Miracle-dz/UniFAR-FTUs huggingface dataset:
PSDNet and DPDNet: Efficient channel expansion, Depthwise-Pointwise-Depthwise Inverted Bottleneck Block

In many real-time applications, the deployment of deep neural networks is constrained by high computational cost and efficient lightweight neural networks are widely concerned. In this paper, we propose that depthwise convolution (DWC) is used to expand the number of channels in a bottleneck block, which is more efficient than 1 x 1 convolution. The proposed Pointwise-Standard-Depthwise network (PSDNet) based on channel expansion with DWC has fewer number of parameters, less computational cost and higher accuracy than corresponding ResNet on CIFAR datasets. To design more efficient lightweight concolutional neural netwok, Depthwise-Pointwise-Depthwise inverted bottleneck block (DPD block) is proposed and DPDNet is designed by stacking DPD block. Meanwhile, the number of parameters of DPDNet is only about 60% of that of MobileNetV2 for networks with the same number of layers, but can achieve approximated accuracy. Additionally, two hyperparameters of DPDNet can make the trade-off between accuracy and computational cost, which makes DPDNet suitable for diverse tasks. Furthermore, we find the networks with more DWC layers outperform the networks with more 1x1 convolution layers, which indicates that extracting spatial information is more important than combining channel information.
Translating Visual Art Into Music

The Synesthetic Variational Autoencoder (SynVAE) introduced in this research is able to learn a consistent mapping between visual and auditive sensory modalities in the absence of paired datasets. A quantitative evaluation on MNIST as well as the Behance Artistic Media dataset (BAM) shows that SynVAE is capable of retaining sufficient information content during the translation while maintaining cross-modal latent space consistency. In a qualitative evaluation trial, human evaluators were furthermore able to match musical samples with the images which generated them with accuracies of up to 73%.
Faster Algorithms for Edge Connectivity via Random 2-Out Contractions

We provide a simple new randomized contraction approach to the global minimum cut problem for simple undirected graphs. The contractions exploit 2-out edge sampling from each vertex rather than the standard uniform edge sampling. We demonstrate the power of our new approach by obtaining better algorithms for sequential, distributed, and parallel models of computation. Our end results include the following randomized algorithms for computing edge connectivity with high probability: -- Two sequential algorithms with complexities $O(m \log n)$ and $O(m+n \log^3 n)$. These improve on a long line of developments including a celebrated $O(m \log^3 n)$ algorithm of Karger [STOC'96] and the state of the art $O(m \log^2 n (\log\log n)^2)$ algorithm of Henzinger et al. [SODA'17]. Moreover, our $O(m+n \log^3 n)$ algorithm is optimal whenever $m = \Omega(n \log^3 n)$. Within our new time bounds, whp, we can also construct the cactus representation of all minimal cuts. -- An $O(n^{0.8} D^{0.2} + n^{0.9})$ round distributed algorithm, where D denotes the graph diameter. This improves substantially on a recent breakthrough of Daga et al. [STOC'19], which achieved a round complexity of $O(n^{1-1/353}D^{1/353} + n^{1-1/706})$, hence providing the first sublinear distributed algorithm for exactly computing the edge connectivity. -- The first $O(1)$ round algorithm for the massively parallel computation setting with linear memory per machine.
Question-type Driven Question Generation

Question generation is a challenging task which aims to ask a question based on an answer and relevant context. The existing works suffer from the mismatching between question type and answer, i.e. generating a question with type how while the answer is a personal name. We propose to automatically predict the question type based on the input answer and context. Then, the question type is fused into a seq2seq model to guide the question generation, so as to deal with the mismatching problem. We achieve significant improvement on the accuracy of question type prediction and finally obtain state-of-the-art results for question generation on both SQuAD and MARCO datasets.
The Galactic Halo Contribution to the Dispersion Measure of Extragalactic Fast Radio Bursts

