December 2023 arXiv papers — page 125

Debasis Dutta, Amit Agarwal

Collective plasmon modes, riding on top of drifting electrons, acquire a fascinating nonreciprocal dispersion characterized by $\omega_p(\bm{q}) \neq \omega_p(-\bm{q})$. The {\it classical} plasmonic Doppler shift arises from the polarization of the Fermi surface due to the applied DC bias voltage. Going beyond this paradigm, we predict a {\it quantum} plasm

Saheb Soroushfar, Behnam Pourhassan, İzzet Sakallı

This study presents an investigation into the thermodynamic properties of a dirty black hole immersed in a uniform electric field within the framework of the Einstein-Nonlinear Electrodynamics (ENE)-dilaton theory. The analysis delves into various thermodynamic aspects, including heat capacity, Helmholtz free energy, and internal energy, providing insights i

Janani Venkatasubramanian, Johannes Köhler, Mark Cannon, Frank Allgöwer

We present a novel targeted exploration strategy for linear time-invariant systems without stochastic assumptions on the noise, i.e., without requiring independence or zero mean, allowing for deterministic model misspecifications. This work utilizes classical data-dependent uncertainty bounds on the least-squares parameter estimates in the presence of energy

Rafael Lahoz-Beltra

In recent years, the emergence of the first quantum computers at a time when AI is undergoing a fruitful era has led many AI researchers to be tempted into adapting their algorithms to run on a quantum computer. However, in many cases the initial enthusiasm has ended in frustration, since the features and principles underlying quantum computing are very diff

Angel Daruna, Yunye Gong, Abhinav Rajvanshi, Han-Pang Chiu

Predictive uncertainty estimation remains a challenging problem precluding the use of deep neural networks as subsystems within safety-critical applications. Aleatoric uncertainty is a component of predictive uncertainty that cannot be reduced through model improvements. Uncertainty propagation seeks to estimate aleatoric uncertainty by propagating input unc

Sean Kim, Michael E. Picollelli

A 2018 conjecture of Brewster, McGuinness, Moore, and Noel asserts that for $k \ge 3$, if a graph has chromatic number greater than $k$, then it contains at least as many cycles of length $0 \bmod k$ as the complete graph on $k+1$ vertices. Our main result confirms this in the $k=3$ case by showing every $4$-critical graph contains at least $4$ cycles of len

Yu. G. Ignat'ev, A. R. Samigullina

A study and numerical modeling of the cosmological evolution of a classical scalar field with the Higgs potential was carried out. Based on the formulated similarity properties of cosmological models, their main characteristics are studied for models with different interaction scales: the Planck scale, the Grand Unified scale and the Standard Model scale. Ba

Wladimir Ostrovsky

This paper explores the utility of agent-based simulations in realistically modelling market structures and sheds light on the nuances of optimal dealer strategies. It underscores the contrast between conclusions drawn from probabilistic modelling and agent-based simulations, but also highlights the importance of employing a realistic test bed to analyse int

Jayashree Behera, Mehdi Rezaie, Lado Samushia, Julia Ereza

We investigate how well a simple leading order perturbation theory model of the bispectrum can fit the BAO feature in the measured bispectrum monopole of galaxies. Previous works showed that perturbative models of galaxy bispectrum start failing at the wavenumbers of k ~ 0.1 Mpc/h. We show that when the BAO feature in the bispectrum is separated it can be su

Haokai Pang, Heming Zhu, Adam Kortylewski, Christian Theobalt

Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics. While recent advances in neural implicit rendering have unlocked unprecedented photorealism for digital avatars, real-time performance has mostly been demonstrated for static scenes only. To address this, we propose ASH, an animatable

Mike Ranzinger, Greg Heinrich, Jan Kautz, Pavlo Molchanov

A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks. VFMs like CLIP, DINOv2, SAM are trained with distinct objectives, exhibiting unique characteristics for various downstream tasks. We find that despite their conceptual differences, these models can be effectively merged into a unified model thro

Massimo Lanza de Cristoforis

The present informal set of notes covers the material that has been presented by the author in a series of lectures for the Doctoral School in Mathematics of the Southern Federal State University of Rostov-on-Don in the Fall of 2020 and that develops from the first part of the notes that collect the material of the lectures of the author at the Eurasian Nati

