arXiv papers from January 2023

J. Cortés-Vega, J. F. Barra, L. Pereira, A. Delgado

Entangled states play a fundamental role in Quantum Mechanics and are at the core of many contemporary applications, such as quantum communication and quantum computing. Therefore, determining whether a state is entangled or not is an important task. Here, we propose a method to detect the entanglement of unknown two-qubit quantum states. Our method is based

E. E. Díaz-Figueroa, G. Ares de Parga, J. J. González-Avilés

We perform a series of numerical simulations to recreate small-scale two-fluid jets using the JOANNA code, considering the magnetohydrodynamics of two fluids (ions + electrons and neutral particles). We first excite the jets in a uniform magnetic field by using velocity pulse perturbations located at $y_{0}=$1.3, 1.5, and 1.8 Mm, considering the base of the

Xiaowei Yu, Lu Zhang, Haixing Dai, Lin Zhao

The human cerebral cortex is highly convoluted into convex gyri and concave sulci. It has been demonstrated that gyri and sulci are significantly different in their anatomy, connectivity, and function, besides exhibiting opposite shape patterns, long-distance axonal fibers connected to gyri are much denser than those connected to sulci, and neural signals on

Thiago Cavalheiro, Alexandre Santana, João Cossich, Victor Ayala

The objective of this paper is to study the controllability of discrete-time linear control systems in solvable Lie groups. In the special case of nilpotent Lie groups, a necessary and sufficient condition for controllability is established. Furthermore, the class of discrete-time linear systems in the two-dimensional affine Lie group is constructed and a co

M. Yu. Gubin, A. Yu. Leksin, A. V. Shesterikov, A. V. Prokhorov

Nonlinear plasmonic effects in perspective 2D materials containing low-dimensional quantum emitters can be a basis of a novel technological platform for the fabrication of fast all-plasmonic triggers, transistors, and sensors. This article considers the conditions for achieving a strong plasmon-exciton coupling in the system with quantum nanowire (NW) placed

Lucy Martinez, Doron Zeilberger

Noga Alon and Yaakov Malinovsky recently studied the following game: you start at 0, and keep rolling a fair standard die, and add the outcomes until the sum happens to be prime. We generalize this in several ways, illustrating the power of symbolic, rather than merely numeric, computation. We conclude with polemics why the beautiful rigorous error estimate

Carolus Vitalis, Tobias Wenzel

Microfluidic droplet screens serve as an innovative platform for high-throughput biotechnology, enabling significant advancements in discovery, product optimization, and analysis. This review sheds light on the emerging trend of interaction assays in microfluidic droplets, underscoring the unique suitability of droplets for these applications. Encompassing a

Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael R. Kosorok

Offline model selection (OMS), that is, choosing the best policy from a set of many policies given only logged data, is crucial for applying offline RL in real-world settings. One idea that has been extensively explored is to select policies based on the mean squared Bellman error (MSBE) of the associated Q-functions. However, previous work has struggled to

Yuan Gao, Yizhu Zhang, Kaixuan Zhang, Ziyang Gan

The orientation and ellipticity of terahertz (THz) polarization generated by two-color strong field not only cast light on underlying mechanisms of laser-matter interaction, but also play an important role for various applications. We develop the Coulomb-corrected classical trajectory Monte Carlo (CTMC) method to well reproduce the joint measurements, that t

Jesús Bonilla, Juan Vicente Gutiérrez-Santacreu

The Keller-Segel-Navier-Stokes system governs chemotaxis in liquid environments. This system is to be solved for the organism and chemoattractant densities and for the fluid velocity and pressure. It is known that if the total initial cell density mass is below $2\pi$ there exist globally defined generalised solutions, but what is less understood is whether

Omead Pooladzandi, Pasha Khosravi, Erik Nijkamp, Baharan Mirzasoleiman

Generative models have the ability to synthesize data points drawn from the data distribution, however, not all generated samples are high quality. In this paper, we propose using a combination of coresets selection methods and ``entropic regularization'' to select the highest fidelity samples. We leverage an Energy-Based Model which resembles a variational

Huy The Nguyen, Shengwen Wang

We consider the varifold associated to the Allen--Cahn phase transition problem in $\mathbb R^{n+1}$(or $n+1$-dimensional Riemannian manifolds with bounded curvature) with integral $L^{q_0}$ bounds on the Allen--Cahn mean curvature (first variation of the Allen--Cahn energy) in this paper. It is shown here that there is an equidistribution of energy between