A new model of the Milky Way (MW) halo component of the dispersion measure (DM) for extragalactic sources, such as fast radio bursts, is presented in light of recent diffuse X-ray observations. In addition to the spherical component of isothermal gas (kT ∼ 0.3 keV) in hydrostatic equilibrium with the Galactic gravitational potential, our model includes a disk-like nonspherical hot gas component to reproduce the directional dependence of the observed X-ray emission measure (EM). The total gas mass (1.2 × 1011 M⊙) is dominated by the spherical component, and is consistent with the total baryon mass of the MW expected from the dark matter mass and the cosmic baryon-to-dark-matter ratio. Our model predicts a mean halo DM of , with a full range of 30– over the whole sky. The large scatter seen in the X-ray EM data implies a ∼0.2 dex (rms) fluctuation of the MW halo DM. We provide an analytic formula to estimate the MW halo DM of our model along any line of sight, which can be easily used to compute the total MW component of DM toward extragalactic sources, in combination with existing DM models of the warm ionized medium associated with the Galactic disk.
A first look at blockchain‐based decentralized applications

With the increasing popularity of blockchain technologies in recent years, blockchain‐based decentralized applications (DApps for short in this paper) have been rapidly developed and widely adopted in many areas, being a hot topic in both academia and industry. Despite of the importance of DApps, we still have quite little understanding of DApps along with its ecosystem. To bridge the knowledge gap, this paper presents the first comprehensive empirical study of blockchain‐based DApps to date, based on an extensive dataset of 995 Ethereum DApps and 29,846,075 transaction logs over them. We make a descriptive analysis of the popularity of DApps, summarize the patterns of how DApps use smart contracts to access the underlying blockchain, and explore the worth‐addressing issues of deploying and operating DApps. Based on the findings, we propose some implications for DApp users to select proper DApps, for DApp developers to improve the efficiency of DApps, and for blockchain vendors to enhance the support of DApps.
Gridless variational Bayesian inference of line spectral from quantized samples

Efficient estimation of line spectral from quantized samples is of significant importance in information theory and signal processing, e.g., channel estimation in energy efficient massive MIMO systems and direction of arrival estimation. The goal of this paper is to recover the line spectral as well as its corresponding parameters including the model order, frequencies and amplitudes from heavily quantized samples. To this end, we propose an efficient grid-less Bayesian algorithm named VALSE-EP, which is a combination of the high resolution and low complexity gridless variational line spectral estimation (VALSE) and expectation propagation (EP). The basic idea of VALSE-EP is to iteratively approximate the challenging quantized model of line spectral estimation as a sequence of simple pseudo unquantized models, where VALSE is applied. Moreover, to obtain a benchmark of the performance of the proposed algorithm, the Cramer Rao bound (CRB) is derived. Finally, numerical experiments on both synthetic and real data are performed, demonstrating the near CRB performance of the proposed VALSE-EP for line spectral estimation from quantized samples.
Homotopic curve shortening and the affine curve-shortening flow

We define and study a discrete process that generalizes the convex-layer decomposition of a planar point set. Our process, which we call "homotopic curve shortening" (HCS), starts with a closed curve (which might self-intersect) in the presence of a set $P\subset \mathbb R^2$ of point obstacles, and evolves in discrete steps, where each step consists of (1) taking shortcuts around the obstacles, and (2) reducing the curve to its shortest homotopic equivalent. We find experimentally that, if the initial curve is held fixed and $P$ is chosen to be either a very fine regular grid or a uniformly random point set, then HCS behaves at the limit like the affine curve-shortening flow (ACSF). This connection between HCS and ACSF generalizes the link between "grid peeling" and the ACSF observed by Eppstein et al. (2017), which applied only to convex curves, and which was studied only for regular grids. We prove that HCS satisfies some properties analogous to those of ACSF: HSC is invariant under affine transformations, preserves convexity, and does not increase the total absolute curvature. Furthermore, the number of self-intersections of a curve, or intersections between two curves (appropriately defined), does not increase. Finally, if the initial curve is simple, then the number of inflection points (appropriately defined) does not increase.
Incrementally Updated Spectral Embeddings

