December 2023 arXiv papers — page 53

Geoffrey Mboya

We conduct a systematic search of codimension 2 Complete Intersection Calabi--Yau threefolds (CICY3) in rank 2 toric ambient spaces and fibered by complete intersection of a quadric and a cubic in $\C\P^4$. We classify both the nonsingular ones as well as those with isolated singularities.

Francesco Toppan

In a recent paper (Balbino-de Freitas-Rana-FT, arXiv:2309.00965) we proved that the supercharges of the supersymmetric quantum mechanics can be statistically transmuted and accommodated into a $Z_2^n$-graded parastatistics. In this talk I derive the $6=1+2+3$ transmuted spectrum-generating algebras (whose respective $Z_2^n$ gradings are $n=0,1,2$) of the ${\

Akshita Reddy Mavurapu, Haoqi Shan, Xiaolong Guo, Orlando Arias

The hardware security community has made significant advances in detecting Hardware Trojan vulnerabilities using software fuzzing-inspired automated analysis. However, the Electronic Design Automation (EDA) code base itself remains under-examined by the same techniques. Our experiments in fuzzing EDA tools demonstrate that, indeed, they are prone to software

Haoqi Shan, Dean Sullivan, Orlando Arias

In recent years we have seen an explosion in the usage of low-cost, low-power microcontrollers (MCUs) in embedded devices around us due to the popularity of Internet of Things (IoT) devices. Although this is good from an economics perspective, it has also been detrimental for security as microcontroller-based systems are now a viable attack target. In respon

Georg Oberdieck, Aaron Pixton

We determine the quantum multiplication with divisor classes on the Hilbert scheme of points on an elliptic surface $S \to \Sigma$ for all curve classes which are contracted by the induced fibration $S^{[n]} \to \Sigma^{[n]}$. The formula is expressed in terms of explicit operators on Fock space. The structure constants are meromorphic quasi-Jacobi forms of

Alice Bellettini, Andrea Richaud, Vittorio Penna

We consider massive vortices in binary condensates, where the immiscibility condition entails the trapping of the minority component in the vortex cores of the majority component. We study such vortices by means of a 2D point-like model, and show how the relevant dynamical equations exhibit vortex-pair solutions characterized by different vortex masses and c

Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman

Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context. A key limitation of previously proposed models is their reliance on a predefined action space size and structure. The introduction of a new action space often requires data re-collection and

Sebastián Pinto, Alejandro Pardo Pintos, Pablo Balenzuela, Marcos Trevisan

The digital revolution has transformed the exchange of information between people, blurring the traditional roles of sources and recipients. In this study, we explore the influence of this bidirectional feedback using a publicly available database of quotes, which act as distinct units of information flowing through the digital environment with minimal disto

Hendrik Poulsen Nautrup, Hans J. Briegel

Measurement-based quantum computation (MBQC) is a paradigm for quantum computation where computation is driven by local measurements on a suitably entangled resource state. In this work we show that MBQC is related to a model of quantum computation based on Clifford quantum cellular automata (CQCA). Specifically, we show that certain MBQCs can be directly co

Isabel Hubard, Elías Mochán, Antonio Montero

Voltage operations extend traditional geometric and combinatorial operations (such as medial, truncation, prism, and pyramid over a polytope) to operations on maniplexes, maps, polytopes, and hypertopes. In classical operations, the symmetries of the original object remain in the operated one, but sometimes additional symmetries are created; the same situati

Jing Gao, Arieh Iserles

The subject of this paper is the design of efficient and stable spectral methods for time-dependent partial differential equations in unit balls. We commence by sketching the desired features of a spectral method, which is defined by a choice of an orthonormal basis acting in the spatial domain. We continue by considering in detail the choice of a $W$-functi

Wenchao Wu, Yuanqing Yang, Yansha Deng, A. Hamid Aghvami

The ultra-reliable and low-latency communication (URLLC) service of the fifth-generation (5G) mobile communication network struggles to support safe robot operation. Nowadays, the sixth-generation (6G) mobile communication network is proposed to provide hyper-reliable and low-latency communication to enable safer control for robots. However, current 5G/ 6G r

Takashi Yanagisawa

We discuss the development of Kondo physics from the resolution of the resistance minimum by J. Kondo to recent developments in physics. The Kondo effect has given a great impact to all areas of physics. This reminds us that physics is one unified science. Kondo's pioneering work has led major developments in physics. We show brief history of the Kondo effec

