December 2023 arXiv papers — page 17

Yanwei Han, Zijian Zhang

The nonlinear energy harvesting systems of the forced vibration with an electron-mechanical coupling are widely used to capture ambient vibration energy and convert mechanical energy into electrical energy. However, the nonlinear response mechanism of the friction induced vibration (FIV) energy harvesting system with multiple stability and stick-slip motion

Xiaosong Sun, Shuai Zeng

In this paper, we study the double Danielewski varieties which arose from the research on the classical Cancellation Problem. We describe the Makar-Limanov invariant and locally nilpotent derivations of these varieties. And in a subsequent paper we will describe the automorphisms groups of the varieties and verify that the varieties are counterexamples to th

Haifeng Jia, Yichen Wei, Zhan Wang, Jiani Jin

Traditional wisdom for network management allocates network resources separately for the measurement and data transmission tasks. Heavy measurement tasks may take up resources for data transmission and significantly reduce network performance. It is therefore challenging for interference graphs, deemed as incurring heavy measurement overhead, to be used in p

Yangyang Si, Tianfu Zhang, Chenhan Liu, Sujit Das

Antiferroelectrics have received blooming interests because of a wide range of potential applications in energy storage, solid-state cooling, thermal switch, transducer, actuation, and memory devices. Many of those applications are the most prospective in thin film form. The antiferroelectric ordering in thin films is highly sensitive to a rich set of factor

Jiazhang Zheng, Lei Li, Qiuping Liao, Cheng Li

Nighttime photography encounters escalating challenges in extremely low-light conditions, primarily attributable to the ultra-low signal-to-noise ratio. For real-world deployment, a practical solution must not only produce visually appealing results but also require minimal computation. However, most existing methods are either focused on improving restorati

Pravin K. Dahal, Swayamsiddha Maharana, Fil Simovic, Ioannis Soranidis

We study various aspects of modeling astrophysical black holes using the recently introduced semiclassical formalism of physical black holes (PBHs). This approach is based on the minimal requirements of observability and regularity of the horizons. We demonstrate that PBHs do not directly couple to the cosmological background in the current epoch, and their

Nguyen Sum, Pham Do Tai

Let $P_k$ be the graded polynomial algebra $\mathbb F_2[x_1,x_2,\ldots ,x_k]$ over the prime field with two elements, $\mathbb F_2$, with the degree of each $x_i$ being 1. We study the hit problem, set up by Frank Peterson, of finding a minimal set of generators for $P_k$ as a module over the mod-$2$ Steenrod algebra, $\mathcal{A}.$ It is an open problem in

P. Domenichini, G. Pasquini, M. G. Capeluto

The geometry and morphology of magnetic domain walls (DWs) are closely related to their dynamics when driven by external forces. Under some reliable approximations DWs can be considered self-affine interfaces, so universal laws govern their behavior. On the other hand, large-scale DW structure has been less explored so far. Recently, it has been shown that b

Santiago Laplagne

We present an example of a strictly positive polynomial with rational coefficients that can be decomposed as a sum of squares of polynomials over $\R$ but not over $\Q$. This answers an open question by C. Scheiderer posed as the second question in Section 5.1 of the paper Sums of squares of polynomials with rational coefficients (2012). We verify that the e

Zikang Yuan, Jie Deng, Ruiye Ming, Fengtian Lang

Existing LiDAR-inertial-visual odometry and mapping (LIV-SLAM) systems mainly utilize the LiDAR-inertial odometry (LIO) module for structure reconstruction and the visual-inertial odometry (VIO) module for color rendering. However, the accuracy of VIO is often compromised by photometric changes, weak textures and motion blur, unlike the more robust LIO. This

Hongda Shen, Eren Kurshan

Detecting anomalies has become an increasingly critical function in the financial service industry. Anomaly detection is frequently used in key compliance and risk functions such as financial crime detection fraud and cybersecurity. The dynamic nature of the underlying data patterns especially in adversarial environments like fraud detection poses serious ch

Ryugo Iwami, Takatomo Mihana, Kazutaka Kanno, Makoto Naruse

Photonic computing has been widely used to accelerate the computational performance in machine learning. Photonic decision-making is a promising approach that uses photonic computing technologies to solve the multi-armed bandit problem based on reinforcement learning. Photonic decision making using chaotic mode competition dynamics has been proposed. However

Yajing Zhai, Yawen Zeng, Zhiyong Huang, Zheng Qin

The fine-grained attribute descriptions can significantly supplement the valuable semantic information for person image, which is vital to the success of person re-identification (ReID) task. However, current ReID algorithms typically failed to effectively leverage the rich contextual information available, primarily due to their reliance on simplistic and c

