Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evidence-based Assessment of Genes in Dilated Cardiomyopathy

Background: The cardiomyopathies are classically categorized as hypertrophic (HCM), dilated (DCM), and arrhythmogenic right ventricular (ARVC), and each have a signature genetic theme. HCM and ARVC are largely understood as genetic diseases of sarcomere or desmosome proteins, respectively. In contrast, >250 genes spanning more than 10 gene ontologies have been implicated in DCM, representing a complex and diverse genetic architecture. To clarify this, a systematic curation of evidence to establish the relationship of genes with DCM was conducted. Methods: An international Panel with clinical and scientific expertise in DCM genetics was assembled to evaluate evidence supporting monogenic relationships of genes with idiopathic DCM. The Panel utilized the ClinGen semi-quantitative gene-disease clinical validity classification framework. Results: Fifty-one genes with human genetic evidence were curated. Twelve genes (23%) from eight gene ontologies were classified as having definitive (BAG3, DES, FLNC, LMNA, MYH7, PLN, RBM20, SCN5A, TNNC1, TNNT2, TTN) or strong (DSP) evidence. Seven genes (14%) (ACTC1, ACTN2, JPH2, NEXN, TNNI3, TPM1, VCL) including two additional ontologies were classified as moderate evidence; these genes are likely to emerge as strong or definitive with additional evidence. Of the 19 genes classified as definitive, strong or moderate, six were similarly classified for HCM and three for ARVC. Of the remaining 32 genes (63%), 25 (49%) had limited evidence, 4 (8%) were disputed, 2 (4%) had no disease relationship, and 1 (2%) was supported by animal model data only. Of 16 commercially available genetic testing panels evaluated, most definitive genes were included, but panels also included numerous genes with minimal human evidence. Conclusions: In a systematic curation of published evidence for genes considered relevant for monogenic DCM, 12 were classified as definitive or strong and seven as moderate evidence spanning 10 gene ontologies. Notably, these 19 genes only explain a minority of DCM cases, leaving the remainder of DCM genetic architecture incompletely addressed. While clinical genetic testing panels include most high evidence genes, genes lacking robust evidence are also commonly included. Until the genetic architecture of DCM is more fully defined, care should be taken in the interpretation of variable evidence DCM genes in clinical practice.
Active Learning for Deep Gaussian Process Surrogates

Abstract Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning for their nonstationary flexibility and ability to cope with abrupt regime changes in training data. Here, we explore DGPs as surrogates for computer simulation experiments whose response surfaces exhibit similar characteristics. In particular, we transport a DGP’s automatic warping of the input space and full uncertainty quantification, via a novel elliptical slice sampling Bayesian posterior inferential scheme, through to active learning strategies that distribute runs nonuniformly in the input space—something an ordinary (stationary) GP could not do. Building up the design sequentially in this way allows smaller training sets, limiting both expensive evaluation of the simulator code and mitigating cubic costs of DGP inference. When training data sizes are kept small through careful acquisition, and with parsimonious layout of latent layers, the framework can be both effective and computationally tractable. Our methods are illustrated on simulation data and two real computer experiments of varying input dimensionality. We provide an open source implementation in the deepgp package on CRAN.
Neural Collapse with Cross-Entropy Loss

We consider the variational problem of cross-entropy loss with $n$ feature vectors on a unit hypersphere in $\mathbb{R}^d$. We prove that when $d \geq n - 1$, the global minimum is given by the simplex equiangular tight frame, which justifies the neural collapse behavior. We also prove that as $n \rightarrow \infty$ with fixed $d$, the minimizing points will distribute uniformly on the hypersphere and show a connection with the frame potential of Benedetto & Fickus.
Robust Multi-Agent Reinforcement Learning with Social Empowerment for Coordination and Communication

