Miracle-dz/UniFAR-FTUs huggingface dataset:
Mesoscale Convective Systems in Weakly Forced Large-Scale Environments. Part III: Numerical Simulations and Implications for Operational Forecasting

Abstract During a 24-h period, beginning 1200 UTC 11 May 1982, a series of mesoscale convective systems developed within a weakly forced large-scale environment. Two of these systems had a large component of motion against the midtropospheric flow and propagated in a direction nearly opposite to that of the travelling upper-level disturbances. This evolution of convection is very different from traditional ones in which convection develops and moves more or less in phase with traveling upper-level disturbances. It presents a tremendous challenge for three-dimensional numerical models, since the initiation and evolution of convection are tied to mesoscale features that are not well observed by the conventional upper-air network and may not be well approximated in the model parameterization schemes. Mesoscale model simulations are conducted to evaluate the ability of the model to reproduce this complex event and to examine the model sensitivities to differences in the convective trigger function and model i.
Localizing Scan Targets from Human Pose for Autonomous Lung Ultrasound Imaging

Ultrasound is progressing toward becoming an affordable and versatile solution to medical imaging. With the advent of COVID-19 global pandemic, there is a need to fully automate ultrasound imaging as it requires trained operators in close proximity to patients for long period of time. In this work, we investigate the important yet seldom-studied problem of scan target localization, under the setting of lung ultrasound imaging. We propose a purely vision-based, data driven method that incorporates learning-based computer vision techniques. We combine a human pose estimation model with a specially designed regression model to predict the lung ultrasound scan targets, and deploy multiview stereo vision to enhance the consistency of 3D target localization. While related works mostly focus on phantom experiments, we collect data from 30 human subjects for testing. Our method attains an accuracy level of 15.52 (9.47) mm for probe positioning and 4.32 (3.69){\deg} for probe orientation, with a success rate above 80% under an error threshold of 25mm for all scan targets. Moreover, our approach can serve as a general solution to other types of ultrasound modalities. The code for implementation has been released.
RTMDet: An Empirical Study of Designing Real-Time Object Detectors

In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance segmentation and rotated object detection. To obtain a more efficient model architecture, we explore an architecture that has compatible capacities in the backbone and neck, constructed by a basic building block that consists of large-kernel depth-wise convolutions. We further introduce soft labels when calculating matching costs in the dynamic label assignment to improve accuracy. Together with better training techniques, the resulting object detector, named RTMDet, achieves 52.8% AP on COCO with 300+ FPS on an NVIDIA 3090 GPU, outperforming the current mainstream industrial detectors. RTMDet achieves the best parameter-accuracy trade-off with tiny/small/medium/large/extra-large model sizes for various application scenarios, and obtains new state-of-the-art performance on real-time instance segmentation and rotated object detection. We hope the experimental results can provide new insights into designing versatile real-time object detectors for many object recognition tasks. Code and models are released at https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet.
Animal Synchrony and agents' segregation

In recent years it has become evident the need of understanding how failure of coordination imposes constraints on the size of stable groups that highly social mammals can live in. We examine here the forces that keep animals together as a herd and others that drive them apart. Different phenotypes (e.g. genders) have different rates of gut ﬁll, causing them to spend different amounts of time performing activities. By modeling a group as a set of semi-coupled oscillators on a disc, we show that the members of the group may become less and less coupled until the group dissolves and breaks apart. We show that when social bonding creates a stickiness, or gravitational pull, between pairs of individuals, fragmentation is reduced.
Leveraging Natural Language Processing to Augment Structured Social Determinants of Health Data in the Electronic Health Record

