula with coupled channels $K\pi$, $K\eta$ and

Yarema Prykarpatskyy

The paper investigates the Poisson structures associated with dynamical systems of the heavenly type, focusing on the Mikhalev-Pavlov and Pleba\'nski equation. The dynamical system is represented as a Hamiltonian system on a functional manifold, and Poisson brackets are defined based on a non-degenerate Poisson operator. The study explores the Lax-type integ

Arthur Mehta, William Slofstra, Yuming Zhao

It is well known that an element of the algebra of noncommutative *-polynomials is positive in all *-representations if and only if it is a sum of squares. This provides an effective way to determine if a given *-polynomial is positive, by searching through sums of squares decompositions. We show that no such procedure exists for the tensor product of two no

Chaofeng Chen, Shangchen Zhou, Liang Liao, Haoning Wu

Real-world image super-resolution (RWSR) is a long-standing problem as low-quality (LQ) images often have complex and unidentified degradations. Existing methods such as Generative Adversarial Networks (GANs) or continuous diffusion models present their own issues including GANs being difficult to train while continuous diffusion models requiring numerous in

Pritish Sinha, Ankit Yadav

We study the Poisson geometrical formulation of quantum mechanics for finite dimensional mixed and pure states. Equivalently, we show that quantum mechanics can be understood in the language of classical mechanics. We review the symplectic structure of the Hilbert space and identify its canonical coordinates. We extend the geometric picture to the space of d

Shiyu Xia, Miaosen Zhang, Xu Yang, Ruiming Chen

We propose expanding the shared Transformer module to produce and initialize Transformers of varying depths, enabling adaptation to diverse resource constraints. Drawing an analogy to genetic expansibility, we term such module as learngene. To identify the expansion mechanism, we delve into the relationship between the layer's position and its corresponding

R. Shafieepour, H. R. Moshfegh, J. Piekarewicz

The question of how and which physical observables or thermodynamic parameters can best predict the onset of a possible phase transition in the inner crust of neutron stars remains largely unresolved. Using semiclassical Monte Carlo simulations, we investigate the isothermal compressibility and density fluctuations in a region of relevance to the dynamics of

Dmitry A. Mylnikov, Mikhail A. Kashchenko, Kirill N. Kapralov, Davit A. Ghazaryan

Graphene/hBN/graphene tunnel devices offer promise as sensitive mid-infrared photodetectors but the microscopic origin underlying the photoresponse in them remains elusive. In this work, we investigated the photocurrent generation in graphene/hBN/graphene tunnel structures with localized defect states under mid-IR illumination. We demonstrate that the photoc

Yeonjoon Jung, Sungsoo Ahn

This work investigates neural algorithmic reasoning to develop neural networks capable of learning from classical algorithms. The main challenge is to develop graph neural networks that are expressive enough to predict the given algorithm outputs while generalizing well to out-of-distribution data. In this work, we introduce a new graph neural network layer

Yifei Ma, Zimo Zhao, Jiahe Cui, Jingyu Wang

Vectorial adaptive optics (V-AO) is a cutting-edge technique extending conventional AO into the vectorial domain encompassing both polarization and phase feedback correction for optical systems. However, previous V-AO approaches focus on point correction. In this letter, we extend this AO approach into the imaging domain. We show how V-AO can benefit an aber

SujayKumar Reddy M, Chandra Mohan B

Quantum Key Distribution (QKD) is a technique that enables secure communication between two parties by sharing a secret key. One of the most well-known QKD protocols is the BB84 protocol, proposed by Charles Bennett and Gilles Brassard in 1984. In this protocol, Alice and Bob use a quantum channel to exchange qubits, allowing them to generate a shared key th

Douglas D. Novaes, Pedro C. C. R. Pereira

Floquet's Theorem is a celebrated result in the theory of ordinary differential equations. Essentially, the theorem states that, when studying a linear differential system with $T$-periodic coefficients, we can apply a, possibly complex, $T$-periodic change of variables that transforms it into a linear system with constant coefficients. In this paper, we exp

Aren Karapetyan, Efe C. Balta, Andrea Iannelli, John Lygeros

Suboptimal methods in optimal control arise due to a limited computational budget, unknown system dynamics, or a short prediction window among other reasons. Although these methods are ubiquitous, their transient performance remains relatively unstudied. We consider the control of discrete-time, nonlinear time-varying dynamical systems and establish sufficie

