April 2023 arXiv papers — page 62

Ravid Shwartz-Ziv, Yann LeCun

Deep neural networks excel in supervised learning tasks but are constrained by the need for extensive labeled data. Self-supervised learning emerges as a promising alternative, allowing models to learn without explicit labels. Information theory, and notably the information bottleneck principle, has been pivotal in shaping deep neural networks. This principl

Anton Izosimov, Boris Khesin, Ilia Kirillov

We give a classification of generic coadjoint orbits for the group of area-preserving diffeomorphisms of a closed non-orientable surface. This completes V. Arnold's program of studying invariants of incompressible fluids in 2D. As an auxiliary problem, we also classify simple Morse pseudo-functions on non-orientable surfaces up to area-preserving diffeomorph

Nathan J. Szymanski, Pragnay Nevatia, Christopher J. Bartel, Yan Zeng

To aid in the automation of inorganic materials synthesis, we introduce an algorithm (ARROWS3) that guides the selection of precursors used in solid-state reactions. Given a target phase, ARROWS3 iteratively proposes experiments and learns from their outcomes to identify an optimal set of precursors that leads to maximal yield of that target. Initial experim

Yizheng Wang, Markus Zechner, Gege Wen, Anthony Louis Corso

To combat global warming and mitigate the risks associated with climate change, carbon capture and storage (CCS) has emerged as a crucial technology. However, safely sequestering CO2 in geological formations for long-term storage presents several challenges. In this study, we address these issues by modeling the decision-making process for carbon storage ope

Salik Ram Khanal, Ranjan Sapkota, Dawood Ahmed, Uddhav Bhattarai

Early-stage identification of fruit flowers that are in both opened and unopened condition in an orchard environment is significant information to perform crop load management operations such as flower thinning and pollination using automated and robotic platforms. These operations are important in tree-fruit agriculture to enhance fruit quality, manage crop

Alexander Heger, Bernhard Müller, Ilya Mandel

The collapse of massive stars is one of the most-studied paths to black hole formation. In this chapter, we review black hole formation during the collapse of massive stars in the broader context of single and binary stellar evolution and the theory of supernova explosions. We provide a concise overview of the evolutionary channels that may lead to black hol

Jinjie Mai, Jun Chen, Bing Li, Guocheng Qian

Embodied AI focuses on the study and development of intelligent systems that possess a physical or virtual embodiment (i.e. robots) and are able to dynamically interact with their environment. Memory and control are the two essential parts of an embodied system and usually require separate frameworks to model each of them. In this paper, we propose a novel a

John C. Sunil, Richard A. Blythe, Martin R. Evans, Satya N. Majumdar

Resetting a stochastic process has been shown to expedite the completion time of some complex tasks, such as finding a target for the first time. Here we consider the cost of resetting by associating to each reset a cost, which is a function of the distance travelled during the reset event. We compute the Laplace transform of the joint probability of first p

Weixing Zhou, Qi Peng, Zijie Zhang, Yanfeng Zhang

Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknow

Tobias Weich, Lasse L. Wolf

Let $X=X_1\times X_2$ be a product of two rank one symmetric spaces of non-compact type and $Γ$ a torsion-free discrete subgroup in $G_1\times G_2$. We show that the spectrum of $Γ\backslash X$ is related to the asymptotic growth of $Γ$ in the two direction defined by the two factors. We obtain that $L^2(Γ\backslash G)$ is tempered for large class of $Γ$.

Axel Huebl, Remi Lehe, Edoardo Zoni, Olga Shapoval

Developing complex, reliable advanced accelerators requires a coordinated, extensible, and comprehensive approach in modeling, from source to the end of beam lifetime. We present highlights in Exascale Computing to scale accelerator modeling software to the requirements set for contemporary science drivers. In particular, we present the first laser-plasma mo

Gabriel Tjio, Ping Liu, Chee-Keong Kwoh, Joey Tianyi Zhou

Obtaining sufficient labeled data for training deep models is often challenging in real-life applications. To address this issue, we propose a novel solution for single-source domain generalized semantic segmentation. Recent approaches have explored data diversity enhancement using hallucination techniques. However, excessive hallucination can degrade perfor

