Miracle-dz/UniFAR-FTUs huggingface dataset:
Crystalline Weyl semimetal phase in Quantum Spin Hall systems under magnetic fields

We investigate an unconventional topological phase transition that occurs in quantum spin Hall (QSH) systems when applying an external in-plane magnetic field. We show that this transition between QSH and trivial insulator phases is separated by a stable topological gapless phase, which is protected by the combination of particle-hole and reflection symmetries, and thus, we dub it as crystalline Weyl semimetal. We explore the stability of this new phase when particle-hole symmetry breaking terms are present. Especially, we predict a robust unconventional topological phase transition to be visible for materials described by Kane and Mele model even if particle-hole symmetry is significantly broken.
Variational Bayes: A report on approaches and applications

Deep neural networks have achieved impressive results on a wide variety of tasks. However, quantifying uncertainty in the network's output is a challenging task. Bayesian models offer a mathematical framework to reason about model uncertainty. Variational methods have been used for approximating intractable integrals that arise in Bayesian inference for neural networks. In this report, we review the major variational inference concepts pertinent to Bayesian neural networks and compare various approximation methods used in literature. We also talk about the applications of variational bayes in Reinforcement learning and continual learning.
Empirical determination of the simulation capacity of a near-term quantum computer

Experimentally realizable quantum computers are rapidly approaching the threshold of quantum supremacy. Quantum Hamiltonian simulation promises to be one of the first practical applications for which such a device could demonstrate an advantage over all classical systems. However, these early devices will inevitably remain both noisy and small, precluding the use of quantum error correction. We use high-performance classical tools to construct, optimize, and simulate quantum circuits subject to realistic error models in order to empirically determine the "simulation capacity" of near-term simulation experiments implemented via quantum signal processing (QSP), describing the relationship between simulation time, system size, and resolution of QSP circuits which are optimally configured to balance algorithmic precision and external noise. From simulation capacity models, we estimate maximum tolerable error rate for meaningful simulation experiments on a near-term quantum computer. By exploiting symmetry inherent to the QSP circuit, we further demonstrate that its capacity for quantum simulation can be increased by at least two orders of magnitude if errors are systematic and unitary. We find that a device with $\epsilon^2=10^{-5}$ systematic amplitude errors could meaningfully simulate systems up to $n\approx16$ with an expected failure rate below $10\%$, whereas the largest system a device with a stochastic error rate of $p_\epsilon=10^{-5}$ could meaningfully simulate with the same rate of failure is between $n=3$ and $n=5$ (depending on the stochastic channel). Extrapolating from empirical results, we estimate that one would typically need a stochastic error rate below $p_\epsilon=10^{-8}$ to perform a meaningful $n=50$ simulation experiment with a failure rate below $10\%$, while the same experiment could tolerate systematic unitary errors with strength $\epsilon^2\approx10^{-6}$.
Dispersion and reaction in random flows: single realisation vs ensemble average

We examine the dispersion of a passive scalar released in an incompressible fluid flow in an unbounded domain. The flow is assumed to be spatially periodic, with zero spatial average, and random in time, in the manner of the random-phase alternating sine flow which we use as an exemplar. In the long-time limit, the scalar concentration takes the same, predictable form for almost all realisations of the flow, with a Gaussian core characterised by an effective diffusivity, and large-deviation tails characterised by a rate function (which can be evaluated by computing the largest Lyapunov exponent of a family of random-in-time partial differential equations). We contrast this single-realisation description with that which applies to the average of the concentration over an ensemble of flow realisations. We show that the single-realisation and ensemble-average effective diffusivities are identical but that the corresponding rate functions are not, and that the ensemble-averaged description overestimates the concentration in the tails compared with that obtained for single-flow realisations. This difference has a marked impact for scalars reacting according to the Fisher--Kolmogorov--Petrovskii--Piskunov (FKPP) model. Such scalars form an expanding front whose shape is approximately independent of the flow realisation and can be deduced from the single-realisation large-deviation rate function. We test our predictions against numerical simulations of the alternating sine flow.
Linear Convergence of Variable Bregman Stochastic Coordinate Descent Method for Nonsmooth Nonconvex Optimization by Level-set Variational Analysis

