July 2023 arXiv papers — page 154

Zhenwei Zhang, Xin Wang, Jingyuan Xie, Heling Zhang

Unlocking the potential of deep learning in Peak-Hour Series Forecasting (PHSF) remains a critical yet underexplored task in various domains. While state-of-the-art deep learning models excel in regular Time Series Forecasting (TSF), they struggle to achieve comparable results in PHSF. This can be attributed to the challenges posed by the high degree of non-

Rana Badreddine

We consider the Calogero-Sutherland derivative nonlinear Schr\"odinger equation \begin{equation}\tag{CS} i\partial_tu+\partial_x^2u\,\pm\,\frac{2}{i}\,\partial_x\Pi(|u|^2)u=0\,,\qquad x\in\mathbb{T}\,, \end{equation} where $\Pi$ is the Szeg\H{o} projector $$\Pi\Big(\sum_{n\in \mathbb{Z}}\widehat{u}(n)\mathrm{e}^{inx}\Big)=\sum_{n\geq 0 }\widehat{u}(n)\mathrm

Johanna Busch, Dietmar Paschek

Recently, an analytical expression for the system size dependence and direction-dependence of self-diffusion coefficients for neat liquids due to hydrodynamic interactions has been derived for molecular dynamics (MD) simulations using orthorhombic unit cells. Based on this description, we show that for systems with a "magic" box length ratio of $L_z/L_x\!=\!

Divyang Bhimani, Joachim Toft

We deduce factorization properties for a quasi-Banach module over a quasi-Banach algebra. Especially we extend a result by Hewitt and prove that if any such algebra which possess a bounded left approximate identity, then any element in the module can be factorized. As applications, we deduce factorization properties for Wiener amalgam spaces, for an extended

Fayez Abu-Ajamieh, Pratik Chattopadhyay, Anish Ghoshal, Nobuchika Okada

We investigate anomalies in the class of non-local field theories that have been proposed as an ultraviolet completion of 4-D Quantum Field Theory (QFT) with generalizing the kinetic energy operators to an infinite series of higher derivatives inspired by string field theory and ghost-free non-local approaches to quantum gravity. We explicitly calculate the

Frederic Marazzato

Kirigami are part of the larger class of mechanical metamaterials, which exhibit exotic properties. This article focuses on rhombi-slits, which is a specific type of kirigami. A nonlinear kinematic model was previously proposed as a second order divergence-form PDE with a possibly degenerate, and sign-changing coefficient matrix. We first propose to study th

Paul Schwarzmayr, Felix Birkelbach, Heimo Walter, Florian Javernik

Waste heat recovery in the energy intensive industry is one of the most important measures for the mitigation of climate change. The utilization of just a fraction of the theoretically available waste heat potential would lead to a significant reduction of the primary energy consumption and hence a reduction of greenhouse gas emissions. The present study exa

Bernard Bercu, Jeremie Bigot, Gauthier Thurin

We propose center-outward superquantile and expected shortfall functions, with applications to multivariate risk measurements, extending the standard notion of value at risk and conditional value at risk from the real line to $\mathbb{R}^d$. Our new concepts are built upon the recent definition of Monge-Kantorovich quantiles based on the theory of optimal tr

The Tracker Group of the CMS Collaboration

The Large Hadron Collider (LHC) at CERN will undergo an upgrade in order to increase its luminosity to $7.5 \times 10^{34}$ cm$^{-2}$s$^{-1}$. The increased luminosity during this High-Luminosity running phase (HL-LHC), starting around 2029, means a higher rate of proton-proton interactions, hence a larger ionizing dose and particle fluence for the detectors

Jingjing Yang, Jieli Liu, Jiajing Wu

With the popularity of Non-Fungible Tokens (NFTs), NFTs have become a new target of phishing attacks, posing a significant threat to the NFT trading ecosystem. There has been growing anecdotal evidence that new means of NFT phishing attacks have emerged in Ethereum ecosystem. Most of the existing research focus on detecting phishing scam accounts for native

Nicole Bäuerle, Sebastian Höfer

We consider a finite number of $N$ statistically equal agents, each moving on a finite set of states according to a continuous-time Markov Decision Process (MDP). Transition intensities of the agents and generated rewards depend not only on the state and action of the agent itself, but also on the states of the other agents as well as the chosen action. Inte

Amy Bonsor, Mark C. Wyatt, Sebastian Marino, Björn J. R. Davidsson

Kuiper-like belts of planetesimals orbiting stars other than the Sun are most commonly detected from the thermal emission of small dust produced in collisions. Emission from gas, most notably CO, highlights the cometary nature of these planetesimals. Here we present models for the release of gas from comet-like bodies in these belts, both due to their thermo

Sasipim Srivallapanondh, Pedro J. Freire, Ashraful Alam, Nelson Costa

For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems. A "single" NN-based equalizer improves Q-factor by up to 4 dB compared to CDC, without re-training, even with variations in launch power, symbol rate, or transmission distance.