Several fundamental tasks in data science rely on computing an extremal eigenspace of size $r \ll n$, where $n$ is the underlying problem dimension. For example, spectral clustering and PCA both require the computation of the leading $r$-dimensional subspace. Often, this process is repeated over time due to the possible temporal nature of the data; e.g., graphs representing relations in a social network may change over time, and feature vectors may be added, removed or updated in a dataset. Therefore, it is important to efficiently carry out the computations involved to keep up with frequent changes in the underlying data and also to dynamically determine a reasonable size for the subspace of interest. We present a complete computational pipeline for efficiently updating spectral embeddings in a variety of contexts. Our basic approach is to "seed" iterative methods for eigenproblems with the most recent subspace estimate to significantly reduce the computations involved, in contrast with a na\"ive approach which recomputes the subspace of interest from scratch at every step. In this setting, we provide various bounds on the number of iterations common eigensolvers need to perform in order to update the extremal eigenspace to a sufficient tolerance. We also incorporate a criterion for determining the size of the subspace based on successive eigenvalue ratios. We demonstrate the merits of our approach on the tasks of spectral clustering of temporally evolving graphs and PCA of an incrementally updated data matrix.
Brachial artery flow mediated vasodilatation changes in endothelium due to hyperuricemia in type 2 diabetes mellitus.

Aim: This study aims to evaluate serum uric acid levels and assess its effect on endothelial dysfunction by measuring flow mediated vasodilatation (FMD) of brachial artery in type 2 diabetes mellitus. Study Design: Observational and prospective Place and Duration of Study: The study was carried out in Department of Biochemistry and Department of Radiology, MGM Medical College, Navi-Mumbai from August 2010 to 2012 Methodology: Total 90 patients were selected and divided in to three groups. Group I (n=30) – Controls, Group II A & B (n=60) –Diabetic patients without & with hyperuricemia (HUA). All subjects were examined by high resolution ultrasound to measure FMD. Serum uric acid and nitric oxide (NO) levels along with other biochemical parameters were estimated. Result: Group II B showed significantly increased serum uric acid levels (p<0.001) along with decreased levels of serum NO (p< 0.0001) and decreased vasodilatation when measured by FMD. (p<0.001). Conclusion: The FMD of
Towards a simple, comprehensive model of regular earthquakes and slow slip events, part I: one-dimensional model

We have developed a model that describes the major characteristics of a rupture, ranging from regular earthquakes (EQs) to slow slip events (SSEs), including episodic tremor and slip (ETS). Previous model predictions, while accurate, are based on a highly idealized initial stress distribution and a simple velocity-dependent expression for friction. The full scope of the model has, therefore, not been fully demonstrated. Further developments, presented here, include more physically realistic treatments of both the initial conditions and friction. Model predictions are: (1) The type of a seismic event, i.e. regular EQ or SSE, is determined by the fault strength, the shear to normal stress ratio, and the gradient in the ratio. Quantitative values for these crucial parameters are also obtained here; (2) Rupture velocities for regular EQs range from a fraction of the shear wave velocity up to the supershear velocity. The maximum slip velocity for regular EQs is typically on the order of 1 m/s. For SSEs, the slip velocity ranges widely from a few cm/year up to 0.1 m/s. The magnitude of the stress drop may vary in the range from 1% to 10% of the initial shear stress for regular EQs and from 0.1% to 10% for the SSEs; (3) The rupture can expend as a crack-like mode or as a self-healing pulse mode. The type of rupture mode is determined by the stress ratio and its gradient. If stress heterogeneity has a step-like shape, the rupture is always crack-like. If the heterogeneity is localized, the rupture is crack-like for sufficiently large values of the stress and its gradient and is pulse-like for smaller values; (4) After an instability develops, rupture dynamics do not depend on the relative values of the rate-state a and b parameters, i.e., it does not matter if frictional sliding is velocity-weakening or velocity-strengthening. Models based on the rate-state are not consistent with this result.
Stochastic control of Tolman-Oppenheimer collapse of zero-pressure fluid stars: rigorous criteria for density boundedness and singularity smoothing

