Miracle-dz/UniFAR-FTUs huggingface dataset:
The $\beta$ Pictoris b Hill sphere transit campaign. Paper I: Photometric limits to dust and rings

Photometric monitoring of Beta Pictoris in 1981 showed anomalous fluctuations of up to 4% over several days, consistent with foreground material transiting the stellar disk. The subsequent discovery of the gas giant planet Beta Pictoris b and the predicted transit of its Hill sphere to within 0.1 au projected distance of the planet provided an opportunity to search for the transit of a circumplanetary disk in this $21\pm 4$ Myr-old planetary system. Continuous broadband photometric monitoring of Beta Pictoris requires ground-based observatories at multiple longitudes to provide redundancy and to provide triggers for rapid spectroscopic followup. These observatories include the dedicated Beta Pictoris monitoring observatory bRing at Sutherland and Siding Springs, the ASTEP400 telescope at Concordia, and observations from the space observatories BRITE and Hubble Space Telescope. We search the combined light curves for evidence of short period transient events caused by rings and for longer term photometric variability due to diffuse circumplanetary material. We find no photometric event that matches with the event seen in November 1981, and there is no systematic photometric dimming of the star as a function of the Hill sphere radius. We conclude that the 1981 event was not caused by the transit of a circumplanetary disk around Beta Pictoris b. The upper limit on the long term variability of Beta Pictoris places an upper limit of $1.8\times 10^{22}$ g of dust within the Hill sphere. Circumplanetary material is either condensed into a non-transiting disk, is condensed into a disk with moons that has a small obliquity, or is below our detection threshold. This is the first time that a dedicated international campaign has mapped the Hill sphere transit of a gas giant extrasolar planet at 10 au.
Online Deterministic Annealing for Classification and Clustering

Inherent in virtually every iterative machine learning algorithm is the problem of hyperparameter tuning, which includes three major design parameters: 1) the complexity of the model, e.g., the number of neurons in a neural network; 2) the initial conditions, which heavily affect the behavior of the algorithm; and 3) the dissimilarity measure used to quantify its performance. We introduce an online prototype-based learning algorithm that can be viewed as a progressively growing competitive-learning neural network architecture for classification and clustering. The learning rule of the proposed approach is formulated as an online gradient-free stochastic approximation algorithm that solves a sequence of appropriately defined optimization problems, simulating an annealing process. The annealing nature of the algorithm contributes to avoiding poor local minima, offers robustness with respect to the initial conditions, and provides a means to progressively increase the complexity of the learning model, through an intuitive bifurcation phenomenon. The proposed approach is interpretable, requires minimal hyperparameter tuning, and allows online control over the performance-complexity tradeoff. Finally, we show that Bregman divergences appear naturally as a family of dissimilarity measures that play a central role in both the performance and the computational complexity of the learning algorithm.
Novel multi-step predictor-corrector schemes for backward stochastic differential equations

Novel multi-step predictor-corrector numerical schemes have been derived for approximating decoupled forward-backward stochastic differential equations (FBSDEs). The stability and high order rate of convergence of the schemes are rigorously proved. We also present a sufficient and necessary condition for the stability of the schemes. Numerical experiments are given to illustrate the stability and convergence rates of the proposed methods.
Reproducibility Report: La-MAML: Look-ahead Meta Learning for Continual Learning

The Continual Learning (CL) problem involves performing well on a sequence of tasks under limited compute. Current algorithms in the domain are either slow, offline or sensitive to hyper-parameters. La-MAML, an optimization-based meta-learning algorithm claims to be better than other replay-based, prior-based and meta-learning based approaches. According to the MER paper [1], metrics to measure performance in the continual learning arena are Retained Accuracy (RA) and Backward Transfer-Interference (BTI). La-MAML claims to perform better in these values when compared to the SOTA in the domain. This is the main claim of the paper, which we shall be verifying in this report.
Pion form factor and charge radius from lattice QCD at the physical point

Xiang Gao, 2, ∗ Nikhil Karthik, 4 Swagato Mukherjee, Peter Petreczky, Sergey Syritsyn, 6 and Yong Zhao 1 Physics Department, Brookhaven National Laboratory, Upton, NY 11973, USA Physics Department, Tsinghua University, Beijing 100084, China Department of Physics, College of William & Mary, Williamsburg, VA 23185, USA Thomas Jefferson National Accelerator Facility, Newport News, VA 23606, USA Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794, USA RIKEN-BNL Research Center, Brookhaven National Lab, Upton, NY, 11973, USA Physics Division, Argonne National Laboratory, Lemont, IL 60439, USA (Dated: February 12, 2021)
Two Component Doublet-Triplet Scalar Dark Matter stabilising the Electroweak vacuum