Ping Ge, Sven Ahrens, Baifei Shen

Many calculations in strong field quantum field theory are carried out by using a simple field geometry, often neglecting the spacial field envelope. In this article, we simulate the electron diffraction quantum dynamics of the Kapitza-Dirac effect in a Gaussian beam standing light wave. The two-dimensional simulation is computed in a relativistic framework,

Yu-Shan Wang, Zhen-Ming Xu, Bin Wu

Phase transition is important for understanding the nature and evolution of the black hole thermodynamic system. In this study, the connection between the phase transition of a black hole and the winding number derived by the complex analysis is used to predict the type of the black hole phase transition. For the third-order Lovelock black holes, at the hype

Yipeng Liu, Qi Yang, Yujie Zhang, Yiling Xu

The visual quality of point clouds plays a crucial role in the development and broadcasting of immersive media. Therefore, investigating point cloud quality assessment (PCQA) is instrumental in facilitating immersive media applications, including virtual reality and augmented reality applications. Considering reference point clouds are not available in many

Aurélie Bigot

We extend the central limit theorem under the Dedecker-Rio condition to adapted stationary and ergodic sequences of random variables taking values in a class of smooth Banach spaces. This result applies to the case of random variables taking values in $L^p(\mu)$, with $2 \leq p < \infty$ and $\mu$ a $\sigma$-finite real measure. As an application we give a s

Louis Airale, Dominique Vaufreydaz, Xavier Alameda-Pineda

Animating still face images with deep generative models using a speech input signal is an active research topic and has seen important recent progress.However, much of the effort has been put into lip syncing and rendering quality while the generation of natural head motion, let alone the audio-visual correlation between head motion and speech, has often bee

Paul Lafourcade, Jean-Bernard Maillet, Christophe Denoual, Eléonore Duval

The increased time- and length-scale of classical molecular dynamics simulations have led to raw data flows surpassing storage capacities, necessitating on-the-fly integration of structural analysis algorithms. As a result, algorithms must be computationally efficient, accurate, and stable at finite temperature to reliably extract the relevant features of th

Gianni Pascoli

Many models have been proposed to minimize the dark matter (DM) content in various astronomical objects at every scale in the Universe. The most widely known model isMOdified Newtonian Dynamics (MOND). MOND was first published by Mordehai Milgromin 1983 (Milgrom, 1983; 2015; see also Banik and Zhao, 2022 for a review). A second concurrent model is modified g

Olivier Couronné

We give a characterization of the percolation threshold for a multirange model on oriented trees, as the first positive root of a polynomial, with the use of a multi-type Galton-Watson process. This gives in particular the exact value of the critical point for the model studied in [2] and [3] for k = 2.

K. Burazin, M. Erceg, M. Waurick

We revisit homogenisation theory for Friedrichs systems. In particular, we show that $G$-compactness can be obtained under severely weaker assumptions than in the original work of Burazin and Vrdoljak (2014). In this way we extend the applicability of $G$-compactness results for Friedrichs systems to equations that yield memory effects in the homogenised lim

Kazutoshi Takahashi, Masaki Imamura, Haruto Ikeda, Ryosuke Koyama

Two-dimensional band dispersion of (2$\times$2) superstructure with Bi grown on Ag(111), which has been urged as an ultraflat hexagonal bismuthene, is investigated using angle-resolved photoemission spectroscopy (ARPES). The (2$\times$2)-Bi superstructure can be grown on the Ag(111) surface at low temperatures; it transforms into a surface alloy with a ($\sq

Nishant Kumar, Lukas Krause, Thomas Wondrak, Sven Eckert

Electrolysis is crucial for eco-friendly hydrogen production, but gas bubbles generated during the process hinder reactions, reduce cell efficiency, and increase energy consumption. Additionally, these gas bubbles cause changes in the conductivity inside the cell, resulting in corresponding variations in the induced magnetic field around the cell. Therefore,

