Miracle-dz/UniFAR-FTUs huggingface dataset:
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

Federated learning is a distributed framework according to which a model is trained over a set of devices, while keeping data localized. This framework faces several systems-oriented challenges which include (i) communication bottleneck since a large number of devices upload their local updates to a parameter server, and (ii) scalability as the federated network consists of millions of devices. Due to these systems challenges as well as issues related to statistical heterogeneity of data and privacy concerns, designing a provably efficient federated learning method is of significant importance yet it remains challenging. In this paper, we present FedPAQ, a communication-efficient Federated Learning method with Periodic Averaging and Quantization. FedPAQ relies on three key features: (1) periodic averaging where models are updated locally at devices and only periodically averaged at the server; (2) partial device participation where only a fraction of devices participate in each round of the training; and (3) quantized message-passing where the edge nodes quantize their updates before uploading to the parameter server. These features address the communications and scalability challenges in federated learning. We also show that FedPAQ achieves near-optimal theoretical guarantees for strongly convex and non-convex loss functions and empirically demonstrate the communication-computation tradeoff provided by our method.
Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning

Resembling the rapid learning capability of human, low-shot learning empowers vision systems to understand new concepts by training with few samples. Leading approaches derived from meta-learning on images with a single visual object. Obfuscated by a complex background and multiple objects in one image, they are hard to promote the research of low-shot object detection/segmentation. In this work, we present a flexible and general methodology to achieve these tasks. Our work extends Faster /Mask R-CNN by proposing meta-learning over RoI (Region-of-Interest) features instead of a full image feature. This simple spirit disentangles multi-object information merged with the background, without bells and whistles, enabling Faster /Mask R-CNN turn into a meta-learner to achieve the tasks. Specifically, we introduce a Predictor-head Remodeling Network (PRN) that shares its main backbone with Faster /Mask R-CNN. PRN receives images containing low-shot objects with their bounding boxes or masks to infer their class attentive vectors. The vectors take channel-wise soft-attention on RoI features, remodeling those R-CNN predictor heads to detect or segment the objects consistent with the classes these vectors represent. In our experiments, Meta R-CNN yields the new state of the art in low-shot object detection and improves low-shot object segmentation by Mask R-CNN. Code: \url{https://yanxp.github. io/metarcnn.html}.
A closer look at network resolution for efficient network design

There is growing interest in designing lightweight neural networks for mobile and embedded vision applications. Previous works typically reduce computations from the structure level. For example, group convolution based methods reduce computations by factorizing a vanilla convolution into depth-wise and point-wise convolutions. Pruning based methods prune redundant connections in the network structure. In this paper, we explore the importance of network input for achieving optimal accuracy-efficiency trade-off. Reducing input scale is a simple yet effective way to reduce computational cost. It does not require careful network module design, specific hardware optimization and network retraining after pruning. Moreover, different input scales contain different representations to learn. We propose a framework to mutually learn from different input resolutions and network widths1. With the shared knowledge, our framework is able to find better width-resolution balance and capture multi-scale representations. It achieves consistently better ImageNet top-1 accuracy over US-Net (Yu & Huang, 2019) under different computation constraints, and outperforms the best compound scale model of EfficientNet (Tan & Le, 2019) by 1.5%. The superiority of our framework is also validated on COCO object detection and instance segmentation as well as transfer learning.
T CrB: Radio Observations during the 2016–2017 “Super-active” State

We obtained radio observations of the symbiotic binary and known recurrent nova T Coronae Borealis following a period of increased activity in the optical and X-ray bands. A comparison of our observations with those made prior to 2015 indicates that the system is in a state of higher emission in the radio as well. The spectral energy distributions are consistent with optically thick thermal bremsstrahlung emission from a photoionized source. Our observations indicate that the system was in a state of increased ionization in the companion wind, possibly driven by an increase in accretion rate, with the radio photosphere located well outside the binary system.
Ab initio
 analysis of magnetic properties of the prototype B20 chiral magnet FeGe

FeGe in the B20 phase is an experimentally well-studied prototypical chiral magnet exhibiting helical spirals, skyrmion lattices and individual skyrmions with a robust length of 70~nm. While the helical spiral ground state can be verified by first-principles calculations based on density functional theory, this feature size could not be reproduced even approximately. To develop a coherent picture of the discrepancy between experiment and theory, we investigate in this work the magnetic properties of FeGe from first-principles using different electronic-structure methods. We study atomistic as well as micromagnetic parameters describing exchange and Dzyaloshinskii-Moriya interactions, and discuss their subtle dependence on computational, structural, and correlation parameters. In particular, we quantify how these magnetic properties are affected by changes of the lattice parameter, different atomic arrangements, exchange and correlation effects, finite Fermi-function broadening, and momentum-space sampling. In addition, we use the obtained atomistic parameters to determine the corresponding Curie temperature, which agrees well with experiments. Our results indicate that the well-known and well-accepted relation between the micromagnetic parameters and the period of the helical structure, is not valid for FeGe. This calls for new experiments exploring the relation by measuring independently the spin stiffness, the spiralization and the period of the helical spin spiral.
Polarimetric imaging mode of VLT/SPHERE/IRDIS