We consider the problem of robust multi-agent reinforcement learning (MARL) for cooperative communication and coordination tasks. MARL agents, mainly those trained in a centralized way, can be brittle because they can adopt policies that act under the expectation that other agents will act a certain way rather than react to their actions. Our objective is to bias the learning process towards finding strategies that remain reactive towards others' behavior. Social empowerment measures the potential influence between agents' actions. We propose it as an additional reward term, so agents better adapt to other agents' actions. We show that the proposed method results in obtaining higher rewards faster and a higher success rate in three cooperative communication and coordination tasks.
Physics prospects with the second oscillation maximum at the Deep Underground Neutrino Experiment

Current long-baseline neutrino-oscillation experiments such as NO$\nu$A and T2K are mainly sensitive to physics in the neighbourhood of the first oscillation maximum of the $\nu_\mu \to \nu_e$ oscillation probability. The future Deep Underground Neutrino Experiment (DUNE) utilizes a wide-band beam tune optimized for CP violation sensitivity that fully covers the region of the first maxima and part of the second. In the present study, we elucidate the role of second oscillation maximum in addressing issues pertaining to unknowns in the standard three flavour paradigm. We consider a new DUNE beam tune optimized for coverage of the region of the second oscillation maxima which could be realized using proposed accelerator upgrades that provide multi-MW of power at proton energies of 8 GeV. We find that addition of the multi-MW 8 GeV beam to DUNE wide-band running leads to modest improvement in sensitivity to CP violation, mass hierarchy, the octant of $\theta_{23}$ as well as the resolution of $\delta$ and the Jarlskog invariant. Significant improvements to the DUNE neutrino energy resolution yield a much larger improvement in performance. We conclude that the standard DUNE wide-band beam when coupled with excellent detector resolution capabilities is sufficient to resolve $\delta$ to better than $\sim 12^\circ$ for all values of $\delta$ in a decade of running. For second maxima (8 GeV 3MW) beam running concurrently with the standard wide-band (80 GeV 2.2 MW) beam for 5 of the 10 years, it is found that $\delta$ can be further resolved better than $\sim 10^\circ$ for all values of $\delta$.
Magic angles and current-induced topology in twisted nodal superconductors

Motivated by the recent achievements in the realization of strongly correlated and topological phases in twisted van der Waals heterostructures, we study the low-energy properties of a twisted bilayer of nodal superconductors. It is demonstrated that the spectrum of the superconducting Dirac quasiparticles close to the gap nodes is strongly renormalized by twisting and can be controlled with magnetic fields, current, or interlayer voltage. In particular, the application of an interlayer current transforms the system into a topological superconductor, opening a topological gap and resulting in a quantized thermal Hall effect with gapless, neutral edge modes. Close to the "magic angle," where the Dirac velocity of the quasiparticles is found to vanish, a correlated superconducting state breaking time-reversal symmetry is shown to emerge. Estimates for a number of superconducting materials, such as cuprate, heavy fermion, and organic nodal superconductors, show that twisted bilayers of nodal superconductors can be readily realized with current experimental techniques.
Unsupervised Domain Adaptation from Synthetic to Real Images for Anchorless Object Detection

Synthetic images are one of the most promising solutions to avoid high costs associated with generating annotated datasets to train supervised convolutional neural networks (CNN). However, to allow networks to generalize knowledge from synthetic to real images, domain adaptation methods are necessary. This paper implements unsupervised domain adaptation (UDA) methods on an anchorless object detector. Given their good performance, anchorless detectors are increasingly attracting attention in the field of object detection. While their results are comparable to the well-established anchor-based methods, anchorless detectors are considerably faster. In our work, we use CenterNet, one of the most recent anchorless architectures, for a domain adaptation problem involving synthetic images. Taking advantage of the architecture of anchorless detectors, we propose to adjust two UDA methods, viz., entropy minimization and maximum squares loss, originally developed for segmentation, to object detection. Our results show that the proposed UDA methods can increase the mAPfrom61 %to69 %with respect to direct transfer on the considered anchorless detector. The code is available: https://github.com/scheckmedia/centernet-uda.
Practical Auto-Calibration for Spatial Scene-Understanding from Crowdsourced Dashcamera Videos