Large-scale nonconvex and nonsmooth problems have attracted considerable attention in the fields of compress sensing, big data optimization and machine learning. Exploring effective methods is still the main challenge of today's research. Stochastic coordinate descent type methods have been widely used to solve large-scale optimization problems. In this paper, we derive the convergence of variable Bregman stochastic coordinate descent (VBSCD) method for a broad class of nonsmooth and nonconvex optimization problems, i.e., any accumulation of the sequence generated by VBSCD is almost surely a critical point. Moreover, we develop a new variational approach on level sets that aim towards the convergence rate analysis. If the level-set subdifferential error bound holds, we derive a linear rate of convergence for the expected values of the objective function and expected values of random variables generated by VBSCD.
The Revised TESS Input Catalog and Candidate Target List

We describe the catalogs assembled and the algorithms used to populate the revised TESS Input Catalog (TIC), based on the incorporation of the Gaia second data release. We also describe a revised ranking system for prioritizing stars for 2 minute cadence observations, and we assemble a revised Candidate Target List (CTL) using that ranking. The TIC is available on the Mikulski Archive for Space Telescopes server, and an enhanced CTL is available through the Filtergraph data visualization portal system at http://filtergraph.vanderbilt. edu/tess_ctl.
Performance evaluation of multiple-target tracking systems

To evaluate the performance of real multiple-target tracking systems, instead of their simulative systems, the signal origin of each track should be determined in the different form because the data association in real systems is absolutely unknown. In addition, the calculation of performance indices should be suitable for the case of variable sampling interval. Here the state estimate of each track is projected into measurement space and the statistical distance between this projection and each simultaneous measurement is calculated. Hence the signal origin of that track can be determined via the rule of Nearest Neighbor. Based on the relationship matrix with respect to track and signal origin, a new method to calculate a suit of performance indices for both TAS and TWS modes is proposed. This method has been adopted in our TrackEnviro — a evaluation system for the multiple-target tracking performance of real phased array radar.
Synthesis and compression study of orthorhombic Fe7(C, Si)3: a possible constituent of the Earth’s core

ABSTRACT The orthorhombic phase of Si-doped Fe carbide is synthesized at high-pressures and temperatures using laser-heated diamond anvil cell (LHDAC), followed by its characterization using X-ray diffraction (XRD), Transmission Electron Microscopy (TEM) and Raman spectroscopy. Room-temperature high-pressure XRD measurements are carried out up to about 104 GPa for the determination of the equation of state parameters. No evidence of structural transition is observed. Pressure evolution of isothermal bulk modulus shows elastic stiffening around 28 GPa followed by softening around 78 GPa, which are possibly related to magnetic transitions driven by pressure-induced anisotropic strain in the unit cell. Extrapolation of the density profile of our study to the inner core conditions agrees very well with PREM data with an uncertainty of about 3–4%. Our estimated bulk modulus value at core pressures seems to be 8-9% less than that of PREM data and is best matched in comparison to other reported values.
Natural Compression for Distributed Deep Learning