Marius Roland, Alexandre Forel, Thibaut Vidal

This paper studies chance-constrained stochastic optimization problems with finite support. It presents an iterative method that solves reduced-size chance-constrained models obtained by partitioning the scenario set. Each reduced problem is constructed to yield a bound on the optimal value of the original problem. We show how to adapt the partitioning of th

Arshad Kaji, Manan Shah

Large language models (LLMs) have exerted a considerable impact on diverse language-related tasks in recent years. Their demonstrated state-of-the-art performance is achieved through methodologies such as zero-shot or few-shot prompting. These models undergo training on extensive datasets that encompass segments of the Internet and subsequently undergo fine-

Sepehr Assadi, Christian Konrad, Kheeran K. Naidu, Janani Sundaresan

In the semi-streaming model for processing massive graphs, an algorithm makes multiple passes over the edges of a given $n$-vertex graph and is tasked with computing the solution to a problem using $O(n \cdot \text{polylog}(n))$ space. Semi-streaming algorithms for Maximal Independent Set (MIS) that run in $O(\log\log{n})$ passes have been known for almost a

Bin Yu

Let $M_k$ ($k\in \mathbb Z$ and $|k|>4$) be the $3$-manifold obtained by doing $k$ Dehn surgery on the figure-eight knot, and $X_t$ be the canonical Anosov flow on $M_k$ that is constructed by Goodman. The main result of this article is that if $|k|\gg 0$, then $Mod (M_k) \cong \mathbb {Z}_2 \oplus \mathbb {Z}_2$ and every element of the mapping class group

V. F. Molchanov

We present an approach to Berezin quantization (a variant of quantization in the spirit of Berezin) on para-Hermitian symmetric spaces using the notion of an "overgroup". This approach gives covariant and contravariant symbols and the Berezin transform in a natural and transparent way

Julian D. Schiller, Matthias A. Müller

We propose a moving horizon estimation scheme to estimate the states and the unknown constant parameters of general nonlinear uncertain discrete-time systems. The proposed framework and analysis explicitly do not involve the a priori verification of a particular excitation condition for the parameters. Instead, we use online information about the actual exci

Kevyn Gallegos-Moncayo, Justine Jean, Nicolas Folastre, Arash Jamali

The study focuses on NMC811, a promising material for high-capacity batteries, and investigates the challenges associated with its use, specifically the formation of the Cathode Electrolyte Interphase (CEI) layer due to chemical reactions. This layer is a consequence of the position of the LUMO energy level of NMC811 that is close to the HOMO level of liquid

Zhuangzhuang Jia, Grani A. Hanasusanto, Phebe Vayanos, Weijun Xie

We consider the problem of learning fair policies for multi-stage selection problems from observational data. This problem arises in several high-stakes domains such as company hiring, loan approval, or bail decisions where outcomes (e.g., career success, loan repayment, recidivism) are only observed for those selected. We propose a multi-stage framework tha

V. N. Zirakashvili, V. S. Ptuskin, S. I. Rogovaya

It is shown that the acceleration of particles by a powerful relativistic jet associated with the activity of a supermassive black hole in the Galactic center several million years ago may explain the observed cosmic ray spectrum at energies higher than $10^{15}$ eV. The accelerated particles are efficiently confined in the extended magnetized gas halo creat

Sidra Gibeault, Temitayo N. Adeyeye, Liam A. Pocher, Daniel P. Lathrop

Superparamagnetic tunnel junctions (SMTJs) are promising sources of randomness for compact and energy efficient implementations of probabilistic computing techniques. Augmenting an SMTJ with electronic circuits, to convert the random telegraph fluctuations of its resistance state to stochastic digital signals, gives a basic building block known as a probabil

Ettore Tiotto, Víctor Pérez, Whitney Tsang, Lukas Sommer

Similar to other programming models, compilers for SYCL, the open programming model for heterogeneous computing based on C++, would benefit from access to higher-level intermediate representations. The loss of high-level structure and semantics caused by premature lowering to low-level intermediate representations and the inability to reason about host and d

A. Galindo-Tellez, V. Sharyy, C. -H. Sung, M. Follin

The ClearMind project aims to develop a TOF-PET position-sensitive detection module optimized for time and spatial resolutions and detection efficiency. For this, we use a 59 mm $\times$ 59 mm $\times$ 5 mm monolithic PbWO$_4$ (PWO) crystal, which is encapsulated within a commercial Micro-Channel Plate Photomultiplier tube MAPMT253 with a bialkali photocatho