Yikang Wang, Hiromitsu Nishizaki, Ming Li

Finding synthetic artifacts of spoofing data will help the anti-spoofing countermeasures (CMs) system discriminate between spoofed and real speech. The Conformer combines the best of convolutional neural network and the Transformer, allowing it to aggregate global and local information. This may benefit the CM system to capture the synthetic artifacts hidden

Vera K. Postnikova, Dmitry Bizyaev

We explore sources of ionization of diffuse gas at different altitudes in galaxies in dependence of their stellar mass, \Ha\ luminosity, and specific star formation rate. We use the MaNGA data from SDSS-IV data release DR16 together with photoionization and shock ionization models provided by the 3MdB database. Our sample comprises 239 edge-on galaxies, whic

Zheng-Cheng Liang, Zhi-Yuan Li, En-Kun Li, Jian-dong Zhang

Single gravitational-wave detectors face inherent limitations in detecting the anisotropy of the stochastic background. In this work, we explore the sensitivity to anisotropic backgrounds with a network of space-borne detectors. We find that the separation between detectors plays an important role in determining the sensitivity. For the first time, we observ

Emily Theophilou, Cansu Koyuturk, Mona Yavari, Sathya Bursic

Artificial intelligence's (AI) progress holds great promise in tackling pressing societal concerns such as health and climate. Large Language Models (LLM) and the derived chatbots, like ChatGPT, have highly improved the natural language processing capabilities of AI systems allowing them to process an unprecedented amount of unstructured data. However, the e

Ryan J. Murphy, Oliver J. Maclaren, Matthew J. Simpson

Throughout the life sciences we routinely seek to interpret measurements and observations using parameterised mechanistic mathematical models. A fundamental and often overlooked choice in this approach involves relating the solution of a mathematical model with noisy and incomplete measurement data. This is often achieved by assuming that the data are noisy

Simon Holbach, Olivier Raimond

We consider a self-interacting diffusion $X$ on a smooth compact Riemannian manifold $\mathbb M$, described by the stochastic differential equation \[ dX_t = \sqrt{2} dW_t(X_t)- \beta(t) \nabla V_t(X_t)dt, \] where $\beta$ is suitably lower-bounded and grows at most logarithmically, and $V_t(x)=\frac{1}{t}\int_0^t V(x,X_s)ds$ for a suitable smooth function $

Anil Osman Tur, Nicola Dall&#39;Asen, Cigdem Beyan, Elisa Ricci

This paper aims to address the unsupervised video anomaly detection (VAD) problem, which involves classifying each frame in a video as normal or abnormal, without any access to labels. To accomplish this, the proposed method employs conditional diffusion models, where the input data is the spatiotemporal features extracted from a pre-trained network, and the

Asim Khan, Taimur Hassan, Muhammad Shafay, Israa Fahmy

Tomatoes are a major crop worldwide, and accurately classifying their maturity is important for many agricultural applications, such as harvesting, grading, and quality control. In this paper, the authors propose a novel method for tomato maturity classification using a convolutional transformer. The convolutional transformer is a hybrid architecture that co

Mohaddese Heydari-Fard, Malihe Heydari-Fard, Nematollah Riazi

By considering Kehagias-Sfetsos black hole in the framework of the Ho\v{r}ava-Lifshitz gravity, we study the optical appearance of such black holes surrounded by spherical accretion flow. For the static/infalling spherical accretion flow, we compute the observed specific intensity as a function of impact parameter. We also investigate the effect of the Ho\v{

H. Keppler, T. Krajewski, T. Muller, A. Tanasa

In a recent series of papers, a duality between orthogonal and symplectic random tensor models has been proven, first for quartic models and then for models with interactions of arbitrary order. However, the tensor models considered so far in the literature had no symmetry under permutation of the indices. In this paper, we generalize these results for tenso

Dongkai Zhou, Jing Guo, Siqiang Wang, Zhong Zheng

Over-the-air computation (AirComp), as a data aggregation method that can improve network efficiency by exploiting the superposition characteristics of wireless channels, has received much attention recently. Meanwhile, the orthogonal time frequency space (OTFS) modulation can provide a strong Doppler resilience and facilitate reliable transmission for high-

Ravi Kakaiya, Rakshith Sathish, Ramanathan Sethuraman, Debdoot Sheet

Autonomous vehicles and Advanced Driving Assistance Systems (ADAS) have the potential to radically change the way we travel. Many such vehicles currently rely on segmentation and object detection algorithms to detect and track objects around its surrounding. The data collected from the vehicles are often sent to cloud servers to facilitate continual/life-lon