Ilya Trofimov, Daniil Cherniavskii, Eduard Tulchinskii, Nikita Balabin

We propose a method for learning topology-preserving data representations (dimensionality reduction). The method aims to provide topological similarity between the data manifold and its latent representation via enforcing the similarity in topological features (clusters, loops, 2D voids, etc.) and their localization. The core of the method is the minimizatio

Prasad Jayanti, Siddhartha Jayanti, Sucharita Jayanti

We present durable implementations for two well known universal primitives -- CAS (compare-and-swap), and its ABA-free counter-part LLSC (load-linked, store-conditional). All our implementations are: writable, meaning they support a Write() operation; have constant time complexity per operation; allow for dynamic joining, meaning newly created processes (a.k

Samuel Triest, Mateo Guaman Castro, Parv Maheshwari, Matthew Sivaprakasam

The process of designing costmaps for off-road driving tasks is often a challenging and engineering-intensive task. Recent work in costmap design for off-road driving focuses on training deep neural networks to predict costmaps from sensory observations using corpora of expert driving data. However, such approaches are generally subject to over-confident mis

Bin Fu, Yumei Huo, Hairong Zhao

We study the classical scheduling problem on parallel machines %with precedence constraints where the precedence graph has the bounded depth $h$. Our goal is to minimize the maximum completion time. We focus on developing approximation algorithms that use only sublinear space or sublinear time. We develop the first one-pass streaming approximation schemes us

Seick Kim, Georgios Sakellaris

We construct the Neumann Green function and establish scale invariant regularity estimates for solutions to the Neumann problem for the elliptic operator $Lu=-{\rm div}({\bf A} \nabla u+ \boldsymbol{b}u)+ \boldsymbol{c} \cdot \nabla u+du$ in a Lipschitz domain $\Omega$. We assume that ${\bf A}$ is elliptic and bounded, that the lower order coefficients belon

T. Ramburuth-Hurt, A. De Cia, J. -K. Krogager, C. Ledoux

The chemical composition of gas in galaxies can be measured in detail from absorption spectroscopy. By studying gas in galaxies in this way, it is possible to investigate the small and faint galaxies, which are the most numerous in the universe. In particular, the chemical distribution of gas in absorbing systems gives us insight into cycles of gas in and ar

P. Malgaretti, T. Nizkaia, G. Oshanin

In many practically important problems which rely on particles' transport in realistic corrugated channels, one is interested to know the probability that either of the extremities, (e.g., the one containing a chemically active site, or connected to a broader channel), is reached before the other one. In mathematical literature, the latter are called the "sp

Fermín Moscoso del Prado Martín

Human syntactic structures are usually represented as graphs. Much research has focused on the mapping between such graphs and linguistic sequences, but less attention has been paid to the shapes of the graphs themselves: their topologies. This study investigates how the topologies of syntactic graphs reveal traces of the processes that led to their emergenc

Usman Anjum, Vladimir Zadorozhny, Prashant Krishnamurthy

Twitter (one example of microblogging) is widely being used by researchers to understand human behavior, specifically how people behave when a significant event occurs and how it changes user microblogging patterns. The changing microblogging behavior can reveal patterns that can help in detecting real-world events. However, the Twitter data that is availabl

Giacomo Albi, Marco Caliari, Elisa Calzola, Fabio Cassini

In this paper we consider mean-field optimal control problems with selective action of the control, where the constraint is a continuity equation involving a non-local term and diffusion. First order optimality conditions are formally derived in a general framework, accounting for boundary conditions. Hence, the optimality system is used to construct a reduc

Haoyue Guo, Matthew R. Carbone, Chuntian Cao, Jianzhou Qu

X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In this work, we develop a database of sulfur K-edge XAS spectra of crystalline and amorphous lithium thiophosphate materials based on the atomic structures reported in Chem. Mater., 3

Á. Rincón, G. Panotopoulos, I. Lopes

We investigate exotic stars composed of dark energy within the context of Einstein's General Relativity, by applying an extended Chaplygin gas equation-of-state. To account for anisotropies, we utilize a formalism based on the complexity factor to obtain numerical solutions. By applying well-established criteria, we demonstrate that the solutions are physica