Antonello Pellecchia

The muon system of the CMS experiment is expected to upgrade all of its subdetectors for the Phase-2 of the Large Hadron Collider (LHC) that will begin in 2029. The upgrade plans for drift tubes (DTs), cathode strip chambers (CSCs) and resistive place chambers (RPCs) include a new electronics for better performance in high background rate conditions and to s

K Vishnu Namboothiri, Vinod Sivadasan

For an arithmetical function $f$, its Ramanujan expansion is a series expansion in the form $f(n)=\sum\limits_{k=1}^{\infty}a(k) c_k(n)$ where $a(k)$ are complex numbers and $c_k(n):= \sum\limits_{\substack{m=1\\(m, k)=1}}^{k}e^{\frac{2\pi imn}{k}}$ is the Ramanujan sum. Here we prove a reciprocity result on Cohen-Ramanujan sums $c_k^s(n) :=\sum\limits_{\sub

Lekan Molu, Shaoru Chen, Audrey Sedal

The characteristic ``in-plane" bending associated with soft robots' deformation make them preferred over rigid robots in sophisticated manipulation and movement tasks. Executing such motion strategies to precision in soft deformable robots and structures is however fraught with modeling and control challenges given their infinite degrees-of-freedom. Imposing

Vinod Sivadasan, K Vishnu Namboothiri

Srinivasa Ramanujan provided Fourier series expansions of certain arithmetical functions in terms of the exponential sum defined by $c_q(n)=\sum\limits_{\substack{{m=1}\\(m,q)=1}}^{q}e^{\frac{2 \pi imn}{q}}$. Later, H. Delange derived the bound $\sum\limits_{q|k}|c_q(n)|\leq n\, 2^{\omega(k)}$ and gave a sufficient condition for such expansions to exist. A.

Nikolai Chemetov, Fernanda Cipriano

We study a stochastic velocity tracking problem for the 2D-Navier-Stokes equations perturbed by a multiplicative Gaussian noise. From a physical point of view, the control acts through a boundary injection/suction device with uncertainty, modeled by stochastic non-homogeneous Navier-slip boundary conditions. We show the existence and uniqueness of the soluti

Oded Ovadia, Menachem Brief, Moshik Mishaeli, Oren Elisha

Large language models (LLMs) encapsulate a vast amount of factual information within their pre-trained weights, as evidenced by their ability to answer diverse questions across different domains. However, this knowledge is inherently limited, relying heavily on the characteristics of the training data. Consequently, using external datasets to incorporate new

Shahriar Noroozizadeh, Jeremy C. Weiss, George H. Chen

We consider the problem of predicting how the likelihood of an outcome of interest for a patient changes over time as we observe more of the patient data. To solve this problem, we propose a supervised contrastive learning framework that learns an embedding representation for each time step of a patient time series. Our framework learns the embedding space t

Zoe Shapcott

In this note, we assess the accuracy of CLT-based approximations for the volume of intersection of the $d$-dimensional cube $[-1,1]^d$ and an $L_q$-ball centred at the origin; this is clearly equivalent to approximating the distribution of the $L_q$-norm of a random point in a $d$-dimensional cube centered at 0. The approximations are CLT-based where to impr

Kentaro Saji

We construct a form of the $D_4^+$-singularity of fronts in $R^3$ which uses coordinate transformation on the source and isometry on the target. As an application, we calculate differential geometric invariants near the $D_4^+$-singularity, and give a Gauss-Bonnet type theorem for fronts allowing to have this singularity.

Linxi Zhao, Jiankai Tang, Dongyu Chen, Xiaohong Liu

Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a

Joonwoo Kwon, Sooyoung Kim, Yuewei Lin, Shinjae Yoo

Neural style transfer (NST) has evolved significantly in recent years. Yet, despite its rapid progress and advancement, existing NST methods either struggle to transfer aesthetic information from a style effectively or suffer from high computational costs and inefficiencies in feature disentanglement due to using pre-trained models. This work proposes a ligh

Yuanyuan Shu, Tianxing Pan

This study elaborates a text-based metric to quantify the unique position of stylized scientific research, characterized by its innovative integration of diverse knowledge components and potential to pivot established scientific paradigms. Our analysis reveals a concerning decline in stylized research, highlighted by its comparative undervaluation in terms o

Sunjae Yoon, Gwanhyeong Koo, Ji Woo Hong, Chang D. Yoo

Text-conditioned image editing has succeeded in various types of editing based on a diffusion framework. Unfortunately, this success did not carry over to a video, which continues to be challenging. Existing video editing systems are still limited to rigid-type editing such as style transfer and object overlay. To this end, this paper proposes Neutral Editin