Franz Achleitner, Goro Akagi, Christian Kuehn, Jens Markus Melenk

In this chapter we provide an introduction to fractional dissipative partial differential equations (PDEs) with a focus on trying to understand their dynamics. The class of PDEs we focus on are reaction-diffusion equations but we also provide an outlook on closely related classes of PDEs. To simplify the exposition, we only discuss the cases of fractional ti

Aleksandar Terzic, Michael Hersche, Geethan Karunaratne, Luca Benini

MEGA is a recent transformer-based architecture, which utilizes a linear recurrent operator whose parallel computation, based on the FFT, scales as $O(LlogL)$, with $L$ being the sequence length. We build upon their approach by replacing the linear recurrence with a special temporal convolutional network which permits larger receptive field size with shallow

Corentin Léger, Gautier Hamon, Eleni Nisioti, Xavier Hinaut

Animals often demonstrate a remarkable ability to adapt to their environments during their lifetime. They do so partly due to the evolution of morphological and neural structures. These structures capture features of environments shared between generations to bias and speed up lifetime learning. In this work, we propose a computational model for studying a m

Anna Balci

We present a general framework for constructing examples on Lavrentiev energy gap for nonlocal problems and apply it to several nonlocal and mixed models of double-phase type.

Rafael Andersson Lipcsey

This thesis aims to assess the importance of the air transport sector for Sweden's economy in the context of the COVID-19 pandemic. Two complementary research goals are formulated. Firstly, investigating economic linkages of the Swedish air transport sector. Secondly, estimating the effects of the pandemic on Swedish air transport, and the spin-off effects o

Shuhe Wang, Beiming Cao, Shengyu Zhang, Xiaoya Li

Due to the lack of a large collection of high-quality labeled sentence pairs with textual similarity scores, existing approaches for Semantic Textual Similarity (STS) mostly rely on unsupervised techniques or training signals that are only partially correlated with textual similarity, e.g., NLI-based datasets. To tackle this issue, in this paper, we propose

Mengnan Zhao, Lihe Zhang, Yuqiu Kong, Baocai Yin

3D instance segmentation plays a crucial role in comprehending 3D scenes. Despite recent advancements in this field, existing approaches exhibit certain limitations. These methods often rely on fixed instance positions obtained from sampled representative points in vast 3D point clouds, using center prediction or farthest point sampling. However, these selec

Han Zhang, Raymond Chan, Xue-Cheng Tai

Investigating blood flow in the cardiovascular system is crucial for assessing cardiovascular health. Computational approaches offer some non-invasive alternatives to measure blood flow dynamics. Numerical simulations based on traditional methods such as finite-element and other numerical discretizations have been extensively studied and have yielded excelle

Andreas Aigner, Thomas Weber, Alwin Wester, Stefan A. Maier

Enhancing and controlling light-matter interactions is crucial in nanotechnology and material science, propelling research on green energy, laser technology, and quantum cryptography. Central to enhanced light-matter coupling are two parameters: the spectral overlap between an optical cavity mode and the material's spectral features (e.g., excitonic or molec

Suorong Yang, Hongchao Yang, Suhan Guo, Furao Shen

While deep neural networks have demonstrated remarkable performance across various tasks, they typically require massive training data. Due to the presence of redundancies and biases in real-world datasets, not all data in the training dataset contributes to the model performance. To address this issue, dataset pruning techniques have been introduced to enha

Lirui Zhao, Yuxin Zhang, Fei Chao, Rongrong Ji

The poor cross-architecture generalization of dataset distillation greatly weakens its practical significance. This paper attempts to mitigate this issue through an empirical study, which suggests that the synthetic datasets undergo an inductive bias towards the distillation model. Therefore, the evaluation model is strictly confined to having similar archit

Mario Burgui-Burgui

In this study, we conducted a comparative analysis of the automated coding provided by three Artificial Intelligence functionalities (At-las.ti, ChatGPT and Google Bard) in relation to the manual coding of 12 research interviews focused on Landscape Quality Objectives for a small island in the north of Cuba (Cayo Santa Mar\'ia). For this purpose, the followi

Rundong Huang, Farhad Shirani, Dongsheng Luo

Graph Neural Networks (GNNs) have received increasing attention due to their ability to learn from graph-structured data. To open the black-box of these deep learning models, post-hoc instance-level explanation methods have been proposed to understand GNN predictions. These methods seek to discover substructures that explain the prediction behavior of a trai