March 2023 arXiv papers — page 83

Jeffrey Zhang, Wei Li, Wang Miao, Eric Tchetgen Tchetgen

We consider identification and inference about a counterfactual outcome mean when there is unmeasured confounding using tools from proximal causal inference (Miao et al. [2018], Tchetgen Tchetgen et al. [2020]). Proximal causal inference requires existence of solutions to at least one of two integral equations. We motivate the existence of solutions to the i

Senthil Hariharan Arul, Jong Jin Park, Dinesh Manocha

We present an algorithm for safe robot navigation in complex dynamic environments using a variant of model predictive equilibrium point control. We use an optimization formulation to navigate robots gracefully in dynamic environments by optimizing over a trajectory cost function at each timestep. We present a novel trajectory cost formulation that significan

Javad Nazarian Sarkooh, Azam Ehsani, Zeynal Pashaei, Roghayeh Abdi

Ovadia and Rodriguez-Hertz (Neutralized local entropy, arXiv:2302.10874) defined the neutralized Bowen open ball for an autonomous dynamical system on a compact metric space. Replacing the usual Bowen open ball with neutralized Bowen open ball, we introduce the notions of neutralized Bowen topological entropy of subsets, neutralized weighted Bowen topologica

Christoph Treude, Hideaki Hata

Implicit gender bias in software development is a well-documented issue, such as the association of technical roles with men. To address this bias, it is important to understand it in more detail. This study uses data mining techniques to investigate the extent to which 56 tasks related to software development, such as assigning GitHub issues and testing, ar

Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock

We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating b

Edward Y. Chang

The CoCoMo model proposes a computational solution to the challenge of incorporating ethical and emotional intelligence considerations into AI systems, with the aim of creating AI agents that combine knowledge with compassion. To achieve this goal, CoCoMo prioritizes fairness, beneficence, non-maleficence, empathy, adaptability, transparency, and critical an

Xiaoxiong Liu, Stepan S. Tsirkin, Ivo Souza

The derivatives of the Berry curvature $\Omega$ and intrinsic orbital magnetic moment m in momentum space are relevant to various problems, including the nonlinear anomalous Hall effect and magneto-transport within the Boltzmann-equation formalism. To investigate these properties using first-principles methods, we have developed a Wannier interpolation schem

Sonia Petrini, Antoni Casas-i-Muñoz, Jordi Cluet-i-Martinell, Mengxue Wang

Zipf's law of abbreviation, the tendency of more frequent words to be shorter, is one of the most solid candidates for a linguistic universal, in the sense that it has the potential for being exceptionless or with a number of exceptions that is vanishingly small compared to the number of languages on Earth. Since Zipf's pioneering research, this law has been

Robin Delabays, Francesco Bullo

This paper studies the celebrated Kuramoto-Sakaguchi model of coupled oscillators adopting two recent concepts. First, we consider appropriately-defined subsets of the $n$-torus called winding cells. Second, we analyze the semicontractivity of the model, i.e., the property that the distance between trajectories decreases when measured according to a seminorm

Yidan Zhang, Ting Zhang, Dong Chen, Yujing Wang

While generative modeling has become prevalent across numerous research fields, its integration into the realm of image retrieval remains largely unexplored and underjustified. In this paper, we present a novel methodology, reframing image retrieval as a variant of generative modeling and employing a sequence-to-sequence model. This approach is harmoniously

Gauhar Abbas, Rathin Adhikari, Eung Jin Chun

We first time show that a common solution to dark matter and the flavor problem of the standard model can be obtained in the framework of the $\mathcal{Z}_{\rm N} \times \mathcal{Z}_{\rm M}$ flavor symmetry where the flavonic Goldstone boson of this flavor symmetry acts as a good dark matter candidate through the misalignment mechanism. Hierarchical mass pat

Luca Fabbri

We consider quantum mechanics written in hydrodynamic formulation for the case of relativistic spinor fields to study their velocity: within such a hydrodynamic formulation it is possible to see that the velocity as is usually defined can not actually represent the tangent vector to the trajectories of particles. We propose an alternative definition for this

Hedieh Ajam, Marzieh Najafi, Vahid Jamali, Robert Schober

The line-of-sight (LOS) requirement of free-space optical (FSO) systems can be relaxed by employing optical relays or optical intelligent reflecting surfaces (IRSs). In this paper, we show that the power reflected from FSO IRSs and collected at the receiver (Rx) lens may scale quadratically or linearly with the IRS size or may saturate at a constant value. W