Anwesh Ray

Let $\mathbb{F}_q$ be the finite field with $q\geq 5$ elements, $A:=\mathbb{F}_q[T]$ and $F:=\mathbb{F}_q(T)$. Assume that $q$ is odd and take $|\cdot|$ to be the absolute value at $\infty$ that is normalized by $|T|=q$. Given a pair $w=(g_1, g_2)\in A^2$ with $g_2\neq 0$, consider the associated Drinfeld module $\phi^w: A\rightarrow A\{\tau\}$ of rank $2$ d

Leyou Xu, Bo Zhou

Answers are offered to the Gould's question to find spectral sufficient conditions for a graph to have a chorded cycle via signless Laplacian spectral radius. The conditions are tight.

Shanglin Li, Bohan Zeng, Yutang Feng, Sicheng Gao

Recent advances in vision-language models like Stable Diffusion have shown remarkable power in creative image synthesis and editing.However, most existing text-to-image editing methods encounter two obstacles: First, the text prompt needs to be carefully crafted to achieve good results, which is not intuitive or user-friendly. Second, they are insensitive to

Quanquan Gu, Zhaoran Wang, Han Liu

In this paper, we study the estimation of the $k$-dimensional sparse principal subspace of covariance matrix $\Sigma$ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori. To this end, we propose a family of estimators based on th

Masato Fujitake

This paper proposes a novel logo image recognition approach incorporating a localization technique based on reinforcement learning. Logo recognition is an image classification task identifying a brand in an image. As the size and position of a logo vary widely from image to image, it is necessary to determine its position for accurate recognition. However, b

Stefan Winter, Abraham Chan, Habib Saissi, Karthik Pattabiraman

Fault injection is a technique to measure the robustness of a program to errors by introducing faults into the program under test. Following a fault injection experiment, Error Propagation Analysis (EPA) is deployed to understand how errors affect a program's execution. EPA typically compares the traces of a fault-free (golden) run with those from a faulty r

Zhihao Yu, Liantao Ma, Yasha Wang, Junfeng Zhao

Deep learning algorithms, especially Transformer-based models, have achieved significant performance by capturing long-range dependencies and historical information. However, the power of convolution has not been fully investigated. Moreover, most existing works ignore the dynamic interaction among variables and evolutionary noise in series. Addressing these

Mira Frick, Ryota Iijima, Yuhta Ishii

We consider moral hazard problems where a principal has access to rich monitoring data about an agent's action. Rather than focusing on optimal contracts (which are known to in general be complicated), we characterize the optimal rate at which the principal's payoffs can converge to the first-best payoff as the amount of data grows large. Our main result sug

Van Thuy Hoang, O-Joun Lee

This study utilizes community structures to address node degree biases in message-passing (MP) via learnable graph augmentations and novel graph transformers. Recent augmentation-based methods showed that MP neural networks often perform poorly on low-degree nodes, leading to degree biases due to a lack of messages reaching low-degree nodes. Despite their su

Feiya Li, Chunyun Fu, Dongye Sun

The majority of existing LiDAR odometry solutions are based on simple geometric features such as points, lines or planes which cannot fully reflect the characteristics of surrounding environments. In this study, we propose a novel LiDAR odometry which effectively utilizes the overall exterior characteristics of environmental landmarks. The vehicle pose estim

Ari Cruz, Pamela E. Harris, Kimberly J. Harry, Jan Kretschmann

Recall that $\alpha=(a_1,a_2,\ldots,a_n)\in[n]^n$ is a parking function if its nondecreasing rearrangement $\beta=(b_1,b_2,\ldots,b_n)$ satisfies $b_i\leq i$ for all $1\leq i\leq n$. In this article, we study parking functions based on their ascents (indices at which $a_i<a_{i+1}$), descents (indices at which $a_i>a_{i+1}$), and ties (indices at which $a_i=a

Karthik Dulam, Hrishikesh Ghate, Michael Lau, Suyash Pathak

Let ${\mathfrak{g}}$ be a complex semisimple Lie algebra with Borel subalgebra ${\mathfrak{b}}$ and corresponding nilradical ${\mathfrak{n}}$. We show that singular Whittaker modules $M$ are simple if and only if the space $\hbox{Wh}\,M$ of Whittaker vectors is $1$-dimensional. For arbitrary locally ${\mathfrak{n}}$-finite ${\mathfrak{g}}$-modules $V$, an im