A two component scalar dark matter scenario comprising an additional scalar doublet and a Y = 0 scalar triplet is proposed. Key features of the ensuing dark matter phenomenology are highlighted with emphasis on inter-conversion between the two dark matter components. For suitable choices of the model parameters, we show that such inter-conversion can explain the observed relic abundance when the doublet dark matter component has mass in the desert region while the triplet component has sub-TeV mass. This ﬁnding is important in the context of such mass regions known to predict under-abundant relic density for the standalone cases of the scalar doublet and triplet. In addition, we also show that the present scenario can stabilise the electroweak vacuum up to the Planck scale in the parameter space responsible for the requisite dark matter observables.
Resonance Raman spectra of vitamin B12 and some cobalt corrinoid derivatives

Resonance Raman spectra in the frequency region 200–3200 cm–1 are reported for very dilute aqueous solutions of the following compounds: cyanocobalamin (vitamin B12), aquocobalamin (B12a), hydroxocobalamin (B12b), dicyanocobinamide, the alkaline hydrolysis products of B12(penta- and hexa-carboxylate derivatives), aquocobalamin (B12r), and {cobalamin}–cobalt(I)(B12s). The spectra were excited by lines from an argon laser, selected so as to coincide with bands in the visible absorption spectra of the compounds, and to satisfy the conditions for rigorous resonance enhancement of selected vibrational band intensities. The CoIII compounds all gave closely similar spectra, with no major changes being induced by coordination displacement even of the large 5,6-dimethylbenzimidazole base group from the metal. However, large changes in the structure of the spectra resulted from reduction of B12 to B12r and B12s; these changes appear to be related to conformational changes in the corrin ring chromophor.
PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification

Classification of time-series data is pivotal for a wide range of applications and comes with many challenges. Although the amount of publicly available datasets increases rapidly, deep neural models are only partially exploited in contrast to the traditional methods. These methods get preferred in safety-critical, financial, or medical fields because of their interpretable results. However, their performance and scalability are limited, and finding suitable explanations for time-series classification tasks is challenging due to the intrinsic nature of concealed concepts in the time-series data. Visual analysis of complete time-series comes with an extensive cognitive overload, as it is difficult to perceive and leads to confusion. Therefore, we believe that patch-wise processing of the data results in a more interpretable representation. To bridge this gap, and to reduce the cognitive overload for interpretation of time series data, we propose a novel hybrid approach that utilizes deep neural networks and traditional machine learning algorithms for an interpretable and scale-able time-series classification approach. Both quantitively and qualitatively PatchX shows superiority to its counterparts with an edge of interoperability.
Feature Selection for Multivariate Time Series via Network Pruning

In recent years, there has been an ever increasing amount of multivariate time series (MTS) data in various domains, typically generated by a large family of sensors such as wearable devices. This has led to the development of novel learning methods on MTS data, with deep learning models dominating the most recent advancements. Prior literature has primarily focused on designing new network architectures for modeling temporal dependencies within MTS. However, a less studied challenge is associated with high dimensionality of MTS data. In this paper, we propose a novel neural component, namely Neural Feature Selector (NFS), as an end-2-end solution for feature selection in MTS data. Specifically, NFS is based on decomposed convolution design and includes two modules: firstly each feature stream1 within MTS is processed by a temporal CNN independently; then an aggregating CNN combines the processed streams to produce input for other downstream networks. We evaluated the proposed NFS model on four real-world MTS datasets and found that it achieves comparable results with state-of-the-art methods while providing the benefit of feature selection. Our paper also highlights the robustness and effectiveness of feature selection with NFS compared to using recent autoencoder-based methods.
The action of the Kauffman bracket skein algebra of the torus on the Kauffman bracket skein module of the 3-twist knot complement

We determine the action of the Kauffman bracket skein algebra of the torus on the Kauffman bracket skein module of the complement of the 3-twist knot. The point is to study the relationship between knot complements and their boundary tori, an idea that has proved very fruitful in knot theory. We place this idea in the context of Chern-Simons theory, where such actions arose in connection with the computation of the noncommutative version of the A-polynomial that was defined by Frohman, Gelca and Lofaro, but they can also be interpreted as quantum mechanical systems. Our goal is to exhibit a detailed example in a part of Chern-Simons theory where examples are scarce.
Advances in conversion of hemicellulosic biomass to furfural and upgrading to biofuels

Recent approaches to furfural synthesis from hemicellulosic biomass and pentose sugars with both homogeneous and solid acidic catalysts have been summarized by addressing the associated sustainability issues. The features of deconstruction of hemicellulosic biomass by acid hydrolysis to produce pentose sugar feedstock for furfural have been discussed in brief. Several strategies including solvent extraction in a biphasic process, application of surface functionalized materials such as acidic resins, mesoporous solids and mechanistic insight in limited cases are discussed. The present status of the promising furfural platform in producing second generation biofuels (furanics and hydrocarbon) is reviewed. The performances of each catalytic system are assessed in terms of intrinsic reactivity and selectivity toward furfural production. Overall, this minireview attempts to highlight the scope of further developments for a sustainable furfural process and upgrading to fuels.
Lagrangian configurations and Hamiltonian maps