Michael J. Curran

Assuming the Riemann hypothesis, we investigate the shifted moments of the zeta function \[ M_{\alpha,{\beta}}(T) = \int_T^{2T} \prod_{k = 1}^m |\zeta(\tfrac{1}{2} + i (t + \alpha_k))|^{2 \beta_k} dt \] introduced by Chandee, where ${\alpha} = {\alpha}(T) = (\alpha_1, \ldots, \alpha_m)$ and ${\beta} = (\beta_1 \ldots , \beta_m)$ satisfy $|\alpha_k| \leq T/2$

Jacopo Cardellini, Andrea Ridolfi, Melissa Donati, Valentina Giampietro

Although promising for biomedicine, the clinical translation of inorganic nanoparticles (NPs) is limited by low biocompatibility and stability in biological fluids. A common strategy to circumvent this drawback consists in disguising the active inorganic core with a lipid bilayer coating, reminiscent of the structure of the cell membrane to redefine the chem

Valerio La Gatta, Chiyu Wei, Luca Luceri, Francesco Pierri

Nowadays, false and unverified information on social media sway individuals' perceptions during major geo-political events and threaten the quality of the whole digital information ecosystem. Since the Russian invasion of Ukraine, several fact-checking organizations have been actively involved in verifying stories related to the conflict that circulated onli

Michael Shanks, Uduak Inyang-Udoh, Neera Jain

Latent thermal energy storage (TES) devices could enable advances in many thermal management applications, including peak load shifting for reducing energy demand and cost of HVAC or providing supplemental heat rejection in transient thermal management systems. However, real-time feedback control of such devices is currently limited by the absence of suitabl

Katrin Hecker, Luca Banszerus, Aaron Schäpers, Samuel Möller

The coherent dynamics of a quantum mechanical two-level system passing through an anti-crossing of two energy levels can give rise to Landau-Zener-St\"uckelberg-Majorana (LZSM) interference. LZSM interference spectroscopy has proven to be a fruitful tool to investigate charge noise and charge decoherence in semiconductor quantum dots (QDs). Recently, bilayer

Susana Hahn, Orkunt Sabuncu, Torsten Schaub, Tobias Stolzmann

We present the ASP-based visualization tool, clingraph, which aims at visualizing various concepts of ASP by means of ASP itself. This idea traces back to the aspviz tool and clingraph redevelops and extends it in the context of modern ASP systems. More precisely, clingraph takes graph specifications in terms of ASP facts and hands them over to the graph vis

Degui Li, Bin Peng, Songqiao Tang, Weibiao Wu

This paper introduces a flexible time-varying network vector autoregressive model framework for large-scale time series. A latent group structure is imposed on the heterogeneous and node-specific time-varying momentum and network spillover effects so that the number of unknown time-varying coefficients to be estimated can be reduced considerably. A classic a

Wojciech Dudek, Narcis Miguel, Tomasz Winiarski

Evaluating early design concepts is crucial as it impacts quality and cost. This process is often hindered by vague and uncertain design information. This article introduces the SysML-based Simulated-Physical Systems Modelling Language (SPSysML). It is a Domain-Specification Language for evaluating component reusability in Cyber-Physical Systems incorporatin

Petr Hliněný, Adam Straka

A long-standing question of the mutual relation between the stack and queue numbers of a graph, explicitly emphasized by Dujmovi\'c and Wood in 2005, was "half-answered" by Dujmovi\'c, Eppstein, Hickingbotham, Morin and Wood in 2022; they proved the existence of a graph family with the queue number at most 4 but unbounded stack number. We give an alternative

Simon Blouin, Huaqing Mao, Tyrone E. Woods, Pavel Denissenkov

Supermassive stars are Population III stars with masses exceeding $10^4\,M_{\odot}$ that could be the progenitors of the first supermassive black holes. Their interiors are in a regime where radiation pressure dominates the equation of state. In this work, we use the explicit gas dynamics code PPMstar to simulate the hydrogen-burning core of a $10^4\,M_{\odo

Jacob J. Repicky, Brad Goff, Shuyu Cheng, Roland K. Kawakami

Magnetic multilayers are a promising platform for storage and logic devices based on skyrmion spin textures, due to the large materials phase space for tuning properties. Epitaxial superlattice structures of [Pt/Co/Cu]n thin films were grown by molecular beam epitaxy at room temperature. Spin-polarized scanning tunneling microscopy (SP-STM) of these samples