Tianyi Zhang, Haoteng Yin, Rongzhe Wei, Pan Li

Graph neural networks (GNNs) have shown great potential in learning on graphs, but they are known to perform sub-optimally on link prediction tasks. Existing GNNs are primarily designed to learn node-wise representations and usually fail to capture pairwise relations between target nodes, which proves to be crucial for link prediction. Recent works resort to

Zhiyong Liu, Qiuyan Xu

We prove the convergence of meshfree method for solving the elliptic Monge-Ampere equation with Dirichlet boundary on the bounded domain. L2 error is obtained based on the kernel-based trial spaces generated by the compactly supported radial basis functions. We obtain the convergence result when the testing discretization is finer than the trial discretizati

M. Agarwal, O. A. Starykh, D. A. Pesin, E. G. Mishchenko

We investigate collective spin excitations of graphene electrons with short-ranged interactions and subject to the external Zeeman magnetic field. We find that in addition to the familiar Silin spin wave, a collective spin-flip excitation that reduces to the uniform precession when the wave's momentum approaches zero, the magnetized graphene supports another

R. Di Renna, R. C. de Lamare

This paper presents an iterative detection and decoding scheme along with an adaptive strategy to improve the selection of access points (APs) in a grant-free uplink cell-free scenario. With the requirement for the APs to have low-computational power in mind, we introduce a low-complexity scheme for local activity and data detection. At the central processin

Changwei Xiong

First we establish a weighted Reilly formula for differential forms on a smooth compact oriented Riemannian manifold with boundary. Then we give two applications of this formula when the manifold satisfies certain geometric conditions. One is a sharp lower bound for the first positive eigenvalue of the Steklov eigenvalue problem on differential forms investi

Pilar Herreros

In this paper we define a new operator $J$ for the study of $$ \Delta u +f(u)=0,\quad x\in R ^N, N> 2. $$ Using $J$ we can easily see some qualitative properties of the solutions, for example we can determine how many times $u$ changes sign, which are the values of the local maxima and minima, and where $u$ changes concavity. We also use this functional to c

Yuntao Shou, Tao Meng, Wei Ai, Nan Yin

With the release of increasing open-source emotion recognition datasets on social media platforms and the rapid development of computing resources, multimodal emotion recognition tasks (MER) have begun to receive widespread research attention. The MER task extracts and fuses complementary semantic information from different modalities, which can classify the

Binbing Wu, Zhengfeng Fan, Difa Ye, Tao Ye

We investigate the fusion cross sections of light nuclei in the presence of linearly polarized intense laser fields. By combining the Coulomb-Volkov solutions with the complex spherical square-well nuclear potential, we derive an explicit formulation of the multiphoton cross section in a self-consistent manner. Our analysis is specifically focused on deutero

Eric Marberg, Kam Hung Tong

We describe two crystal structures on set-valued decomposition tableaux. These provide the first examples of interesting "$K$-theoretic" crystals on shifted tableaux. Our first crystal is modeled on a similar construction of Monical, Pechenik, and Scrimshaw for semistandard (unshifted) set-valued tableaux. Our second crystal is adapted from the "square root"

Feng-Yi Liao, Lijun Ding, Yang Zheng

Many practical optimization problems lack strong convexity. Fortunately, recent studies have revealed that first-order algorithms also enjoy linear convergences under various weaker regularity conditions. While the relationship among different conditions for convex and smooth functions is well-understood, it is not the case for the nonsmooth setting. In this

Miguel E. Villalobos, Alejandra Valencia, Alonso Botero

We present the asymptotic analysis of the quantum two-port interferometer in the $n \rightarrow \infty$ limit of $n$ partially indistinguishable photons. Using the unitary-unitary duality between port and inner-mode degrees of freedom, the probability distribution of output port counts can be decomposed as a sum of contributions from independent channels, ea

Pilar Herreros

We consider the problem of multiplicity and uniqueness of radial solutions of a nonlinear elliptic equation of the form \begin{eqnarray*} \begin{gathered} \Delta u +f(u)=0,\quad x\in \mathbb{R}^N, N\geq 2, \lim\limits_{|x|\to\infty}u(x)=0. \end{gathered} \end{eqnarray*} where $f$ is a prescribed function, satisfying appropriate conditions. This paper is a re

Jun Bai, Annie Jin, Madison Adams, Clifford Yang

Breast cancer continues to be a significant cause of mortality among women globally. Timely identification and precise diagnosis of breast abnormalities are critical for enhancing patient prognosis. In this study, we focus on improving the early detection and accurate diagnosis of breast abnormalities, which is crucial for improving patient outcomes and redu

