Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying RR Lyrae Variable Stars in Six Years of the Dark Energy Survey

We present a search for RR Lyrae stars using the full six-year data set from the Dark Energy Survey covering ∼5000 deg2 of the southern sky. Using a multistage multivariate classification and light-curve template-fitting scheme, we identify RR Lyrae candidates with a median of 35 observations per candidate. We detect 6971 RR Lyrae candidates out to ∼335 kpc, and we estimate that our sample is >70% complete at ∼150 kpc. We find excellent agreement with other wide-area RR Lyrae catalogs and RR Lyrae studies targeting the Magellanic Clouds and other Milky Way satellite galaxies. We fit the smooth stellar halo density profile using a broken-power-law model with fixed halo flattening (q = 0.7), and we find strong evidence for a break at R0=32.1−0.9+1.1kpc with an inner slope of n1=−2.54−0.09+0.09 and an outer slope of n2=−5.42−0.14+0.13 . We use our catalog to perform a search for Milky Way satellite galaxies with large sizes and low luminosities. Using a set of simulated satellite galaxies, we find that our RR Lyrae-based search is more sensitive than those using resolved stellar populations in the regime of large (r h ≳ 500 pc), low-surface-brightness dwarf galaxies. A blind search for large, diffuse satellites yields three candidate substructures. The first can be confidently associated with the dwarf galaxy Eridanus II. The second has a distance and proper motion similar to the ultrafaint dwarf galaxy Tucana II but is separated by ∼5 deg. The third is close in projection to the globular cluster NGC 1851 but is ∼10 kpc more distant and appears to differ in proper motion.
The insecticidal properties of a garlic oil, with special reference to its use against two dipteran pests

Garclh Michael Prowse: The Insecticidal Pr·o11erties of a Garlic Oil, with S11ecial Reference to its Use Against Two Di11teran Pests. llte potential for a quantitatively produced garlic oil for use as an insecticide was investigated. The study assessed the garlic oils efficacy in the laboratory and in the field, as well as its physiological effect on three key enzymes noted for their involvement either in toxicity, or detoxication of exogenous toxins in two Dipteran pests; Delia radicum (L.) and Musca domestica (L.). The garlic oil absorbed onto wood flour pellets (prills) had no significant repellent effect on D. radicum, in choice tests when presented with artificial oviposition sites. Exposure of 241tr old D. radicum eggs to garlic prills placed on the snbstrate significantly reduced egg hatch, whilst exposure to vapours of the prills had no significant effect. When used in the field, damage to swede plmll roots by D. radicum was reduced in a dose dependent fashion, with the highest application rate (140 kg ha·') providing control comparable to that of the 'current best practice' organophosphonrs pesticide. In liquid fonn the garlic oil had a dose dependent effect on the mortality of all life stages (egg, 2'"' instar larvae, and adult) of D. rndicum and Al. domestica, and corresponding LC5<, values were calculated. A 5% concentration had comparable mortality rates to the 'current best practice' organophosphonrs pesticide. Field application reduced swede root dmmge in a concentration dependent f.1shion with mean root damage scores in the 0.5% and 2% concentration treatment plots significanlly lower than those of the control, but not significantly different to those in the organophosphonrs pesticide treatments. The liquid had no significant effect on swede root circumference at any concentration, thus not significanlly affecting potential crop yield. Exposure of pure extracts of acetylcholinesterase, carboxylesterase, and glutathione S-transferase to a series of garlic oil concentrations inhibited carboxylesterase activity and glutathione S-transferase activity in a dose dependent fashion; no significant inhibition of acetylcholinesterase activity was noted. No significant inhibit ion of the three e11zymes studied was noted In vitro or in vivo exposure of larvae and adults of both Dipteran species. Quantitative assessmenl of the glutathione and lactate pools indicated that there was generally a net loss of glutathione with increasing gnrlic oil concentration and no significnnt correlation between lactate levels and concentration in the larval and adult life-stages of D. rndicum and M. domeslica. It was concluded that the garlic oil studied, both in liquid form and in prill fom1 could be used as an insecticide against the two Dipteran pests studied, although the mode of action is still tmclear. Implications for use of the products and the potential mode of action nre discussed.
Eigenstate thermalization hypothesis through the lens of autocorrelation functions