Brad M. Goff, Alexander J. Bishop, Wenyi Zhou, Ryan Bailey-Crandell

Introducing magnetism to the surface state of topological insulators, such as Bi2Te3, can lead to a variety of interesting phenomena. We use scanning tunneling microscopy (STM) to study a single quintuple layer (QL) of the van der Waals magnet Fe3GeTe2 (FGT) that is grown on Bi2Te3 via molecular beam epitaxy. STM topographic images show that the FGT grows as

Chang Gong, Di Yao, Chuzhe Zhang, Wenbin Li

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and p

Braden M. Weight, Xinyang Li, Yu Zhang

Light-matter interaction not only plays an instrumental role in characterizing materials' properties via various spectroscopic techniques but also provides a general strategy to manipulate material properties via the design of novel nanostructures. This perspective summarizes recent theoretical advances in modeling light-matter interactions in chemistry, mai

Evan B. Bauer

We enhance the treatment of crystallization for models of white dwarfs (WDs) in the stellar evolution software MESA by implementing carbon-oxygen (C/O) phase separation. The phase separation process during crystallization leads to transport of oxygen toward the center of WDs, resulting in a more compact structure that liberates gravitational energy as additi

Aleksey Girich, Liubov Ivzhenko, Artem Hrinchenko, Sergey Tarapov

Spoof surface plasmon-polariton is a type of surface wave propagating at the artificially engineered structures in microwave and terahertz ranges. These surface waves are highly important in planar photonic and on-chip devices, integrated circuits, lenses, sensors, and antennas applications. However, it is still a challenge to control the propagation regime

Gang Liu, Eric Inae, Tong Zhao, Jiaxin Xu

Graph property prediction tasks are important and numerous. While each task offers a small size of labeled examples, unlabeled graphs have been collected from various sources and at a large scale. A conventional approach is training a model with the unlabeled graphs on self-supervised tasks and then fine-tuning the model on the prediction tasks. However, the

Yichen Hu, Sirui Ning, Yehao Zhou

$\mathcal{N}=1$ superconformal minimal models are the first series of unitary conformal field theories (CFTs) extending beyond Virasoro algebra. Using coset constructions, we characterize CFTs in $\mathcal{N}=1$ superconformal minimal models using combinations of a parafermion theory, an Ising theory and a free boson theory. Supercurrent operators in the ori

Vincent J. Straub, Deborah Morgan, Youmna Hashem, John Francis

Calls for new metrics, technical standards and governance mechanisms to guide the adoption of Artificial Intelligence (AI) in institutions and public administration are now commonplace. Yet, most research and policy efforts aimed at understanding the implications of adopting AI tend to prioritize only a handful of ideas; they do not fully connect all the dif

N. A. Webb, C. Bot, S. Charpinet, T. Contini

Following the survey Well-being in astrophysics that was sent out in March 2021, to establish how astrophysics researchers, primarily in France, experience their career, some of the results were published in Webb et al. (2021). Here we further analyse the data to determine if gender can cause different experiences in astrophysics. We also study the impact on

Alexander Schell

Blind source separation (BSS) aims to recover an unobserved signal $S$ from its mixture $X=f(S)$ under the condition that the effecting transformation $f$ is invertible but unknown. As this is a basic problem with many practical applications, a fundamental issue is to understand how the solutions to this problem behave when their supporting statistical prior

John M. Ball, Christopher L. Horner

A nonlinear elasticity model for comparing images is formulated and analyzed, in which optimal transformations between images are sought as minimizers of an integral functional. The existence of minimizers in a suitable class of homeomorphisms between image domains is established under natural hypotheses. We investigate whether for linearly related images th

Yian Chen, Mihai Anitescu

Physics-based covariance models provide a systematic way to construct covariance models that are consistent with the underlying physical laws in Gaussian process analysis. The unknown parameters in the covariance models can be estimated using maximum likelihood estimation, but direct construction of the covariance matrix and classical strategies of computing

Jan Rolfes, Robert Schüler, Marc Christian Zimmermann

Finding point configurations, that yield the maximum polarization (Chebyshev constant) is gaining interest in the field of geometric optimization. In the present article, we study the problem of unconstrained maximum polarization on compact sets. In particular, we discuss necessary conditions for local optimality, such as that a locally optimal configuration

Liulei Li, Wenguan Wang, Tianfei Zhou, Jianwu Li

The objective of this paper is self-supervised learning of video object segmentation. We develop a unified framework which simultaneously models cross-frame dense correspondence for locally discriminative feature learning and embeds object-level context for target-mask decoding. As a result, it is able to directly learn to perform mask-guided sequential segm