Huanyu Ma, Xincheng Wang, Linxuan Zhang, Zhihan Zou

Photoionization of the rubidium (Rb) atoms cooled in a magneto-optical trap, characterized by the coexistence of the ground 5$S_{1/2}$ and the excited 5$P_{3/2}$ states, is investigated experimentally and theoretically with the 400 nm femtosecond laser pulses at intensities of $I=3\times10^9$ W/cm$^2$ - $4.5\times10^{12}$ W/cm$^2$. Recoil-ion momentum distri

Lei Li, Tianfang Zhang, Zhongfeng Kang, Wenhan Zhang

The detection of small and medium-sized objects in three dimensions has always been a frontier exploration problem. This technology has a very wide application in sports analysis, games, virtual reality, human animation and other fields. The traditional three-dimensional small target detection technology has the disadvantages of high cost, low precision and

Lohit Vandanapu, Michael D. Shileds

This paper presents a methodology for the simulation of non-Gaussian wind field as a stochastic wave using the 3rd-order Spectral Representation Method. Traditionally, the wind field is modeled as a stochastic vector process at discrete locations in space. But the simulation of vector process is well-known to be computationally challenging and numerically un

Charles Dawson, Ashkan Jasour, Andreas Hofmann, Brian Williams

Many practical applications of robotics require systems that can operate safely despite uncertainty. In the context of motion planning, two types of uncertainty are particularly important when planning safe robot trajectories. The first is environmental uncertainty -- uncertainty in the locations of nearby obstacles, stemming from sensor noise or (in the cas

Peipei Lu, Wei Wang, Guido Kanschat, Andreas Rupp

We propose a multigrid method to solve the linear system of equations arising from a hybrid discontinuous Galerkin (in particular, a single face hybridizable, a hybrid Raviart--Thomas, or a hybrid Brezzi--Douglas--Marini) discretization of a Stokes problem. Our analysis is centered around the augmented Lagrangian approach and we prove uniform convergence in

Xi Wu, Shaleen Deep, Joe Benassi, Fengan Li

Many data insight questions can be viewed as searching in a large space of tables and finding important ones, where the notion of importance is defined in some adhoc user defined manner. This paper presents Holistic Cube Analysis (HoCA), a framework that augments the capabilities of relational queries for such problems. HoCA first augments the relational dat

Bryan Zhang, Amita Misra

Previous work suggests that performance of cross-lingual information retrieval correlates highly with the quality of Machine Translation. However, there may be a threshold beyond which improving query translation quality yields little or no benefit to further improve the retrieval performance. This threshold may depend upon multiple factors including the sou

Elisa Monchietti, César Massri, J. Acacio de Barros, Federico Holik

In this work, we elaborate on a measure-theoretic approach to negative probabilities. We study a natural notion of contextuality measure and characterize its main properties. Then, we apply this measure to relevant examples of quantum physics. In particular, we study the role played by contextuality in quantum computing circuits.

Mahdieh Yazdani, Maziar Raissi

The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self-supervised vision transformers, a recent breakthrough in computer vision and deep learning. Our proposed algorithm uses a combination of machine learning, computer vision and hedo

Camille Phiquepal, Marc Toussaint

This paper presents a new approach to Model Predictive Control for environments where essential, discrete variables are partially observed. Under this assumption, the belief state is a probability distribution over a finite number of states. We optimize a \textit{control-tree} where each branch assumes a given state-hypothesis. The control-tree optimization

Dawson Fox, Jose Monsalve Diaz, Xiaoming Li

For decades, memory capabilities have scaled up much slower than compute capabilities, leaving memory utilization as a major bottleneck. Prefetching and cache hierarchies mitigate this in applications with easily predictable memory accesses or those with high locality. In other applications like sparse linear algebra or graph-based applications, these strate

Collin Foster, Sreevishnu Oruganti, Francesco Panerai

Quantitative microstructural analysis of Room Temperature Vulcanized (RTV) silicone pyrolysis at high temperatures is presented. RTV is used as a bonding agent in multiple industries, particularly filling gaps in ablative tiles for hypersonic (re-)entry vehicles and fire prevention. Decomposition of RTV is resolved in real time using in situ high-temperature

Prince E. Kuevor, Maani Ghaffari, Ella M. Atkins, James W. Cutler

This work presents a number of techniques to improve the ability to create magnetic field maps on a UAV which can be used to quickly and reliably gather magnetic field observations at multiple altitudes in a workspace. Unfortunately, the electronics on the UAV can introduce their own magnetic fields, distorting the resultant magnetic field map. We show metho