Matrix elements of observables in eigenstates of generic Hamiltonians are described by the Srednicki ansatz within the eigenstate thermalization hypothesis (ETH). We study a quantum chaotic spin-fermion model in a one-dimensional lattice, which consists of a spin-1/2 XX chain coupled to a single itinerant fermion. In our study, we focus on translationally invariant observables including the charge and energy current, thereby also connecting the ETH with transport properties. We ask to which extent one can use autocorrelation functions of observables to extract specific properties of their matrix elements. Considering observables with a Hilbert-Schmidt norm of one, we first demonstrate the validity of the ETH ansatz to a remarkable accuracy. A particular emphasis is on the analysis of the structure of the offdiagonal matrix elements $|\langle \alpha | \hat O | \beta \rangle|^2$ in the limit of small eigenstate energy differences $\omega = E_\beta - E_\alpha$. Removing the dominant exponential suppression of $|\langle \alpha | \hat O | \beta \rangle|^2$, we find that: (i) the current matrix elements, in contrast to all other observables under investigation, exhibit no additional system-size dependence, (ii) matrix elements of several other observables exhibit a Drude-like structure with a Lorentzian frequency dependence, whose amplitude and width scales linearly with the lattice size, eventually suggesting ballistic transport for large systems. We then show how this information can be extracted from the autocorrelation functions as well. Finally, our study is complemented by a numerical analysis of the fluctuation-dissipation relation for eigenstates in the bulk of the spectrum. We identify the regime of $\omega$ in which the well-known fluctuation-dissipation relation is valid with high accuracy for finite systems.
All-optical long-distance quantum communication with Gottesman-Kitaev-Preskill qubits

Quantum repeaters are a promising platform for realizing long-distance quantum communication and thus could form the backbone of a secure quantum internet, a scalable quantum network, or a distributed quantum computer. Repeater protocols that encode information in single- or multi-photon states are limited by transmission losses and the cost of implementing entangling gates or Bell measurements. In this work, we consider implementing a quantum repeater protocol using Gottesman-Kitaev-Preskill (GKP) qubits. These qubits are natural elements for quantum repeater protocols, because they allow for deterministic Gaussian entangling operations and Bell measurements, which can be implemented at room temperature. The GKP encoding is also capable of correcting small displacement errors. At the cost of additional Gaussian noise, photon loss can be converted into a random displacement error channel by applying a phase-insensitive amplifier. Here we show that a similar conversion can be achieved in two-way repeater protocols by using phase-sensitive amplification applied in the post-processing of the measurement data, resulting in less overall Gaussian noise per (sufficiently short) repeater segment. We also investigate concatenating the GKP code with higher level qubit codes while leveraging analog syndrome data, post-selection, and path-selection techniques to boost the rate of communication. We compute the secure key rates and find that GKP repeaters can achieve a comparative performance relative to methods based on photonic qubits while using orders-of-magnitude fewer qubits.
Comparative Analysis of Extreme Verification Latency Learning Algorithms

One of the more challenging real-world problems in computational intelligence is to learn from non-stationary streaming data, also known as concept drift. Perhaps even a more challenging version of this scenario is when -- following a small set of initial labeled data -- the data stream consists of unlabeled data only. Such a scenario is typically referred to as learning in initially labeled nonstationary environment, or simply as extreme verification latency (EVL). Because of the very challenging nature of the problem, very few algorithms have been proposed in the literature up to date. This work is a very first effort to provide a review of some of the existing algorithms (important/prominent) in this field to the research community. More specifically, this paper is a comprehensive survey and comparative analysis of some of the EVL algorithms to point out the weaknesses and strengths of different approaches from three different perspectives: classification accuracy, computational complexity and parameter sensitivity using several synthetic and real world datasets.
Patterns of Rheumatic Heart Disease and Treatment Practices at Tertiary Care Center in Nepal: A Descriptive Cross-sectional Study

ABSTRACT Introduction: Rheumatic heart disease is a sequel of rheumatic fever which causes heart valve damage. This study was conducted to look at the pattern of valve lesions and treatment practices in patients with rheumatic heart disease. Methods: A cross-sectional study conducted at the tertiary care center with a diagnosis of rheumatic heart disease from July 2018 to January 2020 by convenient sampling. Ethical clearance was obtained from the Institutional Review Committee (ref no. 55/2018). Data were analyzed by using Statistical package for social sciences version 20. Results: Out of 600 patients, 428 (71.3%) were female. The mean age was 44.24±14.24 years. The isolated mitral valve was affected in 280 (46.6%). Dual involvement of mitral and aortic valve was present in 294 (49%). Only 14 (2.3%) had involvement of isolated aortic valve involvement. Overall, mitral stenosis was the most common abnormality 508 (84.6%) followed by mitral regurgitation 418 (69.6%), aortic regurgitation 320 (53.3%), and aortic stenosis 63 (10.5%). Assessment of the severity of lesions showed that 247 (41.2%) patients had severe mitral stenosis, 119 (19.8%) severe mitral regurgitation, 14 (2.3%) severe aortic stenosis, and 11 (1.8%) severe aortic regurgitation. Majority 493 (82.2%) were treated with medical therapies. Surgical procedures were performed in 51 (8.5%). The use of anticoagulation was in 212 (35.3%) of eligible patients. Conclusions: Mitral valve was affected commonly both in isolation and combination. The majority of patients who were eligible for cardiac interventions were treated medically with suboptimal use of anticoagulation and secondary prophylaxis.
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning

Thompson sampling (TS) has emerged as a robust technique for contextual bandit problems. However, TS requires posterior inference and optimization for action generation, prohibiting its use in many internet applications where latency and ease of deployment are of concern. We propose a novel imitation-learning-based algorithm that distills a TS policy into an explicit policy representation by performing posterior inference and optimization offline. The explicit policy representation enables fast online decision-making and easy deployment in mobile and server-based environments. Our algorithm iteratively performs offline batch updates to the TS policy and learns a new imitation policy. Since we update the TS policy with observations collected under the imitation policy, our algorithm emulates an off-policy version of TS. Our imitation algorithm guarantees Bayes regret comparable to TS, up to the sum of single-step imitation errors. We show these imitation errors can be made arbitrarily small when unlabeled contexts are cheaply available, which is the case for most large-scale internet applications. Empirically, we show that our imitation policy achieves comparable regret to TS, while reducing decision-time latency by over an order of magnitude.
RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction

Semantic scene understanding from point clouds is particularly challenging as the points reflect only a sparse set of the underlying 3D geometry. Previous works often convert point cloud into regular grids (e.g. voxels or bird-eye view images), and resort to grid-based convolutions for scene understanding. In this work, we introduce RfD-Net that jointly detects and reconstructs dense object surfaces directly from raw point clouds. Instead of representing scenes with regular grids, our method leverages the sparsity of point cloud data and focuses on predicting shapes that are recognized with high objectness. With this design, we decouple the instance reconstruction into global object localization and local shape prediction. It not only eases the difficulty of learning 2-D manifold surfaces from sparse 3D space, the point clouds in each object proposal convey shape details that support implicit function learning to reconstruct any high-resolution surfaces. Our experiments indicate that instance detection and reconstruction present complementary effects, where the shape prediction head shows consistent effects on improving object detection with modern 3D proposal network backbones. The qualitative and quantitative evaluations further demonstrate that our approach consistently outperforms the state-of-the-arts and improves over 11 of mesh IoU in object reconstruction.
A strict inequality for the minimization of the Willmore functional under isoperimetric constraint

Abstract Inspired by previous work of Kusner and Bauer–Kuwert, we prove a strict inequality between the Willmore energies of two surfaces and their connected sum in the context of isoperimetric constraints. Building on previous work by Keller, Mondino and Rivière, our strict inequality leads to existence of minimizers for the isoperimetric constrained Willmore problem in every genus, provided the minimal energy lies strictly below 8 ⁢ π {8\pi} . Besides the geometric interest, such a minimization problem has been studied in the literature as a simplified model in the theory of lipid bilayer cell membranes.
Progress in the Glauber Model at Collider Energies

We review the theoretical and experimental progress in the Glauber model of multiple nucleon and/or parton scatterings, after the last 10--15 years of operation with proton and nuclear beams at the CERN Large Hadron Collider (LHC) and with various light and heavy colliding ions at the BNL Relativistic Heavy Ion Collider (RHIC). The main developments and the state-of-the-art of the field are summarized. These encompass measurements of the inclusive inelastic proton and nuclear cross sections, advances in the description of the proton and nuclear density profiles and their fluctuations, inclusion of subnucleonic degrees of freedom, experimental procedures and issues related to the determination of the collision centrality, validation of the binary scaling prescription for hard scattering cross sections, and constraints on transport properties of quark-gluon matter from varying initial-state conditions in relativistic hydrodynamics calculations. These advances confirm the validity and usefulness of the Glauber formalism for quantitative studies of QCD matter produced in collisions of systems, from protons to uranium, of vastly different size.
Revisiting Unsupervised Meta-Learning: Amplifying or Compensating for the Characteristics of Few-Shot Tasks