Alexandros Tsouros, Gordian Edenhofer, Torsten Enßlin, Michalis Mastorakis

(abridged) Ultra-high energy cosmic rays (UHECRs) are highly energetic charged particles with energies exceeding $10^{18}$ eV. Identifying their sources and production mechanism can provide insight into many open questions in astrophysics and high energy physics. However, the Galactic magnetic field (GMF) deflects UHECRs, and the high uncertainties in our cu

Sara Angela Filippini, Lorenzo Guerrieri

Let $M$ be a perfect module of projective dimension 3 in a Gorenstein, local or graded ring $R$. We denote by $\FF$ the minimal free resolution of $M$. Using the generic ring associated to the format of $\FF$ we define higher structure maps, according to the theory developed by Weyman in "Generic free resolutions and root systems" (Annales de l'Institut Four

Natalie A. Webb, Francisco J. Carrera, Axel Schwope, Christian Motch

XMM-Newton, a European Space Agency observatory, has been observing the X-ray, ultra-violet and optical sky for 23 years. During this time, astronomy has evolved from mainly studying single sources to populations and from a single wavelength, to multi-wavelength or messenger data. We are also moving into an era of time domain astronomy. New software and meth

Karl Schrader, Pascal Peter, Niklas Kämper, Joachim Weickert

With well-selected data, homogeneous diffusion inpainting can reconstruct images from sparse data with high quality. While 4K colour images of size 3840 x 2160 can already be inpainted in real time, optimising the known data for applications like image compression remains challenging: Widely used stochastic strategies can take days for a single 4K image. Rec

Peifeng Peng, Nigel Metcalfe

In this paper, the cosmological parameters are determined by applying six cosmological models to fit the magnitude-redshift relation of the Pantheon Sample consisting of 1048 Type Ia supernovae (SNe Ia) in the range of $0.01 < z < 2.26$. Apart from the well-known flat $\Lambda$CDM model as well as other models that have been broadly studied, this paper inclu

Alessandro Benedetti, Anna Ruggero

Interleaving is an online evaluation approach for information retrieval systems that compares the effectiveness of ranking functions in interpreting the users' implicit feedback. Previous work such as Hofmann et al (2011) has evaluated the most promising interleaved methods at the time, on uniform distributions of queries. In the real world, ordinarily, ther

Kyle Buettner, Adriana Kovashka

Vision-language alignment learned from image-caption pairs has been shown to benefit tasks like object recognition and detection. Methods are mostly evaluated in terms of how well object class names are learned, but captions also contain rich attribute context that should be considered when learning object alignment. It is unclear how methods use this contex

Guangyu Hu, Marat I. Latypov

Microstructure--property relationships are key to effective design of structural materials for advanced applications. Advances in computational methods enabled modeling microstructure-sensitive properties using 3D models (e.g., finite elements) based on microstructure representative volumes. 3D microstructure data required as input to these models are typica

Thomas Brian Winkler, Marijan Beg, Martin Lang, Mathias Kläui

Magnetic Bloch points (BPs) are highly confined magnetization configurations, that often occur in transient spin dynamics processes. However, opposing chiralities of adjacent layers for instance in a FeGe bilayer stack can stabilize such magnetic BPs at the layer interface. These BPs configurations are metastable and consist of two coupled vortices (one in e

ATLAS Collaboration

Parton energy loss in the quark-gluon plasma (QGP) is studied with a measurement of photon-tagged jet production in 1.7 nb$^{-1}$ of Pb+Pb data and 260 pb$^{-1}$ of $pp$ data, both at $\sqrt{s_\mathrm{NN}} = 5.02$ TeV, with the ATLAS detector. The process $pp \to \gamma$+jet+$X$ and its analogue in Pb+Pb collisions is measured in events containing an isolate

Weiyi Zhang, Yushi Guo, Liting Niu, Peijun Li

Simultaneous localization and mapping (SLAM) is a critical technology that enables autonomous robots to be aware of their surrounding environment. With the development of deep learning, SLAM systems can achieve a higher level of perception of the environment, including the semantic and text levels. However, current works are limited in their ability to achie

Martin Balko, David Chodounský, Natasha Dobrinen, Jan Hubička

As a result of 33 intercontinental Zoom calls, we characterise big Ramsey degrees of the generic partial order. This is an infinitary extension of the well known fact that finite partial orders endowed with linear extensions form a Ramsey class (this result was announced by Ne\v{s}et\v{r}il and R\"odl in 1984 with first published proof by Paoli, Trotter and

