Miracle-dz/UniFAR-FTUs huggingface dataset:
JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

In 2019, around 57% of the population of the world has broadband access to the Internet. Moreover, there are 5.9 billion mobile broadband subscriptions, i.e., 1.3 subscriptions per user. So there is an enormous interconnected computational power held by users all around the world. Also, it is estimated that Internet users spend more than six and a half hours online every day. But in spite of being a great amount of time, those resources are idle most of the day. Therefore, taking advantage of them presents an interesting opportunity. In this study, we introduce JSDoop, a prototype implementation to profit from this opportunity. In particular, we propose a volunteer web browser-based high-performance computing library. JSdoop divides a problem into tasks and uses different queues to distribute the computation. Then, volunteers access the web page of the problem and start processing the tasks in their web browsers. We conducted a proof-of-concept using our proposal and TensorFlow.js to train a recurrent neural network that predicts text. We tested it in a computer cluster and with up to 32 volunteers. The experimental results show that training a neural network in distributed web browsers is feasible and accurate, has a high scalability, and it is an interesting area for research.
A class of prime fusion categories of dimension $2^N$

We study a class of strictly weakly integral fusion categories $\mathfrak{I}_{N, \zeta}$, where $N \geq 1$ is a natural number and $\zeta$ is a $2^N$th root of unity, that we call $N$-Ising fusion categories. An $N$-Ising fusion category has Frobenius-Perron dimension $2^{N+1}$ and is a graded extension of a pointed fusion category of rank 2 by the cyclic group of order $\mathbb Z_{2^N}$. We show that every braided $N$-Ising fusion category is prime and also that there exists a slightly degenerate $N$-Ising braided fusion category for all $N > 2$. We also prove a structure result for braided extensions of a rank 2 pointed fusion category in terms of braided $N$-Ising fusion categories.
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize

This paper proposes an adaptive metric selection strategy called diagonal Barzilai-Borwein (DBB) stepsize for the popular Variable Metric Proximal Gradient (VM-PG) algorithm [1], [2]. The proposed approach better captures the local geometry of the problem while keeping the per-step computation cost similar to the widely used scalar Barzilai-Borwein (BB) stepsize. We provide the theoretical convergence analysis for VM-PG using DBB stepsize. Finally, our empirical results show ∼10 - 40 % improvement in convergence times for the VM-PG using DBB compared to the BB stepsize for different machine learning problems on several datasets.
Imperial College London Submission to VATEX Video Captioning Task

This paper describes the Imperial College London team's submission to the 2019' VATEX video captioning challenge, where we first explore two sequence-to-sequence models, namely a recurrent (GRU) model and a transformer model, which generate captions from the I3D action features. We then investigate the effect of dropping the encoder and the attention mechanism and instead conditioning the GRU decoder over two different vectorial representations: (i) a max-pooled action feature vector and (ii) the output of a multi-label classifier trained to predict visual entities from the action features. Our baselines achieved scores comparable to the official baseline. Conditioning over entity predictions performed substantially better than conditioning on the max-pooled feature vector, and only marginally worse than the GRU-based sequence-to-sequence baseline.
To err is human. Building a safer health system

Human beings, make errors Healthcare Services is a complex industry prone to accidents.The IOM Report [1] points out that some systems are more prone to accidents than others. When a system fails there are often multiple faults. In healthcare,human errors are the greatest contributors to accidents,however when human error is to blame it often depends upon failures within the system.These failures exists in the system before the error occurs, the same as with latent errors which are difficult to identify since they may be hidden in computers or within the various managerial layers. Most of the errors can be prevented by designing systems that make it hard for people to do the wrong thing and easy for people to do the right thing. In healthcare, this means designing processes that are able to ensure that patients are safe from accidental injury. As healthcare and the system that delivers it become more complex, the opportunities for errors abound. The IOM report “To Err is Human” proposes an approach for reducing medical errors and improving patient safety.The environment within which this occurs has a critical influence on quality.This influence may contain two dimensions; the first consists of the domain of quality which includes the practice that is consistent with current medical knowledge. The second dimension consists of forces in the external environment that can drive quality improvement in the delivery system. Although the risk of dying as a result of a medical error, far surpasses the risk of dying in an airline accident, public attention has been more focused on improving safety in the airline industry than in healthcare systems. Because of the absence of standardized nomenclature, it is important to define what an error is and what is an adverse event, the IOM Report defines them in the following way: “An error is the failure of a planned action to be completed as intended or the use of a wrong plan to achieve an aim. ” An adverse event is an injury caused by medical management rather than the underlying condition of the patient. An adverse event attributable to error is a “preventable adverse event”.
An Exponential Learning Rate Schedule for Deep Learning