Yi-Kuan Hsieh, Jun-Wei Hsieh, Yu-Chee Tseng, Ming-Ching Chang

Traditional crowd counting networks suffer from information loss when feature maps are downsized through pooling layers, leading to inaccuracies in counting crowds at a distance. Existing methods often assume correct annotations during training, disregarding the impact of noisy annotations, especially in crowded scenes. Furthermore, the use of a fixed Gaussi

Jiren Liu

The discovery in 2014 of the pulsation from the ultra-luminous X-ray source (ULX) M82 X-2 in 2014 has changed our view of ULXs. Because of the relatively short baseline over which pulsations have been detected so far, M82 X-2's spin state had been assumed to be in an equilibrium state. Using \cha and \xmm archive data, we are able to investigate the pulsatio

Xin Zhou, Yin Xia, Lexin Li

A growth curve model (GCM) aims to characterize how an outcome variable evolves, develops and grows as a function of time, along with other predictors. It provides a particularly useful framework to model growth trend in longitudinal data. However, the estimation and inference of GCM with a large number of response variables faces numerous challenges, and re

Xiaohao Mo, Lin Gui, Kai Ying, Xichao Sang

The performance of wireless communication systems is fundamentally constrained by random and uncontrollable wireless channels. Recently, reconfigurable intelligent surfaces (RIS) has emerged as a promising solution to enhance wireless network performance by smartly reconfiguring the radio propagation environment. While significant research has been conducted

Thomy Phan, Taoan Huang, Bistra Dilkina, Sven Koenig

Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i.e., the neighborhood, of the solution, using

Michael Lau, Olivier Mathieu

We consider bounded weight modules for the universal central extension ${\mathfrak{sl}}_2(J)$ of the Tits-Kantor-Koecher algebra of a unital Jordan algebra $J$. Universal objects called Weyl modules are introduced and studied, and a combinatorial dominance criterion is given for analogues of highest weights. Specializing $J$ to the free Jordan algebra $J(r)$

Eric Culf, Hamoon Mousavi, Taro Spirig

Noncommutative constraint satisfaction problems (NC-CSPs) are higher-dimensional operator extensions of classical CSPs. Despite their significance in quantum information, their approximability remains largely unexplored. A notable example of a noncommutative CSP that is not solvable in polynomial time is NC-Max-$3$-Cut. We present a $0.864$-approximation alg

Félix-Louis Julié

We show that Schwarzschild black hole binaries can undergo dynamical scalarization (DS) in the inspiral phase, in a subclass of $\mathbb{Z}_2$-symmetric Einstein-scalar-Gauss-Bonnet (ESGB) theories of gravity. The mechanism is analogous to neutron star DS in scalar-tensor gravity, and it differs from the late merger and ringdown black hole (de)scalarization

Simon W. McKnight, Aidan O. T. Hogg, Vincent W. Neo, Patrick A. Naylor

This paper studies modulation spectrum features ($\Phi$) and mel-frequency cepstral coefficients ($\Psi$) in joint speaker diarization and identification (JSID). JSID is important as speaker diarization on its own to distinguish speakers is insufficient for many applications, it is often necessary to identify speakers as well. Machine learning models are set

Fan Shi

This comprehensive paper delves into the forefront of personalized voice synthesis within artificial intelligence (AI), spotlighting the Dynamic Individual Voice Synthesis Engine (DIVSE). DIVSE represents a groundbreaking leap in text-to-voice (TTS) technology, uniquely focusing on adapting and personalizing voice outputs to match individual vocal characteri

Shanshan Wang, Mamadou Diagne, Miroslav Krstić

Deep neural network approximation of nonlinear operators, commonly referred to as DeepONet, has proven capable of approximating PDE backstepping designs in which a single Goursat-form PDE governs a single feedback gain function. In boundary control of coupled PDEs, coupled Goursat-form PDEs govern two or more gain kernels-a PDE structure unaddressed thus far

H. F. Feng, B. Liu, Xin-Gao Gong, Zhi-Xin Guo

First principles-based predictions of lattice thermal conductivity (TC) from perturbation theory have achieved significant success. Usually, it only included three-phonon (3ph) scattering processes, only recently four-phonon (4ph) scattering processes were found to have a comparable impact as 3ph scattering at medium and high temperatures in various material

Christopher Brix, Stanley Bak, Changliu Liu, Taylor T. Johnson

This report summarizes the 4th International Verification of Neural Networks Competition (VNN-COMP 2023), held as a part of the 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), that was collocated with the 35th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objecti

