Miracle-dz/UniFAR-FTUs huggingface dataset:
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images

The retina is the only part of the human body in which blood vessels can be accessed non-invasively using imaging techniques such as digital fundus images (DFI). The spatial distribution of the retinal microvasculature may change with cardiovascular diseases and thus the eyes may be regarded as a window to our hearts. Computerized segmentation of the retinal arterioles and venules (A/V) is essential for automated microvasculature analysis. Using active learning, we created a new DFI dataset containing 240 crowd-sourced manual A/V segmentations performed by fifteen medical students and reviewed by an ophthalmologist, and developed LUNet, a novel deep learning architecture for high resolution A/V segmentation. LUNet architecture includes a double dilated convolutional block that aims to enhance the receptive field of the model and reduce its parameter count. Furthermore, LUNet has a long tail that operates at high resolution to refine the segmentation. The custom loss function emphasizes the continuity of the blood vessels. LUNet is shown to significantly outperform two state-of-the-art segmentation algorithms on the local test set as well as on four external test sets simulating distribution shifts across ethnicity, comorbidities, and annotators. We make the newly created dataset open access (upon publication).
Non-Hausdorff germinal groupoids for actions of countable groups

We study conditions under which the germinal groupoid associated to a minimal equicontinuous action of a countable group on a Cantor set has non-Hausdorff topology. We develop a new criterion, which serves as an obstruction for the \'etale topology on the groupoid to be non-Hausdorff. We use this and other criteria to study the topology of germinal groupoids for a few classes of actions. In particular, we give examples of families of contracting and non-contracting self-similar groups, which are amenable and whose actions have associated germinal groupoids with non-Hausdorff topology.
A New Framework to Estimate Return on Investment for Player Salaries in the National Basketball Association

The National Basketball Association (NBA) imposes a player salary cap. It is therefore useful to develop tools to measure the relative realized return of a player's salary given their on court performance. Very few such studies exist, however. We thus present the first known framework to estimate a return on investment (ROI) for NBA player contracts. The framework operates in five parts: (1) decide on a measurement time horizon, such as the standard 82-game NBA regular season; (2) calculate the novel game contribution percentage (GCP) measure we propose, which is a single game summary statistic that sums to unity for each competing team and is comprised of traditional, playtype, hustle, box outs, defensive, tracking, and rebounding per game NBA statistics; (3) estimate the single game value (SGV) of each regular season NBA game using a standard currency conversion calculation; (4) multiply the SGV by the vector of realized GCPs to obtain a series of realized per-player single season cash flows; and (5) use the player salary as an initial investment to perform the traditional ROI calculation. We illustrate our framework by compiling a novel, sharable dataset of per game GCP statistics and salaries for the 2022-2023 NBA regular season. A scatter plot of ROI by salary for all players is presented, including the top and bottom 50 performers. Notably, missed games are treated as defaults because GCP is a per game metric. This allows for break-even calculations between high-performing players with frequent missed games and average performers with few missed games, which we demonstrate with a comparison of the 2023 NBA regular seasons of Anthony Davis and Brook Lopez. We conclude by suggesting uses of our framework, discussing its flexibility through customization, and outlining potential future improvements.
EANet: Expert Attention Network for Online Trajectory Prediction

Trajectory prediction plays a crucial role in autonomous driving. Existing mainstream research and continuoual learning-based methods all require training on complete datasets, leading to poor prediction accuracy when sudden changes in scenarios occur and failing to promptly respond and update the model. Whether these methods can make a prediction in real-time and use data instances to update the model immediately(i.e., online learning settings) remains a question. The problem of gradient explosion or vanishing caused by data instance streams also needs to be addressed. Inspired by Hedge Propagation algorithm, we propose Expert Attention Network, a complete online learning framework for trajectory prediction. We introduce expert attention, which adjusts the weights of different depths of network layers, avoiding the model updated slowly due to gradient problem and enabling fast learning of new scenario's knowledge to restore prediction accuracy. Furthermore, we propose a short-term motion trend kernel function which is sensitive to scenario change, allowing the model to respond quickly. To the best of our knowledge, this work is the first attempt to address the online learning problem in trajectory prediction. The experimental results indicate that traditional methods suffer from gradient problems and that our method can quickly reduce prediction errors and reach the state-of-the-art prediction accuracy.
Spin-Hall effect due to the bulk states of topological insulators: Extrinsic contribution to the conserved spin current