Yuxuan Shi, Yongzhou Cheng

Based on the fluid volume fraction large eddy simulation discrete phase coupling model, the water and sediment flow around the vibrating monopile was simulated. The discrete phase momentum model was enhanced using the force distribution of the particle phase on the bed surface. Additionally, the flow dynamics and sediment incipience around a vibrating monopi

Jannik Sheikh, Andrew Melnik, Gora Chand Nandi, Robert Haschke

Reinforcement learning and Imitation Learning approaches utilize policy learning strategies that are difficult to generalize well with just a few examples of a task. In this work, we propose a language-conditioned semantic search-based method to produce an online search-based policy from the available demonstration dataset of state-action trajectories. Here

Xiaojian Yuan, Kejiang Chen, Wen Huang, Jie Zhang

The popularity of Machine Learning as a Service (MLaaS) has led to increased concerns about Model Stealing Attacks (MSA), which aim to craft a clone model by querying MLaaS. Currently, most research on MSA assumes that MLaaS can provide soft labels and that the attacker has a proxy dataset with a similar distribution. However, this fails to encapsulate the m

Xinyan Liu, Guorong Li, Yuankai Qi, Ziheng Yan

Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. % Existing methods learn representations based on trajectory labels for individuals, which are annotation-expensive. % To provide a more realistic reflection of the underlying practical challenge, we introduce a weakly supervised VIC task, wherein trajectory l

Zhilin Du, Haozhen Li, Zhenyu Liu, Shilong Fan

The advent of deep learning (DL)-based models has significantly advanced Channel State Information (CSI) feedback mechanisms in wireless communication systems. However, traditional approaches often suffer from high communication overhead and potential privacy risks due to the centralized nature of CSI data processing. To address these challenges, we design a

Haoning Dang, Fei Wang

This paper introduces a new numerical approach that integrates local randomized neural networks (LRNNs) and the hybridized discontinuous Petrov-Galerkin (HDPG) method for solving coupled fluid flow problems. The proposed method partitions the domain of interest into several subdomains and constructs an LRNN on each subdomain. Then, the HDPG scheme is used to

Zhibo Chen

Logical frameworks are successful in modeling proof systems. Recently, CoLF extended the logical framework LF to support higher-order rational terms that enable adequate encoding of circular objects and derivations. In this paper, we propose CoLF$^\omega$ as an alternative interpretation of CoLF-style signatures where terms are taken to be all possibly infin

Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre, Vinit Katariya

Addressing public safety effectively requires incorporating diverse stakeholder perspectives, particularly those of the community, which are often underrepresented compared to other stakeholders. This study presents a comprehensive analysis of the community's general public safety concerns, their view of existing surveillance technologies, and their percepti

Pedro Aceves-Sanchez, Rafael Bailo, Pierre Degond, Zoe Mercier

We study the validity of the dissipative Aw-Rascle system as a macroscopic model for pedestrian dynamics. The model uses a congestion term (a singular diffusion term) to enforce capacity constraints in the crowd density while inducing a steering behaviour. Furthermore, we introduce a semi-implicit, structure-preserving, and asymptotic-preserving numerical sc

Kai-He Ding, Zhen-Gang Zhu

Negative magnetoresistance (NMR) is a marked feature of Dirac semimetals, and may be caused by multiple mechanisms, such as the chiral anomaly, the Zeeman energy, the quantum interference effect, and the orbital moment. Recently, an experiment on Dirac semimetal Cd$_3$As$_2$ thin films revealed a new NMR feature that depends strongly on the thickness of the

Nora Weickgenannt, Jean-Paul Blaizot

We derive an expression for the local transverse polarization of a boost-invariant expanding system of massive particles, which involves a set of dynamical spin moments. Starting from spin kinetic theory, we obtain a closed set of equations of motion for these spin moments. These equations are valid during the full evolution of the system, from free streamin

Luca M. Hartmann, Orcun Karaca, Tinus Dorfling, Tobias Geyer

Past work proposed an extension to finite control set model predictive control to track both a current reference and a switching frequency reference, simultaneously. Such an objective can jeopardize the current tracking performance, and this can potentially be alleviated by instead limiting the switching frequency. To this end, we propose to limit the switch

Yihan Hu, Yiheng Lin, Wei Wang, Yao Zhao

We aim to leverage diffusion to address the challenging image matting task. However, the presence of high computational overhead and the inconsistency of noise sampling between the training and inference processes pose significant obstacles to achieving this goal. In this paper, we present DiffMatte, a solution designed to effectively overcome these challeng