We study configurations of disjoint Lagrangian submanifolds in certain low-dimensional symplectic manifolds from the perspective of the geometry of Hamiltonian maps. We detect infinite-dimensional flats in the Hamiltonian group of the two-sphere equipped with Hofer's metric, prove constraints on Lagrangian packing, find instances of Lagrangian Poincaré recurrence, and present a new hierarchy of normal subgroups of area-preserving homeomorphisms of the two-sphere. The technology involves Lagrangian spectral invariants with Hamiltonian term in symmetric product orbifolds.
Common Information Belief based Dynamic Programs for Stochastic Zero-sum Games with Competing Teams

Decentralized team problems where players have asymmetric information about the state of the underlying stochastic system have been actively studied, but games between such teams are less understood. We consider a general model of zero-sum stochastic games between two competing teams. This model subsumes many previously considered team and zero-sum game models. For this general model, we provide bounds on the upper (min-max) and lower (max-min) values of the game. Furthermore, if the upper and lower values of the game are identical (i.e., if the game has a value), our bounds coincide with the value of the game. Our bounds are obtained using two dynamic programs based on a sufficient statistic known as the common information belief (CIB). We also identify certain information structures in which only the minimizing team controls the evolution of the CIB. In these cases, we show that one of our CIB based dynamic programs can be used to find the min-max strategy (in addition to the min-max value). We propose an approximate dynamic programming approach for computing the values (and the strategy when applicable) and illustrate our results with the help of an example.
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries

Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models. In this paper, we show how complex inference scenarios for these models that commonly arise in machine learning -- from computing the expectations of decision tree ensembles to information-theoretic divergences of deep mixture models -- can be represented in terms of tractable modular operations over circuits. Specifically, we characterize the tractability of a vocabulary of simple transformations -- sums, products, quotients, powers, logarithms, and exponentials -- in terms of sufficient structural constraints of the circuits they operate on, and present novel hardness results for the cases in which these properties are not satisfied. Building on these operations, we derive a unified framework for reasoning about tractable models that generalizes several results in the literature and opens up novel tractable inference scenarios.
Hemorrhagic Shock Induces a Rapid Transcriptomic Shift of the Immune Balance in Leukocytes after Experimental Multiple Injury

The immune response following trauma represents a major driving force of organ dysfunction and poor outcome. Therefore, we investigated the influence of an additional hemorrhagic shock (HS) on the early posttraumatic immune dysbalance in the whole population of blood leukocytes. A well-established murine polytrauma (PT) model with or without an additional pressure-controlled HS (mean arterial pressure of 30 mmHg (±5 mmHg) for 60 mins, afterwards fluid resuscitation with balanced electrolyte solution four times the volume of blood drawn) was used. C57BL/6 mice were randomized into a control, PT, and PT + HS group with three animals in each group. Four hours after trauma, corresponding to three hours after induction of hemorrhage, RNA was isolated from all peripheral blood leukocytes, and a microarray analysis was performed. Enrichment analysis was conducted on selected genes strongly modulated by the HS. After additional HS in PT mice, the gene expression of pathways related to the innate immunity, such as IL-6 production, neutrophil chemotaxis, cell adhesion, and toll-like receptor signaling was upregulated, whereas pathways of the adaptive immune system, such as B- and T-cell activation as well as the MHC class II protein complex, were downregulated. These results demonstrate that an additional HS plays an important role in the immune dysregulation early after PT by shifting the balance to increased innate and reduced adaptive immune responses.
Association between Optical Coherence Tomography Measurements and Clinical Parameters in Idiopathic Intracranial Hypertension

Purpose To correlate optical coherence tomography (OCT) measurements with clinical parameters in idiopathic intracranial hypertension (IIH). Methods A cross-sectional study was conducted with 22 patients with IIH and 11 controls. All participants underwent comprehensive ophthalmological examination followed by spectral-domain OCT (SD-OCT) and standard automated perimetry using the 30–2 program of the Humphrey visual field analyzer. Correlations between ganglion cell complex (GCC) thickness and retinal nerve fiber layer (RNFL) thickness, as measured by SD-OCT, and clinical parameters were assessed using generalized estimating equations. Result The mean age of the participants was 35.0 ± 10.83 years. The groups were similar regarding age, but were significantly different regarding sex and visual acuity (p=0.001 and p=0.038, respectively). The GCC was significantly thinner in the IIH group, with a mean of 90.535 ± 9.766 μm compared to 98.119 ± 6.988 μm for the controls (p=0.023). There was a significant association between GCC thickness and optic disc pallor (p=0.016) and between edema and visual acuity (p=0.037). No significant difference was found in RNFL thickness between patients and controls. Conclusion The GCC was thinner in the patients with IIH compared to the controls, and there was an association between GCC and optic disc pallor. This might suggest a role for OCT parameters when the structural changes that occur in IIH are investigated, possibly guiding clinical decision making.
Infected Non-Union of the Distal Femur