Shuai Chen, Yash Bhalgat, Xinghui Li, Jiawang Bian

Absolute Pose Regression (APR) methods use deep neural networks to directly regress camera poses from RGB images. However, the predominant APR architectures only rely on 2D operations during inference, resulting in limited accuracy of pose estimation due to the lack of 3D geometry constraints or priors. In this work, we propose a test-time refinement pipelin

Serge Deside, Matthieu Arnhem, Célia Griffet, Nicolas J. Cerf

Entanglement is among the most fundamental-and at the same time puzzling-properties of quantum physics. Its modern description relies on a resource-theoretical approach, which treats entangled systems as a means to enable or accelerate certain informational tasks. Hence, it is of crucial importance to determine whether-and how-different entangled states can

Abhisek Chakraborty, Anirban Bhattacharya, Debdeep Pati

Flexible Bayesian models are typically constructed using limits of large parametric models with a multitude of parameters that are often uninterpretable. In this article, we offer a novel alternative by constructing an exponentially tilted empirical likelihood carefully designed to concentrate near a parametric family of distributions of choice with respect

E. Filandri, L. Girlanda

A complete set of parity- and time-reversal conserving relativistic nucleon-nucleon contact operators is identified up to the order $O(p^4)$ of the expansion in soft momenta $p$. A basis is also provided for the corresponding non-relativistic operators contributing in the general reference frame. We show that the non-relativistic expansions of the relativist

Tianhao Wu, Hanxue Liang, Fangcheng Zhong, Gernot Riegler

Implicit surface representations such as the signed distance function (SDF) have emerged as a promising approach for image-based surface reconstruction. However, existing optimization methods assume solid surfaces and are therefore unable to properly reconstruct semi-transparent surfaces and thin structures, which also exhibit low opacity due to the blending

Jnaneshwar Baslingker, Shankar Bhamidi, Nicolas Broutin, Sanchayan Sen

We develop a general universality technique for establishing metric scaling limits of critical random discrete structures exhibiting mean-field behavior that requires four ingredients: (i) from the barely subcritical regime to the critical window, components merge approximately like the multiplicative coalescent, (ii) asymptotics of the susceptibility functi

Shucheng Kang, Yuxiao Chen, Heng Yang, Marco Pavone

We study the problem of verification and synthesis of robust control barrier functions (CBF) for control-affine polynomial systems with bounded additive uncertainty and convex polynomial constraints on the control. We first formulate robust CBF verification and synthesis as multilevel polynomial optimization problems (POP), where verification optimizes -- in

Chongze Wang, Jia Yu, Zhenyu Zhang, Jun-Hyung Cho

The nature of the superconducting pairing state in the pristine phase of a compressed kagome metal CsV$_3$Sb$_5$ under pressure is studied by the Migdal-Eliashberg formalism and density-functional theory calculations. We find that the superconducting gap distribution driven by electron-phonon coupling is nodeless and anisotropic. It is revealed that the hybr

Yang Liu, Weimeng Wang

It is widely believed that a joint factor analysis of item responses and response time (RT) may yield more precise ability scores that are conventionally predicted from responses only. For this purpose, a simple-structure factor model is often preferred as it only requires specifying an additional measurement model for item-level RT while leaving the origina

Michael Scherbela, Leon Gerard, Philipp Grohs

Deep neural networks have become a highly accurate and powerful wavefunction ansatz in combination with variational Monte Carlo methods for solving the electronic Schr\"odinger equation. However, despite their success and favorable scaling, these methods are still computationally too costly for wide adoption. A significant obstacle is the requirement to opti

Jannis Walk, Niklas Kühl, Michael Saidani, Jürgen Schatte

The usage and impact of deep learning for cleaner production and sustainability purposes remain little explored. This work shows how deep learning can be harnessed to increase sustainability in production and product usage. Specifically, we utilize deep learning-based computer vision to determine the wear states of products. The resulting insights serve as a

Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang

With the development of adversarial attacks, adversairal examples have been widely used to enhance the robustness of the training models on deep neural networks. Although considerable efforts of adversarial attacks on improving the transferability of adversarial examples have been developed, the attack success rate of the transfer-based attacks on the surrog

Sarah Casura

In this thesis, we derive a catalogue of robust structural parameters for the components of a large sample of nearby GAMA galaxies while at the same time contributing to the advancement of image analysis, surface brightness fitting and post-processing routines for quality assurance in the context of automated large-scale bulge-disk decomposition studies. The