Victor D. Mendoza-Rubio, Victor H. Jorge-Pérez

Let $R$ be a Noetherian local ring, and let $C$ be a semidualizing $R$-module. In this paper, we present some results concerning the vanishing of $\operatorname{Ext}$ and finite injective dimension of $\operatorname{Hom}$. Additionally, we extend these results in terms of finite $C$-injective dimension of $\operatorname{Hom}$. We also investigate the consequ

Djamila Oudrar, Maurice Pouzet

We present a structural approach of some results about jumps in the behavior of the profile (alias generating function) of hereditary classes of finite structures. We consider the following notion due to N.Thi\'ery and the second author. A \emph{monomorphic decomposition} of a relational structure $R$ is a partition of its domain $V(R)$ into a family of sets

Rui Yang, Thijs van den Ham, Roberto Verzicco, Detlef Lohse

We report on the melting dynamics of ice suspended in fresh water and subject to natural convective flows. Using direct numerical simulations we investigate the melt rate of ellipsoidal objects for $2.32\times 10^4 \leq \text{Ra} \leq 7.61\times 10^8$, where \text{Ra} is the Rayleigh number defined with the temperature difference between the ice and the surr

Zhigang Bao, Qiyang Han, Xiaocong Xu

Approximate message passing (AMP) has emerged both as a popular class of iterative algorithms and as a powerful analytic tool in a wide range of statistical estimation problems and statistical physics models. A well established line of AMP theory proves Gaussian approximations for the empirical distributions of the AMP iterate in the high dimensional limit,

Rishika Dwaraghanath, Rahul Majethia, Sanjana Gautam

Qualitative research studies often employ a contextual inquiry, or a field study that involves in-depth observation and interviews of a small sample of study participants, in-situ, to gain a robust understanding of the reasons and circumstances that led to the participant's thoughts, actions, and experiences regarding the domain of interest. Contextual inqui

Zhidi Lin, Yiyong Sun, Feng Yin, Alexandre Hoang Thiéry

The Gaussian process state-space models (GPSSMs) represent a versatile class of data-driven nonlinear dynamical system models. However, the presence of numerous latent variables in GPSSM incurs unresolved issues for existing variational inference approaches, particularly under the more realistic non-mean-field (NMF) assumption, including extensive training e

Hongbin Lin, Juangui Xu, Qingfeng Xu, Zhengyu Hu

In the realm of 3D reconstruction from 2D images, a persisting challenge is to achieve high-precision reconstructions devoid of 3D Ground Truth data reliance. We present UNeR3D, a pioneering unsupervised methodology that sets a new standard for generating detailed 3D reconstructions solely from 2D views. Our model significantly cuts down the training costs t

Semyon Petrov, Fedor Petrov, Alexander Okhotin

The communication matrix for two-way deterministic finite automata (2DFA) with $n$ states is defined for an automaton over a full alphabet of all $(2n+1)^n$ possible symbols: its rows and columns are indexed by strings, and the entry $(u, v)$ is $1$ if $uv$ is accepted by the automaton, and $0$ otherwise. With duplicate rows and columns removed, this is a sq

Bingjun Luo, Zewen Wang, Jinpeng Wang, Junjie Zhu

Illumination variation has been a long-term challenge in real-world facial expression recognition(FER). Under uncontrolled or non-visible light conditions, Near-infrared (NIR) can provide a simple and alternative solution to obtain high-quality images and supplement the geometric and texture details that are missing in the visible domain. Due to the lack of

Bingjun Luo, Haowen Wang, Jinpeng Wang, Junjie Zhu

With the strong robusticity on illumination variations, near-infrared (NIR) can be an effective and essential complement to visible (VIS) facial expression recognition in low lighting or complete darkness conditions. However, facial expression recognition (FER) from NIR images presents more challenging problem than traditional FER due to the limitations impo

Mohammad Mamun, Scott Buffett

Audit logs containing system level events are frequently used for behavior modeling as they can provide detailed insight into cyber-threat occurrences. However, mapping low-level system events in audit logs to highlevel behaviors has been a major challenge in identifying host contextual behavior for the purpose of detecting potential cyber threats. Relying o