Federico Castelletti

Estimating dependence relationships between variables is a crucial issue in many applied domains, such as medicine, social sciences and psychology. When several variables are entertained, these can be organized into a network which encodes their set of conditional dependence relations. Typically however, the underlying network structure is completely unknown

Krzysztof J. Ciosmak

We present a range of applications of localisation for constrained transports for pairs of probability measures in order with respect to a lattice cone. These examples comprise irreducible convex paving for martingale transports in infinite-dimensional spaces, irreducible convex paving for submartingale transports, localisation of the Monge--Kantorovich prob

Valentina Cammarota, Riccardo-W. Maffucci, Domenico Marinucci, Maurizia Rossi

The geometry of Arithmetic Random Waves has been extensively investigated in the last fifteen years, starting from the seminal papers [RW08, ORW08]. In this paper we study the correlation structure among different functionals such as nodal length, boundary length of excursion sets, and the number of intersection of nodal sets with deterministic curves in dif

Yuriy Tumarkin

We consider a special case of the question of classification of invariant Radon measures of $\mathbb{Z}^m$-valued skew-products over interval exchange transformations, which arise as Poincar\'e sections of the linear flow on periodic infinite translation surfaces. In the case of periodic type skew-products, we obtain a full classification of ergodic invarian

Goran Duplančić, Peter Kroll, Kornelija Passek-K., Lech Szymanowski

The twist-3 contribution, consisting of twist-2 transversity generalized parton distributions (GPDs) and a twist-3 meson wave function, to deeply virtual pion electroproduction is discussed. The twist-3 meson wave function includes both the $q\bar{q}$ and the $q\bar{q}g$ Fock components. Two methods to regularize the end-point singularities are introduced -

V. Temlyakov

In this paper we analyze approximation and recovery properties with respect to systems satisfying universal sampling discretization property and a special incoherence property. We apply a powerful nonlinear approximation method -- the Weak Chebyshev Greedy Algorithm (WCGA). We establish that the WCGA based on good points for the $L_p$-universal discretizatio

Habib Boloorchi Tabrizi, Christopher Crick

In this study, we address the critical challenge of balancing speed and accuracy while maintaining interpretablity in visual odometry (VO) systems, a pivotal aspect in the field of autonomous navigation and robotics. Traditional VO systems often face a trade-off between computational speed and the precision of pose estimation. To tackle this issue, we introd

Leon Frischauf, Otmar Scherzer, Cong Shi

In this paper we investigate the solution of inverse problems with neural network ansatz functions with generalized decision functions. The relevant observation for this work is that such functions can approximate typical test cases, such as the Shepp-Logan phantom, better, than standard neural networks. Moreover, we show that the convergence analysis of num

Richard S. Falk, Ragnar Winther

The bubble transform is a procedure to decompose differential forms, which are piecewise smooth with respect to a given triangulation of the domain, into a sum of local bubbles. In this paper, an improved version of a construction in the setting of the de Rham complex previously proposed by the authors is presented. The major improvement in the decomposition

Sanjay Nayak, Tun-Wei Hsu, Robert Boyd, Jens Gibmeier

Understanding the nucleation and growth of polycrystalline thin films is a long-standing goal. Polycrystalline films have many grains with different orientations that affect thin-film properties. Numerous studies have been done to determine these grain size and their preferred crystallographic orientation as well as stress in films. However most past studies

Wenjie Fang, Éric Fusy, Philippe Nadeau

We introduce a simple bijection between Tamari intervals and the blossoming trees (Poulalhon and Schaeffer, 2006) encoding planar triangulations, using a new meandering representation of such trees. Its specializations to the families of synchronized, Kreweras, new/modern, and infinitely modern intervals give a combinatorial proof of the counting formula for

Bart Wijns, Ralf Eichhorn, Bart Cleuren

A microscopic model for a translational Brownian motor, dubbed as Brownian Translator, is introduced. It is inspired by the Brownian Gyrator of Filliger and Reimann (Filliger and Reimann 2007). The Brownian Translator consists of a spatially asymmetric object moving freely along a line due to perpetual collisions with a surrounding ideal gas. When this gas h