Wassim Hamidouche, Lina Bariah, Merouane Debbah

The advent of the Metaverse concept has further expedited the evolution of haptic, tactile internet, and multimedia applications with their VR/AR/XR services, and therefore, fully-immersive sensing is most likely to define the next generation of wireless networks as a key to realize the speculative vision of the Metaverse. In this magazine, we articulate dif

Kaihui Cheng, Chule Yang, Xiao Liu, Naiyang Guan

Few-shot classification aims to adapt to new tasks with limited labeled examples. To fully use the accessible data, recent methods explore suitable measures for the similarity between the query and support images and better high-dimensional features with meta-training and pre-training strategies. However, the potential of multi-modality information has barel

Sunder Ali Khowaja, Kapal Dev, Syed Muhammad Anwar, Marius George Linguraru

Self-supervised learning in the federated learning paradigm has been gaining a lot of interest both in industry and research due to the collaborative learning capability on unlabeled yet isolated data. However, self-supervised based federated learning strategies suffer from performance degradation due to label scarcity and diverse data distributions, i.e., d

Eirill Hauge, Håkon Emil Kristiansen, Lukas Konecny, Marius Kadek

We present a novel function fitting method for approximating the propagation of the time-dependent electric dipole moment from real-time electronic structure calculations. Real-time calculations of the electronic absorption spectrum require discrete Fourier transforms of the electric dipole moment. The spectral resolution is determined by the total propagati

Minrui Wei, Lu Zhang, Guangjun Tian, Weijie Hua

The vibronic coupling effect in nitrogen 1s X-ray photoelectron spectra (XPS) was systematically studied for a family of 17 bicyclic indole molecules by combining Franck-Condon simulations (including the Duschinsky rotation effect) and density functional theory. The simulated vibrationally-resolved spectra of 4 molecules agree well with available experiments

Yuri Yu. Tarasevich, Andrei V. Eserkepov, Irina V. Vodolazskaya

Crack-template-based transparent conductive films (TCFs) are promising kinds of junction-free, metallic network electrodes that can be used, e.g., for transparent electromagnetic interference (EMI) shielding. Using image processing of published photos of TCFs, we have analyzed the topological and geometrical properties of such crack templates. Additionally,

Louise Arno, Desmond Kabus, Hans Dierckx

Many extended chemical and biological systems self-organise into complex patterns that drive the medium behaviour in a non-linear fashion. An important class of such systems are excitable media, including neural and cardiac tissues. In extended excitable media, wave breaks can form rotating patterns and turbulence. However, the onset, sustaining and eliminat

Mengying Jiang, Guizhong Liu, Biao Zhao, Yuanchao Su

Relation-aware graph structure embedding is promising for predicting multi-relational drug-drug interactions (DDIs). Typically, most existing methods begin by constructing a multi-relational DDI graph and then learning relation-aware graph structure embeddings (RaGSEs) of drugs from the DDI graph. Nevertheless, most existing approaches are usually limited in

Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu

Recently, ''pre-training and fine-tuning'' has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annotations from each application. However, graph tasks with node level, edge level, and graph level are far diversified, making the pre-training pretext often incompatible with these m

Iman Jafarian

The Internet of Drones has emerged as a transformative technology with applications spanning various domains, including surveillance, delivery services, and disaster management. Secure communication between controller users and drones is paramount to ensure the transmitted data's confidentiality, integrity, and authenticity. Key agreement protocols are cruci

Stefan Hildebrand, Sandra Klinge

Machine Learning methods belong to the group of most up-to-date approaches for solving partial differential equations. The current work investigates two classes, Neural FEM and Neural Operator Methods, for the use in elastostatics by means of numerical experiments. The Neural Operator methods require expensive training but then allow for solving multiple bou

Philipp D. Lösel, Jacob J. Relle, Samuel Voß, Ramesch Raschidi

Background: Abdominal wall defects, such as incisional hernias, are a common source of pain and discomfort and often require repeated surgical interventions. Traditional mesh repair techniques typically rely on fixed overlap based on defect size, without considering important biomechanical factors like muscle activity, internal pressure, and tissue elasticit

Tajron Jurić, Hrvoje Nikolić

We develop a new method for finding the quantum probability density of arrival at the detector. The evolution of the quantum state restricted to the region outside of the detector is described by a restricted Hamiltonian that contains a non-hermitian boundary term. The non-hermitian term is shown to be proportional to the flux of the probability current oper