Anna Mpanti, Stavros D. Nikolopoulos, Leonidas Palios

The minimum completion (fill-in) problem is defined as follows: Given a graph family $\mathcal{F}$ (more generally, a property $\Pi$) and a graph $G$, the completion problem asks for the minimum number of non-edges needed to be added to $G$ so that the resulting graph belongs to the graph family $\mathcal{F}$ (or has property $\Pi$). This problem is NP-compl

J Harry Caufield, Tim Putman, Kevin Schaper, Deepak R Unni

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of k

Yilun Du, Mengjiao Yang, Bo Dai, Hanjun Dai

A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks. Recent progress in text-guided image synthesis has yielded models with an impressive ability to generate complex novel images, exhibiting combinatorial generalization across domains. Motivated by this success, we investigate whether such tools can be used to co

Riccardo Torsi, Kyle T. Munson, Rahul Pendurthi, Esteban A. Marques

Substitutionally-doped 2D transition metal dichalcogenides are primed for next-generation device applications such as field effect transistors (FET), sensors, and optoelectronic circuits. In this work, we demonstrate substitutional Rhenium (Re) doping of MoS2 monolayers with controllable concentrations down to 500 parts-per-million (ppm) by metal-organic che

Hanjo Schnellbaecher, Florian Dufresne, Tommy Nilsson, Leonie Becker

The general profile and overarching goal of this proposed mission is to pioneer potentially highly beneficial (even vital) and cost-effective techniques for the future human colonization of Mars. Adopting radically new and disruptive solutions untested in the Martian context, our approach is one of high risk and high reward. The real possibility of such a so

Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis, Soji Adeshina

Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications. By contrast, Graph-regularized MLPs (GR-MLPs) implicitly inject the graph structure information into model weights, while their performance can hardly match

Lorenzo Mori, Maria Rosaria Ferrante

We propose a Small Area Estimation model based on Generalized Additive Models for Location, Scale and Shape (SAE-GAMLSS), for the estimation of household economic indicators. SAE-GAMLSS release the exponential family distributional assumption and allow each distributional parameter to depend on covariates. A bootstrap approach to estimate MSE is proposed. Th

Z. F. Wang, X. Y. Zhang, Y-c I. Chang

The analysis of data stored in multiple sites has become more popular, raising new concerns about the security of data storage and communication. Federated learning, which does not require centralizing data, is a common approach to preventing heavy data transportation, securing valued data, and protecting personal information protection. Therefore, determini

Yuxi Zhao, Xiaowen Gong, Shiwen Mao

Federated learning (FL) has emerged as a promising paradigm that trains machine learning (ML) models on clients' devices in a distributed manner without the need of transmitting clients' data to the FL server. In many applications of ML, the labels of training data need to be generated manually by human agents. In this paper, we study FL with crowdsourced da

Parfait Atchade-Adelomou, Kent Larson

In this work, we aim to confirm the impact of the Fourier series on the quantum machine learning model. We will propose models, tests, and demonstrations to achieve this objective. We designed a quantum machine learning leveraged on the Hamiltonian encoding. With a subtle change, we performed the trigonometric interpolation, binary and multiclass classifier,

Xinru Wei, Shuai Dong, Zhao Su, Lili Tang

Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard symptom-based diagnosis often unreliable and unrepeatable. We developed a novel network-based machine-learning

Changlong Wu, Ananth Grama, Wojciech Szpankowski

We study the problem of online learning and online regret minimization when samples are drawn from a general unknown non-stationary process. We introduce the concept of a dynamic changing process with cost $K$, where the conditional marginals of the process can vary arbitrarily, but that the number of different conditional marginals is bounded by $K$ over $T

Melanie Subbiah, Amrita Bhattacharjee, Yilun Hua, Tharindu Kumarage

Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread. We argue that while misinformation and disinformation detection have been studied, there has been a lack of investment in the important open challenge of detecting harmful agendas in news articles; identifying harmful agendas is critical to fla

Amanda L. Steinhebel, Regina Caputo, Henrike Fleischhack, Nicolas Striebig

Space-based gamma-ray telescopes such as the Fermi Large Area Telescope have used single sided silicon strip detectors to measure the position of charged particles produced by incident gamma rays with high resolution. At energies in the Compton regime and below, two dimensional position information within a single detector is required. Double sided silicon s