Dipendu Bhandari, Arghyajit Datta, Arunansu Sil

We show that a phase transition may take place in the early Universe at a temperature $T_*$ via a Standard Model singlet scalar field which happens to couple to right handed neutrinos (RHN) resulting a temperature dependent mass for them that finally relaxes to a constant value after electroweak phase transition (EWPT). As a result, a requisite amount of lep

Lening Wang, Yilong Ren, Han Jiang, Pinlong Cai

Traffic accidents, being a significant contributor to both human casualties and property damage, have long been a focal point of research for many scholars in the field of traffic safety. However, previous studies, whether focusing on static environmental assessments or dynamic driving analyses, as well as pre-accident predictions or post-accident rule analy

Erez Peterfreund, Iryna Burak, Ofir Lindenbaum, Jim Gimlett

Fusing measurements from multiple, heterogeneous, partial sources, observing a common object or process, poses challenges due to the increasing availability of numbers and types of sensors. In this work we propose, implement and validate an end-to-end computational pipeline in the form of a multiple-auto-encoder neural network architecture for this task. The

Zelin Hu, Qibin Ye, Yixuan Huang, Su Hu

Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) is promising for future sixth-generation mobile communication systems. Existing works focus on the joint estimation of the targets' range and velocity for OFDM-based ISAC systems. In contrast, this paper studies the three-dimensional joint estimation (3DJE) of

Przemysław Berk, Martyna Górska, Thierry de la Rue

We study the class $Erg^\perp$ of automorphisms which are disjoint with all ergodic systems. We prove that the identities are the only multipliers of $Erg^\perp,$ that is, each automorphism whose every joining with an element of $Erg^{\perp}$ yields a system which is again an element of $Erg^{\perp}$, must be an identity. Despite this fact, we show that $Erg

Zhongchang Sun, Yousef El-Laham, Svitlana Vyetrenko

Stochastic differential equations (SDEs) have been widely used to model real world random phenomena. Existing works mainly focus on the case where the time series is modeled by a single SDE, which might be restrictive for modeling time series with distributional shift. In this work, we propose a change point detection algorithm for time series modeled as neu

Lauren A. Hurley, Juan G. Restrepo, Sean E. Shaheen

Reservoir computing is a machine learning framework where the readouts from a nonlinear system (the reservoir) are trained so that the output from the reservoir, when forced with an input signal, reproduces a desired output signal. A common implementation of reservoir computers is to use a recurrent neural network as the reservoir. The design of this network

Stanislaw Szymanowicz, Christian Rupprecht, Andrea Vedaldi

We introduce the \method, an ultra-efficient approach for monocular 3D object reconstruction. Splatter Image is based on Gaussian Splatting, which allows fast and high-quality reconstruction of 3D scenes from multiple images. We apply Gaussian Splatting to monocular reconstruction by learning a neural network that, at test time, performs reconstruction in a

Leandro Da Rold, Manuel Epele, Anibal D. Medina, Nicolás I. Mileo

We study di-Higgs production via gluon fusion at the high luminosity LHC in the presence of new physics, focusing on the $b\bar b\gamma\gamma$ final states. Taking a minimal set of three scalar leptoquarks (LQs) with cubic and quartic interactions with the Higgs and choosing four benchmark points with a light LQ, we perform a detailed analysis of differentia

Max Goplerud, Omiros Papaspiliopoulos, Giacomo Zanella

While generalized linear mixed models are a fundamental tool in applied statistics, many specifications, such as those involving categorical factors with many levels or interaction terms, can be computationally challenging to estimate due to the need to compute or approximate high-dimensional integrals. Variational inference is a popular way to perform such

Lars Bengel, Lydia Blümel, Elfia Bezou-Vrakatseli, Federico Castagna

This volume contains revised versions of the papers selected for the fourth volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial i

Charles Arnal, David Cohen-Steiner, Vincent Divol

In general, the critical points of the distance function $d_{\mathsf{M}}$ to a compact submanifold $\mathsf{M} \subset \mathbb{R}^D$ can be poorly behaved. In this article, we show that this is generically not the case by listing regularity conditions on the critical and $\mu$-critical points of a submanifold and by proving that they are generically satisfie

Ernestine Großmann, Jonas Sauer, Christian Schulz, Patrick Steil

We present FLASH-TB, a journey planning algorithm for public transit networks that combines Trip-Based Public Transit Routing (TB) with the Arc-Flags speedup technique. The basic idea is simple: The network is partitioned into a configurable number of cells. For each cell and each possible transfer between two vehicles, the algorithm precomputes a flag that