Modern deep learning models are often trained in parallel over a collection of distributed machines to reduce training time. In such settings, communication of model updates among machines becomes a significant performance bottleneck and various lossy update compression techniques have been proposed to alleviate this problem. In this work, we introduce a new, simple yet theoretically and practically effective compression technique: {\em natural compression (NC)}. Our technique is applied individually to all entries of the to-be-compressed update vector and works by randomized rounding to the nearest (negative or positive) power of two, which can be computed in a "natural" way by ignoring the mantissa. We show that compared to no compression, NC increases the second moment of the compressed vector by not more than the tiny factor $\nicefrac{9}{8}$, which means that the effect of NC on the convergence speed of popular training algorithms, such as distributed SGD, is negligible. However, the communications savings enabled by NC are substantial, leading to {\em $3$-$4\times$ improvement in overall theoretical running time}. For applications requiring more aggressive compression, we generalize NC to {\em natural dithering}, which we prove is {\em exponentially better} than the common random dithering technique. Our compression operators can be used on their own or in combination with existing operators for a more aggressive combined effect, and offer new state-of-the-art both in theory and practice.
Population Estimates for Electromagnetically Distinguishable Supermassive Binary Black Holes

Distinguishing the photon output of an accreting supermassive black hole binary system from that of a single supermassive black hole accreting at the same rate is intrinsically difficult because the majority of the light emerges from near the innermost stable orbits of the black holes. However, there are two possible signals that can distinctively mark binaries, both arising from the gap formed in circumbinary accretion flows inside approximately twice the binary separation. One of these is a “notch” cut into the thermal spectra of these systems in the IR/optical/UV, the other a periodically varying excess hard X-ray luminosity whose period is of order the binary orbital period. Using data from detailed galaxy evolution simulations, we estimate the distribution function in mass, mass ratio, and accretion rate for accreting supermassive binary black holes (SMBBHs) as a function of redshift and then transform this distribution function into predicted source counts for these two potential signals. At flux levels ≳10−13 erg cm−2 s−1, there may be ∼O(102) such systems in the sky, mostly in the redshift range 0.5 ≲ z ≲ 1. Roughly 10% should have periods short enough (≲5 yr) to detect the X-ray modulation; this is also the period range accessible to Pulsar Timing Array observations.
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-Based Real-World Noise Modeling

Recent research on image denoising has progressed with the development of deep learning architectures, especially convolutional neural networks. However, real-world image denoising is still very challenging because it is not possible to obtain ideal pairs of ground-truth images and real-world noisy images. Owing to the recent release of benchmark datasets, the interest of the image denoising community is now moving toward the real-world denoising problem. In this paper, we propose a grouped residual dense network (GRDN), which is an extended and generalized architecture of the state-of-the-art residual dense network (RDN). The core part of RDN is defined as grouped residual dense block (GRDB) and used as a building module of GRDN. We experimentally show that the image denoising performance can be significantly improved by cascading GRDBs. In addition to the network architecture design, we also develop a new generative adversarial network-based real-world noise modeling method. We demonstrate the superiority of the proposed methods by achieving the highest score in terms of both the peak signal-to-noise ratio and the structural similarity in the NTIRE2019 Real Image Denoising Challenge - Track 2:sRGB.
Towards reliable and fair probabilistic predictions: field-aware calibration with neural networks

In machine learning, it is observed that probabilistic predictions sometimes disagree with averaged actual outcomes on certain subsets of data. This is also known as miscalibration that is responsible for unreliability and unfairness of practical machine learning systems. In this paper, we put forward an evaluation metric for calibration, coined field-level calibration error, that measures bias in predictions over the input fields that the decision maker concerns. We show that existing calibration methods perform poorly under our new metric. Specifically, after learning a calibration mapping over the validation dataset, existing methods have limited improvements in our error metric and completely fail to improve other non-calibration metrics such as the AUC score. We propose Neural Calibration, a new calibration method, which learns to calibrate by making full use of all input information over the validation set. We test our method on five large-scale real-world datasets. The results show that Neural Calibration significantly improves against uncalibrated predictions in all well-known metrics such as the negative log-likelihood, the Brier score, the AUC score, as well as our proposed field-level calibration error.
Circulating serum lectins of patients with IgA nephropathy stimulate IL-6 release from mesangial cells.