Martin Veresko, Ming-Cheng Cheng

Multi-dimensional direct numerical simulation (DNS) of the Schr\"odinger equation is needed for design and analysis of quantum nanostructures that offer numerous applications in biology, medicine, materials, electronic/photonic devices, etc. In large-scale nanostructures, extensive computational effort needed in DNS may become prohibitive due to the high deg

Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lazaro-Gredilla

Noisy-OR Bayesian Networks (BNs) are a family of probabilistic graphical models which express rich statistical dependencies in binary data. Variational inference (VI) has been the main method proposed to learn noisy-OR BNs with complex latent structures (Jaakkola & Jordan, 1999; Ji et al., 2020; Buhai et al., 2020). However, the proposed VI approaches either

Simiao Ren, Saad Lahrichi, Yang Deng, Willie J. Padilla

Deep active learning (DAL) methods have shown significant improvements in sample efficiency compared to simple random sampling. While these studies are valuable, they nearly always assume that optimal DAL hyperparameter (HP) settings are known in advance, or optimize the HPs through repeating DAL several times with different HP settings. Here, we argue that

Duncan Dauvergne

The parabolic Airy line ensemble $\mathfrak A$ is a central limit object in the KPZ universality class and related areas. On any compact set $K = \{1, \dots, k\} \times [a, a + t]$, the law of the recentered ensemble $\mathfrak A - \mathfrak A(a)$ has a density $X_K$ with respect to the law of $k$ independent Brownian motions. We show that $$ X_K(f) = \exp \

Venkatesh Sivaraman, Leigh A. Bukowski, Joel Levin, Jeremy M. Kahn

Artificial intelligence (AI) in healthcare has the potential to improve patient outcomes, but clinician acceptance remains a critical barrier. We developed a novel decision support interface that provides interpretable treatment recommendations for sepsis, a life-threatening condition in which decisional uncertainty is common, treatment practices vary widely

Sarabjeet Singh, Xiong Fan, Ananth Krishna Prasad, Lin Jia

This paper makes a case for accelerating lattice-based post quantum cryptography (PQC) with memristor based crossbars, and shows that these inherently error-tolerant algorithms are a good fit for noisy analog MAC operations in crossbars. We compare different NIST round-3 lattice-based candidates for PQC, and identify that SABER is not only a front-runner whe

Son Quoc Tran, Phong Nguyen-Thuan Do, Uyen Le, Matt Kretchmar

Pretrained language models have achieved super-human performances on many Machine Reading Comprehension (MRC) benchmarks. Nevertheless, their relative inability to defend against adversarial attacks has spurred skepticism about their natural language understanding. In this paper, we ask whether training with unanswerable questions in SQuAD 2.0 can help impro

Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales

Large language models have achieved impressive performance on various natural language processing tasks. However, so far they have been evaluated primarily on benchmarks where all information in the input context is relevant for solving the task. In this work, we investigate the distractibility of large language models, i.e., how the model problem-solving ac

Zhenghao Zeng, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi

When estimating causal effects, it is important to assess external validity, i.e., determine how useful a given study is to inform a practical question for a specific target population. One challenge is that the covariate distribution in the population underlying a study may be different from that in the target population. If some covariates are effect modif

Brock C. Price, Xiangsheng Xu

In this article we prove the global existence of weak solutions to an initial boundary value problem with an exponential and p-Laplacian nonlinearity. The equation is a continuum limit of a family of kinetic Monte Carlo models of crystal surface relaxation. In our investigation we find a weak solution where the exponent in the equation, $-\Delta_p u$, can ha

Hussein Hazimeh, Natalia Ponomareva

Adversarial nets have proved to be powerful in various domains including generative modeling (GANs), transfer learning, and fairness. However, successfully training adversarial nets using first-order methods remains a major challenge. Typically, careful choices of the learning rates are needed to maintain the delicate balance between the competing networks.