Context. Circumstellar disks and self-luminous giant exoplanets or companion brown dwarfs can be characterized through direct-imaging polarimetry at near-infrared wavelengths. SPHERE/IRDIS at the Very Large Telescope has the capabilities to perform such measurements, but uncalibrated instrumental polarization effects limit the attainable polarimetric accuracy. Aims. We aim to characterize and correct the instrumental polarization effects of the complete optical system, that is, the telescope and SPHERE/IRDIS. Methods. We created a detailed Mueller matrix model in the broadband filters Y, J, H, and Ks and calibrated the model using measurements with SPHERE’s internal light source and observations of two unpolarized stars. We developed a data-reduction method that uses the model to correct for the instrumental polarization effects, and applied it to observations of the circumstellar disk of T Cha. Results. The instrumental polarization is almost exclusively produced by the telescope and SPHERE’s first mirror and varies with telescope altitude angle. The crosstalk primarily originates from the image derotator (K-mirror). At some orientations, the derotator causes severe loss of signal (> 90% loss in the H- and Ks-band) and strongly offsets the angle of linear polarization. With our correction method we reach, in all filters, a total polarimetric accuracy of ≲0.1% in the degree of linear polarization and an accuracy of a few degrees in angle of linear polarization. Conclusions. The correction method enables us to accurately measure the polarized intensity and angle of linear polarization of circumstellar disks, and is a vital tool for detecting spatially unresolved (inner) disks and measuring the polarization of substellar companions. We have incorporated the correction method in a highly-automated end-to-end data-reduction pipeline called IRDAP, which we made publicly available online.
Spatio-Temporal FAST 3D Convolutions for Human Action Recognition

Effective processing of video input is essential for the recognition of temporally varying events such as human actions. Motivated by the often distinctive temporal characteristics of actions in either horizontal or vertical direction, we introduce a novel convolution block for CNN architectures with video input. Our proposed Fractioned Adjacent Spatial and Temporal (FAST) 3D convolutions are a natural decomposition of a regular 3D convolution. Each convolution block consist of three sequential convolution operations: a 2D spatial convolution followed by spatio-temporal convolutions in the horizontal and vertical direction, respectively. Additionally, we introduce a FAST variant that treats horizontal and vertical motion in parallel. Experiments on benchmark action recognition datasets UCF-101 and HMDB-51 with ResNet architectures demonstrate consistent increased performance of FAST 3D convolution blocks over traditional 3D convolutions. The lower validation loss indicates better generalization, especially for deeper networks. We also evaluate the performance of CNN architectures with similar memory requirements, based either on Two-stream networks or with 3D convolution blocks. DenseNet-121 with FAST 3D convolutions was shown to perform best, giving further evidence of the merits of the decoupled spatio-temporal convolutions.
Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer

We consider the problem of automatically generating textual paraphrases with modified attributes or properties, focusing on the setting without parallel data (Hu et al., 2017; Shen et al., 2017). This setting poses challenges for evaluation. We show that the metric of post-transfer classification accuracy is insufficient on its own, and propose additional metrics based on semantic preservation and fluency as well as a way to combine them into a single overall score. We contribute new loss functions and training strategies to address the different metrics. Semantic preservation is addressed by adding a cyclic consistency loss and a loss based on paraphrase pairs, while fluency is improved by integrating losses based on style-specific language models. We experiment with a Yelp sentiment dataset and a new literature dataset that we propose, using multiple models that extend prior work (Shen et al., 2017). We demonstrate that our metrics correlate well with human judgments, at both the sentence-level and system-level. Automatic and manual evaluation also show large improvements over the baseline method of Shen et al. (2017). We hope that our proposed metrics can speed up system development for new textual transfer tasks while also encouraging the community to address our three complementary aspects of transfer quality.
Perturbative method for resolving contact interactions in quantum mechanics