Previously, the authors reported that the serum of patients with immunoglobulin (Ig) A nephropathy stimulated peripheral leukocytes, and this effect was inhibited by nominal haptens for lectins. In vitro studies have shown that lectins can bind to rat mesangial cells and cause their activation. This study was performed to investigate whether the serum of IgA nephropathy patients contains lectins that activate mesangial cells, i.e., induce release of interleukin (IL)-6, a nephritogenic cytokine. The serum of patients was adsorbed by affinity chromatography on resins loaded with lectin-binding sugars. After adsorption, serum supernatant was collected and the resins were then eluted. Human mesangial cells were conditioned with native serum, post-adsorption supernatant, and eluate (all three at 10%) for 24 h, and the release of IL-6 was determined by ELISA. Normal serum was used as control. Incubation of mesangial cells with IgA nephropathy patients serum raised average IL-6 release from 8.5 pg/mL to 274.1 pg/mL. Adsorption in beta-D-glucose and N-acetyl-D-glucosamine caused a fall in the activity of patients' serum, to 17.0 and 63.7 pg/mL, respectively, and the activity lost was recovered in the eluate (185.2 and 142.7 pg/mL, respectively). Neither adsorption on N-acetyl-D-galactosamine nor on fucosylamine was associated with any effect on serum activity; accordingly, no activity was found in the eluates. Serum of patients with non-IgA mesangiocapillary nephritis did not stimulate mesangial cells. These results show that the serum of IgA nephropathy patients contains specific lectins that stimulate IL-6 nephropathy by mesangial cells and are, therefore, potential nephritogenic.
Parallel and Communication Avoiding Least Angle Regression

We are interested in parallelizing the Least Angle Regression (LARS) algorithm for fitting linear regression models to high-dimensional data. We consider two parallel and communication avoiding versions of the basic LARS algorithm. The two algorithms apply to data that have different layout patterns (one is appropriate for row-partitioned data, and the other is appropriate for column-partitioned data), and they have different asymptotic costs and practical performance. The first is bLARS, a block version of LARS algorithm where we update b columns at each iteration. Assuming that the data are row-partitioned, bLARS reduces the number of arithmetic operations, latency, and bandwidth by a factor of b. The second is Tournament-bLARS (T-bLARS), a tournament version of LARS, in which case processors compete, by running several LARS computations in parallel, to choose b new columns to be added into the solution. Assuming that the data are column-partitioned, T-bLARS reduces latency by a factor of b. Similarly to LARS, our proposed methods generate a sequence of linear models. We present extensive numerical experiments that illustrate speed-ups up to 25x compared to LARS.
Rapid, Facile Detection of Heterodimer Partners for Target Human G-Protein-Coupled Receptors Using a Modified Split-Ubiquitin Membrane Yeast Two-Hybrid System

Potentially immeasurable heterodimer combinations of human G-protein-coupled receptors (GPCRs) result in a great deal of physiological diversity and provide a new opportunity for drug discovery. However, due to the existence of numerous combinations, the sets of GPCR dimers are almost entirely unknown and thus their dominant roles are still poorly understood. Thus, the identification of GPCR dimer pairs has been a major challenge. Here, we established a specialized method to screen potential heterodimer partners of human GPCRs based on the split-ubiquitin membrane yeast two-hybrid system. We demonstrate that the mitogen-activated protein kinase (MAPK) signal-independent method can detect ligand-induced conformational changes and rapidly identify heterodimer partners for target GPCRs. Our data present the abilities to apply for the intermolecular mapping of interactions among GPCRs and to uncover potential GPCR targets for the development of new therapeutic agents.
Traffic adaptive relaying topology control

The optimization of the relaying topology in multihop cellular network should provide the answer to the question who is transmitting to whom, and when, in such a way to insure the best system performance. In the case of temporally and spatially varying traffic distribution the optimal topology will also vary in time and an efficient way for topology control is needed in order to maximize the system performance. In this paper we present an algorithm for efficient relaying topology control, which is aware of the intercell interference, requiring coordinated action between the cells and resulting in multicell jointly optimal relaying topology. Numerical results demonstrate that an adaptive relaying topology control provides the network utility improvements and presents the framework for quantifying these improvements for spatially and temporally varying traffic.
Improved Distributed Approximations for Minimum-Weight Two-Edge-Connected Spanning Subgraph