The reported levels of non-union in the lateral locking plate differ widely, with some early studies showing rates of less than 6% and up to 17%-21% in more recent studies. We report a case where better results were shown by a non-union treated with distal femoral nailing with allogenic grafting.
Protective Effects of Astragalus Polysaccharide on Sepsis-Induced Acute Kidney Injury

Objective To explore the protective roles of Astragalus polysaccharide (APS) on acute renal injury (AKI) induced by sepsis. Methods Firstly, an animal model of sepsis-induced AKI was established by injecting lipopolysaccharide (LPS) into mice. The mice were pretreated with an intraperitoneal injection of 1, 3, and 5 mg/(kg·d) APS for 3 consecutive days. The severity of kidney injury was then scored by histopathological analysis, and the concentrations of serum urea nitrogen (BUN) and serum creatinine (SCr) and the levels of tumor necrosis factor α (TNF-α) and interleukin-1β (IL-1β) were determined as well. In in vitro experiments, lipopolysaccharide (LPS) was used to induce HK-2 cell injury to establish a sepsis-induced AKI cell model, and the cell counting kit-8 (CCK-8) method was performed to determine the cytotoxicity and appropriate experimental concentration of APS. Then, cells were divided into the control, LPS, and APS+LPS groups. Cell apoptosis and inflammation-related TNF-α, IL-1β, IL-6, and IL-8 were determined by flow cytometry and enzyme-linked immunosorbent assay (ELISA), respectively. The microscope was used to observe the morphological changes of cells, and the cell migration ability was measured by wound healing assay. RT-qPCR and Western blot assay were used to determine the mRNA and protein levels of apoptosis-related factors including caspase-3, caspase-9, Bax, and Bcl-2; endoplasmic reticulum stress- (ERS-) related biomarkers including C/EBP homologous protein (CHOP) and glucose-regulated protein78 (GRP78); and epithelial-mesenchymal transition- (EMT-) related biomarkers including E-cadherin, Snail, α-smooth muscle actin (α-SMΑ), and Vimentin. Results In vivo experiments in mice showed that APS can reverse LPS-induced kidney damage in a concentration-dependent manner (P < 0.05); the concentrations of BUN and Scr were increased (all P < 0.05); similarly, the levels of TNF-α and IL-1β were increased as well (all P < 0.05). In in vitro experiments, the results showed that LPS can significantly cause HK-2 cell damage and induce apoptosis, inflammation, ERS, and EMT. When APS concentration was in the range of 0-200 μg/mL, it had no cytotoxicity in HK-2 cells, and 100 μg/mL APS pretreatment could significantly mitigate the decrease of cell activity induced by LPS (P < 0.05). Compared with the LPS group, APS pretreatment could inhibit the expression of inflammatory factors including TNF-α, IL-1 β, IL-6, and IL-8 (all P < 0.05), reducing the number of apoptotic cells (P < 0.05), suppressing the expression of caspase-3, caspase-9, and Bax, but upregulating the expression levels of Bcl-2. In ERS, APS pretreatment inhibited LPS-induced upregulation of CHOP and GRP78. Moreover, in EMT, APS pretreatment could inhibit the morphological changes of cells, downregulate the migration, decrease the expression of EMT biomarkers, and inhibit the process of EMT. Conclusion APS could alleviate sepsis-induced AKI by regulating inflammation, apoptosis, ERS, and EMT.
Extended Mie-Grüneisen molecular model for time dependent dielectric breakdown in silica detailing the critical roles of O–SiO3 tetragonal bonding, stretched bonds, hole capture, and hydrogen release

An extended Mie-Gruneisen molecular model is presented, which describes a bond-breakage process for O–SiO3 tetragonal molecules in silica and the trap-generation process that occurs during time dependent dielectric breakdown (TDDB) testing. This quantitative molecular model correctly describes important physics routinely reported for silica TDDB testing: the generation of E′ centers, a breakdown strength of Ebd∼15MV∕cm, an effective dipole-moment range of peff=7–13eA, and a zero-field activation energy range for bond breakage of ΔHo*=1−2eV. The bond-breakage/trap-generation mechanism is shown to occur when the Si ion transitions from its primary energy minimum (with fourfold coordination) to a secondary saddle point (with threefold coordination). The molecular model also shows clearly that current induced hole capture and hydrogen release can play critically important roles in the TDDB process.