Collin Cademartori, Cynthia Rush

Approximate Message Passing (AMP) algorithms are a class of iterative procedures for computationally-efficient estimation in high-dimensional inference and estimation tasks. Due to the presence of an 'Onsager' correction term in its iterates, for $N \times M$ design matrices $\mathbf{A}$ with i.i.d. Gaussian entries, the asymptotic distribution of the estima

Lucas Valença, Ian Maquignaz, Hadi Moazen, Rishikesh Madan

We present LM-GAN, an HDR sky model that generates photorealistic environment maps with weathered skies. Our sky model retains the flexibility of traditional parametric models and enables the reproduction of photorealistic all-weather skies with visual diversity in cloud formations. This is achieved with flexible and intuitive user controls for parameters, i

Alfredo Iorio, Boris Ivetić, Salvatore Mignemi, Pablo Pais

We discuss here how, when higher-order effects in the parameter $\frac{\ell}{\hbar} |\vec{p}|$, related to the lattice spacing $\ell$, are considered, pristine graphene, and other Dirac materials, can be used as tabletop systems where generalized commutation relations are naturally realized. Such generalized algebras of quantization, which lead to generalize

Johanna Casado, Beatriz García, Poshak Gandhi, Wanda Díaz-Merced

Even when actual technologies present the potential to augment inclusion and the United Nations has been stablished the digital access to information as a human right, people with disabilities continuously faced barriers in their profession. In many cases, in sciences, the lack of accessible and user centred tools left behind researches with disabilities and

Jeffrey Bub

Richard Feynman famously said that nobody understands quantum mechanics and cautioned against asking: "But how can it be like that?" Something about the conceptual foundations of the theory is profoundly puzzling, but just what is so disturbing is not easy to pin down. Three books by Olival Freire (The Quantum Dissidents), Adam Becker (What is Real?), and Ph

Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay

Retrieval-Augmented Language Modeling (RALM) methods, which condition a language model (LM) on relevant documents from a grounding corpus during generation, were shown to significantly improve language modeling performance. In addition, they can mitigate the problem of factually inaccurate text generation and provide natural source attribution mechanism. Exi

Yuanhao Li, Badong Chen, Okito Yamashita, Natsue Yoshimura

Sparseness and robustness are two important properties for many machine learning scenarios. In the present study, regarding the maximum correntropy criterion (MCC) based robust regression algorithm, we investigate to integrate the MCC method with the automatic relevance determination (ARD) technique in a Bayesian framework, so that MCC-based robust regressio

Gonzalo De La Vega, Leonardo Martin Exequiel Dominguez, Johanna Casado, Beatriz García

Sonification as a complement of visualization is been under research for decades as a new ways of data deployment. ICAD conferences, gather together specialists from different disciplines to discuss about sonification. Different tools as sonoUno, starSound and Web Sandbox are attempt to reach a tool to open astronomical data sets and sonify it in conjunction

Yucong Tang, Bin Wang, Guanghui Wang, Guiying Yan

In this paper, we develop a new rainbow Hamilton framework, which is of independent interest, settling the problem proposed by Gupta, Hamann, M\"{u}yesser, Parczyk, and Sgueglia when $k=3$, and draw the general conclusion for any $k\geq3$ as follows. A $k$-graph system $\textbf{H}=\{H_i\}_{i\in[n]}$ is a family of not necessarily distinct $k$-graphs on the s

Noyan Evirgen, Xiang 'Anthony' Chen

Generative adversarial networks (GANs) have many application areas including image editing, domain translation, missing data imputation, and support for creative work. However, GANs are considered 'black boxes'. Specifically, the end-users have little control over how to improve editing directions through disentanglement. Prior work focused on new GAN archit

C. A. Sackett, J. A. Stickney

We present a design for an atom chip trap that uses the time-orbiting potential technique. The design offers several advantages compared to other chip-trap methods. It uses a simple crossed-wire pattern on the chip, along with a rotating bias field. The trap is naturally close to spherically symmetric, and it can be modified to be exactly symmetric in quadra

Cuong Tran, Ferdinando Fioretto

A number of learning models used in consequential domains, such as to assist in legal, banking, hiring, and healthcare decisions, make use of potentially sensitive users' information to carry out inference. Further, the complete set of features is typically required to perform inference. This not only poses severe privacy risks for the individuals using the

Karl Karu, Eva Roos Nerut, Xueran Tao, Sergei A. Kislenko

Progress in electrochemical applications of ionic liquids builds on an understanding of electrical double-layer. This computational study focuses on structure-determined quantities -- maximum packing density, potentials and capacitances -- evaluated using a one-electrode electrical double-layer model. Interfaces of 40 studied ions are grouped into four disti

Neil G. MacLaren, Lingqi Meng, Melissa Collier, Naoki Masuda

The social brain hypothesis states that the relative size of the neocortex is larger for species with higher social complexity as a result of evolution. Various lines of empirical evidence have supported the social brain hypothesis, including evidence from the structure of social networks. Social complexity may itself positively impact cooperation among indi