The substantial amount of recent research into spin torques has been accompanied by a revival of interest in the spin-Hall effect. This effect contributes to the spin torque in many materials, including topological insulator/ferromagnet devices, Weyl semimetals, and van der Waals heterostructures. In general the relative sizes of competing spin torque mechanisms remain poorly understood. Whereas a consensus is beginning to emerge on the evaluation of a conserved spin current, the role of extrinsic disorder mechanisms in the spin-Hall effect has not been clarified. In this work we present a comprehensive calculation of the extrinsic spin Hall effect while focussing on the bulk states of topological insulators as a prototype system and employing a fully quantum mechanical formalism to calculate the proper spin current. Our calculation of the proper spin current employs a 4x4 k.p Hamiltonian describing the bulk states of topological insulators. At the same time, we provide a qualitative explanation of the proper spin currents calculated based on an effective $2\times2$ Hamiltonian obtained via a Schrieffer-Wolf transformation. We find that the extrinsic contribution to the proper spin current, driven by side jump, skew scattering and related mechanisms, is of a comparable magnitude to the intrinsic contribution, making it vital to take such disorder effects into account when seeking to understand experiments. Among the scattering effects considered, side jump scattering is the primary contributor to the extrinsic spin Hall effect. The total spin susceptibility calculated here is too small to explain experimentally measured spin torques, hence we expect the spin Hall effect to make a negligible contribution to the spin torque in topological insulator structures.
ViHOPE: Visuotactile In-Hand Object 6D Pose Estimation With Shape Completion

In this letter, we introduce ViHOPE, a novel framework for estimating the 6D pose of an in-hand object using visuotactile perception. Our key insight is that the accuracy of the 6D object pose estimate can be improved by explicitly completing the shape of the object. To this end, we introduce a novel visuotactile shape completion module that uses a conditional Generative Adversarial Network to complete the shape of an in-hand object based on volumetric representation. This approach improves over prior works that directly regress visuotactile observations to a 6D pose. By explicitly completing the shape of the in-hand object and jointly optimizing the shape completion and pose estimation tasks, we improve the accuracy of the 6D object pose estimate. We train and test our model on a synthetic dataset and compare it with the state-of-the-art. In the visuotactile shape completion task, we outperform the state-of-the-art by 265% using the Intersection of Union metric and achieve 88% lower Chamfer Distance. In the visuotactile pose estimation task, we present results that suggest our framework reduces position and angular errors by 35% and 64%, respectively. Furthermore, we ablate our framework to confirm the gain on the 6D object pose estimate from explicitly completing the shape. Ultimately, we show that our framework produces models that are robust to sim-to-real transfer on a real-world robot platform.
Evolution of Negative Superhumps, Quasiperiodic Oscillations, and Outbursts in the Z Cam-type Dwarf Nova AH Her

AH Her is a Z Cam-type dwarf nova with an orbital period of ∼0.258 days. Dwarf nova oscillations and long-period dwarf nova oscillations have been detected, but no quasiperiodic oscillations (QPOs) and negative superhumps (NSHs) have been found. We investigated the association between NSHs, QPOs, and outbursts of AH Her based on Transiting Exoplanet Survey Satellite photometry. We find for the first time NSHs with a period of 0.24497(1) days in AH Her, and trace the variation in the amplitude and period of NSHs with the outburst. The amplitude of the NSHs is the most significant at quiescence, weakening as the outburst rises, becoming undetectable at the top, rebounding and weakening at the plateau, and strengthening again as the outburst declines. The variation in the accretion disk radius can explain the NSH amplitude variation except for the plateau, so we suggest that the relationship between the NSH amplitude and outburst can be used as a window to study the accretion disk instability and the origin of NSHs. In addition, we find periodic variations in the amplitude, maxima, and shape of the NSHs ranging from 2.33(2) to 2.68(5) days, which may be related to the precession of the tilted disk. Finally, we find QPOs at the top of AH Her’s long outburst with ∼2800 s similar to those of HS 2325+8205, suggesting that the presence of QPOs at the top of Z Cam’s long outburst may be a general phenomenon.
Unimodular Plebanski gravity