Spatial scene-understanding, including dense depth and ego-motion estimation, is an important problem in computer vision for autonomous vehicles and advanced driver assistance systems. Thus, it is beneficial to design perception modules that can utilize crowdsourced videos collected from arbitrary vehicular onboard or dashboard cameras. However, the intrinsic parameters corresponding to such cameras are often unknown or change over time. Typical manual calibration approaches require objects such as a chessboard or additional scene-specific information. On the other hand, automatic camera calibration does not have such requirements. Yet, the automatic calibration of dashboard cameras is challenging as forward and planar navigation results in critical motion sequences with reconstruction ambiguities. Structure reconstruction of complete visual-sequences that may contain tens of thousands of images is also computationally untenable. Here, we propose a system for practical monocular onboard camera auto-calibration from crowdsourced videos. We show the effectiveness of our proposed system on the KITTI raw, Oxford RobotCar, and the crowdsourced D$^2$-City datasets in varying conditions. Finally, we demonstrate its application for accurate monocular dense depth and ego-motion estimation on uncalibrated videos.
Manifold-based time series forecasting

Prediction for high dimensional time series is a challenging task due to the curse of dimensionality problem. Classical parametric models like ARIMA or VAR require strong modeling assumptions and time stationarity and are often overparametrized. This paper offers a new flexible approach using recent ideas of manifold learning. The considered model includes linear models such as the central subspace model and ARIMA as particular cases. The proposed procedure combines manifold denoising techniques with a simple nonparametric prediction by local averaging. The resulting procedure demonstrates a very reasonable performance for real-life econometric time series. We also provide a theoretical justification of the manifold estimation procedure.
Intrinsic oceanic decadal variability of upper-ocean heat content

Atmosphere and ocean are coupled via air–sea interactions. The atmospheric conditions fuel the ocean circulation and its variability, but the extent to which ocean processes can affect the atmosphere at decadal time scales remains unclear. In particular, such low-frequency variability is difficult to extract from the short observational record, meaning that climate models are the primary tools deployed to resolve this question. Here, we assess how the ocean’s intrinsic variability leads to patterns of upper-ocean heat content that vary at decadal time scales. These patterns have the potential to feed back on the atmosphere and thereby affect climate modes of variability, such as El Niño or the Interdecadal Pacific Oscillation. We use the output from a global ocean–sea ice circulation model at three different horizontal resolutions, each driven by the same atmospheric reanalysis. To disentangle the variability of the ocean’s direct response to atmospheric forcing from the variability due to intrinsic ocean dynamics, we compare model runs driven with inter-annually varying forcing (1958-2019) and model runs driven with repeat-year forcing. Models with coarse resolution that rely on eddy parameterizations, show (i) significantly reduced variance of the upper-ocean heat content at decadal time scales and (ii) differences in the spatial patterns of low-frequency variability compared with higher resolution simulations. Climate projections are typically done with general circulation models with coarse-resolution ocean components. Therefore, these biases affect our ability to predict decadal climate modes of variability and, in turn, hinder climate projections. Our results suggest that for improving climate projections, the community should move towards coupled climate models with higher oceanic resolution.
Effect of oxygen concentration on the spike formation during reactive ion etching of SiC using the mixed gas plasma of NF3 and O2

Reactive ion etching (RIE) of poly-β‐SiC was investigated in the NF3∕O2 mixture gas plasma. The addition of 10% oxygen concentration to the NF3 plasma increased the etching rate to ∼80nm∕min at a total pressure of 10Pa and 997nm∕min at a total pressure of 20Pa. The ratio of increase in etching rate against that in the pure NF3 plasma was ∼43%. RIE for longer than 30min in the 90% NF3 and 10% O2 mixture gas plasma gave a much smoother surface than that etched in the pure NF3 plasma. However, the further addition of O2 decreased the etching rate. Optical-emission spectra indicated the presence of an oxygen radical, in addition to fluorine radical and molecular nitrogen cations, in the NF3∕O2 mixture gas plasma. X-ray photoemission spectroscopy analysis of the etched samples revealed that the SiO2 layer was formed on the surface at the higher O2 concentration. The role of oxygen in the NF3∕O2 mixture gas plasma was elucidated. Scanning electron microscopy observation revealed that many thornlike substances, .
Local Dispersive and Strichartz Estimates for the Schrödinger Operator on the Heisenberg Group