Pablo Burset, Benjamin Roussel, Michael Moskalets, Christian Flindt

Electron quantum optics explores coherent single-electron charge pulse propagation in electronic nanoscale circuits akin to table-top photon setups. While past experiments focused on normal-state conductors, incorporating superconductors holds promise for exploiting the electron-hole degree of freedom in quantum sensing applications and quantum information p

Fabio Frommer

Interacting particle systems in a finite-volume in equilibrium are often described by a grand-canonical ensemble induced by the corresponding Hamiltonian, i.e. a finite-volume Gibbs measure. However, in practice, directly measuring this Hamiltonian is not possible, as such, methods need to be developed to calculate the Hamiltonian potentials from measurable

Simone Lauria, Mohammed F. Saleh

We present a novel implementation of conditional Long Short-Term Memory Recurrent Neural Networks that successfully predict the spectral evolution of a pulse in nonlinear periodically-poled waveguides. The developed networks offer large flexibility by allowing the propagation of optical pulses with ranges of energies and temporal widths in waveguides with di

Minghao Chen

With the rapid advancement of technology, the recognition of underwater acoustic signals in complex environments has become increasingly crucial. Currently, mainstream underwater acoustic signal recognition relies primarily on time-frequency analysis to extract spectral features, finding widespread applications in the field. However, existing recognition met

S. A. Alavi, T. Fallahi Serish

Investigation of neutrino spin oscillation in the gravitational fields of black holes (BHs) is one of the interesting topics in neutrino physics. On the other hand, in recent years, many studies have been devoted to the exploration of different physical phenomena in higher dimensions. Non-commutative geometry has also been focus of researchers in recent year

Yousef El-Laham, Elizabeth Fons, Dillon Daudert, Svitlana Vyetrenko

Data augmentation techniques play an important role in enhancing the performance of deep learning models. Despite their proven benefits in computer vision tasks, their application in the other domains remains limited. This paper proposes a Mixup regularization scheme, referred to as UMAP Mixup, designed for ``on-manifold" automated data augmentation for deep

Finantius E. M. Rahangiar, Adam B. Cahaya, Melania S. Muntini, Isa Anshori

Half-metal ferromagnets were predicted [in IEEE Trans. Mag. 51, 1 (2015)] to give large thermoelectric performance in anti-parallel spin valve configuration. Despite being metals that suffer from the Wiedemann-Franz law, the additional spin degrees of freedom allow for tuning of the thermoelectric properties due to the spin-valve enhancement factor (SVEF). W

Hongtao Wu, Ya Jing, Chilam Cheang, Guangzeng Chen

Generative pre-trained models have demonstrated remarkable effectiveness in language and vision domains by learning useful representations. In this paper, we extend the scope of this effectiveness by showing that visual robot manipulation can significantly benefit from large-scale video generative pre-training. We introduce GR-1, a straightforward GPT-style

Inmaculada Baldomá, Maciej J. Capiński, Mar Giralt, Marcel Guardia

The Restricted Planar Circular 3-Body Problem models the motion of a body of negligible mass under the gravitational influence of two massive bodies, called the primaries, which perform circular orbits coplanar with that of the massless body. In rotating coordinates, it can be modelled by a two degrees of freedom Hamiltonian system, which has five critical p

Christopher Aubin, Bipasha Chakraborty, Will Detmold, Sofie Martins

We review the level of welcomeness that members of the lattice field theory community feel based on the results of a survey performed in May and June 2023. While respondents reported generally high levels of feeling welcome at the lattice conference, women and people with diverse gender identities, sexual orientations, ethnic backgrounds and religious affili

Junwu Chen, Philippe Schwaller

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher-order connections like multi-center bonds and conjugated structures. To tackle this challenge, we introduce molecular hypergraphs an

Xiao-Fei Liu, Yuta Matsumoto, Takafumi Fujita, Arne Ludwig

Adiabatic processes can keep the quantum system in its instantaneous eigenstate, which is robust to noises and dissipation. However, it is limited by sufficiently slow evolution. Here, we experimentally demonstrate the transitionless quantum driving (TLQD) of the shortcuts to adiabaticity in gate-defined semiconductor quantum dots (QDs) to greatly accelerate