João Henrique Inacio de Souza, Victor Croisfelt, Fabio Saggese, Taufik Abrão

A reconfigurable intelligent surface (RIS) can shape the wireless propagation channel by inducing controlled phase shift variations to the impinging signals. Multiple works have considered the use of RIS by time-varying configurations of reflection coefficients. In this work we use the RIS to control the channel coherence time and introduce a generalized dis

Blaise Delmotte

We investigate the individual and collective dynamics of torque-driven particles, called microrollers, near fluid-fluid interfaces. We find that the viscosity ratio across the interface controls the speed and direction of the particles, their relative motion, the growth of a fingering instability, and the self-assembled motile structures that emerge from it.

Pietro Dona, Pietropaolo Frisoni

We introduce a strategy to compute EPRL spin foam amplitudes with many internal faces numerically. We work with sl2cfoam-next, the state-of-the-art framework to numerically evaluate spin foam transition amplitudes. We find that uniform sampling Monte Carlo is exceptionally effective in approximating the sum over internal quantum numbers of a spin foam amplit

R. Ramesh, C. Kathiravan, Anshu Kumari

We report spectral and polarimeter observations of two weak, low frequency (${\approx}$85-60\,MHz) solar coronal type II radio bursts that occurred on 2020 May 29 within a time interval ${\approx}$2\,min. The bursts had fine structures, and were due to harmonic plasma emission. Our analysis indicates that the magnetohydrodynamic (MHD) shocks responsible for

Ching-Yao Chuang, Varun Jampani, Yuanzhen Li, Antonio Torralba

Machine learning models have been shown to inherit biases from their training datasets. This can be particularly problematic for vision-language foundation models trained on uncurated datasets scraped from the internet. The biases can be amplified and propagated to downstream applications like zero-shot classifiers and text-to-image generative models. In thi

Ji Qi, Wei Ding, Qi Zhang, Yuxuan Wang

To solve the conventional conflict between maintaining good charge transport property and achieving high light extraction efficiency when using micro/nanostructure patterned substrates to extract light from organic light emitting diodes (OLEDs), we developed a novel OLED structure, termed High-index Deep-Groove Dielectric Nanomesh OLED (HDNM-OLED), fabricate

Mahsa Doosthosseini, Mahdi Khajeh Talkhoncheh, Jeffrey L. Silberberg, Sandy Weininger

This paper investigates the impact of implantable cardioverter defibrillator (ICD)'s load on its lithium battery power sources through a coupled electro-thermal dynamic model simulation. ICDs are one of the effective treatments available to significantly improve survival of patients with fatal arrhythmia (abnormal heart rhythm) disorders. Using a lithium bat

Joseph P. Vantassel, Jodie A. Crocker, Brady R. Cox, Khiem Tran

There is a growing need to characterize the engineering material properties of the shallow subsurface in three-dimensions for advanced engineering analyses. However, imaging the near-surface in three-dimensions at spatial resolutions required for such purposes remains in its infancy and requires further study before it can be adopted into practice. To enable

Ido Ben-Dayan, Utkarsh Kumar

Addressing the discrepancy between the late and early time measurements of the Hubble parameter, $H_0$, and the so-called $S_8$ parameter has been a challenge in precision cosmology. Several models are present to address these tensions, but very few of them can do so simultaneously. In the past, we have suggested Banks-Zaks/Unparticles as an emergent Dark En

Ji Qi, Wei Ding, Qi Zhang, Yuxuan Wang

To improve the light extraction efficiency of organic light emitting diodes (OLEDs), we developed a novel substrate, i.e., a metamaterial based flat Moire micro-lens array formed using double nanoimprint, termed Mlens-array, consisting of a hexagonal moir\'e pattern pillar array. By choosing a low refractive index dielectric material for the pillar array and

Rhys Howard, Lars Kunze

Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. The creation of models to describe these relationships is typically accomplished through the application of causal discovery techniques. However, as it stands observational causal discovery techniques struggle to adequately cope with conditions such as causal

Xinru Hua, Truyen Nguyen, Tam Le, Jose Blanchet

The scarcity of labeled data is a long-standing challenge for many machine learning tasks. We propose our gradient flow method to leverage the existing dataset (i.e., source) to generate new samples that are close to the dataset of interest (i.e., target). We lift both datasets to the space of probability distributions on the feature-Gaussian manifold, and t