It was proved by H. Bahouri, P. G{\'e}rard and C. -J. Xu in [9] that the Schr{\"o}dinger equation on the Heisenberg group $\mathbb{H}^d$, involving the sublaplacian, is an example of a totally non-dispersive evolution equation: for this reason global dispersive estimates cannot hold. This paper aims at establishing local dispersive estimates on $\mathbb{H}^d$ for the linear Schr{\"o}dinger equation, by a refined study of the Schr{\"o}dinger kernel $S_t$ on $\mathbb{H}^d$. The sharpness of these estimates is discussed through several examples. Our approach, based on the explicit formula of the heat kernel on $\mathbb{H}^d$ derived by B. Gaveau in [20], is achieved by combining complex analysis and Fourier-Heisenberg tools. As a by-product of our results, we establish local Strichartz estimates and prove that the kernel $S_t$ concentrates on quantized horizontal hyperplanes of $\mathbb{H}^d$.
A Statistical Estimation of the Occurrence of Extraterrestrial Intelligence in the Milky Way Galaxy

In the field of astrobiology, the precise location, prevalence, and age of potential extraterrestrial intelligence (ETI) have not been explicitly explored. Here, we address these inquiries using an empirical galactic simulation model to analyze the spatial–temporal variations and the prevalence of potential ETI within the Galaxy. This model estimates the occurrence of ETI, providing guidance on where to look for intelligent life in the Search for ETI (SETI) with a set of criteria, including well-established astrophysical properties of the Milky Way. Further, typically overlooked factors such as the process of abiogenesis, different evolutionary timescales, and potential self-annihilation are incorporated to explore the growth propensity of ETI. We examine three major parameters: (1) the likelihood rate of abiogenesis (λA); (2) evolutionary timescales (Tevo); and (3) probability of self-annihilation of complex life (Pann). We found Pann to be the most influential parameter determining the quantity and age of galactic intelligent life. Our model simulation also identified a peak location for ETI at an annular region approximately 4 kpc from the galactic center around 8 billion years (Gyrs), with complex life decreasing temporally and spatially from the peak point, asserting a high likelihood of intelligent life in the galactic inner disk. The simulated age distributions also suggest that most of the intelligent life in our galaxy are young, thus making observation or detection difficult.
Overview description of the Gambian GABECE Educational Data and associated algorithms and unsupervized learning process

As the first paper of a series of exploratory analysis and statistical investigation works on the Gambian GABECE data based on a variety of statistical tools, we wish to begin with a thorough unsupervised learning process through descriptive and exploratory methods. This will lead to a variety of discoveries and hypotheses that will direct future research works related to this data
Thermodynamically stable skyrmion lattice in a tetragonal frustrated antiferromagnet with dipolar interaction

Motivated by recent experimental results on GdRu$_2$Si$_2$ [Khanh, N.D. , Nakajima, T. , Yu, X. et al., \emph{Nat. Nanotechnol. } \textbf{15}, 444-449 (2020)], where nanometric square skyrmion lattice was observed, we propose simple analytical mean-field description of the high-temperature part of the phase diagram of centrosymmetric tetragonal frustrated antiferromagnets with dipolar interaction in the external magnetic field. In the reciprocal space dipolar forces provide momentum dependent biaxial anisotropy. It is shown that in tetragonal lattice in the large part of the Brillouin zone for mutually perpendicular modulation vectors in the $ab$ plane this anisotropy has mutually perpendicular easy axes and collinear middle axes, what leads to double-Q modulated spin structure stabilization. The latter turns out to be a square skyrmion lattice in the large part of its stability region with the topological charge $\pm 1$ per magnetic unit cell, which is determined by the frustrated exchange coupling, and, thus, nanometer-sized. In the presence of additional single-ion easy-axis anisotropy, easy and middle axes can be swapped, which leads to different phase diagram. It is argued that the latter case is relevant to GdRu$_2$Si$_2$.
Optimization Techniques to Improve Inference Performance of a Forward Propagating Neural Network on an FPGA