Meta-learning becomes a practical approach towards few-shot image classification, where a visual recognition system is constructed with limited annotated data. Inductive bias such as embedding is learned from a base class set with ample labeled examples and then generalizes to few-shot tasks with novel classes. Surprisingly, we find that the base class set labels are not necessary, and discriminative embeddings could be meta-learned in an unsupervised manner. Comprehensive analyses indicate two modifications -- the semi-normalized distance metric and the sufficient sampling -- improves unsupervised meta-learning (UML) significantly. Based on the modified baseline, we further amplify or compensate for the characteristic of tasks when training a UML model. First, mixed embeddings are incorporated to increase the difficulty of few-shot tasks. Next, we utilize a task-specific embedding transformation to deal with the specific properties among tasks, maintaining the generalization ability into the vanilla embeddings. Experiments on few-shot learning benchmarks verify that our approaches outperform previous UML methods by a 4-10% performance gap, and embeddings learned with our UML achieve comparable or even better performance than its supervised variants.
Ultra-fast Model Emulation with PRISM: Analyzing the Meraxes Galaxy Formation Model

We demonstrate the potential of an emulator-based approach to analyzing galaxy formation models in the domain where constraining data is limited. We have applied the open-source Python package Prism to the galaxy formation model Meraxes. Meraxes is a semianalytic model, purposely built to study the growth of galaxies during the Epoch of Reionization. Constraining such models is however complicated by the scarcity of observational data in the EoR. Prism’s ability to rapidly construct accurate approximations of complex scientific models using minimal data is therefore key to performing this analysis well. This paper provides an overview of our analysis of Meraxes using measurements of galaxy stellar mass densities, luminosity functions, and color–magnitude relations. We demonstrate the power of using Prism instead of a full Bayesian analysis when dealing with highly correlated model parameters and a scarce set of observational data. Our results show that the various observational data sets constrain Meraxes differently and do not necessarily agree with each other, signifying the importance of using multiple observational data types when constraining such models. Furthermore, we show that Prism can detect when model parameters are too correlated or cannot be constrained effectively. We conclude that a mixture of different observational data types, even when they are scarce or inaccurate, is a priority for understanding galaxy formation and that emulation frameworks such as Prism can guide the selection of such data.
Load progression criteria in exercise programmes in lower limb tendinopathy: a systematic review

Objective The objective of this study is to summarise and analyse the current literature about what progression criteria are applied in loading exercise programmes in lower limb tendinopathies and their evidence and effectiveness. Design Systematic review. Methods PubMed, Embase, Scopus and PEDro were searched from inception to 24 September 2020. The inclusion criteria were randomised controlled trials that included patients with midportion Achilles, patellar or gluteal tendinopathy; assessed function, pain or performance; included at least one group where progressive physical exercise was administered as monotherapy; included at least a control group. We excluded studies that included subjects with previous tendon surgical treatment; studies with control group that conducted a supplemented modality of the exercise performed in the intervention group. A narrative synthesis was conducted. Cohen’s d and the percentage of change of main clinical and performance outcomes were obtained. Methodological quality was assessed using the PEDro scale. Results Thirty studies that described progression criteria were included. Six types of criteria grouped in two categories were identified and included in a new classification proposal: pain as a primary criterion (evoking and avoid-pain based), and pain and symptom control as a secondary criterion (conditioning stages, fatigue-based, subjective perception and temporary linear increase). Most of the studies applied a pain-based criterion. Criteria based on conditioning stages were also commonly applied. Other criteria such as fatigue, a temporary linear increase, or the subjective perception of the patient’s abilities were occasionally applied. Conclusions There is a predominant use of pain-based criteria, but the utilisation of these criteria is not supported by strong evidence. This review evidences the need for studies that compare the same exercise programme using different progression criteria. A new classification of the existing progression criteria is proposed based on the use of pain as the primary or secondary criterion. PROSPERO registration number CRD42018110997.
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images

Prevailing defense schemes against adversarial face images tend to overfit to the perturbations in the training set and fail to generalize to unseen adversarial attacks. We propose a new self-supervised adversarial defense framework, namely FaceGuard, that can automatically detect, localize, and purify a wide variety of adversarial faces without utilizing pre-computed adversarial training samples. During training, FaceGuard automatically synthesizes challenging and diverse adversarial attacks, enabling a classifier to learn to distinguish them from real faces. Concurrently, a purifier attempts to remove the adversarial perturbations in the image space. Experimental results on LFW, Celeb-A, and FFHQ datasets show that FaceGuard can achieve 99.81%, 98.73%, and 99.35% detection accuracies, respectively, on six unseen adversarial attack types. In addition, the proposed method can enhance the face recognition performance of ArcFace from 34.27% TAR @ 0.1% FAR under no defense to 77.46% TAR @ 0.1% FAR. Code, pre-trained models and dataset will be publicly available.