Hannah Brodsky, Rocío Calderón, Douglas S. Hamilton, Longlei Li

Pathogenic fungi are a leading cause of crop disease and primarily spread through microscopic, durable spores adapted differentially for both persistence and dispersal. Computational Earth System Models and air pollution models have been used to simulate atmospheric spore transport for aerial-dispersal-adapted (airborne) rust diseases, but the importance of

Alex Krasnok, Pashupati Dhakal, Arkady Fedorov, Pedro Frigola

Superconducting microwave cavities with ultra-high Q-factors are revolutionizing the field of quantum computing, offering long coherence times exceeding 1 ms, which is critical for realizing scalable multi-qubit quantum systems with low error rates. In this work, we provide an in-depth analysis of recent advances in ultra-high Q-factor cavities, integration

Jackson Callaghan, Colleen H. Xu, Jiwen Xin, Marco Alvarado Cano

Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identifi

Dinh Van Tuan, Hanan Dery

We revisit low-temperature optical spectra of transition-metal dichalcogenide monolayers and point to a possible crystallization of electrons (or holes) at low to moderate charge densities. To calculate the excitonic spectra under such conditions, we introduce the recursion method and compute how the charge density affects the energies, linewidths, and oscil

Kabish Wisal, Stephen C. Warren-Smith, Chun-Wei Chen, Hui Cao

Stimulated Brillouin scattering (SBS) is an important nonlinear optical effect which can both enable and impede optical processes in guided wave systems. Highly multi-mode excitation of fibers has been proposed as a novel route towards efficient suppression of SBS in both active and passive fibers. To study the effects of multimode excitation generally, we d

Victoria Blake, Louisa Jorm, Jennifer Yu, Astin Lee

In Australia, cardiovascular diseases (CVD) are managed in a complex and fragmented healthcare system across multiple providers. A data repository that links data sources, and enables advanced analytics and big data technologies, will generate novel insights, and allow development of translational tools that can improve patient care and outcomes. The Cardiac

Camilo Hernández, Ludovic Tangpi

In this work, we study the mean field Schr\"odinger problem from a purely probabilistic point of view by exploiting its connection to stochastic control theory for McKean-Vlasov diffusions. Our main result shows that the mean field Schr\"odinger problem arises as the limit of ``standard'' Schr\"odinger problems over interacting particles. Due to the stochast

David H. Kreitmeir, Paul A. Raschky

We analyse the effects of the ban of ChatGPT, a generative pre-trained transformer chatbot, on individual productivity. We first compile data on the hourly coding output of over 8,000 professional GitHub users in Italy and other European countries to analyse the impact of the ban on individual productivity. Combining the high-frequency data with the sudden a

Yuexin Wu, I-Chan Huang, Xiaolei Huang

Imbalanced token distributions naturally exist in text documents, leading neural language models to overfit on frequent tokens. The token imbalance may dampen the robustness of radiology report generators, as complex medical terms appear less frequently but reflect more medical information. In this study, we demonstrate how current state-of-the-art models fa

Eduard Puig Montellà, Julien Chauchat, Cyrille Bonamy, Dave Weij

In submerged sandy slopes, soil is frequently eroded as a combination of two main mechanisms: breaching, which refers to the retrogressive failure of a steep slope forming a turbidity current, and, instantaneous sliding wedges, known as shear failure, that also contribute to shape the morphology of the soil deposit. Although there are several modes of failur

Stephen Brade, Bryan Wang, Mauricio Sousa, Sageev Oore

Text-to-image generative models have demonstrated remarkable capabilities in generating high-quality images based on textual prompts. However, crafting prompts that accurately capture the user's creative intent remains challenging. It often involves laborious trial-and-error procedures to ensure that the model interprets the prompts in alignment with the use

Mira Watermeyer, Thomas Möbius, Oliver Grothe, Felix Müsgens

The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to a

Maria Chudnovsky, Ian Malcolm Johnson McInnis

The family of cycle completable graphs has several cryptomorphic descriptions, the equivalence of which has heretofore been proven by a laborious implication-cycle that detours through a motivating matrix completion problem. We give a concise proof, partially by introducing a new characterization. Then we generalize this family to ``$k$-quasichordal'' graphs

M. Moreno-Gonzalez, J. Villagra

Model-Free Control has proven its performance in a wide variety of systems. Although its adequate tuning can be achieved using the knowledge of the system and optimization-based approaches, there is not yet a systematic design procedure for this kind of control scheme. In this paper, a non-iterative Three Term Controller tuning procedure is adapted and exten