OBJECTIVE Social determinants of health (SDOH) impact health outcomes and are documented in the electronic health record (EHR) through structured data and unstructured clinical notes. However, clinical notes often contain more comprehensive SDOH information, detailing aspects such as status, severity, and temporality. This work has two primary objectives: (1) develop a natural language processing information extraction model to capture detailed SDOH information and (2) evaluate the information gain achieved by applying the SDOH extractor to clinical narratives and combining the extracted representations with existing structured data. MATERIALS AND METHODS We developed a novel SDOH extractor using a deep learning entity and relation extraction architecture to characterize SDOH across various dimensions. In an EHR case study, we applied the SDOH extractor to a large clinical data set with 225 089 patients and 430 406 notes with social history sections and compared the extracted SDOH information with existing structured data. RESULTS The SDOH extractor achieved 0.86 F1 on a withheld test set. In the EHR case study, we found extracted SDOH information complements existing structured data with 32% of homeless patients, 19% of current tobacco users, and 10% of drug users only having these health risk factors documented in the clinical narrative. CONCLUSIONS Utilizing EHR data to identify SDOH health risk factors and social needs may improve patient care and outcomes. Semantic representations of text-encoded SDOH information can augment existing structured data, and this more comprehensive SDOH representation can assist health systems in identifying and addressing these social needs.
Anomaly Detection in Driving by Cluster Analysis Twice

— Events deviating from normal traffic patterns in driving, anomalies, such as aggressive driving or bumpy roads, may harm delivery efficiency for transportation and logistics (T&L) business. Thus, detecting anomalies in driving is critical for the T&L industry. So far numerous researches have used vehicle sensor data to identify anomalies. Most previous works captured anomalies by using deep learning or machine learning algorithms, which require prior training processes and huge computational costs. This study proposes a method namely Anomaly Detection in Driving by Cluster Analysis Twice (ADDCAT) which clusters the processed sensor data in different physical properties. An event is said to be an anomaly if it never fits with the major cluster, which is considered as the pattern of normality in driving. This method provides a way to detect anomalies in driving with no prior training processes and huge computational costs needed. This paper validated the performance of the method on an open dataset.
Spontaneous scalarization of black holes in Gauss-Bonnet teleparallel gravity

In this paper, we find new scalarized black holes by coupling a scalar field with the Gauss-Bonnet invariant in Teleparallel gravity. The Teleparallel formulation of this theory uses torsion instead of curvature to describe the gravitational interaction and it turns out that, in this language, the usual Gauss-Bonnet term in four dimensions, decays in two distinct boundary terms, the Teleparallel Gauss-Bonnet invariants. Both can be coupled individually, or in any combination to a scalar field, to obtain a Teleparallel Gauss-Bonnet extension of the Teleparallel equivalent of general relativity. The theory we study contains the familiar Riemannian Einstein-Gauss-Bonnet gravity theory as a particular limit and offers a natural extension, in which scalarization is triggered by torsion and with new interesting phenomenology. We demonstrate numerically the existence of asymptotically flat scalarized black hole solutions and show that, depending on the choice of coupling of the boundary terms, they can have a distinct behaviour compared to the ones known from the usual Einstein-Gauss-Bonnet case. More specifically, non-monotonicity of the metric functions and the scalar field can be present, a feature that was not observed until now for static scalarized black hole solutions.
Learning threshold neurons via the "edge of stability"

Existing analyses of neural network training often operate under the unrealistic assumption of an extremely small learning rate. This lies in stark contrast to practical wisdom and empirical studies, such as the work of J. Cohen et al. (ICLR 2021), which exhibit startling new phenomena (the"edge of stability"or"unstable convergence") and potential benefits for generalization in the large learning rate regime. Despite a flurry of recent works on this topic, however, the latter effect is still poorly understood. In this paper, we take a step towards understanding genuinely non-convex training dynamics with large learning rates by performing a detailed analysis of gradient descent for simplified models of two-layer neural networks. For these models, we provably establish the edge of stability phenomenon and discover a sharp phase transition for the step size below which the neural network fails to learn"threshold-like"neurons (i.e., neurons with a non-zero first-layer bias). This elucidates one possible mechanism by which the edge of stability can in fact lead to better generalization, as threshold neurons are basic building blocks with useful inductive bias for many tasks.
Parametrization of Deceleration Parameter in f(Q) Gravity