Sasila Ilandarideva, Anatoli Juditsky, Guanghui Lan, Tianjiao Li

We consider a class of stochastic smooth convex optimization problems under rather general assumptions on the noise in the stochastic gradient observation. As opposed to the classical problem setting in which the variance of noise is assumed to be uniformly bounded, herein we assume that the variance of stochastic gradients is related to the "sub-optimality"

Chang Liu, Dejun Luo

We consider the vorticity form of 2D Navier--Stokes equations perturbed by an Ornstein--Uhlenbeck flow of transport type. Contrary to previous works where the random perturbation was interpreted as Stratonovich transport noise, here we understand the equation in a pathwise manner and show the properties of mixing and enhanced dissipation for suitable choice

Tariq Syed

Cyclic coverings produce many examples of topologically contractible smooth affine complex varieties. In this paper, we study the motivic cohomology groups of cyclic coverings over algebraically closed fields of characteristic $0$. In particular, we prove that in many situations Chow groups of cyclic coverings become trivial after tensoring with $\mathbb{Q}$

Peter Albers, Serge Tabachnikov

Given a planar oval, consider the maximal area of inscribed $n$-gons resp. the minimal area of circumscribed $n$-gons. One obtains two sequences indexed by $n$, and one of Dowker's theorems states that the first sequence is concave and the second is convex. In total, there are four such classic results, concerning areas resp. perimeters of inscribed resp. ci

Ruben Caraballo, Chansophea Wathanak In, Alberto F. Martín, Ricardo Ruiz-Baier

In this paper, we propose a new formulation and a suitable finite element method for the steady coupling of viscous flow in deformable porous media using divergence-conforming filtration fluxes. The proposed method is based on the use of parameter-weighted spaces, which allows for a more accurate and robust analysis of the continuous and discrete problems. F

Hui Zhang, Jianjun Zhang, Xuexiu Zhong

In this paper, we consider the critical Choquard system with prescribed mass \begin{equation*} \begin{aligned} \left\{ \begin{array}{lll} -Δu+λ_1u=(I_μ\ast |u|^{2^*_μ})|u|^{2^*_μ-2}u+νp(I_μ\ast |v|^q)|u|^{p-2}u\ & \text{in}\quad \mathbb{R}^N,\\ -Δv+λ_2v=(I_μ\ast |v|^{2^*_μ})|v|^{2^*_μ-2}v+νq(I_μ\ast |u|^p)|v|^{q-2}v\ & \text{in}\quad \mathbb{R}^N,\\ \int_{\m

Tong Nie, Guoyang Qin, Lijun Sun, Wei Ma

Spatiotemporal traffic data (STTD) displays complex correlational structures. Extensive advanced techniques have been designed to capture these structures for effective forecasting. However, because STTD is often massive in scale, practitioners need to strike a balance between effectiveness and efficiency using computationally efficient models. An alternativ

Peixun Long, Jianjun Zhao

Quantum programs exhibit inherent non-deterministic behavior, which poses more significant challenges for error discovery compared to classical programs. While several testing methods have been proposed for quantum programs, they often overlook fundamental questions in black-box testing. In this paper, we bridge this gap by presenting three novel algorithms

Minsu Ko, Sergey V. Uchaikin, Boris I. Ivanov, JinMyeong Kim

The axion search experiments in the microwave frequency range require high sensitive detectors with intrinsic noise close to quantum noise limit. Josephson parametric amplifiers (JPAs) are the most valuable candidates for the role of the first stage amplifier in the measurement circuit of the microwave frequency range, as they are well-known in superconducti

Shubham Singh, Jayanta Roy, Shyam Sunder, Bhaswati Bhattacharyya

Many pulsars in the known population exhibit nulling, which is characterised by a sudden cessation and subsequent restoration of radio emission. In this work, we present the localization, timing, and emission properties of a GHRSS discovered pulsar J1244-4708. Moreover, we find that this pulsar shows nulling with a nulling fraction close to 60%. A quasi-peri

N. K. Karn, M. M. Sharma, V. P. S. Awana

Doped Iron Chalcogenide (FeCh) superconductors are extensively studied in the context of topological superconductivity. However, the evidence of topological surface states in electrical transport measurements of the doped FeCh system is yet warranted. In the present letter, we performed angle-dependent magneto transport measurements on a single crystal of a

M. Murata, T. Kawabata, S. Adachi, H. Akimune

We simultaneously measured the $^4$He($γ, n$)$^3$He and $^4$He($γ, p$)$^3$H reactions in the energy range around the giant dipole resonance. A quasi-monoenergetic photon beam produced via the laser Compton scattering technique was irradiated on the active-target time-projection chamber filled with helium gas, and trajectories of charged decay particles emitt