César Ayala, Camilo Castro-Arriaza, Gorazd Cvetič

Approximate knowledge of the renormalon structure of the Bjorken polarised sum rule (BSR) ${\overline \Gamma}_1^{{\rm p-n}}(Q^2)$ leads to the corresponding BSR characteristic function that allows us to evaluate the leading-twist part of BSR. In our previous work \cite{pPLB}, this evaluation (resummation) was performed using perturbative QCD (pQCD) coupling

Norton Lee

We study the relation between the quantum integrable systems derived from the dimer graphs and five dimensional $\mathcal{N}=1$ supersymmetric gauge theories on $S^1 \times \mathbb{R}^4$. We construct integrable systems based on new dimer graphs obtained from modification of hexagon dimer diagram. We study the gauge theories in correspondence to the newly pr

Dhananjay Raju, Georgios Bakirtzis, Ufuk Topcu

Securing dynamic networks against adversarial actions is challenging because of the need to anticipate and counter strategic disruptions by adversarial entities within complex network structures. Traditional game-theoretic models, while insightful, often fail to model the unpredictability and constraints of real-world threat assessment scenarios. We refine s

Edoardo Debenedetti, Zishen Wan, Maksym Andriushchenko, Vikash Sehwag

The last six years have witnessed significant progress in adversarially robust deep learning. As evidenced by the CIFAR-10 dataset category in RobustBench benchmark, the accuracy under $\ell_\infty$ adversarial perturbations improved from 44\% in \citet{Madry2018Towards} to 71\% in \citet{peng2023robust}. Although impressive, existing state-of-the-art is sti

Rafael Hanashiro, Patrick Jaillet

To address the needs of modeling uncertainty in sensitive machine learning applications, the setup of distributionally robust optimization (DRO) seeks good performance uniformly across a variety of tasks. The recent multi-distribution learning (MDL) framework tackles this objective in a dynamic interaction with the environment, where the learner has sampling

Federico Binda, Tommy Lundemo, Alberto Merici, Doosung Park

We prove that (logarithmic) prismatic and (logarithmic) syntomic cohomology are representable in the category of logarithmic motives. As an application, we obtain Gysin maps for prismatic and syntomic cohomology, and we explicitly identify their cofibers. We also prove a smooth blow-up formula and we compute prismatic and syntomic cohomology of Grassmannians

Stéphane Alarie, Charles Audet, Miguel Diago, Sébastien Le Digabel

This work introduces a novel blackbox optimization algorithm for computationally expensive constrained multi-fidelity problems. When applying a direct search method to such problems, the scarcity of feasible points may lead to numerous costly evaluations spent on infeasible points. Our proposed fidelity and interruption controlled optimization algorithm addr

Li Wang, Xiaohua Zhang, Longfei Li, Hongyun Meng

Spectral unmixing is a significant challenge in hyperspectral image processing. Existing unmixing methods utilize prior knowledge about the abundance distribution to solve the regularization optimization problem, where the difficulty lies in choosing appropriate prior knowledge and solving the complex regularization optimization problem. To solve these probl

Deepank Verma, Olaf Mumm, Vanessa Miriam Carlow

Evaluating the surroundings to gain understanding, frame perspectives, and anticipate behavioral reactions is an inherent human trait. However, these continuous encounters are diverse and complex, posing challenges to their study and experimentation. Researchers have been able to isolate environmental features and study their effect on human perception and b

Joon-Bin Lee, M. R. Masouminia, Michael H. Seymour, Un-ki Yang

This paper presents the inaugural investigation of beyond the Standard Model (BSM) radiation processes, framed as a generalized, process- and model-independent parton shower algorithm within Herwig 7, based on direct translations of Universal FeynRules Output (UFO) constructed via Herwig's ufo2herwig module. Leveraging the fact that shower kinematics are dic

Artem Dudko, Rostislav Grigorchuk

Using the construction by Bencs and T\'{o}th of invariant random subgroups on weakly branch groups acting on regular rooted trees we produce uncountably many indecomposable characters on these groups. In fact, we study three types of characters coming from the action of a weakly branch group on a regular tree, paying attention to their similarities and diffe

James Hancock, Matthew J. Craven, Craig McNeile, Davide Vadacchino

This paper investigates Windfarm Layout Optimization (WFLO), where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Wind energy plays a critical role in the transition toward sustainable power systems, but the optimal placement of turbines remains a challenging combinatorial problem due

Dengke Zhang, Chen Qing

Compact and robust structures to precisely control and acquire atomic spectra are increasingly important for the pursuit of widespread applications. Sub-Doppler responses of thermal atoms are critical in constructing high-precision devices and systems. In this study, we designed a nanograting metasurface specifically for atomic rubidium vapor and integrated