Wanshui Gan, Ningkai Mo, Hongbin Xu, Naoto Yokoya

The task of estimating 3D occupancy from surrounding-view images is an exciting development in the field of autonomous driving, following the success of Bird's Eye View (BEV) perception. This task provides crucial 3D attributes of the driving environment, enhancing the overall understanding and perception of the surrounding space. In this work, we present a

Arkadip Basak, Branden Allen, Jaesub Hong, Daniel P. Violette

ProtoEXIST2 (P2) was a prototype imaging X-ray detector plane developed for wide-field Time Domain Astrophysics (TDA) in the 5 - 200 keV energy band. It was composed of an 8 $\times$ 8 array of 5 mm thick, 2cm $\times$ 2cm pixelated (32 $\times$ 32) CdZnTe (CZT) detectors with a 0.6 mm pitch that utilize the NuSTAR ASIC(NuASIC) for readout. During the initia

Danica Basarić, Nilasis Chaudhuri

We consider the Navier-Stokes-Fourier system describing the motion of a compressible, viscous and heat-conducting fluid on a domain perforated by tiny holes. First, we identify a class of dissipative solutions to the Oberbeck-Boussinesq approximation as a low Mach number limit of the primitive system. Secondly, by proving the weak-strong uniqueness principle

Jingxuan Wei, Shiyu Wu, Xin Jiang, Yequan Wang

We introduce DialogPaint, a novel framework that bridges conversational interactions with image editing, enabling users to modify images through natural dialogue. By integrating a dialogue model with the Stable Diffusion image transformation technique, DialogPaint offers a more intuitive and interactive approach to image modifications. Our method stands out

V. V. Tsyganok, D. A. Pershin, V. A. Khlebnikov, D. A. Kumpilov

Recently, the thulium atom has been cooled down to the temperature of Bose-Einstein condensation. While the condensate of the thulium atom has a lot of applications in quantum simulations and other areas of physics, it can also serve as a unique diagnostic tool for many atomic experiments. In the present study, the Bose-Einstein condensate of the thulium ato

Qiankun Gao, Chen Zhao, Yifan Sun, Teng Xi

The "pre-training $\rightarrow$ downstream adaptation" presents both new opportunities and challenges for Continual Learning (CL). Although the recent state-of-the-art in CL is achieved through Parameter-Efficient-Tuning (PET) adaptation paradigm, only prompt has been explored, limiting its application to Transformers only. In this paper, we position prompti

Pengfei Xu, Zhiyong Peng, Liwei Wang

The spread of rumors in online networks threatens public safety and results in economic losses. To overcome this problem, a lot of work studies the problem of rumor control which aims at limiting the spread of rumors. However, all previous work ignores the relationship between the influence block effect and counts of impressions on the user. In this paper, w

Zeyd Boukhers, Nagaraj Bahubali Asundi

In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it challenging to assign newly published papers to their respective authors. Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in digital libraries. This paper proposes an Author Name Disambiguati

Adrian Hjältén, Aleksandra Foltynowicz, Ibrahim Sadiek

We present a new spectral analysis of the ${\nu}_1$ and ${\nu}_3$+${\nu}_1$-${\nu}_3$ bands of $^{12}$CH$_3$I around 2971 cm$^{-1}$ based on a high-resolution spectrum spanning from 2800 cm$^{-1}$ to 3160 cm$^{-1}$, measured using an optical frequency comb Fourier transform spectrometer. From this spectrum, we previously assigned the ${\nu}_4$ and ${\nu}_3$+

Jan Frahm, Karl-Hermann Neeb, Gestur Olafsson

Let G be a connected simple linear Lie group and H in G a symmetric subgroup such that the corresponding symmetric space G/H is non-compactly causal. We show that any irreducible unitary representation of G leads naturally to a net of standard subspaces on G/H that is isotone, covariant and has the Reeh--Schlieder and the Bisognano--Wichmann property. We als

Giuseppe Procopio, Massimiliano Giona

We generalize the multipole expansion and the structure of the Fax\'en operator in Stokes flows obtained for bodies with no-slip to generic boundary conditions, addressing the assumptions under which this generalization is conceivable. We show that a disturbance field generated by a body immersed in an ambient flow can be expressed as a multipole expansion t