Raha Ahmadi, Mohammad Javad Rajabi, Mohammad Khalooie, Mohammad Sabokrou

Deep learning models have demonstrated remarkable capabilities in learning complex patterns and concepts from training data. However, recent findings indicate that these models tend to rely heavily on simple and easily discernible features present in the background of images rather than the main concepts or objects they are intended to classify. This phenome

Michel Dekking

We characterize the entries of Hofstadter's G-sequence in terms of the lower and upper Wythoff sequences. This can be used to give a short and comprehensive proof of the equality of Hofstadter's G-sequence and the sequence of averages of the swapped Wythoff sequences. In a second part we give some results that hold when one replaces the golden mean by other

Pavan Kumar Sharma, Pranamesh Chakraborty

Driver gaze plays an important role in different gaze-based applications such as driver attentiveness detection, visual distraction detection, gaze behavior understanding, and building driver assistance system. The main objective of this study is to perform a comprehensive summary of driver gaze fundamentals, methods to estimate driver gaze, and it's applica

Hyesun Choung, Prabu David, John S. Seberger

To realize the potential benefits and mitigate potential risks of AI, it is necessary to develop a framework of governance that conforms to ethics and fundamental human values. Although several organizations have issued guidelines and ethical frameworks for trustworthy AI, without a mediating governance structure, these ethical principles will not translate

Yunqing Zhao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, Chao Du

Few-shot image generation (FSIG) aims to learn to generate new and diverse images given few (e.g., 10) training samples. Recent work has addressed FSIG by leveraging a GAN pre-trained on a large-scale source domain and adapting it to the target domain with few target samples. Central to recent FSIG methods are knowledge preservation criteria, which select an

Juntao Yang, Jeff Adie, Simon See, Adriano Gualandi

Bayesian inverse problems arise in various scientific and engineering domains, and solving them can be computationally demanding. This is especially the case for problems governed by partial differential equations, where the repeated evaluation of the forward operator is extremely expensive. Recent advances in Deep Learning (DL)-based surrogate models have s

Minh-Quan Dao, Julie Stephany Berrio, Vincent Frémont, Mao Shan

Occlusion is a major challenge for LiDAR-based object detection methods. This challenge becomes safety-critical in urban traffic where the ego vehicle must have reliable object detection to avoid collision while its field of view is severely reduced due to the obstruction posed by a large number of road users. Collaborative perception via Vehicle-to-Everythi

Varsha Gupta

In this work, we study quantum chaos by focusing on the evolution of initially close states in the dynamics of the Quantum Kicked Rotor (QKR). We propose a novel measure, the Quantum Lyapunov Exponent (QLE), to quantify the degree of chaos in this quantum system, analogous to its classical counterpart. We begin by modeling the momentum space and then the QLE

Veronica Arena, Stephen Obinna, Dan Abramovich

We give a formula for the integral Chow rings of weighted blow-ups. Along the way, we also compute the integral Chow rings of weighted projective stack bundles, a formula for the Gysin homomorphism of a weighted blow-up, and a generalization of the splitting principle. In addition, in the appendix we compute the Chern class of a weighted blow-up.

Tong Xiang, Liangzhi Li, Wangyue Li, Mingbai Bai

The recent advances in natural language processing (NLP), have led to a new trend of applying large language models (LLMs) to real-world scenarios. While the latest LLMs are astonishingly fluent when interacting with humans, they suffer from the misinformation problem by unintentionally generating factually false statements. This can lead to harmful conseque

Yann Gouttenoire, Sokratis Trifinopoulos, Georgios Valogiannis, Miguel Vanvlasselaer

Recent observations have granted to us two unique insights into the early universe: the presence of a low-frequency stochastic gravitational wave background detected by the NANOGrav and Pulsar Timing Array (PTA) experiments and the emergence of unusually massive galaxy candidates at high redshifts reported by the James Webb Space Telescope (JWST). In this le

Zhaohui Dong, Xianfeng Chen, Luqi Yuan

Spatiotemporal modulation offers a variety of opportunities for light manipulations. In this paper, we propose a way towards arbitrary transformation for pulses sequentially propagating within one waveguide in space via temporal waveguide coupling. The temporal waveguide coupling operation is achieved by spatiotemporally modulating the refractive index of th