John M. Abowd

McCartan et al. (2023) call for "making differential privacy work for census data users." This commentary explains why the 2020 Census Noisy Measurement Files (NMFs) are not the best focus for that plea. The August 2021 letter from 62 prominent researchers asking for production of the direct output of the differential privacy system deployed for the 2020 Cen

Govind S. Krishnaswami, T. R. Vishnu

The Rajeev-Ranken (RR) model is a Hamiltonian system describing screw-type nonlinear waves (screwons) of wavenumber $k$ in a scalar field theory pseudodual to the 1+1D SU(2) principal chiral model. Classically, the RR model based on a quadratic Hamiltonian on a nilpotent/Euclidean Poisson algebra is Liouville integrable. Upon adopting canonical variables in

Elad Zelingher

We prove an identity relating twisted matrix Kloosterman sums to modified Hall-Littlewood polynomials evaluated at the roots of the characteristic polynomial associated to a twisted Kloosterman sheaf. This solves a conjecture of Erd\'elyi and T\'oth.

Haide Wu, Lars Hemmingsen, Stephan P. A. Sauer

Thiolate containing mercury(II) complexes of the general formula [Hg(SR)$_n$]$^{2-n}$ have been of great interest since the toxicity of mercury was recognized. $^{199}$Hg nuclear magnetic resonance spectroscopy (NMR) is a powerful tool for characterization of mercury complexes. In this work, the Hg shielding constants in a series of [Hg(SR)$_n$]$^{2-n}$ comp

Xin Jin, Charalampos Katsis, Fan Sang, Jiahao Sun

The rampant occurrence of cybersecurity breaches imposes substantial limitations on the progress of network infrastructures, leading to compromised data, financial losses, potential harm to individuals, and disruptions in essential services. The current security landscape demands the urgent development of a holistic security assessment solution that encompas

Tao Wu, Tie Luo, Donald C. Wunsch

The transferability of adversarial examples is of central importance to transfer-based black-box adversarial attacks. Previous works for generating transferable adversarial examples focus on attacking \emph{given} pretrained surrogate models while the connections between surrogate models and adversarial trasferability have been overlooked. In this paper, we

Yingxia Xi, Jiguang Sun

We consider nonlinear eigenvalue problems to compute all eigenvalues in a bounded region on the complex plane. Based on domain decomposition and contour integrals, two robust and scalable parallel multi-step methods are proposed. The first method 1) uses the spectral indicator method to find eigenvalues and 2) calls a linear eigensolver to compute the associ

Haili Ye, Xiaoqing Zhang, Yan Hu, Huazhu Fu

The morphologies of vessel-like structures, such as blood vessels and nerve fibres, play significant roles in disease diagnosis, e.g., Parkinson's disease. Deep network-based refinement segmentation methods have recently achieved promising vessel-like structure segmentation results. There are still two challenges: (1) existing methods have limitations in reh

Youjia Li, Jianjun Shi, Zheng Zhang

Code generation stands as a powerful technique in modern software development, improving development efficiency, reducing errors, and fostering standardization and consistency. Recently, ChatGPT has exhibited immense potential in automatic code generation. However, existing researches on code generation lack guidance for practical software development proces

Oguzhan Ulucan, Diclehan Ulucan, Marc Ebner

Color constancy and color illusion perception are two phenomena occurring in the human visual system, which can help us reveal unknown mechanisms of human perception. For decades computer vision scientists have developed numerous color constancy methods, which estimate the reflectance of the surface by discounting the illuminant. However, color illusions hav

David A. Towers

This paper starts by showing that for algebras in a certain class the concepts of weak nilpotency and nilpotency coincide. It goes on to describe some solvability and nilpotency properties of bicommutative algebras, of assosymmetric and of Novikov algebras and to introduce a Frattini theory for all of them. A description is also given for semisimple bicommut

Shai M. Chester, Silviu S. Pufu, Yifan Wang, Xi Yin

We analyze correlation functions of $SU(k) \times SU(2)_F$ flavor currents in a family of three-dimensional ${\cal N}=4$ superconformal field theories, combining analytic bootstrap methods with input from supersymmetric localization. Via holographic duality, we extract gluon and graviton scattering amplitudes of M-theory on ${\rm AdS}_4\times S^7/\mathbb{Z}_