The Tolman-Oppenheimer description gives exact analytical solutions for an Einstein-matter system describing total gravitational collapse of a zero-pressure perfect-fluid sphere, representing a massive star which has exhausted its nuclear fuel. The star collapses to a point of infinite density within a finite comoving proper time interval $[0,t_{*}]$, and the exterior metric matches the Schwarzchild black hole metric. The description is re-expressed in terms of a 'density function' $u(t)=(\rho(t)/\rho_{o}))^{1/3}=R^{-1}(t)$ for initial density $u_{0}=R^{-1}(0)=1$ and radius $R(0)$, whereby the general-relativistic formulation reduces to an autonomous nonlinear ODE for $u(t)$. The solution blows up or is singular at $t=t_{*}=\pi/2(8\pi G/3\rho_{o})^{1/2}$. The blowup interval $[0,t_{*}]$ is partitioned into domains $[0,t_{\epsilon}]\bigcup[t_{\epsilon},t_{*}]$,with $t_{*}=t_{\epsilon}+|\epsilon|$ and $|\epsilon|\ll 1$, so that $t_{\epsilon}$ can be infinitesimally close to $t_{*}$. Randomness or 'stochastic control' is introduced via the 'switching on' of specific (white-noise) perturbations at $t=t_{\epsilon}$. Hybrid nonlinear ODES-SDES are then 'engineered' over the partition. Within the Ito interpretation, the resulting density function diffusion $\overline{u(t)}$ is proved to be a martingale whose supremum, volatility and higher-order moments are finite, bounded and singularity free for all finite $t>t_{\epsilon}$. The collapse is (comovingly) eternal but never becomes singular. Extensive and rigorous boundedness and no-blowup criteria are established via various methods, and blowup probability is always zero. The density singularity is therefore smoothed or 'noise-suppressed'. Within the Stratanovitch interpretation, the singularity formation probability is unity; however, null recurrence ensures the expected comoving time for this to occur is now infinite.
Partial Observations and Conservation Laws: Gray-Box Modeling in Biotechnology and Optogenetics

Developing accurate dynamical system models from physical insight or data can be impeded when only partial observations of the system state are available. Here, we combine conservation laws used in physics and engineering with artificial neural networks to construct "grey-box" system models that make accurate predictions even with limited information. These models use a time delay embedding (c.f., Takens embedding theorem) to reconstruct effect of the intrinsic states, and can be used for multiscale systems where macroscopic balance equations depend on unmeasured micro/meso scale phenomena. By incorporating physics knowledge into the neural network architecture, we regularize variables and may train the model more accurately on smaller data sets than black-box neural network models. We present numerical examples from biotechnology, including a continuous bioreactor actuated using light through optogenetics (an emerging technology in synthetic biology) where the effect of unmeasured intracellular information is recovered from the histories of the measured macroscopic variables.
Li Evolution and the Open Cluster NGC 6819: A Correlation between Li Depletion and Spindown in Dwarfs More Massive Than the F-Dwarf Li-Dip

Spectroscopy of 333 NGC 6819 stars and Gaia astrometry are used to map Li evolution from the giant branch tip to 0.5 mag below the Li dip. Isochrone comparison with [Fe/H] = −0.04, based upon neural network spectroscopic analysis, produces an age of 2.25 (2.4) Gyr for E(B − V) = 0.16 (0.14) and (m − M) = 12.40 (12.29). Despite originating outside the Li dip, only 10% of single subgiants/giants have measurable Li. Above the Li dip, the limiting A(Li) for single stars is 3.2 ± 0.1 but the lower range is comparable to that found within the dip. The F-dwarf Li dip profile agrees with the Hyades/Praesepe, evolved forward. The Li level among stars populating the plateau fainter than the Li dip is A(Li) = 2.83 ± 0.16; the dispersion is larger than expected from spectroscopic error alone. Comparison of Li and VROT distributions among turnoff stars in NGC 7789, NGC 2506, NGC 3680, and NGC 6819 indicates that rotational spindown from the main sequence is critical in defining the boundaries of the Li dip. For higher-mass dwarfs, spindown is likewise correlated with Li depletion, creating a second dip, but at higher mass and on a longer timescale. The Li distribution among evolved stars of NGC 6819 is more representative of the older M67, where subgiant and giant stars emerge from within the Li dip, than the younger NGC 7789, where a broad range in VROT among the turnoff stars likely produces a range in mass among the giants.
Learning Fitness Functions for Genetic Algorithms