Xingyu Bao, Shengtao Lin, Jiaojiao Zhang, Yongxin Liang

Er-doped random fiber laser (ERFL) is a complex physical system, and understanding its intrinsic physical mechanisms is crucial for promoting applications. In this paper, we experimentally investigate the time-domain statistical properties of ERFL under full-bandwidth condition for the first time. We also analyze the effects of the transmission process and a

Nurudin Alvarez-Gonzalez, Andreas Kaltenbrunner, Vicenç Gómez

We present a novel edge-level ego-network encoding for learning on graphs that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing additional node and edge features or extending message-passing formats. The proposed encoding is sufficient to distinguish Strongly Regular Graphs, a family of challenging 3-WL equivalent graphs. We show theore

Kaberi Goswami, K. Narayan

We continue the study of 4-dimensional Schwarzschild de Sitter black holes in the regime where the black hole mass is small compared with the de Sitter scale, following arXiv:2207.10724 [hep-th]. The de Sitter temperature is very low compared with that of the black hole. We consider the future boundary as the location where the black hole Hawking radiation i

Imara Lima Fernandes, Samir Lounis

Skyrmions are spin-swirling textures hosting wonderful properties with potential implications in information technology. Such magnetic particles carry a magnetization, whose amplitude is crucial to establish them as robust magnetic bits, while their topological nature gives rise to a plethora of exquisite features such as topological protection, the skyrmion

Yu. N. Eroshenko, E. A. Popova

In recent years, evidence has been obtained that in the outer region of the Solar System (in the inner part of the Oort cloud), at a distance $\sim300-700$ AU from the Sun, there may be a captured planet or a primordial black hole. In this paper, we show that the gravitational scattering of dust particles in the same region on this object can transfer them t

Miguel Ángel Escobedo

The evolution of hard probes in a medium is a complex multiscale problem that significantly benefits from the use of Effective Field Theories (EFTs). Within the EFT framework, we aim to define a series of EFTs in a way that addresses each energy scale individually in separate steps. However, studying hard probes in a medium presents challenges. This is becau

Iztok Banic, Goran Erceg, Judy Kennedy, Chris Mouron

We construct a mixing homeomorphism on the Lelek fan. We also construct a mixing homeomorphism on the Cantor fan. Then, we construct a family of uncountably many pairwise non-homeomorphic (non-)smooth fans that admit a mixing homeomorphism.

Hakim Bouchal, Ahror Belaid

Segmentation of Arabic manuscripts into lines of text and words is an important step to make recognition systems more efficient and accurate. The problem of segmentation into text lines is solved since there are carefully annotated dataset dedicated to this task. However, To the best of our knowledge, there are no dataset annotating the word position of Arab

Negin Ghamsarian

Following the technological advancements in medicine, the operation rooms are evolving into intelligent environments. The context-aware systems (CAS) can comprehensively interpret the surgical state, enable real-time warning, and support decision-making, especially for novice surgeons. These systems can automatically analyze surgical videos and perform index

Hsuan-Wei Lee, Colin Cleveland, Attila Szolnoki

Taxes are an essential and uniformly applied institution for maintaining modern societies. However, the levels of taxation remain an intensive debate topic among citizens. If each citizen contributes to common goals, a minimal tax would be sufficient to cover common expenses. However, this is only achievable at high cooperation level; hence, a larger tax bra

V. Manuilov

Let $X$ be a metric measure space. A Delone subset $D\subset X$ is a uniformly discrete set coarsely equivalent to $X$. We consider the space $\mathcal D_F$ of controlled Delone subsets of $X$ with an appropriate metric, and show that it, together with $X$ itself, is a compact space. By assigning to each point $D$ of $\mathcal D_F$ (resp., to $X$) the unifor

Philipp Otto, Osman Doğan, Süleyman Taşpınar

This paper explores the estimation of a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model. The log-volatility term in this model can depend on (i) the spatial lag of the log-squared outcome variable, (ii) the time-lag of the log-squared outcome variable, (iii) the spatiotemporal lag of the log-squared outcome variable, (iv) ex

Jiquan Yuan, Xinyan Cao, Linjing Cao, Jinlong Lin

In recent years, Artificial Intelligence Generated Content (AIGC) has gained widespread attention beyond the computer science community. Due to various issues arising from continuous creation of AI-generated images (AIGI), AIGC image quality assessment (AIGCIQA), which aims to evaluate the quality of AIGIs from human perception perspectives, has emerged as a

Yves-Henri Sanejouand

Magnitude predictions of $\Lambda$CDM, as parametrized by the Planck collaboration, are not consistent with the supernova data of the whole Pantheon+ sample even when, in order to take into account the uncertainty about its value, the Hubble constant is adjusted. This is a likely consequence of the increase of the number of low-redshift supernovae in the Pan

Hannah K. Wayment-Steele

We provide an English translation of "\"Uber positive Darstellungen von Polynomen" by Ernst Meissner, originally published 1911 in Mathematische Annalen (70) 223-235.