Ran Li, Xuanhua Wang, Kun Zhang, Jin Wang

The non-isometric holographic model of the black hole interior stands out as a potential resolution of the long-standing black hole information puzzle since it remedies the friction between the effective calculation and the microscopic description. In this study, combining the final-state projection model, the non-isometric model of black hole interior and H

Zhihong Deng, Jing Jiang, Guodong Long, Chengqi Zhang

Reinforcement learning is an essential paradigm for solving sequential decision problems under uncertainty. Despite many remarkable achievements in recent decades, applying reinforcement learning methods in the real world remains challenging. One of the main obstacles is that reinforcement learning agents lack a fundamental understanding of the world and mus

Abhikbrata Sarkar, Zhanning Wang, Mathew Rendell, Nico W. Hendrickx

In this work we present a comprehensive theory of spin physics in planar Ge hole quantum dots in an in-plane magnetic field, where the orbital terms play a dominant role in qubit physics, and provide a brief comparison with experimental measurements of the angular dependence of electrically driven spin resonance. We focus the theoretical analysis on electric

Harsh Parikh, Marco Morucci, Vittorio Orlandi, Sudeepa Roy

Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational studies, allowing practitioners to test for assumption violations and estimate treatment effects consistently. Our framework proposes a falsification test for external validity and i

Jiacheng He, Bei Peng, Zhenyu Feng, Xuemei Mao

The information transmission between nodes in a wireless sensor networks (WSNs) often causes packet loss due to denial-of-service (DoS) attack, energy limitations, and environmental factors, and the information that is successfully transmitted can also be contaminated by non-Gaussian noise. The presence of these two factors poses a challenge for distributed

Xiangyu Gao, Sumit Roy, Lyutianyang Zhang

Anti-collision assistance, integral to the current drive towards increased vehicular autonomy, relies heavily on precise detection and localization of moving targets in the vehicle's vicinity. A crucial step towards achieving this is the removal of static objects from the scene, thereby enhancing the detection and localization of dynamic targets - a pivotal

Jiacheng He, Gang Wang, Kun Zhang, Shan Zhong

The robustness of the kernel recursive least square (KRLS) algorithm has recently been improved by combining them with more robust information-theoretic learning criteria, such as minimum error entropy (MEE) and generalized MEE (GMEE), which also improves the computational complexity of the KRLS-type algorithms to a certain extent. To reduce the computationa

Thomas N. Woods, Bennet Schwab, Robert Sewell, Anant Kumar Telikicherla Kandala

Three generations of the Miniature X-ray Solar Spectrometer (MinXSS) have flown on small satellites with the goal "to explore the energy distribution of soft X-ray (SXR) emissions from the quiescent Sun, active regions, and during solar flares, and to model the impact on Earth's ionosphere and thermosphere". The primary science instrument is the Amptek X123

Jiacheng He, Gang Wang, Zhenyu Feng, Shan Zhong

In real applications, non-Gaussian distributions are frequently caused by outliers and impulsive disturbances, and these will impair the performance of the classical cubature Kalman filter (CKF) algorithm. In this letter, a modified generalized minimum error entropy criterion with fiducial point (GMEEFP) is studied to ensure that the error comes together to

Alan Demlow, Michael Neilan

Surface Stokes and Navier-Stokes equations are used to model fluid flow on surfaces. They have attracted significant recent attention in the numerical analysis literature because approximation of their solutions poses significant challenges not encountered in the Euclidean context. One challenge comes from the need to simultaneously enforce tangentiality and

Mohammad Hosseinirad, Fatemeh Tabatabaei, Mojtaba Raouf, Mahmood Roshan

The Universe is pervaded by magnetic fields in different scales, although for simplicity, they are ignored in most cosmological simulations. In this paper, we use the TNG50, which is a large cosmological galaxy formation simulation that incorporates magnetic fields with an unprecedented resolution. We study the correlation of the magnetic field with various

Zhen Zhu, Weijie Lyu, Yao Xiao, Derek Hoiem

We introduce a method for flexible and efficient continual learning in open-vocabulary image classification, drawing inspiration from the complementary learning systems observed in human cognition. Specifically, we propose to combine predictions from a CLIP zero-shot model and the exemplar-based model, using the zero-shot estimated probability that a sample'

Baorui Dai, Gaëtan Frusque, Tianfu Li, Qi Li

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, different units of a fleet or between simulated and real data. However, in real industrial scenarios, unkn

Zhiyuan Yan, Yong Zhang, Xinhang Yuan, Siwei Lyu

A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. This issue leads to unfair performance comparisons and potentially misleading results. Specifically, there is a lack of uniformity in data processing pipelines, resulting in inconsistent data inputs for detection