Debi Prasad Pattnaik, Abu Bakar Siddique, Alex T. Bregazzi, Pavel Borisov

Zero-dimensional graphene quantum dots (GQD) dispersed in conducting polymer matrix display a striking range of optical, mechanical, and thermoelectric properties which can be utilized to design next-generation sensors and low-cost thermoelectric. This exotic electrical property in GQDs is achieved by exploiting the concentration of the GQDs and by tailoring

Zuowen Liu

In these proceedings, we present transverse momentum dependence of the mid-rapidity slope of directed flow ($dv_1/dy|_{y=0}$) for $\pi^+$ and $K_S^0$ in Au + Au collisions at $\sqrt{s_{NN}}$ = 3.0, 3.2, 3.5, and 3.9 GeV. Both $\pi^+$ and $K_S^0$ show negative $v_1$ slope at low $p_T$ ($p_T < 0.6$ GeV/$c$). Collision energy dependence of $v_1$ slope and $p_T$

Hankyul Baek, Donghyeon Kim, Joongheon Kim

Spurred by consistent advances and innovation in deep learning, object detection applications have become prevalent, particularly in autonomous driving that leverages various visual data. As convolutional neural networks (CNNs) are being optimized, the performances and computation speeds of object detection in autonomous driving have been significantly impro

Kun Liu, Jian Zhou

Perovskites have been applied in a wide range of fields such as solar cells and non-volatile memories due to their multiferroic nature and excellent photo-electric conversion capabilities. Recently, two-dimensional (2D) perovskites with a few atomic layers have been successfully synthesized, attracting significant attention for potential applications. In thi

Jinfei Wang, Yi Ma, Rahim Tafazolli, Zhibo Pang

The cumulative distribution function (CDF) of a non-central $\chi^2$-distributed random variable (RV) is often used when measuring the outage probability of communication systems. For ultra-reliable low-latency communication (URLLC), it is important but mathematically challenging to determine the outage threshold for an extremely small outage target. This mo

Martina Toshevska, Slobodan Kalajdziski, Sonja Gievska

Social media resurgence of antisocial behavior has exerted a downward spiral on stereotypical beliefs, and hateful comments towards individuals and social groups, as well as false or distorted news. The advances in graph neural networks employed on massive quantities of graph-structured data raise high hopes for the future of mediating communication on socia

Guram Bezhanishvili, Chase Meadors

It is a classic result of Segerberg and Maksimova that a variety of $\mathsf{S4}$-algebras is locally finite iff it is of finite depth. Since the logic $\mathsf{MS4}$ (monadic $\mathsf{S4}$) axiomatizes the one-variable fragment of $\mathsf{QS4}$ (predicate $\mathsf{S4}$), it is natural to try to generalize the Segerberg--Maksimova theorem to this setting. W

Cheng Chen, Bing Yan, Ju-Jun Xie

Within the extended vector meson dominance model, we investigate the $e^+ e^- \to \Lambda^+_c \bar{\Lambda}^-_c$ reaction and the electromagnetic form factors of the charmed baryon $\Lambda_c^+$. The model parameters are determined by fitting them to the cross sections of the process $e^+e^-\rightarrow \Lambda_c^+ \bar{\Lambda}_c^-$ and the magnetic form fac

A. Fulop

The concept of concurrence is researched to characterize the dynamical behavior of the bipartite systems. The quantum kicked top model has great significance in the qubit systems and the chaotic properties of the entanglement. The eigenvalues of the reduced symmetric density matrix are determined, it allows us to understand this driven system to distinguish

Matthew D. Kvalheim

The author's extensions of Brockett's and Coron's necessary conditions for stabilizability are shown to be independent in the fiber bundle picture of control, but the latter is shown to be stronger in the vector bundle picture if the state space is orientable and the Cech-Euler characteristic of the set to be stabilized is nonzero.