In this paper, we investigate the modified symmetric teleparallel gravity or f(Q) gravity, where Q is the nonmetricity, to study the evolutionary history of the universe by considering the functional form of f(Q)=αQn, where α and n are constants. Here, we consider the parametrization form of the deceleration parameter as q=q0+q1z/(1+z)2 (with the parameters q0(q at z=0), q1, and the redshift, z), which provides the desired property for a sign flip from a decelerating to an accelerating phase. We obtain the solution of the Hubble parameter by examining the mentioned parametric form of q, and then we impose the solution in Friedmann equations. Employing the Bayesian analysis for the Observational Hubble data (OHD), we estimated the constraints on the associated free parameters (H0,q0,q1) with H0 the current Hubble parameter to determine if this model may challenge the ΛCDM (Λ cold dark matter with the cosmological constant, Λ) limitations. Furthermore, the constrained current value of the deceleration parameter q0=−0.832−0.091+0.091 shows that the present universe is accelerating. We also investigate the evolutionary trajectory of the energy density, pressure, and EoS (equation-of-state) parameters to conclude the accelerating behavior of the universe. Finally, we try to demonstrate that the considered parametric form of the deceleration parameter is compatible with f(Q) gravity.
Strain-induced superconductivity in 
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Multi-orbital quantum materials with strong interactions can host a variety of novel phases. In this work we study the possibility of interaction-driven superconductivity in the iridate compound Sr$_2$IrO$_4$ under strain and doping. We find numerous regimes of strain-induced superconductivity in which the pairing structure depends on model parameters. Spin-fluctuation mediated superconductivity is modeled by a Hubbard-Kanamori model with an effective particle-particle interaction, calculated via the random phase approximation. Magnetic orders are found using the Stoner criterion. The most likely superconducting order we find has $d$-wave pairing, predominantly in the total angular momentum, $J=1/2$ states. Moreover, an $s_{\pm}$-order which mixes different bands is found at high Hund's coupling, and at high strain anisotropic $s$- and $d$-wave orders emerge. Finally, we show that in a fine-tuned region of parameters a spin-triplet $p$-wave order exists. The combination of strong spin-orbit coupling, interactions, and a sensitivity of the band structure to strain proves a fruitful avenue for engineering new quantum phases.
Probing gamma-ray burst afterglows with the cherenkov telescope array

Detection of delayed sub-TeV photons from Gamma-Ray Bursts (GRBs) by MAGIC and HESS has proven the promising future of GRB afterglow studies with the Cherenkov Telescope Array (CTA), the next-generation gamma-ray observatory. With the unprecedented sensitivity of CTA, afterglow detection rates are expected to increase dramatically. In this paper, we explore the multi-dimensional afterglow parameter space to see the detectability of sub-TeV photons by CTA. We use a one-zone electron synchrotron and synchrotron self-Compton model to obtain the spectral energy distribution. We consider bursts going off in a medium of homogenous density. The blast wave is assumed to be radiatively inefficient and evolving adiabatically. Considering that the electron acceleration is not efficient if the acceleration timescale exceeds the radiative cooling timescale, we find that the Sub-TeV emission is always due to the self-Compton process. We find that jets with high kinetic energy or large bulk Lorentz factor decelerating into a dense ambient medium offer better detection prospects for CTA. For relatively lower values of the downstream magnetic field, electrons are slow-cooling, and the emitted radiation is positively correlated with the magnetic field. For larger magnetic fields, the electron population enters the fast cooling phase where the radiated flux is inversely proportional to the magnetic field. We apply our results in the context of bright TeV afterglows detected in recent years. Our results indicate that cosmological short GRBs have only moderate prospects of detection by CTA while local Neutron Star merger counterparts can be detected if the jet is launched towards the observer.
Light‐Induced Metastable Hidden Skyrmion Phase in the Mott Insulator Cu2OSeO3