Felipe Elorrieta, Lucas Osses, Matias Cáceres, Susana Eyheramendy

In the last decades, due to the huge technological growth observed, it has become increasingly common that a collection of temporal data rapidly accumulates in vast amounts. This provides an opportunity for extracting valuable information through the estimation of increasingly precise models. But at the same time it imposes the challenge of continuously upda

Rui Oliveira, Siddharth H. Nair, Bo Wahlberg

Motion planning for autonomous vehicles sharing the road with human drivers remains challenging. The difficulty arises from three challenging aspects: human drivers are 1) multi-modal, 2) interacting with the autonomous vehicle, and 3) actively making decisions based on the current state of the traffic scene. We propose a motion planning framework based on B

Alexandre Heuillet, Hedi Tabia, Hichem Arioui

Siamese networks are one of the most trending methods to achieve self-supervised visual representation learning (SSL). Since hand labeling is costly, SSL can play a crucial part by allowing deep learning to train on large unlabeled datasets. Meanwhile, Neural Architecture Search (NAS) is becoming increasingly important as a technique to discover novel deep l

Thi Kieu Khanh Ho, Ali Karami, Narges Armanfard

With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challeng

Alessandro Guidotti, Alessandro Vanelli-Coralli, Carla Amatetti

While 5G networks are being rolled out, the definition of the potential 5G-Advanced features and the identification of disruptive technologies for 6G systems are being addressed by the scientific and academic communities to tackle the challenges that 2030 communication systems will face, such as terabit-capacity and always-on networks. In this framework, it

C. J. Marvin, A. Reiners, G. Anglada-Escudé, S. V. Jeffers

Context: With the recent surge of planetary surveys focusing on detecting Earth-mass planets around M dwarfs, it is becoming more important to understand chromospheric activity in M dwarfs. Stellar chromospheric calcium emission is typically measured using the $R'_\mathrm{HK}$ calibrations of Noyes et al. (1984), which are only valid for $0.44 \le B-V \le 0.

Victor Chéron, Fabien Evrard, Berend van Wachem

A novel smooth immersed boundary method (IBM) based on a direct-forcing formulation is proposed to simulate incompressible dense particle-laden flows. This IBM relies on a regularization of the transfer function between the Eulerian grid points (to discretise the fluid governing equations) and Lagrangian markers (to represent the particle surface) to fulfill

Ilse Plaisier, Sjoerd Bouma, Anna Nelles

In-ice radio detectors are a promising tool for the discovery of EeV neutrinos. For astrophysics, the implications of such a discovery will rely on the reconstruction of the neutrino arrival direction. This paper describes a first complete neutrino arrival direction reconstruction for detectors employing deep antennas such as RNO-G or planning to employ them

Akshara Viswanathan, Else Starkenburg, Helmer H. Koppelman, Amina Helmi

The Milky Way halo is one of the few galactic haloes that provides a unique insight into galaxy formation by resolved stellar populations. Here, we present a catalogue of $\sim$47 million halo stars selected independent of parallax and line-of-sight velocities, using a combination of Gaia DR3 proper motion and photometry by means of their reduced proper moti

Subhash Bhagat, Andrzej Pelc

A mobile agent, modeled as a deterministic finite automaton, navigates in the infinite anonymous oriented grid $\mathbb{Z} \times \mathbb{Z}$. It has to explore a given infinite subgraph of the grid by visiting all of its nodes. We focus on the simplest subgraphs, called {\em wedges}, spanned by all nodes of the grid located between two half-lines in the pla

Manuel Amann, Anand Dessai

In C. R. Math. Acad. Sci. Paris 348 (2010) pp. 283--285 (arXiv:0811.0840) we constructed examples of $S^1$-manifolds with finite second homotopy group and non-vanishing $\hat A$-genus. The reasoning was based on an equivariant surgery lemma which only holds under additional assumptions. To remedy the situation we give a construction using explicit equivarian

Jiri Adamek, Miroslav Husek, Jiri Rosicky, Walter Tholen

Quillen's notion of small object and the Gabriel-Ulmer notion of finitely presentable or generated object are fundamental in homotopy theory and categorical algebra. Do these notions always lead to rather uninteresting classes of objects in categories of topological spaces, such as all finite discrete spaces, or just the empty space, as the examples and rema