Junwen Zheng, Martin Fischer

Efficient information retrieval (IR) from building information models (BIMs) poses significant challenges due to the necessity for deep BIM knowledge or extensive engineering efforts for automation. We introduce BIM-GPT, a prompt-based virtual assistant (VA) framework integrating BIM and generative pre-trained transformer (GPT) technologies to support NL-bas

Jaroslaw S. Jaracz

In [5], Bray and Khuri proposed coupling the generalized Jang equation to several different auxiliary equations. The solutions to these coupled systems would then imply the Penrose inequality. One of these involves coupling the generalized Jang equation to $\overline{div}(\phi q)=0$, as this would guarantee the non-negativity of the scalar curvature in the J

Alexander Fedorov, Diba Hashemi, Giorgi Nadiradze, Dan Alistarh

Determining the degree of inherent parallelism in classical sequential algorithms and leveraging it for fast parallel execution is a key topic in parallel computing, and detailed analyses are known for a wide range of classical algorithms. In this paper, we perform the first such analysis for the fundamental Union-Find problem, in which we are given a graph

Federico Capone, Prahar Mitra, Aaron Poole, Bilyana Tomova

We perform a complete and systematic analysis of the solution space of six-dimensional Einstein gravity. We show that a particular subclass of solutions -- those that are analytic near $\mathcal{I}^+$ -- admit a non-trivial action of the generalised Bondi-Metzner-van der Burg-Sachs (GBMS) group which contains \emph{infinite-dimensional} supertranslations and

Boning Li, Jake Perazzone, Ananthram Swami, Santiago Segarra

We propose a novel data-driven approach to allocate transmit power for federated learning (FL) over interference-limited wireless networks. The proposed method is useful in challenging scenarios where the wireless channel is changing during the FL training process and when the training data are not independent and identically distributed (non-i.i.d.) on the

Tadele Mengesha, Abner J. Salgado, Joshua M. Siktar

We study a non-local optimal control problem involving a linear, bond-based peridynamics model. In addition to existence and uniqueness of solutions to our problem, we investigate their behavior as the horizon parameter $\delta$, which controls the degree of nonlocality, approaches zero. We then study a finite element-based discretization of this problem, it

Erum Mushtaq, Yavuz Faruk Bakman, Jie Ding, Salman Avestimehr

Federated Learning (FL) aims to train a machine learning (ML) model in a distributed fashion to strengthen data privacy with limited data migration costs. It is a distributed learning framework naturally suitable for privacy-sensitive medical imaging datasets. However, most current FL-based medical imaging works assume silos have ground truth labels for trai

Owen Goff

The $q$-Onsager algebra, denoted $O_q$, is defined by two generators $W_0, W_1$ and two relations called the $q$-Dolan-Grady relations. Recently, Terwilliger introduced some elements of $O_q$, said to be alternating. These elements are denoted $\{{W}_{-k}\}_{k=0}^{\infty}, \{{W}_{k+1}\}_{k=0}^{\infty}, \{{G}_{k+1}\}_{k=0}^{\infty}, \{{\tilde{G}}_{k+1}\}_{k=0

Xilai Li, Goeric Huybrechts, Srikanth Ronanki, Jeff Farris

Recently, there has been an increasing interest in unifying streaming and non-streaming speech recognition models to reduce development, training and deployment cost. The best-known approaches rely on either window-based or dynamic chunk-based attention strategy and causal convolutions to minimize the degradation due to streaming. However, the performance ga

Sheng He, Rina Bao, Jingpeng Li, Jeffrey Stout

Background: The segment-anything model (SAM), introduced in April 2023, shows promise as a benchmark model and a universal solution to segment various natural images. It comes without previously-required re-training or fine-tuning specific to each new dataset. Purpose: To test SAM's accuracy in various medical image segmentation tasks and investigate potenti

Xiaolei Zhang, Ning Bian, Refat Abdelmawla Khaled Assaad, Wei Qi

In this paper, the $\tau_q$-weak global dimension $\tau_q$-\cwd$(R)$ of a commutative ring $R$ is introduced. Rings with $\tau_q$-weak global dimension equal to $0$ are studied in terms of homologies, direct products, polynomial extensions and amalgamations. Besides, we investigate the $\tau_q$-weak global dimensions of polynomial rings.