We present new action principles for unimodular gravity, defined in the chiral Plebanski formulation based on (complex) two-forms and a complex SO(3) connection. In these theories, just as in their analogues in the metric formulation, the cosmological constant does not take a prescribed value but is an integration constant whose value can differ between different (classical) solutions. We discuss some subtleties when identifying Lorentzian solutions in the generally complex theory, and show how these theories can be reduced to a "pure connection" form similar to Krasnov's pure connection formalism for general relativity.
Progress in direct measurements of the Hubble constant

One of the most exciting and pressing issues in cosmology today is the discrepancy between some measurements of the local Hubble constant and other values of the expansion rate inferred from the observed temperature and polarization fluctuations in the cosmic microwave background (CMB) radiation. Resolving these differences holds the potential for the discovery of new physics beyond the standard model of cosmology: Lambda Cold Dark Matter (ΛCDM), a successful model that has been in place for more than 20 years. Given both the fundamental significance of this outstanding discrepancy, and the many-decades-long effort to increase the accuracy of the extragalactic distance scale, it is critical to demonstrate that the local measurements are convincingly free from residual systematic errors. We review the progress over the past quarter century in measurements of the local value of the Hubble constant, and discuss remaining challenges. Particularly exciting are new data from the James Webb Space Telescope (JWST), for which we present an overview of our program and first results. We focus in particular on Cepheids and the Tip of the Red Giant Branch (TRGB) stars, as well as a relatively new method, the JAGB (J-Region Asymptotic Giant Branch) method, all methods that currently exhibit the demonstrably smallest statistical and systematic uncertainties. JWST is delivering high-resolution near-infrared imaging data to both test for and to address directly several of the systematic uncertainties that have historically limited the accuracy of extragalactic distance scale measurements (e.g., the dimming effects of interstellar dust, chemical composition differences in the atmospheres of stars, and the crowding and blending of Cepheids contaminated by nearby previously unresolved stars). For the first galaxy in our program, NGC 7250, the high-resolution JWST images demonstrate that many of the Cepheids observed with the Hubble Space Telescope (HST) are significantly crowded by nearby neighbors. Avoiding the more significantly crowded variables, the scatter in the JWST near-infrared (NIR) Cepheid PL relation is decreased by a factor of two compared to those from HST, illustrating the power of JWST for improvements to local measurements of H 0. Ultimately, these data will either confirm the standard model, or provide robust evidence for the inclusion of additional new physics.
Human Action Co-occurrence in Lifestyle Vlogs using Graph Link Prediction

We introduce the task of automatic human action co-occurrence identification, i.e., determine whether two human actions can co-occur in the same interval of time. We create and make publicly available the ACE (Action Co-occurrencE) dataset, consisting of a large graph of ~12k co-occurring pairs of visual actions and their corresponding video clips. We describe graph link prediction models that leverage visual and textual information to automatically infer if two actions are co-occurring. We show that graphs are particularly well suited to capture relations between human actions, and the learned graph representations are effective for our task and capture novel and relevant information across different data domains. The ACE dataset and the code introduced in this paper are publicly available at https://github.com/MichiganNLP/vlog_action_co-occurrence.
A New Re-redistribution Scheme for Weighted State Redistribution with Adaptive Mesh Refinement

State redistribution (SRD) is a recently developed technique for stabilizing cut cells that result from finite-volume embedded boundary methods. SRD has been successfully applied to a variety of compressible and incompressible flow problems. When used in conjunction with adaptive mesh refinement (AMR), additional steps are needed to preserve the accuracy and conservation properties of the solution if the embedded boundary is not restricted to a single level of the mesh hierarchy. In this work, we extend the weighted state redistribution algorithm to cases where cut cells live at or near a coarse-fine interface within the domain. The resulting algorithm maintains conservation and is demonstrated on several two- and three-dimensional example problems.
tSPM+; a high-performance algorithm for mining transitive sequential patterns from clinical data