The discovery of a novel long‐lived metastable skyrmion phase in the multiferroic insulator Cu2OSeO3 visualized with Lorentz transmission electron microscopy for magnetic fields below the equilibrium skyrmion pocket is reported. This phase can be accessed by exciting the sample non‐adiabatically with near‐infrared femtosecond laser pulses and cannot be reached by any conventional field‐cooling protocol, referred as a hidden phase. From the strong wavelength dependence of the photocreation process and via spin‐dynamics simulations, the magnetoelastic effect is identified as the most likely photocreation mechanism. This effect results in a transient modification of the magnetic free energy landscape extending the equilibrium skyrmion pocket to lower magnetic fields. The evolution of the photoinduced phase is monitored for over 15 min and no decay is found. Because such a time is much longer than the duration of any transient effect induced by a laser pulse in a material, it is assumed that the newly discovered skyrmion state is stable for practical purposes, thus breaking ground for a novel approach to control magnetic state on demand at ultrafast timescales and drastically reducing heat dissipation relevant for next‐generation spintronic devices.
Convection heat transfer at a stretching sheet with suction or blowing

Abstract Analysis of convection flow and heat transfer of a viscous fluid near an infinite, porous, vertical stretching surface is carried out. The effects of free convection and suction or injection on the how and heat transfer characteristics are considered. The equations of conservation of momentum, mass, and energy, which govern the flow and heat transfer problem, are solved numerically by using a variable order, variable step size finite-difference method. The numerical results obtained for the flow and heat transfer characteristics reveal many interesting behaviors. These behaviors warrant further study of the effects of free convection and suction or injection on the flow and heat transfer characteristics.
The Exotic $K(2)$-Local Picard Group at the Prime $2$

We calculate the group $\kappa_2$ of exotic elements in the $K(2)$-local Picard group at the prime $2$ and find it is a group of order $2^9$ isomorphic to $(\mathbb{Z}/8)^2 \times (\mathbb{Z}/2)^3$. In order to do this we must define and exploit a variety of different ways of constructing elements in the Picard group, and this requires a significant exploration of the theory. The most innovative technique, which so far has worked best at the prime $2$, is the use of a $J$-homomorphism from the group of real representations of finite quotients of the Morava stabilizer group to the $K(n)$-local Picard group.
Novel stochastic and entropy-based Expectation-Maximisation algorithm for transcription factor binding site motif discovery

The discovery of transcription factor binding site (TFBS) motifs remains an important and challenging problem in computational biology. This thesis presents MITSU, a novel algorithm for TFBS motif discovery which exploits stochastic methods as a means of both overcoming optimality limitations in current algorithms and as a framework for incorporating relevant prior knowledge in order to improve results. The current state of the TFBS motif discovery field is surveyed, with a focus on probabilistic algorithms that typically take the promoter regions of coregulated genes as input. A case is made for an approach based on the stochastic ExpectationMaximisation (sEM) algorithm; its position amongst existing probabilistic algorithms for motif discovery is shown. The algorithm developed in this thesis is unique amongst existing motif discovery algorithms in that it combines the sEM algorithm with a derived data set which leads to an improved approximation to the likelihood function. This likelihood function is unconstrained with regard to the distribution of motif occurrences within the input dataset. MITSU also incorporates a novel heuristic to automatically determine TFBS motif width. This heuristic, known as MCOIN, is shown to outperform current methods for determining motif width. MITSU is implemented in Java and an executable is available for download. MITSU is evaluated quantitatively using realistic synthetic data and several collections of previously characterised prokaryotic TFBS motifs. The evaluation demonstrates that MITSU improves on a deterministic EM-based motif discovery algorithm and an alternative sEM-based algorithm, in terms of previously established metrics. The ability of the sEM algorithm to escape stable fixed points of the EM algorithm, which trap deterministic motif discovery algorithms and the ability of MITSU to discover multiple motif occurrences within a single input sequence are also demonstrated. MITSU is validated using previously characterised Alphaproteobacterial motifs, before being applied to motif discovery in uncharacterised Alphaproteobacterial data. A number of novel results from this analysis are presented and motivate two extensions of MITSU: a strategy for the discovery of multiple different motifs within a single dataset and a higher order Markov background model. The effects of incorporating these extensions within MITSU are evaluated quantitatively using previously characterised prokaryotic TFBS motifs and demonstrated using Alphaproteobacterial motifs. Finally, an information-theoretic measure of motif palindromicity is presented and its advantages over existing approaches for discovering palindromic motifs discussed.
A Künneth formula in topological homology and its applications to the simplicial cohomology of $ℓ¹(ℤ₊^{k})$