Shun Shimomura

For the fifth Painlev\'e equation it is known that a general solution is represented asymptotically by an elliptic function in cheese-like strips near the point at infinity. We present an explicit asymptotic formula for the error term of this expression, which leads to an estimate for its magnitude as was conjectured. Analogous formula is obtained for the er

Luka Zurak, Christian Wolff, Jessica Meier, Rene Kullock

Classical electrodynamics describes the optical response of systems using bulk electronic properties and infinitesimally thin boundaries. However, due to the quantum nature of electrons, interfaces have a finite thickness. Non-classical surface effects become increasingly important as ever smaller nanoscale systems are realized and eventually dominate over v

Chi Zhang, Seth T. Rittenhouse, Timur V. Tscherbul, H. R. Sadeghpour

We propose to sympathetically slow and cool polar molecules in a cold, low-density beam using laser-cooled Rydberg atoms. The elastic collision cross sections between molecules and Rydberg atoms are large enough to efficiently thermalize the molecules even in a low density environment. Molecules traveling at 100 m/s can be stopped in under 30 collisions with

Yohan Jun, Yamin Arefeen, Jaejin Cho, Shohei Fujita

Purpose: To develop and evaluate methods for 1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T1 and T2 mapping, and 2) improving the fidelity of subspace QALAS by combining scan-specific deep-lea

Tilahun M. Getu, Georges Kaddoum, Mehdi Bennis

SemCom and goal-oriented SemCom are designed to transmit only semantically-relevant information and hence help to minimize power usage, bandwidth consumption, and transmission delay. Consequently, SemCom and goal-oriented SemCom embody a paradigm shift that can change the status quo that wireless connectivity is an opaque data pipe carrying messages whose co

Amit Tiwari, Susmita Bardhan, Vikas Kumar

The present study identifies and assesses the bibliographic trend in Artificial Intelligence (AI) research for the years 2015-2020 using the science mapping method of bibliometric study. The required data has been collected from the Scopus database. To make the collected data analysis-ready, essential data transformation was performed manually and with the h

Mengyuan Zhang, Kai Liu

We study a class of non-convex and non-smooth problems with \textit{rank} regularization to promote sparsity in optimal solution. We propose to apply the proximal gradient descent method to solve the problem and accelerate the process with a novel support set projection operation on the singular values of the intermediate update. We show that our algorithms

Anj Simmons, Rajesh Vasa

This paper proposes exploiting the common sense knowledge learned by large language models to perform zero-shot reasoning about crimes given textual descriptions of surveillance videos. We show that when video is (manually) converted to high quality textual descriptions, large language models are capable of detecting and classifying crimes with state-of-the-

Barnali Basak, Uday P. Khedker, Supratim Biswas

Sparse matrix operations involve a large number of zero operands which makes most of the operations redundant. The amount of redundancy magnifies when a matrix operation repeatedly executes on sparse data. Optimizing matrix operations for sparsity involves either reorganization of data or reorganization of computations, performed either at compile-time or ru

Jinyang Li, Qiang Guan, Dingwen Tao, Weiwen Jiang

The rapid advancement of quantum hardware brings a host of research opportunities and the potential for quantum advantages across numerous fields. In this landscape, quantum circuit simulations serve as an indispensable tool by emulating quantum behavior on classical computers. They offer easy access, noise-free environments, and real-time observation of qua

Shengxuan Ding, Changwei Yuan, Jing Chen, Chenhao Zhang

With the continuous improvement of urbanization and motorization, travel demand in historical blocks is higher than before. The contradiction between supply of transportation facilities and environmental protection is more serious. Traditional public transport planning methods aim to improve mobility. However, several existing studies do not put travelers in

Di Fan, Mingyang Liu, Xiaohan Zhang, Xiaopeng Gong

A novel human emotion recognition method based on automatically selected Galvanic Skin Response (GSR) signal features and SVM is proposed in this paper. GSR signals were acquired by e-Health Sensor Platform V2.0. Then, the data is de-noised by wavelet function and normalized to get rid of the individual difference. 30 features are extracted from the normaliz

Xuewei Cheng, Ke Huang, Yi Zou, Shujie Ma

Deep neural networks have played an important role in automatic sleep stage classification because of their strong representation and in-model feature transformation abilities. However, class imbalance and individual heterogeneity which typically exist in raw EEG signals of sleep data can significantly affect the classification performance of any machine lea