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

A search has been performed for the semileptonic decays $D^{0}\to K_{S}^{0} K^{-} e^{+}\nu_{e}$, $D^{+}\to K_{S}^{0} K_{S}^{0} e^{+}\nu_{e}$ and $D^{+}\to K^{+}K^{-} e^{+}\nu_{e}$, using $7.9~\mathrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected at the center-of-mass energy $\sqrt{s}=3.773$ GeV by the BESIII detector operating at the BEPCII collider. No

David B. Brückner, Gašper Tkačik

A key feature of many developmental systems is their ability to self-organize spatial patterns of functionally distinct cell fates. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understo

Yossi Rosenzweig, Yevgeny Kats, Menachem Givon, Yonathan Japha

Searching for physics beyond the Standard Model is one of the main tasks of experimental physics. Candidates for dark matter include axion-like ultralight bosonic particles. Comagnetometers form ultra-high sensitivity probes for such particles and any exotic field that interacts with the spin of an atom. Here, we propose a multi-atom-species probe that enabl

Evgenii Vityaev

The work demonstrates that brain might reflect the external world causal relationships in the form of a logically consistent and prognostic model of reality, which shows up as consciousness. The paper analyses and solves the problem of statistical ambiguity and provides a formal model of causal relationships as probabilistic maximally specific rules. We supp

Qianqian Chen, Lei Qiao, Fuchun Zhang, Zheng Zhu

Motivated by significant discrepancies between experimental observations of electron-doped cuprates and numerical results of the Hubbard and $t$-$J$ models, we investigate the role of inter-site interactions $V$ by studying the $t$-$J$-$V$ model on square lattices. Based on large-scale density matrix renormalization group simulations, we identify the ground-

Rodrigo Benevides, Maxwell Drimmer, Giacomo Bisson, Francesco Adinolfi

A known source of decoherence in superconducting qubits is the presence of broken Cooper pairs, or quasiparticles. These can be generated by high-energy radiation, either present in the environment or purposefully introduced, as in the case of some hybrid quantum devices. Here, we systematically study the properties of a transmon qubit under illumination by

Cheng Chang, Zhouping Xin, Tieyong Zeng

Maxwell-Amp\`{e}re-Nernst-Planck (MANP) equations were recently proposed to model the dynamics of charged particles. In this study, we enhance a numerical algorithm of this system with deep learning tools. The proposed hybrid algorithm provides an automated means to determine a proper approximation for the dummy variables, which can otherwise only be obtaine

Luca Marzari, Gabriele Roncolato, Alessandro Farinelli

Deep Neural Networks (DNNs) are powerful tools that have shown extraordinary results in many scenarios, ranging from pattern recognition to complex robotic problems. However, their intricate designs and lack of transparency raise safety concerns when applied in real-world applications. In this context, Formal Verification (FV) of DNNs has emerged as a valuab

K. J. Luke, Abhinav Mishra, Vihaan Ghare, Shaurya Chanyal

Science-AI Symbiotic Group at Seven Square Academy, Naigaon was formed in 2023 with the purpose of bringing school students to the forefronts of science research by involving them in hands on research. In October 2023 a new project was started with the goal of studying the lunar surface by real-time observations and open source data. Twelve students/members

Kirill Mazur, Gwangbin Bae, Andrew J. Davison

Joint camera pose and dense geometry estimation from a set of images or a monocular video remains a challenging problem due to its computational complexity and inherent visual ambiguities. Most dense incremental reconstruction systems operate directly on image pixels and solve for their 3D positions using multi-view geometry cues. Such pixel-level approaches

B. A. Kniehl, V. N. Velizhanin

We perform direct diagrammatic calculations of the anomalous dimensions of twist-two operators in extended N=2 and N=4 super Yang-Mills theories (SYM). In the case of N=4 SYM, we compute the four-loop anomalous dimension of the twist-two operator for several fixed values of Lorentz spin. This is the first direct diagrammatic calculation of this kind, and we