Weijia Mao, Yan-Pei Cao, Jia-Wei Liu, Zhongcong Xu

We introduce ShowRoom3D, a three-stage approach for generating high-quality 3D room-scale scenes from texts. Previous methods using 2D diffusion priors to optimize neural radiance fields for generating room-scale scenes have shown unsatisfactory quality. This is primarily attributed to the limitations of 2D priors lacking 3D awareness and constraints in the

Eric Bonvin, Louisiane Devaud, Massimiliano Rossi, Andrei Militaru

Levitated nanoparticles in vacuum are prime candidates for generating macroscopic quantum superposition states of massive objects. Most protocols for preparing these states necessitate coherent expansion beyond the scale of the zero-point motion to produce sufficiently delocalized and pure phase-space distributions. Here, we spatially expand and subsequently

Daiki Koge, Naoaki Ono, Shigehiko Kanaya

Learning representations of molecular structures using deep learning is a fundamental problem in molecular property prediction tasks. Molecules inherently exist in the real world as three-dimensional structures; furthermore, they are not static but in continuous motion in the 3D Euclidean space, forming a potential energy surface. Therefore, it is desirable

Jessica Golm, Jose María García-Barceló, Sergio Arguedas Cuendis, Sergio Calatroni

The axion haloscope is the currently most sensitive method to probe the vanishingly small coupling of this prominent Dark Matter candidate to photons. To scan a sizeable axion Dark Matter parameter space, the cavities that make up the haloscope need to be tuned efficiently. In this article, we describe a novel technique to tune axion haloscopes around $8.4$~

Difei Gao, Lei Ji, Zechen Bai, Mingyu Ouyang

Graphical User Interface (GUI) automation holds significant promise for assisting users with complex tasks, thereby boosting human productivity. Existing works leveraging Large Language Model (LLM) or LLM-based AI agents have shown capabilities in automating tasks on Android and Web platforms. However, these tasks are primarily aimed at simple device usage a

Malavika Kayyil Veedu, Julia Osmólska, Agata Hajda, Joanna Olesiak-Bańska

Gold nanoclusters (AuNCs) have captured significant interest for their photoluminescent properties; however, their rapid photodynamics remain elusive while probed by ensemble-averaging spectroscopy techniques. To address this challenge, we use fluorescence correlation spectroscopy (FCS) to uncover the photoluminescence dynamics of colloidal Au18(SG)14 nanocl

Christian Cachin, Jovana Micic

Decentralized finance revolutionizes traditional financial systems by leveraging blockchain technology to reduce trust. However, some vulnerabilities persist, notably front-running by malicious actors who exploit transaction information to gain financial advantage. Consensus with a fair order aims at preventing such attacks, and in particular, the differenti

Leif Döring, Mladen Savov, Lukas Trottner, Alexander R. Watson

We prove that the spatial Wiener-Hopf factorisation of a L\'evy process or random walk without killing is unique.

Shyamal Mishra, Preetha Chatterjee

Fostering a collaborative and inclusive environment is crucial for the sustained progress of open source development. However, the prevalence of negative discourse, often manifested as toxic comments, poses significant challenges to developer well-being and productivity. To identify such negativity in project communications, especially within large projects,

Sushil Sharma, Aryan Singh, Ganesh Sistu, Mark Halton

Predicting the trajectory of an ego vehicle is a critical component of autonomous driving systems. Current state-of-the-art methods typically rely on Deep Neural Networks (DNNs) and sequential models to process front-view images for future trajectory prediction. However, these approaches often struggle with perspective issues affecting object features in the

Jinge Wu, Yunsoo Kim, Eva C. Keller, Jamie Chow

This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports. We created an evaluation dataset from real-world radiology datasets (including X-rays and CT scans). A subset of original reports was modified to contain synthetic errors by introducing three

Li Ma, Vasu Agrawal, Haithem Turki, Changil Kim

Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However, existing approaches still struggle with the view-dependent appearance of glossy surfaces, especially under complex lighting of indoor environments. Unlike existing methods, which typically assume distant lighting like an environment map, we propose a

S. R. Souza, L. F. Canto, R. Donangelo

The complete fusion of $^6$Li with $^{28}$Si and $^{64}$Ni nuclei, ranging from subbarrier energies up to values well above the Coulomb barrier, is studied using the continuum discretized coupled channels method. We investigate the sensitivity of the results to the largest energy used in the discretization of the continuum, including closed channels. Our res