Nikolay Kutev, Sonia Tabakova

The unsteady Poiseuille flow of Carreau-Yasuda fluid in a pipe, caused by a variable pressure gradient, is studied theoretically. In a particular case, the steady flow is considered separately. It is proved that at some values of the viscosity model parameters, the problem has a generalized solution, while at others - classical solution. For the latter, a ne

Qiaojie Zheng, Xiaoli Zhang

Deep learning appearance-based 3D gaze estimation is gaining popularity due to its minimal hardware requirements and being free of constraint. Unreliable and overconfident inferences, however, still limit the adoption of this gaze estimation method. To address the unreliable and overconfident issues, we introduce a confidence-aware model that predicts uncert

Glenn Webb

A partial differential equation model is analyzed for the two-slit experiment of quantum mechanics. The state variable of the equation is the probability density function of particle positions. The equation has a diffusion term corresponding to the random movement of particles, and a nonlocal advection term corresponding to the movement of particles in the t

Fabio Bagarello, Rosario Corso

We discuss some perturbation results concerning certain pairs of sequences of vectors in a Hilbert space $\Hil$ and producing new sequences which share, with the original ones, { reconstruction formulas on a dense subspace of $\Hil$ or on the whole space}. We also propose some preliminary results on the same issue, but in a distributional settings.

D. Melconian, G. P. A. Berg, P. D. Shidling, M. Couder

A new \textsuperscript{3}He-driven IGISOL production station and mass separator have been designed to produce neutron-deficient low-mass isotopes at the Cyclotron Institute for the TAMUTRAP facility. The LSTAR design has a mass resolution $M/\Delta M\geq 3, 000$ to reject contaminants with $\gt95\%$ efficiency.

Zexi Li, Xinyi Shang, Rui He, Tao Lin

Data heterogeneity is an inherent challenge that hinders the performance of federated learning (FL). Recent studies have identified the biased classifiers of local models as the key bottleneck. Previous attempts have used classifier calibration after FL training, but this approach falls short in improving the poor feature representations caused by training-t

Xiaofeng Liu, Thibault Marin, Tiss Amal, Jonghye Woo

Background: In medical imaging, images are usually treated as deterministic, while their uncertainties are largely underexplored. Purpose: This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probable parameters as well as their uncertainties. Methods: Our de

Can Qin, Ning Yu, Chen Xing, Shu Zhang

Text-to-image (T2I) models based on diffusion processes have achieved remarkable success in controllable image generation using user-provided captions. However, the tight coupling between the current text encoder and image decoder in T2I models makes it challenging to replace or upgrade. Such changes often require massive fine-tuning or even training from sc

Natalie A. Webb

White dwarfs are the most common endpoints of stellar evolution. They are often found in close binary systems in which the white dwarf is accreting matter from a companion star, either via an accretion disc or channelled along the white dwarf magnetic field lines. The nature of this binary depends on the masses and the separation of the two stellar component

Stefano Scopa, Dragi Karevski

We revisit the out-of-equilibrium physics arising during the unitary evolution of a one-dimensional XXZ spin chain initially prepared in a domain wall state $\vert\psi_0\rangle=\vert\dots \uparrow\uparrow\downarrow\downarrow\dots\rangle$. In absence of interactions, we review the exact lattice calculation of several conserved quantities, including e.g. the m

M. -R. Yun, Jin-Lei Wu, L. -L. Yan, Yu Jia

Due to strong zero-phonon line emission, narrow inhomogeneous broadening, and stable optical transition frequencies, the quantum system consisting of negatively charged silicon-vacancy (SiV) centers in diamond is highly expected to develop universal quantum computation. We propose to implement quantum computation for the first time using SiV centers placed i

A. E. Nicholson, N. J. Mayne

The search for biosignatures necessitates developing our understanding of life under different conditions. If life can influence the climate evolution of its planet then understanding the behaviour of life-climate feedbacks under extreme conditions is key to determine the 'edges' of the habitable zone. Additionally understanding the behaviour of a temperatur

T. M. Graham, L. Phuttitarn, R. Chinnarasu, Y. Song

We demonstrate mid-circuit measurements in a neutral atom array by shelving data qubits in protected hyperfine-Zeeman sub-states while non-destructively measuring an ancilla qubit. Measurement fidelity was enhanced using microwave repumping of the ancilla during the measurement. The coherence of the shelved data qubits was extended during the ancilla readout

Laszlo Kozma, Salah Gomaa Elgendi

In this paper, using the Finslerian settings, we study the existence of parallel one forms (or, equivalently parallel vector fields) on a Riemannian manifold. We show that a parallel one form on a Riemannian manifold M is a holonomy invariant function on the tangent bundle T M with respect to the geodesic spray. We prove that if the metrizability freedom of