Bo Yu, Hechang Chen, Chengyou Jia, Hongren Zhou

Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A series of popular deep learning methods have recently been introduced to learn nuance features from RS for binary classifications and achieved outstanding performance than conventional

Julia Chuzhoy, Ruimin Zhang

We study the fully dynamic All-Pairs Shortest Paths (APSP) problem in undirected edge-weighted graphs. Given an $n$-vertex graph $G$ with non-negative edge lengths, that undergoes an online sequence of edge insertions and deletions, the goal is to support approximate distance queries and shortest-path queries. We provide a deterministic algorithm for this pr

Alexander Clow, Ladislav Stacho

This paper considers upper bounds on the oriented chromatic number $\chi_o(G)$, of an oriented graph $G$ in terms of its $2$-dipath chromatic number $\chi_2(G)$, degeneracy $d(G)$, and maximum degree $\Delta(G)$. In particular, we show that for all graphs $G$ with $\chi_2(G) \leq k$ where $k \geq 2$ and $d(G) \leq t$ where $t \geq \log_2(k)$, $\chi_o(G) = 33

Alan Edelman, Sungwoo Jeong

We present the conditional determinantal point process (DPP) approach to obtain new (mostly Fredholm determinantal) expressions for various eigenvalue statistics in random matrix theory. It is well-known that many (especially $\beta=2$) eigenvalue $n$-point correlation functions are given in terms of $n\times n$ determinants, i.e., they are continuous DPPs.

Túlio Chiodi, Arthur dos Santos, Pedro Martins, Bruno Masiero

In this paper, we introduce a data-compilation ensemble, primarily intended to serve as a resource for researchers in the field of dereverberation, particularly for data-driven approaches. It comprises speech and song samples, together with acoustic guitar sounds, with original annotations pertinent to emotion recognition and Music Information Retrieval (MIR

Pinar Satilmis, Thomas Bashford-Rogers

Sky illumination is a core source of lighting in rendering, and a substantial amount of work has been developed to simulate lighting from clear skies. However, in reality, clouds substantially alter the appearance of the sky and subsequently change the scene's illumination. While there have been recent advances in developing sky models which include clouds,

Jianhai Hao, Fengdong Jia, Yue Cui, Yuhan Wang

We have theoretically and experimentally studied the dispersive signal of the Rydberg atomic electromagnetically induced transparency (EIT) - Autler-Townes (AT) splitting spectra obtained using amplitude modulation of the microwave (MW) field. In addition to the two zero-crossing points, the dispersion signal has two positive maxima with an interval defined

Kshiteej J. Deshmukh, Graeme W. Milton

Material resonances are fundamentally important in the field of nano-photonics and optics. So it is of great interest to know what are the limits to which they can be tuned. The bandwidth of the resonances in materials is an important feature which is commonly characterized by using the quality (Q) factor. We present bounds on the quality factor of two-phase

Bo Yu, Hechang Chen, Yunke Zhang, Lele Cong

Artificial intelligence-enabled histopathological data analysis has become a valuable assistant to the pathologist. However, existing models lack representation and inference abilities compared with those of pathologists, especially in cancer subtype diagnosis, which is unconvincing in clinical practice. For instance, pathologists typically observe the lesio

Marek Bolanowski, Alicja Gerka, Andrzej Paszkiewicz, Maria Ganzha

The concept of extended cloud requires efficient network infrastructure to support ecosystems reaching form the edge to the cloud(s). Standard approaches to network load balancing deliver static solutions that are insufficient for the extended clouds, where network loads change often. To address this issue, a genetic algorithm based load optimizer is propose

Román Aranda, Enrique Ramírez-Losada, Jesús Rodríguez-Viorato

This paper continues a program due to Motegi regarding universal bounds for the number of non-isotopic essential $n$-punctured tori in the complement of a hyperbolic knot in $S^3$. For $n=1$, Valdez-S\'anchez showed that there are at most five non-isotopic Seifert tori in the exterior of a hyperbolic knot. In this paper, we address the case $n=2$. We show th

Martina Paccini, Giuseppe Patanè, Michela Spagnuolo

This work addresses the patient-specific characterisation of the morphology and pathologies of muscle-skeletal districts (e.g., wrist, spine) to support diagnostic activities and follow-up exams through the integration of morphological and tissue information. We propose different methods for the integration of morphological information, retrieved from the ge