This paper describes an optimized implementation of a Forward Propagating Classification Neural Network which has been previously trained. The implementation described highlights a novel means of using Python scripts to generate a Verilog hardware implementation. The characteristics of this implementation include optimizations to scale input data, use selected addends instead of multiplication functions, hardware friendly activation functions and simplified output selection. Inference performance comparison of a 28x28 pixel 'hand-written' recognition NN between a software implementation on an Intel i7 vs a Xilinx FPGA will be detailed.
Controlled Synthesis and Water Dispersibility of Hexagonal Phase NaGdF4:Ho3+/Yb3+ Nanoparticles

The luminescence properties of hexagonal sodium gadolinium fluoride nanoparticles co-doped with Ho3+ and Yb3+ (NaGdF4:Ho3+/Yb3+), prepared using a thermal decomposition synthesis route, were evaluated following 980 nm excitation. The synthesized nanoparticles were easily dispersed in nonpolar solvents, showed an extremely narrow particle distribution, and were determined to have a mean diameter of 15.6 ± 1.2 nm. The predominantly green upconversion emission was observed to increase with increasing Ho3+ concentration due to an enhanced energy transfer (ET) from the neighboring Yb3+ ions. Post-synthesis, the nanoparticles were dispersed in water via modification of the capping oleic acid (OA) ligand. Upconversion emission intensity was observed to decrease upon dispersion in aqueous media likely due to an increase in nonradiative decay pathways. A total ligand exchange with poly(acrylic acid) (PAA) resulted in slightly more intense upconversion emission relative to oxidation of the OA to azelaic acid.
Prediction of high-performance computing input/output variability and its application to optimization for system configurations

Abstract Performance variability is an important measure for a reliable high performance computing (HPC) system. Performance variability is affected by complicated interactions between numerous factors, such as CPU frequency, the number of input/output (IO) threads, and the IO scheduler. In this paper, we focus on HPC IO variability. The prediction of HPC variability is a challenging problem in the engineering of HPC systems and there is little statistical work on this problem to date. Although there are many methods available in the computer experiment literature, the applicability of existing methods to HPC performance variability needs investigation, especially, when the objective is to predict performance variability both in interpolation and extrapolation settings. A data analytic framework is developed to model data collected from large-scale experiments. Various promising methods are used to build predictive models for the variability of HPC systems. We evaluate the performance of the methods by measuring prediction accuracy at previously unseen system configurations. We also discuss a methodology for optimizing system configurations that uses the estimated variability map. The findings from method comparisons and developed tool sets in this paper yield new insights into existing statistical methods and can be beneficial for the practice of HPC variability management. This paper has supplementary materials online.
Duality and Higher Buscher Rules in p‐Form Gauge Theory and Linearized Gravity

We perform an in‐depth analysis of the transformation rules under duality for couplings of theories containing multiple scalars, p‐form gauge fields, linearized gravitons or (p, 1) mixed symmetry tensors. Following a similar reasoning to the derivation of the Buscher rules for string background fields under T‐duality, we show that the couplings for all classes of aforementioned multi‐field theories transform according to one of two sets of duality rules. These sets comprise the ordinary Buscher rules and their higher counterpart; this is a generic feature of multi‐field theories in spacetime dimensions where the field strength and its dual are of the same degree. Our analysis takes into account topological theta terms and generalized B‐fields, whose behavior under duality is carefully tracked. For a 1‐form or a graviton in 4D, this reduces to the inversion of the complexified coupling or generalized metric under electric/magnetic duality. Moreover, we write down an action for linearized gravity in the presence of θ‐term from which we obtain previously suggested on‐shell duality and double duality relations. This also provides an explanation for the origin of theta in the gravitational duality relations as a specific additional sector of the linearized gravity action.