Kai Ren, Ke Zou, Xianjie Liu, Yidi Chen

Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual learning features and shared model parameters, while neglecting the reliability of features and performances. In this paper, we propose a novel Uncertainty-informed Mutual Learnin

Pauline Vielzeuf, Matteo Calabrese, Carmelita Carbone, Giulio Fabbian

Cosmic voids are a powerful probe of cosmology and are one of the core observables of upcoming galaxy surveys. The cross-correlations between voids and other large-scale structure tracers such as galaxy clustering and galaxy lensing have been shown to be very sensitive probes of cosmology and among the most promising to probe the nature of gravity and the ne

Andrea Zunino, Lars Gebraad, Alessandro Ghirotto, Andreas Fichtner

The use of the probabilistic approach to solve inverse problems is becoming more popular in the geophysical community, thanks to its ability to address nonlinear forward problems and to provide uncertainty quantification. However, such strategy is often tailored to specific applications and therefore there is a lack of a common platform for solving a range o

Tomoyuki Iori

This paper proposes an efficient symbolic-numeric method to compute the integrals in the successive Galerkin approximation (SGA) of the Hamilton-Jacobi-Bellman (HJB) equation. A solution of the HJB equation is first approximated with a linear combination of the Hermite polynomials. The coefficients of the combination are then computed by iteratively solving

Tomas Berggren, Matthew Nicoletti, Marianna Russkikh

In this work we study a sequence of perfect t-embeddings of uniformly weighted Aztec diamonds. We show that these perfect t-embeddings can be used to prove convergence of gradients of height fluctuations to those of the Gaussian free field. In particular we provide a first proof of the existence of a model satisfying all conditions of the main theorem of arX

Alisher Duspayev, Georg Raithel

We analyze electric fields in ion sources generated by quasi-continuous photo-ionization of cold Rb atoms trapped in the focal spot of a near-concentric, in-vacuum cavity for 1064-nm laser light. Ion streams are extracted with an external electric field, ${\bf{F}}$. Stark effects of Rb 57$F$ and of nearby high-angular-momentum Rydberg levels, which exhibit l

Hugo E. Ramirez, Julián Fernando Sanchez

This paper studies the optimal liquidation of stocks in the presence of temporary and permanent price impacts, and we focus in the case of cryptocurrencies. We start by presenting analytical solutions to the problem with linear temporary impact, and linear and quadratic permanent impact. Then, using data from the order book of the BNB cryptocurrency, we esti

Vanessa Wirth, Anna-Maria Liphardt, Birte Coppers, Johanna Bräunig

Despite their potential, markerless hand tracking technologies are not yet applied in practice to the diagnosis or monitoring of the activity in inflammatory musculoskeletal diseases. One reason is that the focus of most methods lies in the reconstruction of coarse, plausible poses, whereas in the clinical context, accurate, interpretable, and reliable resul

Adam Bobrowski, Elżbieta Ratajczyk

Following our previous work on `perpendicular' boundary conditions, we show that transmission conditions \[ f'(0-)=\alpha(f(0+)-f(0-)), \quad f'(0+)=\beta(f(0+)-f(0-)),\] describing so-called snapping out Brownian motions on the real line, are in a sense complementary to the transmission conditions \[f(0-)=-f(0+), \quad f''(0+) =\alpha f'(0-)+\beta f'(0+). \

Anurag Surapaneni, Daniel Mira Martinez

Solving chemistry is an integral part of reacting flow simulations, usually dominating the computational cost. Among the different strategies to accelerate the solution of chemistry and to achieve realizable simulations, the use of Dynamic Adaptive Chemistry (DAC) stands out among other methods. DAC methods are based on the use of reduced mechanisms generate

Daniël Barenholz, Marco Montali, Artem Polyvyanyy, Hajo A. Reijers

A process discovery algorithm aims to construct a model from data generated by historical system executions such that the model describes the system well. Consequently, one desired property of a process discovery algorithm is rediscoverability, which ensures that the algorithm can construct a model that is behaviorally equivalent to the original system. A sy

Lukas Wessels

We prove the existence of a $B$-continuous viscosity solution for a class of infinite dimensional semilinear partial differential equations (PDEs) using probabilistic methods. Our approach also yields a stochastic representation formula for the solution in terms of a scalar-valued backward stochastic differential equation. The uniqueness is proved under addi