John H. Wise

The most massive black holes at redshifts z = 6 were already over billion solar masses. In this chapter, we discuss the formation and growth of the first black holes in the Universe. The deaths of massive primordial stars provide potential seeds of supermassive black holes. Theoretical models predict that the seed black hole masses range from 10 to 100,000 s

Emadaldin Mozafari-Majd, Visa Koivunen

This paper introduces a new regularized version of the robust $\tau$-regression estimator for analyzing high-dimensional datasets subject to gross contamination in the response variables and covariates. The resulting estimator, termed adaptive $\tau$-Lasso, is robust to outliers and high-leverage points. It also incorporates an adaptive $\ell_1$-norm penalty

Matthew M. Knight, Rosita Kokotanekova, Nalin H. Samarasinha

We summarize the collective knowledge of physical and surface properties of comet nuclei, focusing on those that are obtained from remote observations. We now have measurements or constraints on effective radius for over 200 comets, rotation periods for over 60, axial ratios and color indices for over 50, geometric albedos for over 25, and nucleus phase coef

Walid Maalej

Both recommender systems and bots should proactively and smartly answer the questions of software developers or other project stakeholders to assist them in performing their tasks more efficiently. This paper reflects on the achievements from the more mature area of Recommendation Systems in Software Engineering (RSSE) as well as the rising area of Bots in S

Steffen Kionke, Eduard Schesler

We introduce the notion of a telescope of groups. Very roughly a telescope is a directed system of groups that contains various commuting images of some fixed group $B$. Telescopes are inspired from the theory of groups acting on rooted trees. Imitating known constructions of branch groups, we obtain a number of examples of $B$-telescopes and discuss several

Sarah Frei, Lena Ji

In this note we construct an example of a smooth projective threefold that is irrational over $\mathbb Q$ but is rational at all places. Our example is a complete intersection of two quadrics in $\mathbb P^5$, and we show it has the desired rationality behavior by constructing an explicit element of order $4$ in the Tate--Shafarevich group of the Jacobian of

Lili Zheng, Garvesh Raskutti

Classification with positive and unlabeled (PU) data frequently arises in bioinformatics, clinical data, and ecological studies, where collecting negative samples can be prohibitively expensive. While prior works on PU data focus on binary classification, in this paper we consider multiple positive labels, a practically important and common setting. We intro

Sergei Gukov, James Halverson, Ciprian Manolescu, Fabian Ruehle

We apply Bayesian optimization and reinforcement learning to a problem in topology: the question of when a knot bounds a ribbon disk. This question is relevant in an approach to disproving the four-dimensional smooth Poincar\'e conjecture; using our programs, we rule out many potential counterexamples to the conjecture. We also show that the programs are suc

Giovanni Pellegrini, Jacopo Bertolotti

Phase-retrieval techniques aim to recover the original signal from just the modulus of its Fourier transform, which is usually much easier to measure than its phase, but the standard iterative techniques tend to fail if only part of the modulus information is available. We show that a neural network can be trained to perform phase retrieval using only incomp

Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner, Adam Fishman

We address the important problem of generalizing robotic rearrangement to clutter without any explicit object models. We first generate over 650K cluttered scenes - orders of magnitude more than prior work - in diverse everyday environments, such as cabinets and shelves. We render synthetic partial point clouds from this data and use it to train our CabiNet

Abir Bouraffa, Gian-Luca Fuhrmann, Walid Maalej

Previous works from research and industry have proposed a spatial representation of code in a canvas, arguing that a navigational code space confers developers the freedom to organise elements according to their understanding. By allowing developers to translate logical relatedness into spatial proximity, this code representation could aid in code navigation

Rudy Dissler

For an oriented $4$--dimensional fiber bundle over $S^{1}$, we build a relative trisection from a sutured Heegaard splitting of the fiber. We provide an algorithm to explicitly construct the associated relative trisection diagram, from a sutured Heegaard diagram of the fiber. As an application, we glue our relative trisection diagrams with existing diagrams

Sam Ross, Nicole Sullivan, Jina Yoon

We present design guidelines for incorporating fidgeting into the virtual world as a tool for students in online lectures. Fidgeting is associated with increased attention and self-regulation, and has the potential to help students focus. Currently there are no fidgets, physical or virtual, designed for preserving attention specifically in online learning en