The increasing availability of large clinical datasets collected from patients can enable new avenues for computational characterization of complex diseases using different analytic algorithms. One of the promising new methods for extracting knowledge from large clinical datasets involves temporal pattern mining integrated with machine learning workflows. However, mining these temporal patterns is a computational intensive task and has memory repercussions. Current algorithms, such as the temporal sequence pattern mining (tSPM) algorithm, are already providing promising outcomes, but still leave room for optimization. In this paper, we present the tSPM+ algorithm, a high-performance implementation of the tSPM algorithm, which adds a new dimension by adding the duration to the temporal patterns. We show that the tSPM+ algorithm provides a speed up to factor 980 and a up to 48 fold improvement in memory consumption. Moreover, we present a docker container with an R-package, We also provide vignettes for an easy integration into already existing machine learning workflows and use the mined temporal sequences to identify Post COVID-19 patients and their symptoms according to the WHO definition.
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks

In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption. Future wireless networks mandate exchanging high dimensional data in dynamic and uncertain environments, therefore implementing communication control tasks becomes challenging and highly complex. Multi-agent reinforcement learning with emergent communication (EC-MARL) is a promising solution to address high dimensional continuous control problems with partially observable states in a cooperative fashion where agents build an emergent communication protocol to solve complex tasks. This article articulates the importance of EC-MARL within the context of future 6G wireless networks, which imbues autonomous decision-making capabilities into network entities to solve complex tasks such as autonomous driving, robot navigation, flying base stations network planning, and smart city applications. An overview of EC-MARL algorithms and their design criteria are provided while presenting use cases and research opportuni-ties on this emerging topic.
Etude expérimentale des caractéristiques des structures tourbillonnaires se développant dans les échangeurs de chaleur à tubes et ailettes planes continues

Ce travail s’inscrit dans le cadre des recherches des methodes d'accroissement du transfert thermique dans les echangeurs de chaleurs a tubes et a ailettes planes continues. On etudie principalement l’effet de l’espacement inter-ailettes et de la vitesse d’ecoulement sur les structures tourbillonnaires autour d’un seul tube muni de deux ailettes planes. Pour ce faire, la technique PIV a ete utilisee. On montre que la zone des tourbillons fer a cheval en amont du tube et la region du sillage de celui-ci sont plus larges lorsque le nombre de Reynolds, ReD, et l'espacement inter-ailettes, E/D, augmentent. Par ailleurs, les caracteristiques dynamiques de la structure tourbillonnaire en amont du tube ont ete etudiees en details. L'effet des deux parametres, ReD et E/D, sur l'ecoulement en amont des tubes d'un echangeur de chaleur a deux rangs de tubes et a ailettes planes continues, a ete egalement examine par le moyen de visualisations par PIV et de calculs numeriques a l'aide du modele de turbulence k(SST). Finalement, on analyse dans cette etude l'effet de l’ajout de quatre generateurs de tourbillons sur le sillage d'un tube muni de deux ailettes pour E/D=0. 27. A cet effet, des visualisations ont ete realisees par PIV et par colorants.
The Origin and Evolution of the Mesozoic Ore-forming Fluids in South Korea: Their Genetic Implications