The problem of automatic software generation is known as Machine Programming. In this work, we propose a framework based on genetic algorithms to solve this problem. Although genetic algorithms have been used successfully for many problems, one criticism is that hand-crafting its fitness function, the test that aims to effectively guide its evolution, can be notably challenging. Our framework presents a novel approach to learn the fitness function using neural networks to predict values of ideal fitness function. We also augment the evolutionary process with a minimally intrusive search heuristic. This heuristic improves the framework's ability to discover correct programs from ones that are approximately correct and does so with negligible computational overhead. We compare our approach with two state-of-the-art program synthesis methods and demonstrate that it finds more correct programs with fewer candidate program generations.
Assessing Fashion Recommendations: A Multifaceted Offline Evaluation Approach

Fashion is a unique domain for developing recommender systems (RS). Personalization is critical to fashion users. As a result, highly accurate recommendations are not sufficient unless they are also specific to users. Moreover, fashion data is characterized by a large majority of new users, so a recommendation strategy that performs well only for users with prior interaction history is a poor fit to the fashion problem. Critical to addressing these issues in fashion recommendation is an evaluation strategy that: 1) includes multiple metrics that are relevant to fashion, and 2) is performed within segments of users with different interaction histories. Here, we present our multifaceted offline strategy for evaluating fashion RS. Using our proposed evaluation methodology, we compare the performance of three different algorithms, a most popular (MP) items strategy, a collaborative filtering (CF) strategy, and a content-based (CB) strategy. We demonstrate that only by considering the performance of these algorithms across multiple metrics and user segments can we determine the extent to which each algorithm is likely to fulfill fashion users' needs.
MLOD: A multi-view 3D object detection based on robust feature fusion method

This paper presents Multi-view Labelling Object Detector (MLOD). The detector takes an RGB image and a LIDAR point cloud as input and follows the two-stage object detection framework [1] [2]. A Region Proposal Network (RPN) generates 3D proposals in a Bird’s Eye View (BEV) projection of the point cloud. The second stage projects the 3D proposal bounding boxes to the image and BEV feature maps and sends the corresponding map crops to a detection header for classification and bounding-box regression. Unlike other multi-view based methods, the cropped image features are not directly fed to the detection header, but masked by the depth information to filter out parts outside 3D bounding boxes. The fusion of image and BEV features is challenging, as they are derived from different perspectives. We introduce a novel detection header, which provides detection results not just from fusion layer, but also from each sensor channel. Hence the object detector can be trained on data labelled in different views to avoid the degeneration of feature extractors. MLOD achieves state-of-the-art performance on the KITTI 3D object detection benchmark. Most importantly, the evaluation shows that the new header architecture is effective in preventing image feature extractor degeneration.
A spectroscopic census of the Fornax cluster and beyond: preparing for next generation surveys

The Fornax cluster is the nearest large cluster in the southern sky, and is currently experiencing active assembly of mass. It is thus the target of a number of ongoing observing campaigns at optical, near-infrared, and radio wavelengths, using state-of-the-art facilities in the Southern hemisphere. Spectroscopic redshifts are essential not only for determining cluster membership, but also kinematics within the cluster and identifying substructures. We present a compilation of all available major spectroscopic campaigns undertaken within the Fornax region, including new and previously unpublished spectroscopy. This provides not only a comprehensive census of Fornax cluster membership as a resource for the many ongoing studies of this dynamic system, but also probes the large-scale structure in the background volume.