Danica Kosanović, Rob Schneiderman, Peter Teichner

In this note we give a complete obstruction for two homotopic embeddings of a 2-sphere into a 5-manifold to be isotopic. The results are new even though the methods are classical, the main tool being the elimination of double points via a level preserving Whitney move in codimension~$3$. Moreover, we discuss how this recovers a particular case of a result of

Qiuye Jia, Hai Lin

We perform a detailed analysis of quarter BPS bubbling geometries with AdS asymptotics and their corresponding duality relations with their dual states in the quantum field theory side, among other aspects. We derive generalized Laplace-type equations with sources, obtained from linearized Monge-Ampere equations, and used for asymptotically AdS geometry. Thi

Kalvik Jakkala, Srinivas Akella

Wastewater monitoring is an effective approach for the early detection of viral and bacterial disease outbreaks. It has recently been used to identify the presence of individuals infected with COVID-19. To monitor large communities and accurately localize buildings with infected individuals with a limited number of sensors, one must carefully choose the samp

William Q. Erickson, Markus Hunziker

Let $H$ be a complex reductive group, with finite-dimensional representations $W$ and $U$. The module of covariants for $W$ of type $U$ is the space of all $H$-equivariant polynomial maps $\varphi: W \longrightarrow U$. In this paper, we take $H$ to be one of the classical groups $\operatorname{GL}(V)$, $\operatorname{O}(V)$, or $\operatorname{Sp}(V)$, where

Seung Gyo Jeong, Jin Young Oh, Lin Hao, Jian Liu

Unexpected, yet useful functionalities emerge when two or more materials merge coherently. Artificial oxide superlattices realize atomic and crystal structures that are not available in nature, thus providing controllable correlated quantum phenomena. This review focuses on 4d and 5d oxide superlattices, in which the spin-orbit coupling plays a significant r

Adrian L. Kaufmann, Shawn X. Cui

We study the problem of universality in the anyon model described by the $SU(2)$ Witten-Chern-Simons theory at level $k$. A classic theorem of Freedman-Larsen-Wang states that for $k \geq 3, \ k \neq 4$, braiding of the anyons of topological charge $1/2$ is universal for topological quantum computing. For the case of one qubit, we prove a stronger result tha

A. N. Villano

The $^3$He(n,p) process is excellent for neutron detection between thermal and $\sim$4\,MeV because of the high cross section and near-complete energy transfer from the neutron to the proton. Traditional gaseous $^3$He detectors using this process typically have high levels of radiogenic backgrounds so that they cannot measure the small neutron fluxes presen

Hugo Larocque, Dashiell L. P. Vitullo, Alexander Sludds, Hamed Sattari

Thin-Film Lithium Niobate (TFLN) is an emerging integrated photonic platform showing great promise due to its large second-order nonlinearity at microwave and optical frequencies, cryogenic compatibility, large piezoelectric response, and low optical loss at visible and near-infrared wavelengths. These properties enabled Mach-Zehnder interferometer-based dev

H. Donya, M. Umer

Boron neutron capture therapy is about a century old, but still current and active. In this treatment, a high absorption cross-section of boron is used for thermal neutrons and the excited state is decomposed into alpha and lithium ions. These daughter particles have a high LET value and transfer their energy to the surrounding cells. In this work, the neutr

Vasileios Evangelakos, Emmanuel Paspalakis, Dionisis Stefanatos

We consider a two-level system with a fixed energy spacing (detuning) between the two levels and a single transverse control field which can take values between zero and a maximum amplitude. Using Pontryagin's maximum principle, we completely solve the problem of generating in minimum time a uniform superposition of the two quantum states when starting from

G. Chachamis, M. Hentschinski, A. Sabio Vera

Quantum properties of the state associated to the gluon Green's function in the BFKL approach are studied using a discretization in virtuality space. Considering the coupling constant as imaginary, its density matrix corresponds to a pure state for any energy. Non-linear corrections due to high gluon densities are modelled through a suppression of infrared m

Sebastián Ramírez, Kendry J. Vivas

In this paper we prove that the homotopy class of non-homothety linear endomorphisms on $\mathbb{T}^2$ with determinant greater than 2 contains a $C^1$ open set of non-uniformly hyperbolic endomorphisms. Furthermore, we prove that the homotopy class of non-hyperbolic elements (having either $1$ or $-1$ as an eigenvalue) whose degree is large enough contains

Prem Raj, Sachin Bhadang, Gaurav Chaudhary, Laxmidhar Behera

This paper addresses category-agnostic instance segmentation for robotic manipulation, focusing on segmenting objects independent of their class to enable versatile applications like bin-picking in dynamic environments. Existing methods often lack generalizability and object-specific information, leading to grasp failures. We present a novel approach leverag

Juan Andrés Urrea-Niño, Jacob Finkenrath, Roman Höllwieser, Francesco Knechtli

We use the method of optimal distillation profiles to compute the low-lying charmonium spectrum in an $N_f = 3+1$ ensemble at the $SU(3)$ light flavor symmetric point ($m_{\pi} \approx 420$ MeV), physical charm quark mass and lattice spacing $a\approx 0.0429$ fm. The spectrum and mass splittings display good agreement with their values in nature and the stat