Long-range effective methods are ubiquitous in physics and in quantum theory, in particular. Furthermore, the reliability of such methods is higher when the nature of short-ranged interactions need not be modeled explicitly. This may be necessary for two reasons: (1) there are interactions that occur over a short range that cannot be accurately modeled with a potential function and/or (2) the entire Hamiltonian loses its reliability when applied at short distances. This work is an investigation of the utility and consequences of omitting a finite region of space from quantum mechanical analysis, accomplished by imposition of an artificial boundary behind which obscured short-ranged physical effects may operate. With this method, a free function of integration that depends on momentum is interpreted as a function encoding information needed to match a long-distance wavefunction to an appropriate state function on the other side of the boundary. Omitting part of the space from analysis implies that the strict unitarity requirement of quantum mechanics must be relaxed, since particles can actually propagate beyond the boundary. Strict orthogonality of eigenmodes and hermiticity of the Hamiltonian must also be relaxed in this method; however, all of these canonical relations are obeyed when averaged over sufficiently long times. What is achieved, therefore, appears to be an effective long-wavelength theory, at least for stationary systems. As examples, the quantum defect theory of the one-dimensional Coulomb interaction is recovered, as well as a new perspective of the inverse-square potential and the free particle, as well as the Wigner time delay associated with contact interactions. Potential applications of this method may include three-dimensional atomic systems and two-dimensional systems, such as graphene.
T2-BUDA-gSlider: rapid high isotropic resolution T2 mapping with blip-up/down acquisition, generalized SLIce Dithered Enhanced Resolution and subspace reconstruction

Purpose: To obtain rapid high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity. Methods: A T2 blip-up/down acquisition with generalized Slice-dithered enhanced resolution (T2-BUDA-gSlider) is proposed. An RF-encoded multi-slab SE-EPI acquisition with variable TEs was developed to obtain high SNR efficiency with reduced TR. This was combined with an interleaved 2-shot acquisition using blipup/down phase encoding. An estimated field map was incorporated into the joint multishot EPI reconstruction with a structured low rank constraint to achieve distortion-free and robust reconstruction for each slab without navigation. A Bloch simulated subspace shuffling model was integrated into gSlider reconstruction and utilized for T2 quantification. To further accelerate the acquisition and enable shorter TEs to be sampled, partial Fourier sampling and simultaneous multi-slab encoding were also combined with the proposed method. Results: In vivo results demonstrated that the T2 values estimated by proposed method were consistent with gold standard spin-echo acquisition. Compared to the reference 3D-FSE images, distortion caused by off-resonance and eddy current effects were effectively mitigated. On patients with mesial temporal lobe epilepsy, increased T2 values in hippocampal sclerosis regions were detected. Conclusion: BUDA-gSlider SE-EPI acquisition and shuffling-gSlider joint reconstruction enabled distortion-free whole-brain T2 mapping in 63 seconds at 1 mm isotropic resolution.
On the convergence of gradient descent for two layer neural networks

It has been shown that gradient descent can yield the zero training loss in the over-parametrized regime (the width of the neural networks is much larger than the number of data points). In this work, combining the ideas of some existing works, we investigate the gradient descent method for training two-layer neural networks for approximating some target continuous functions. By making use the generic chaining technique from probability theory, we show that gradient descent can yield an exponential convergence rate, while the width of the neural networks needed is independent of the size of the training data. The result also implies some strong approximation ability of the two-layer neural networks without curse of dimensionality.
Fashion Editing With Adversarial Parsing Learning

Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value. Existing works often treat it as a general inpainting task and do not fully leverage the semantic structural information in fashion images. Moreover, they directly utilize conventional convolution and normalization layers to restore the incomplete image, which tends to wash away the sketch and color information. In this paper, we propose a novel Fashion Editing Generative Adversarial Network (FE-GAN), which is capable of manipulating fashion images by free-form sketches and sparse color strokes. FE-GAN consists of two modules: 1) a free-form parsing network that learns to control the human parsing generation by manipulating sketch and color; 2) a parsing-aware inpainting network that renders detailed textures with semantic guidance from the human parsing map. A new attention normalization layer is further applied at multiple scales in the decoder of the inpainting network to enhance the quality of the synthesized image. Extensive experiments on high-resolution fashion image datasets demonstrate that the proposed FE-GAN significantly outperforms the state-of-the-art methods on fashion image manipulation.
Polariton Assisted Down-Conversion of Photons via Nonadiabatic Molecular Dynamics: A Molecular Dynamical Casimir Effect.