Giulio Paolucci, Isabella Cama, Cristina Campi, Michele Piana

The analysis of the psoas muscle in morphological and functional imaging has proved to be an accurate approach to assess sarcopenia, i.e. a systemic loss of skeletal muscle mass and function that may be correlated to multifactorial etiological aspects. The inclusion of sarcopenia assessment into a radiological workflow would need the implementation of comput

Qing Yang, Yingzhi Tian

Mader [J. Combin. Theory Ser. B 40 (1986) 152-158] proved that every $k$-edge-connected graph $G$ with minimum degree at least $k+1$ contains a vertex $u$ such that $G-\{u\}$ is still $k$-edge-connected. In this paper, we prove that every $k$-edge-connected graph $G$ with minimum degree at least $k+2$ contains an edge $uv$ such that $G-\{u,v\}$ is $k$-edge-c

Shao-Bo Lin

This paper focuses on parameter selection issues of kernel ridge regression (KRR). Due to special spectral properties of KRR, we find that delicate subdivision of the parameter interval shrinks the difference between two successive KRR estimates. Based on this observation, we develop an early-stopping type parameter selection strategy for KRR according to th

E. E. Vityaev

The paper considers a non-reductionist theory of consciousness, which is not reducible to theories of reality and to physiological or psychological theories. Following D.I.Dubrovsky's "informational approach" to the "Mind-Brain Problem", we consider the reality through the prism of information about observed phenomena, which, in turn, is perceived by subject

Chenguang Rao, Zhiguo Ding, Octavia A. Dobre, Xuchu Dai

The resolution is an important performance metric of near-field communication networks. In particular, the resolution of near field beamforming measures how effectively users can be distinguished in the distance-angle domain, which is one of the most significant features of near-field communications. In a comparison, conventional far-field beamforming can di

Mateusz Kocot, Krzysztof Misan, Valentina Avati, Edoardo Bossini

Measurements from particle timing detectors are often affected by the time walk effect caused by statistical fluctuations in the charge deposited by passing particles. The constant fraction discriminator (CFD) algorithm is frequently used to mitigate this effect both in test setups and in running experiments, such as the CMS-PPS system at the CERN's LHC. The

Gi-Ren Liu

The purpose of this paper is to analyze the distribution distance between random vectors derived from the magnitude of the analytic wavelet transform of the squared envelopes of Gaussian processes and their large-scale limits. When the Hurst index of the underlying Gaussian process falls below 1/2, the large-scale limit takes the form of a combination of the

Adrian Marius Deaconu, Javad Tayyebi, Mihai-Lucian Rîtan

The maximum capacity path problem is to find a path from a source to a sink which has the maximum capacity among all paths. This paper addresses an extension of this problem which considers loss factors. It is called the generalized maximum capacity path problem. The problem is a network flow optimization problem whose network contains capacities as well as

Chenhao He, Pramit Saha

The utilization of deep learning-based object detection is an effective approach to assist visually impaired individuals in avoiding obstacles. In this paper, we implemented seven different YOLO object detection models \textit{viz}., YOLO-NAS (small, medium, large), YOLOv8, YOLOv7, YOLOv6, and YOLOv5 and performed comprehensive evaluation with carefully tune

Sören Christensen, Niklas Dexheimer, Claudia Strauch

The standard theory of optimal stopping is based on the idealised assumption that the underlying process is essentially known. In this paper, we drop this restriction and study data-driven optimal stopping for a general diffusion process, focusing on investigating the statistical performance of the proposed estimator of the optimal stopping barrier. More spe

Zico da Silva, Stacy Shield, Penny E. Hudson, Alan M. Wilson

The complex dynamics of animal manoeuvrability in the wild is extremely challenging to study. The cheetah ($\textit{Acinonyx jubatus}$) is a perfect example: despite great interest in its unmatched speed and manoeuvrability, obtaining complete whole-body motion data from these animals remains an unsolved problem. This is especially difficult in wild cheetahs

Shraddha M. Naik, Tanujit Chakraborty, Madhurima Panja, Abdenour Hadid

Real-world datasets often exhibit imbalanced data distribution, where certain class levels are severely underrepresented. In such cases, traditional pattern classifiers have shown a bias towards the majority class, impeding accurate predictions for the minority class. This paper introduces an imbalanced data-oriented classifier using probabilistic neural net

Gilles Audemard, Christophe Lecoutre, Emmanuel Lonca

This document represents the proceedings of the 2023 XCSP3 Competition. The results of this competition of constraint solvers were presented at CP'23 (the 29th International Conference on Principles and Practice of Constraint Programming, held in Toronto, Canada from 27th to 31th August, 2023).