Andreas Löhne

Polyhedral convex set optimization problems are the simplest optimization problems with set-valued objective function. Their role in set optimization is comparable to the role of linear programs in scalar optimization. Vector linear programs and multiple objective linear programs provide proper subclasses. In this article we choose a solution concept for arb

Conrad J. Haupt, Daniel J. Egger

Quantum computers require high fidelity quantum gates. These gates are obtained by routine calibration tasks that eat into the availability of cloud-based devices. Restless circuit execution speeds-up characterization and calibration by foregoing qubit reset in between circuits. Post-processing the measured data recovers the desired signal. However, since th

Taehee Ko, Joseph Heindel, Xingyi Guan, Teresa Head-Gordon

We use local diffusion maps to assess the quality of two types of collective variables (CVs) for a recently published hydrogen combustion benchmark dataset~\cite{guan2022benchmark} that contains ab initio molecular dynamics trajectories and normal modes along minimum energy paths. This approach was recently advocated in~\cite{tlldiffmap20} for assessing CVs

Mohammed H. Abdallah, Rasha M. Samir, Denis A. Leahy, Ashraf A. Shaker

The high mass X-ray binary system 4U 0114+65 was observed by Nustar in October 2019, and by XMM-Newton in August 2015. Here we performed spectral and timing analysis of the Nustar observation, and carry out timing analysis on the XMM-Newton data. We measured the spin period of the neutron star from both observations and found a spin-up rate $\dot{p} = 1.54 \

Alberto Lastra, Sławomir Michalik

The modification of the coefficients of formal power series is analyzed in order that such variation preserves q-Gevrey asymptotic properties, in particular q-Gevrey asymptotic expansions. A characterization of such sequences is determined, providing a handy tool in practice. The sequence of q-factorials is proved to preserve q-Gevrey asymptotic expansions.

Ido Siovitz, Stefan Lannig, Yannick Deller, Helmut Strobel

Isolated many-body systems far from equilibrium may exhibit scaling dynamics with universal exponents indicating the proximity of the time-evolution to a non-thermal fixed point. We find universal dynamics connected with the occurrence of extreme wave excitations in the mutually coupled magnetic components of a spinor gas which propagate in an effectively ra

Alejandro Pardo Pintos, Diego Shalom, Enzo Tagliazucchi, Gabriel Mindlin

Cycles of word usage have been described using an integro-differential Volterra model close to a Hopf bifurcation. Here we transform this system to a phase model, which allows us to phase-couple the words and address the observation of coherent oscillations in word usage.

J. Allam, A. Matzkin

The description of Wigner-friend scenarios -- in which external agents describe a closed laboratory containing a friend making a measurement -- remains problematic due to the ambiguous nature of quantum measurements. One option is to endorse assumptions leading to observer-dependent facts, given that the friend's measurement outcome is not defined from the p

Wenping Wang, Yunxi Guo, Chiyao Shen, Shuai Ding

Embedding based retrieval has seen its usage in a variety of search applications like e-commerce, social networking search etc. While the approach has demonstrated its efficacy in tasks like semantic matching and contextual search, it is plagued by the problem of uncontrollable relevance. In this paper, we conduct an analysis of embedding-based retrieval lau

Young-Ho Kim, Èric Lluch, Gulsun Mehmet, Florin C. Ghesu

Endovascular guidewire manipulation is essential for minimally-invasive clinical applications (Percutaneous Coronary Intervention (PCI), Mechanical thrombectomy techniques for acute ischemic stroke (AIS), or Transjugular intrahepatic portosystemic shunt (TIPS)). All procedures commonly require 3D vessel geometries from 3D CTA (Computed Tomography Angiography

Benjamin D. Killeen, Han Zhang, Jan Mangulabnan, Mehran Armand

Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided percutaneous pelvic fracture fixation, which

Antoine Mahoro, Petri Väisänen, Mirjana Pović, Pheneas Nkundabakura

We present a study of the $\rm{[OIII]\lambda\,5007}$ line profile in a sub-sample of 8 active galactic nuclei (AGN) and 6 non-AGN in the optically-selected green valley at $\rm{z\,<\,0.5}$ using long-slit spectroscopic observations with the 11 m Southern African Large Telescope. Gaussian decomposition of the line profile was performed to study its different

Hans-Peter Lehmann, Peter Sanders, Stefan Walzer

We present {\bf Sli}ding Blo{\bf ck} Hashing (Slick), a simple hash table data structure that combines high performance with very good space efficiency. This preliminary report outlines avenues for analysis and implementation that we intend to pursue.