We establish a Künneth formula for some chain complexes in the categories of Fréchet and Banach spaces. We consider a complex X of Banach spaces and continuous boundary maps dn with closed ranges and prove that H (X ′) ∼= Hn(X )′, where Hn(X )′ is the dual space of the homology group of X and H(X ′) is the cohomology group of the dual complex X ′. A Künneth formula for chain complexes of nuclear Fréchet spaces and continuous boundary maps with closed ranges is also obtained. This enables us to describe explicitly the simplicial cohomology groups H(`(Z+), `(Z+)) and homology groups Hn(`(Z+), `(Z+)) of the semigroup algebra `(Z+).
Pendidikan “Kaum Tertindas”

Educate people is most important to make them aware of their oppressed situation so they may have better future. Those who have critical awareness will be able to see the root of oppression in the community objectively, i.e. system and political structure, economy, social and culture. The idea of Freire on education of the oppressed looks like an oasis in the desert of education in Indonesia. Church and government should be aware of the chaotic situation and try to fix it. I recommend the educators to be aware of the form of the education of the oppressed, and to start fixing the education, especially in Indonesia.
Theory of mutual coupling among minimum-scattering antennas

A novel and rigorou formulation for mutual impedance among a class of idealized but realizable antennas is presented. Unlike past treatments of mutual coupling, which have proceeded from structurally specified antennas, the present treatment deals with a class of antennas, all the electromagnetic properties of which are rigorously expressed explicitly in terms of their radiation patterns alone. Employing a network formulation based on a scattering representation of electromagnetic fields in terms of spherical (or cylindrical) modes, the mutual impedances between such antennas are computed exactly. Several alternate representations for the mutual impedance are derived, one of which is an integral representation involving the power pattern function for both real and complex angles. In special cases, these exact results agree with published results obtained by applying well-known approximate techniques to structurally specified antennas. An important and illuminating example is provided by an infinite planar array of antennas radiating into a half-space for which the present formulation yields the well-known grating-lobe series representation of the active impedance without an explicit reference to structural properties of the radiating elements.
Studying the Fourth Generation Quark Contributions to the Double Charm Decays B (s) → D ( ∗ ) (s) D ( ∗ ) s

Almost all branching ratios and longitudinal polarization fractions of the double charm decays B (s) → D ( ∗ ) (s) D ( ∗ ) s have been measured, and the experimental central value of f L (B 0 s → D ∗+ s D ∗− s ) is quite small comparing to its Standard Model prediction. We study the fourth generation quark contributions to the double charm decays B (s) → D ( ∗ ) (s) D ( ∗ ) s . We find that the loop diagrams involving the fourth generation quark t (cid:4) have great effects on all branching ratios and CP asymmetries, which are very sensitive to the fourth generation parameter λ st (cid:4) and φ t (cid:4) . Nevertheless, the experimental measurements of all branching ratios can not give effective constraints on relevant new physics parameters. In addition, they have no obvious effect on the relevant polarization fractions. These results could be used to search for the fourth heavy quark t (cid:4) via its indirect manifestations in loop diagrams.