Nicolò Margaritella, Vanda Inácio, Ruth King

The statistical analysis of group studies in neuroscience is particularly challenging due to the complex spatio-temporal nature of the data, its multiple levels and the inter-individual variability in brain responses. In this respect, traditional ANOVA-based studies and linear mixed effects models typically provide only limited exploration of the dynamic of

Enes Duran, Muhammed Kocabas, Vasileios Choutas, Zicong Fan

Understanding how humans interact with the world necessitates accurate 3D hand pose estimation, a task complicated by the hand's high degree of articulation, frequent occlusions, self-occlusions, and rapid motions. While most existing methods rely on single-image inputs, videos have useful cues to address aforementioned issues. However, existing video-based

R. Garattini, A. G. Tzikas

We investigate possible manifolds characterizing traversable wormholes in the presence of a scalar field minimally coupled to gravity, which has both kinetic and potential energy. The feature of traversability requires the violation of the null energy condition, which, in turn, signals the existence of exotic matter with negative energy density. To achieve t

Gang Liao, Amol Deshpande, Daniel J. Abadi

Existing serverless data analytics systems rely on external storage services like S3 for data shuffling and communication between cloud functions. While this approach provides the elasticity benefits of serverless computing, it incurs additional latency and cost overheads. We present Flock, a novel cloud-native streaming query engine that leverages the on-de

Yiling Huang, Snigdha Panigrahi, Walter Dempsey

Neighborhood selection is a widely used method used for estimating the support set of sparse precision matrices, which helps determine the conditional dependence structure in undirected graphical models. However, reporting only point estimates for the estimated graph can result in poor replicability without accompanying uncertainty estimates. In fields such

Alind Khare, Dhruv Garg, Sukrit Kalra, Snigdha Grandhi

The increasing deployment of ML models on the critical path of production applications in both datacenter and the edge requires ML inference serving systems to serve these models under unpredictable and bursty request arrival rates. Serving models under such conditions requires these systems to strike a careful balance between the latency and accuracy requir

Giuseppe Cuono, Carmine Autieri, Tomasz Dietl

Using first principle calculations we examine properties of (Cd,V)Te, (Cd,Cr)Te, (Hg,V)Te, and (Hg,Cr)Te relevant to the quantum anomalous Hall effect (QAHE), such as the position of V- and Cr- derived energy levels and the exchange interactions between magnetic ions. We consider CdTe and HgTe, containing 12.5% of cation-substitutional V or Cr ions in compar

Sebastian Dziadzio, Çağatay Yıldız, Gido M. van de Ven, Tomasz Trzciński

The ability of machine learning systems to learn continually is hindered by catastrophic forgetting, the tendency of neural networks to overwrite previously acquired knowledge when learning a new task. Existing methods mitigate this problem through regularization, parameter isolation, or rehearsal, but they are typically evaluated on benchmarks comprising on

Dylan J. Foster, Alexander Rakhlin

These lecture notes give a statistical perspective on the foundations of reinforcement learning and interactive decision making. We present a unifying framework for addressing the exploration-exploitation dilemma using frequentist and Bayesian approaches, with connections and parallels between supervised learning/estimation and decision making as an overarch

Linan Chen, Florence Clerc, Prakash Panangaden

Bisimulation is a concept that captures behavioural equivalence of states in a variety of types of transition systems. It has been widely studied in a discrete-time setting where the notion of a step is fundamental. In our setting we are considering "flow"-processes emphasizing that they evolve in continuous time. In such continuous-time settings, the concep

Yufeng Luo, Antonios Tsokaros, Roland Haas, Koji Uryu

Triaxial neutron stars can be sources of continuous gravitational radiation detectable by ground-based interferometers. The amplitude of the emitted gravitational wave can be greatly affected by the state of the hydrodynamical fluid flow inside the neutron star. In this work we examine the most triaxial models along two sequences of constant rest mass, confi

Subhadeep Bandyopadhyay, Philippe Ghosez

BiNiO$_3$ exhibits an unusual metal-insulator transition from $Pnma$ to $P\overline{1}$ that is related to charge ordering at the Bi sites, which is intriguingly distinct from the charge ordering at Ni sites usually observed in related rare-earth nickelates. Here, using first principles calculations, we first rationalize the phase transition from $Pnma$ to $

Jessica Liu, Huaming Chen, Jun Shen, Kim-Kwang Raymond Choo

As artificial intelligence (AI) increasingly becomes an integral part of our societal and individual activities, there is a growing imperative to develop responsible AI solutions. Despite a diverse assortment of machine learning fairness solutions is proposed in the literature, there is reportedly a lack of practical implementation of these tools in real-wor