Two distinctive Mesozoic hydrothermal systems occurred in South Korea: the Jurassic/Early Cretaceous(ca. 200~130 Ma) deep-level ones during the Daebo orogeny and the Late Cretaceous/Tertiary(ca. 110~45 Ma) shallow hydrothermal ones during the Bulgugsa event. The Mesozoic hydrothermal system and the metallic mineralization in the Korean Peninsula document a close spatial and temporal relationship with syn- to post-tectonic magmatism. The calculated δ 18 OH2O values of the ore-forming fluids from the Mesozoic metallic mineral deposits show limited range for the Jurassic ones but variable range for the Late Cretaceous ones. The orogenic mineral deposits were formed at relatively high temperatures and deep-crustal levels. The mineralizing fluids that were responsible for the formation of theses deposits are characterized by the reasonably homogeneous and similar ranges of δ 18 OH2O values. This implies that the ore-forming fluids were principally derived from spatially associated Jurassic granitoids and related pegmatite. On the contrary, the Late Cretaceous ferroalloy, base-metal and precious-metal deposits in the Taebaeksan, Okcheon and Gyeongsang basins occurred as vein, replacement, breccia-pipe, porphyry-style and skarn deposits. Diverse mineralization styles represent a spatial and temporal distinction between the proximal environment of subvolcanic activity and the distal to transitional condition derived from volcanic environments. The Cu(-Au) or Fe-Mo-W deposits are proximal to a magmatic source, whereas the polymetallic or the precious-metal deposits are more distal to transitional. On the basis of the overall δ 18 OH2O values of various ore deposits in these areas, it can be briefed that the ore fluids show very extensive oxygen isotope exchange with country rocks, though the δDH2O values are relatively homogeneous and similarly restricted.
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation

Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis. For this reason, physicians generally diagnose diseases based on multimodal medical images such as, e.g., PET/CT. The effective fusion of multimodal information is essential to reach a reliable decision and explain how the decision is made as well. In this paper, we propose a fusion framework for multimodal medical image segmentation based on deep learning and the Dempster-Shafer theory of evidence. In this framework, the reliability of each single modality image when segmenting different objects is taken into account by a contextual discounting operation. The discounted pieces of evidence from each modality are then combined by Dempster's rule to reach a final decision. Experimental results with a PET-CT dataset with lymphomas and a multi-MRI dataset with brain tumors show that our method outperforms the state-of-the-art methods in accuracy and reliability.
Metastability and time scales for parabolic equations with drift 1: the first time scale

Consider the elliptic operator given by $$ \mathscr{L}_{\epsilon}f= {b} \cdot \nabla f + \epsilon \Delta f $$ for some smooth vector field $ b\colon \mathbb R^d \to\mathbb R^d$ and a small parameter $\epsilon>0$. Consider the initial-valued problem $$ \left\{ \begin{aligned}&\partial_ t u_\epsilon = \mathscr L_\epsilon u_\epsilon,\\&u_\epsilon (0, \cdot) = u_0(\cdot), \end{aligned} \right. $$ for some bounded continuous function $u_0$. Denote by $\mathcal M_0$ the set of critical points of $b$, $\mathcal M_0 =\{x\in \mathbb R^d : b(x)=0\}$, assumed to be finite. Under the hypothesis that $ b = -(\nabla U + \ell)$, where $ \ell$ is a divergence-free field orthogonal to $\nabla U$, the main result of this article states that there exist a time-scale $\theta^{(1)}_\epsilon$, $\theta^{(1)}_\epsilon \to \infty$ as $\epsilon \rightarrow 0$, and a Markov semigroup $\{p_t : t\ge 0\}$ defined on $\mathcal M_0$ such that $$ \lim_{\epsilon\to 0} u_\epsilon (t\theta^{(1)}_\epsilon, x) =\sum_{m'\in \mathcal M_0} p_t(m, m')\, u_0( m'), $$ for all $t>0$ and $ x$ in the domain of attraction of $m$ for the ODE $\dot{x}(t)= b( x(t))$. The time scale $\theta^{(1)}$ is critical in the sense that, for all time scale $\varrho_\epsilon$ such that $\varrho_\epsilon \to \infty$, $\varrho_\epsilon/\theta^{(1)}_\epsilon \to 0$, $$ \lim_{\epsilon\to 0} u_\epsilon (\varrho_\epsilon, x)=u_0(m) $$ for all $x \in \mathcal D(m)$. Namely, $\theta_\epsilon^{(1)}$ is the first scale at which the solution to the initial-valued problem starts to change. In a companion paper [Landim, Lee, Seo, forthcoming] we extend this result finding all critical time-scales at which the solution $u_\epsilon$ evolves smoothly in time and we show that the solution $u_\epsilon$ is expressed in terms of the semigroup of some Markov chain taking values in sets formed by unions of critical points of $b$.