Michael A. Lomholt, Ralf Metzler

Some proteins can find their targets on DNA faster than by pure diffusion in the three-dimensional cytoplasm, through the process of facilitated diffusion: They can loosely bind to DNA and temporarily slide along it, thus being guided by the DNA molecule itself to the target. This chapter examines this process in mathematical detail with a focus on including

Mengnan Jiang, Jingcun Wang, Amro Eldebiky, Xunzhao Yin

Deep neural networks (DNNs) have demonstrated remarkable success in various fields. However, the large number of floating-point operations (FLOPs) in DNNs poses challenges for their deployment in resource-constrained applications, e.g., edge devices. To address the problem, pruning has been introduced to reduce the computational cost in executing DNNs. Previ

David M T Kuo

The thermoelectric properties of armchair graphene nanoribbons (AGNRs) with array characteristics are investigated theoretically using the tight-binding model and Green's function technique. The AGNR structures with array characteristics are created by embedding a narrow boron nitride nanoribbon (BNNR) into a wider AGNR, resulting in two narrow AGNRs. This s

Tim Salzmann, Jon Arrizabalaga, Joel Andersson, Marco Pavone

While real-world problems are often challenging to analyze analytically, deep learning excels in modeling complex processes from data. Existing optimization frameworks like CasADi facilitate seamless usage of solvers but face challenges when integrating learned process models into numerical optimizations. To address this gap, we present the Learning for CasA

Vladimir Kutsenko, Stanislav Molchanov, Elena Yarovaya

We consider a continuous-time branching random walk on $\mathbb{Z}$ in a random non homogeneous environment. Particles can walk on the lattice points or disappear with random intensities. The process starts with one particle at initial time $t=0$. It can walk on the lattice points or disappear with a random intensity until it reach the point, where initial p

Chang Liu, Terence Jie Chua, Jun Zhao

The concept of the Metaverse has garnered growing interest from both academic and industry circles. The decentralization of both the integrity and security of digital items has spurred the popularity of play-to-earn (P2E) games, where players are entitled to earn and own digital assets which they may trade for physical-world currencies. However, these comput

Yong-rui Chen, Yang-yang Tan, Wei-jie Fu

The Schwinger-Keldysh functional renormalization group (fRG) developed in [1] is employed to investigate critical dynamics related to a second-order phase transition. The effective action of model A is expanded to the order of $O(\partial^2)$ in the derivative expansion for the $O(N)$ symmetry. By solving the fixed-point equations of effective potential and

Matteo Allaix

In the era of extensive data growth, robust and efficient mechanisms are needed to store and manage vast amounts of digital information, such as Data Storage Systems (DSSs). Concurrently, privacy concerns have arisen, leading to the development of techniques like Private Information Retrieval (PIR) to enable data access while preserving privacy. A PIR protoc

Sakshi Ranjan, Subhankar Mishra

Google app market captures the school of thought of users from every corner of the globe via ratings and text reviews, in a multilinguistic arena. The potential information from the reviews cannot be extracted manually, due to its exponential growth. So, Sentiment analysis, by machine learning and deep learning algorithms employing NLP, explicitly uncovers a

Yann Vonlanthen, Jakub Sliwinski, Massimo Albarello, Roger Wattenhofer

This paper presents Banyan, the first rotating leader state machine replication (SMR) protocol that allows transactions to be confirmed in just a single round-trip time in the Byzantine fault tolerance (BFT) setting. Based on minimal alterations to the Internet Computer Consensus (ICC) protocol and with negligible communication overhead, we introduce a novel

Dengke Zhou, Pei Wang, Di Li, Jianhua Fang

Globular clusters harbor numerous millisecond pulsars, but long-period pulsars ($P \gtrsim 100$ ms) are rarely found. In this study, we employed a fast folding algorithm to analyze observational data from multiple globular clusters obtained by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), aiming to detect the existence of long-period puls

Hassan Hooshdar Rostami, Vahid Ahmadi, Saeed Pahlavan

This paper reports an opto-electro-thermal analysis of VCSEL structures with a surface-relief filter and different aperture diameters. 808-nm GaAs/AlGaAs VCSELs with different aperture diameters and different surface-relief filter diameters are studied and the LI curves, the optical spectra, and the temperature peaks of the devices are obtained. Also, the im