Mathias Beiglböck, Gudmund Pammer, Stefan Schrott, Xin Zhang

Random variables $X^i$, $i=1,2$ are 'probabilistically equivalent' if they have the same law. Moreover, in any class of equivalent random variables it is easy to select canonical representatives. The corresponding questions are more involved for processes $X^i$ on filtered stochastic bases $(\Omega^i, \mathcal F^i, \mathbb P^i, (\mathcal F^i_t)_{t\in [0,1]})

Thiago T. Santos, Kleber X. S. de Souza, João Camargo Neto, Luciano V. Koenigkan

Traditionally, sweet orange crop forecasting has involved manually counting fruits from numerous trees, which is a labor-intensive process. Automatic systems for fruit counting, based on proximal imaging, computer vision, and machine learning, have been considered a promising alternative or complement to manual counting. These systems require data associatio

N. Kroó, M. Aladi, M. Kedves, B. Ráczkevi

In this brief report, we present laser induced breakdown spectroscopy (LIBS) evidence of deuterium (D) production in a 3:1 urethane dimethacrylate (UDMA) and triethylene glycol dimethacrylate (TEGDMA) polymer doped with resonant gold nanorods, induced by intense, 40 fs laser pulses. The in situ recorded LIBS spectra revealed that the D/(2D+H) increased to 4-

Christopher Griffin, Li Feng, Rongling Wu

We introduce and study the spatial replicator equation with higher order interactions and both infinite (spatially homogeneous) populations and finite (spatially inhomogeneous) populations. We show that in the special case of three strategies (rock-paper-scissors) higher order interaction terms allow travelling waves to emerge in non-declining finite populat

Edmundo F. Lavia, Guadalupe Cascallares, Juan D. Gonzalez

Modelling the acoustic scattering response due to penetrable objects of arbitrary shapes, such as those of many marine organisms, can be computationally intensive, often requiring high-performance computing equipment when considering a completely general situation. However, when the physical properties (sound speed and density) of the scatterer object under

Siddhartha Datta, Alexander Ku, Deepak Ramachandran, Peter Anderson

Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the images can be repetitive. This paper proposes a Prompt Expansion framework that helps users generate high-quality, diverse images with less effort. The Prompt Expansion model takes a text query as input and outputs a set of expande

Kunal Garg, James Usevitch, Joseph Breeden, Mitchell Black

This tutorial paper presents recent work of the authors that extends the theory of Control Barrier Functions (CBFs) to address practical challenges in the synthesis of safe controllers for autonomous systems and robots. We present novel CBFs and methods that handle safety constraints (i) with time and input constraints under disturbances, (ii) with high-rela

Vahid Noroozi, Somshubra Majumdar, Ankur Kumar, Jagadeesh Balam

In this paper, we propose an efficient and accurate streaming speech recognition model based on the FastConformer architecture. We adapted the FastConformer architecture for streaming applications through: (1) constraining both the look-ahead and past contexts in the encoder, and (2) introducing an activation caching mechanism to enable the non-autoregressiv

Athanasios G. Georgiadis, George Kyriazis, Pencho Petrushev

We lay down the foundation of the theory of spaces of distributions on the product $X_1\times X_2$ of doubling metric measure spaces $X_1$, $X_2$ in the presence of non-negative self-adjoint operators $L_1$, $L_2$, whose heat kernels have Gaussian localization and the Markov property. This theory includes the development of two-parameter functional calculus

Cam Le, Lam Pham, Jasmin Lampert, Matthias Schlögl

Knowledge about historic landslide event occurrence is important for supporting disaster risk reduction strategies. Building upon findings from 2022 Landslide4Sense Competition, we propose a deep neural network based system for landslide detection and segmentation from multisource remote sensing image input. We use a U-Net trained with Cross Entropy loss as

S. V. Ludkowski

The article is devoted to a structure of topological spaces related with topological quasigroups. Regular and complete spaces over topological quasigroups are studied. Separations and embeddings are also investigated for them. Their homeomorphisms and isomorphisms, relations between compactifications and completeness are scrutinized over topological quasigro

Yalin E. Sagduyu, Tugba Erpek

Low-Power Wide-Area Network (LPWAN) technologies, such as LoRa, have gained significant attention for their ability to enable long-range, low-power communication for Internet of Things (IoT) applications. However, the security of LoRa networks remains a major concern, particularly in scenarios where device identification and classification of legitimate and