Ali Fallahi RahmatAbadi, Javad Mohammadzadeh

With the exponentially increasing volume of online data, searching and finding required information have become an extensive and time-consuming task. Recommender Systems as a subclass of information retrieval and decision support systems by providing personalized suggestions helping users access what they need more efficiently. Among the different techniques

William Swartworth, David P. Woodruff

Given a symmetric matrix $A$, we show from the simple sketch $GAG^T$, where $G$ is a Gaussian matrix with $k = O(1/\epsilon^2)$ rows, that there is a procedure for approximating all eigenvalues of $A$ simultaneously to within $\epsilon \|A\|_F$ additive error with large probability. Unlike the work of (Andoni, Nguyen, SODA, 2013), we do not require that $A$

Logan A. Becker, Baowang Li, Nicholas J. Priebe, Eyal Seidemann

The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state neurons fire independently from one another, so that the p

Sem Borst

The present paper is concerned with the stationary workload of queues with heavy-tailed (regularly varying) characteristics. We adopt a transform perspective to illuminate a close connection between the tail asymptotics and heavy-traffic limit in infinite-variance scenarios. This serves as a tribute to some of the pioneering results of J.W. Cohen in this dom

Dianqiang Su, Yuan Jiang, Silvia Cardenas-Lopez, Ana Asenjo-Garcia

We study temporal oscillations, known as dynamical beats, developed by a propagating pulse due to its interaction with a near-resonant collective medium of $^{133}$Cs atoms randomly captured by a nanofiber-based optical lattice. A phenomenological theory provides an intuitive explanation and quantitative predictions, which are improved by an input-output the

João Flach, Alvaro F. Moreira, Luis C. Lamb

Over the last decades, deep neural networks based-models became the dominant paradigm in machine learning. Further, the use of artificial neural networks in symbolic learning has been seen as increasingly relevant recently. To study the capabilities of neural networks in the symbolic AI domain, researchers have explored the ability of deep neural networks to

Muhamed Barakovic, Marco Pizzolato, Chantal M. W. Tax, Umesh Rudrapatna

Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, w

Neng Wan, Dapeng Li, Naira Hovakimyan, Petros G. Voulgaris

Fundamental limitations or performance trade-offs/limits are important properties and constraints of both control and filtering systems. Among various trade-off metrics, total information rate that characterizes the sensitivity trade-offs and time-averaged performance of control and filtering systems was conventionally studied by using the differential entro

So Nakamura, Manuel L. Reyes

In order to diagnose the cause of some defects in the category of canonical hypergroups, we investigate several categories of hyperstructures that generalize hypergroups. By allowing hyperoperations with possibly empty products, one obtains categories with desirable features such as completeness and cocompleteness, free functors, regularity, and closed monoi

Bryce J. Smith, Joyoni Dey, Lacey Medlock, David Solis

Objective: Breast tissue is mainly a mixture of adipose and fibro-glandular tissue. Cancer risk and risk of undetected breast cancer increases with the amount of glandular tissue in the breast. Therefore, radiologists must report the total volume glandular fraction or a BI-RADS classification in screening and diagnostic mammography. A Maximum Likelihood algo

Vladislav Chistyakov, Viktar S. Asadchy, Shanhui Fan, Andrea Alu

Weyl semimetals hold great promise in revolutionizing nonreciprocal optical components due to their unique topological properties. By exhibiting nonreciprocal magneto-optical effects without necessitating an external magnetic field, these materials offer remarkable miniaturization opportunities and reduced energy consumption. However, their intrinsic topolog

Rajiv Movva, Divya Shanmugam, Kaihua Hou, Priya Pathak

Healthcare data in the United States often records only a patient's coarse race group: for example, both Indian and Chinese patients are typically coded as "Asian." It is unknown, however, whether this coarse coding conceals meaningful disparities in the performance of clinical risk scores across granular race groups. Here we show that it does. Using data fr

Frederico Sousa, David T. S. Perkins, Aires Ferreira

At low temperatures, quantum corrections, originating from the interference of the many paths an electron may take between two points, tend to dominate the transport properties of two-dimensional conductors. These quantum corrections increase the resistivity in systems such as two-dimensional electron gases (2DEGs) without spin-orbit coupling (SOC), a phenom

Libiao Xin, Yanbin Li, Baolong Wang, Zhiqiang Li

Some bi or multi-stable Mechanical meta-structures have been implemented as mechanical memory devices which however are with limits such as complex structural forms, low information storage capability and/or fragile structural stability to maintain the stored information bits robustly under external interferences. To address these issues, we refer to the str

Jan Moser

In this paper we prove that there is a continuum set of increments with some minimal structure for the Hardy - Littlewood integral. The result implies a number of new properties of the Hardy - Littlewood integral.