The minimum-weight 2-edge-connected spanning subgraph (2-ECSS) problem is a natural generalization of the well-studied minimum-weight spanning tree (MST) problem, and it has received considerable attention in the area of network design. The latter problem asks for a minimum-weight subgraph with an edge connectivity of 1 between each pair of vertices while the former strengthens this edge-connectivity requirement to 2. Despite this resemblance, the 2-ECSS problem is considerably more complex than MST. While MST admits a linear-time centralized exact algorithm, 2-ECSS is NP-hard and the best known centralized approximation algorithm for it (that runs in polynomial time) gives a 2-approximation. In this paper, we give a deterministic distributed algorithm with round complexity of Õ (D + √n) that computes a (9 + ε)-approximation of 2-ECSS, for any constant ε > 0. Up to logarithmic factors, this complexity matches the Ø (D + √ n) lower bound that can be derived from the technique of Das Sarma et al. [STOC'11], as shown by Censor-Hillel and Dory [OPODIS'17]. Our result is the first distributed constant approximation for 2-ECSS in the nearly optimal time and it improves on a recent randomized algorithm of Dory [PODC'18], which achieved an O(łog n)-approximation in Õ (D+√ ) rounds. We also present an alternative algorithm for O(log n)-approximation, whose round complexity is linear in the low-congestion shortcut parameter of the network---following a framework introduced by Ghaffari and Haeupler [SODA'16]. This algorithm has round complexity Ö (D+√n) in worst-case networks but it provably runs much faster in many well-behaved graph families of interest. For instance, it runs in Õ (D) time in planar networks and those with bounded genus, bounded path-width or bounded tree-width.
Hashing based Answer Selection

Answer selection is an important subtask of question answering (QA), in which deep models usually achieve better performance than non-deep models. Most deep models adopt question-answer interaction mechanisms, such as attention, to get vector representations for answers. When these interaction based deep models are deployed for online prediction, the representations of all answers need to be recalculated for each question. This procedure is time-consuming for deep models with complex encoders like BERT which usually have better accuracy than simple encoders. One possible solution is to store the matrix representation (encoder output) of each answer in memory to avoid recalculation. But this will bring large memory cost. In this paper, we propose a novel method, called hashing based answer selection (HAS), to tackle this problem. HAS adopts a hashing strategy to learn a binary matrix representation for each answer, which can dramatically reduce the memory cost for storing the matrix representations of answers. Hence, HAS can adopt complex encoders like BERT in the model, but the online prediction of HAS is still fast with a low memory cost. Experimental results on three popular answer selection datasets show that HAS can outperform existing models to achieve state-of-the-art performance.
Gated Group Self-Attention for Answer Selection

Answer selection (answer ranking) is one of the key steps in many kinds of question answering (QA) applications, where deep models have achieved state-of-the-art performance. Among these deep models, recurrent neural network (RNN) based models are most popular, typically with better performance than convolutional neural network (CNN) based models. Nevertheless, it is difficult for RNN based models to capture the information about long-range dependency among words in the sentences of questions and answers. In this paper, we propose a new deep model, called gated group self-attention (GGSA), for answer selection. GGSA is inspired by global self-attention which is originally proposed for machine translation and has not been explored in answer selection. GGSA tackles the problem of global self-attention that local and global information cannot be well distinguished. Furthermore, an interaction mechanism between questions and answers is also proposed to enhance GGSA by a residual structure. Experimental results on two popular QA datasets show that GGSA can outperform existing answer selection models to achieve state-of-the-art performance. Furthermore, GGSA can also achieve higher accuracy than global self-attention for the answer selection task, with a lower computation cost.