Quantum dynamics of the photoisomerization of a single 3,3'-diethyl-2,2'-thiacynine iodide molecule embedded in an optical microcavity was theoretically studied. The molecular model consisting of two electronic states and the reaction coordinate was coupled to a single cavity mode via the quantum Rabi Hamiltonian, and the corresponding time-dependent Schr\"odinger equation starting with a purely molecular excitation was solved using the Multiconfigurational Time-Dependent Hartree Method (MCTDH). We show that, for single-molecule strong coupling with the photon mode, nonadiabatic molecular dynamics produces mixing of polariton manifolds with differing number of excitations, without the need of counterrotating light-matter coupling terms. Therefore, an electronic excitation of the molecule at {\it cis} configuration is followed by the generation of two photons in the {\it trans} configuration upon isomerization. Conditions for this phenomenon to be operating in the collective strong light-matter coupling regime are discussed and found to be unfeasible for the present system, based on simulations of two molecules inside the microcavity. Yet, our finding suggests a new mechanism that, without ultrastrong coupling, achieves photon down-conversion by exploiting the emergent molecular dynamics arising in polaritonic architectures.
Model-free Setting-Independent Detection of Dynamic Objects in 3 D Lidar by Juny

Model-free Setting-Independent Detection of Dynamic Objects in 3D Lidar Juny David Yoon Master of Applied Science University of Toronto Institute for Aerospace Studies University of Toronto 2019 This thesis presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data. We focus on a common type of 3D lidar, spinning-lidars, for which its moving-while-scanning operation must be compensated. Our detection method uses motion-compensated scan alignment and a novel freespace querying algorithm to classify between dynamic (currently moving) and static (currently stationary) labels at the point level. A public dataset and benchmark suitable to evaluate our work did not previously exist due to the difficulty of accurately labelling groundtruth. For a quantitative evaluation, we establish a benchmark with motion-distorted lidar data using an open-source 3D simulator for autonomous driving research. We also provide a qualitative evaluation with real data using a commercially available lidar in driving scenarios. Results show that we can do a reasonable job of labelling each lidar point as either static or dynamic without resorting to a model or application-specific assumptions.
The effect of education on fertility: Evidence from a compulsory schooling reform

This paper investigates the effect of education on fertility under inflexible labor market conditions. We exploit exogenous variation from a German compulsory schooling reform to deal with the endogeneity of education. By using data from two complementary data sets, we examine different fertility outcomes over the life cycle. In contrast to evidence for other developed countries, we find that increased education causally reduces completed fertility. This negative effect operates through a postponement of first births away from teenage years, and no catch-up later in life. We attribute these findings to the particularly high opportunity costs of child-rearing in Germany.
Clinical Profile and Outcome of H 1 N 1 Influenza Infection at a Tertiary Care Teaching Hospital

Background: The 2009 flu outbreak in humans, known as "swine influenza" or H1N1 influenza A, refers to influenza A due to a new H1N1 strain called SwineOrigin Influenza Virus A (S-OIV). Global pandemics with high mortality and morbidity occur when a virulent new viral strain emerges. Aim and Objectives: To study demography, clinical profile and outcome of H1N1 influenza infection at a tertiary care teaching hospital. Material and Methods: This was a prospective observational study conducted at the teaching hospital during six month period. It was a time st bound study over a period of six months (from 1 July th 2018 to 30 December 2018). All suspects with throat swab/nasal swab positive for influenza H1N1 virus by Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) with age more than 15 years were included in the present study. Data were analysed for mean, percentage, standard deviation and Chi square test for quantitative data by using Microsoft Excel spread sheet. Results: A total 60 patients were admitted with confirmed diagnosis of H1N1 infection. Out of the 21 (35%) were males and 39 (65%) were females, predominated by female gender (p=0.001). The mean age in male (46.14 ± 20.058) was relatively more compared to female gender (36.33 ± 11.50). The male gender had more co-morbidities and risk factors compared to female patients (p=0.01) and was statistically significant. Out of total 21 male patients 7 patients died because of bilateral pneumonia and Acute Respiratory Distress Syndrome (ARDS) and MultiOrgan Dysfunction (MOD) with case fatality rate of 33.33%. Out of total 39 female patients 5 patients died with case fatality rate of 12.82%. Conclusion: The present study highlighted the disease burden associated with H1N1 infection. The advancing age, male gender, associated co-morbidities and delayed presentation were the risk factors for mortality in present cohort study of H1N1 patients. The community awareness, early case detection and timely management can reduce the disease burden at large.
Development and validation of a scale for measuring intercultural empathy

The research and practice connected to the use of empathic strategies in multicultural educational settings is emerging as one of the most effective ways to combat intergroup bias. The aim of this study is to design an instrument to measure the specific intercultural empathy in its affective component for the Spanish population towards the population of Moroccan origin. Thus, this instrument makes possible the measurement of this feature or capability before implementing educational strategies in multicultural contexts. Therefore, two samples of University students were used to carry out a cross validity study through exploratory (Factor 9.20) and confirmatory factor analysis (SPSS-AMOS). Then, a 3-factor structure was obtained: empathic concern, understanding and affection. The goodness of fit indices, together with the validity and reliability revealed by multidimensional analysis prove that the model is optimal. Eventually, it is discussed the desirability of designing specific instruments that take into account the profile of the populations under this study