Attilio Castano

Since their inception perfectoid spaces have catalyzed a revolution in p-adic geometry. We redevelop the foundations of perfectoid spaces from the point of view of Berkovich Spaces, where the underlying topological space of an affinoid perfectoid space is a compact Hausdorff space -- closely resembling the situation in complex geometry. The key technical ing

Gombojav O. Ariunbold

Quantum dynamics for arbitrary system are traditionally realized by time evolutions of wave functions in Hilbert space and/or density operators in Liouville space. However, the traditional simulations may occasionally turn out to be challenging for the quantum dynamics, particularly those governed by the nonlinear Hamiltonians. In this letter, we introduce a

Maciej Ulas

Let $a, Q\in\Q$ be given and consider the set $\cal{G}(a, Q)=\{aQ^{i}:\;i\in\N\}$ of terms of geometric progression with 0th term equal to $a$ and the quotient $Q$. Let $f\in\Q(x, y)$ and $\cal{V}_{f}$ be the set of finite values of $f$. We consider the problem of existence of $a, Q\in\Q$ such that $\cal{G}(a, Q)\subset\cal{V}_{f}$. In the first part of the

Metehan Calis, David Lloyd, Narasimha Boddeti, J. Scott Bunch

Using a blister test, we measured the work of separation between MoS$_2$ membranes from metal, semiconductor, and graphite substrates. We found a work of separation ranging from 0.11 +- 0.05 J/m^2 for chromium to 0.39 +- 0.1 J/m^2 for graphite substrates. In addition, we measured the work of adhesion of MoS$_2$ membranes over these substrates and observed a

Jonathan Libgober, Beatrice Michaeli, Elyashiv Wiedman

We examine how uncertain veracity of external news influences investor beliefs, market prices and corporate disclosures. Despite assuming independence between the news' veracity and the firm's endowment with private information, we find that favorable news is taken ``with a grain of salt'' in equilibrium -- more precisely, perceived as less likely veracious

Shengqi Yang, Adam Lidz, Aaron Smith, Andrew Benson

The Atacama Large Millimeter/Submillimeter Array (ALMA) in the sub-millimeter and the James Webb Space Telescope (JWST) in the infrared have achieved robust spectroscopic detections of emission lines from the interstellar medium (ISM) in some of the first galaxies. These unprecedented measurements provide valuable information regarding the ISM properties, st

Yifan Sun, Weiye Zhao, Changliu Liu

Trajectory generation in confined environment is crucial for wide adoption of intelligent robot manipulators. In this paper, we propose a novel motion planning approach for redundant robot arms that uses a hybrid optimization framework to search for optimal trajectories in both the configuration space and null space, generating high-quality trajectories that

Jonathan M Spring

Vulnerability management strategy, from both organizational and public policy perspectives, hinges on an understanding of the supply of undiscovered vulnerabilities. If the number of undiscovered vulnerabilities is small enough, then a reasonable investment strategy would be to focus on finding and removing the remaining undiscovered vulnerabilities. If the

Mohammed E. Elbtity, Brendan Reidy, Md Hasibul Amin, Ramtin Zand

Tensor processing units (TPUs), specialized hardware accelerators for machine learning tasks, have shown significant performance improvements when executing convolutional layers in convolutional neural networks (CNNs). However, they struggle to maintain the same efficiency in fully connected (FC) layers, leading to suboptimal hardware utilization. In-memory

Daniel Acosta-Soba, Francisco Guillén-González, J. Rafael Rodríguez Galván

In this work, we present a modification of the phase-field tumor growth model given in [26] that leads to bounded, more physically meaningful, volume fraction variables. In addition, we develop an upwind discontinuous Galerkin (DG) scheme preserving the mass conservation, pointwise bounds and energy stability of the continuous model. Finally, some computatio