Intriguing empirical evidence exists that deep learning can work well with exoticschedules for varying the learning rate. This paper suggests that the phenomenon may be due to Batch Normalization or BN, which is ubiquitous and provides benefits in optimization and generalization across all standard architectures. The following new results are shown about BN with weight decay and momentum (in other words, the typical use case which was not considered in earlier theoretical analyses of stand-alone BN. 1. Training can be done using SGD with momentum and an exponentially increasing learning rate schedule, i.e., learning rate increases by some $(1 +\alpha)$ factor in every epoch for some $\alpha >0$. (Precise statement in the paper.) To the best of our knowledge this is the first time such a rate schedule has been successfully used, let alone for highly successful architectures. As expected, such training rapidly blows up network weights, but the net stays well-behaved due to normalization. 2. Mathematical explanation of the success of the above rate schedule: a rigorous proof that it is equivalent to the standard setting of BN + SGD + StandardRate Tuning + Weight Decay + Momentum. This equivalence holds for other normalization layers as well, Group Normalization, LayerNormalization, Instance Norm, etc. 3. A worked-out toy example illustrating the above linkage of hyper-parameters. Using either weight decay or BN alone reaches global minimum, but convergence fails when both are used.
A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone

Gaze estimation for ordinary smart phone, e.g. estimating where the user is looking at on the phone screen, can be applied in various applications. However, the widely used appearance-based CNN methods still have two issues for practical adoption. First, due to the limited dataset, gaze estimation is very likely to suffer from over-fitting, leading to poor accuracy at run time. Second, the current methods are usually not robust, i.e. their prediction results having notable jitters even when the user is performing gaze fixation, which degrades user experience greatly. For the first issue, we propose a new tolerant and talented (TAT) training scheme, which is an iterative random knowledge distillation framework enhanced with cosine similarity pruning and aligned orthogonal initialization. The knowledge distillation is a tolerant teaching process providing diverse and informative supervision. The enhanced pruning and initialization is a talented learning process prompting the network to escape from the local minima and re-born from a better start. For the second issue, we define a new metric to measure the robustness of gaze estimator, and propose an adversarial training based Disturbance with Ordinal loss (DwO) method to improve it. The experimental results show that our TAT method achieves state-of-the-art performance on GazeCapture dataset, and that our DwO method improves the robustness while keeping comparable accuracy.
Not all stars form in clusters – Gaia-DR2 uncovers the origin of OB associations

Historically, it has often been asserted that most stars form in compact clusters. In this scenario, present-day gravitationally unbound OB associations are the result of the expansion of initially gravitationally bound star clusters. However, this paradigm is inconsistent with recent results, both theoretical and observational, that instead favour a hierarchical picture of star formation in which stars are formed across a continuous distribution of gas densities and most OB associations never were bound clusters. Instead they are formed in situ as the low-density side of this distribution, rather than as the remnants of expanding clusters. We utilize the second Gaia data release to quantify the degree to which OB associations are undergoing expansion and, therefore, whether OB associations are the product of expanding clusters, or whether they were born in situ, as the large-scale globally unbound associations that we see today. We find that the observed kinematic properties of associations are consistent with highly substructured velocity fields and additionally require some degree of localized expansion from subclusters within the association. While most present-day OB associations do exhibit low levels of expansion, there is no significant correlation between radial velocity and radius. Therefore, the large-scale structure of associations is not set by the expansion of clusters, rather it is a relic of the molecular gas cloud from which the association was formed. This finding is inconsistent with a monolithic model of association formation and instead favours a hierarchical model, in which OB associations form in situ, following the fractal structure of the gas from which they form.
Embodiment dictates learnability in neural controllers

Catastrophic forgetting continues to severely restrict the learnability of controllers suitable for multiple task environments. Efforts to combat catastrophic forgetting reported in the literature to date have focused on how control systems can be updated more rapidly, hastening their adjustment from good initial settings to new environments, or more circumspectly, suppressing their ability to overfit to any one environment. When using robots, the environment includes the robot's own body, its shape and material properties, and how its actuators and sensors are distributed along its mechanical structure. Here we demonstrate for the first time how one such design decision (sensor placement) can alter the landscape of the loss function itself, either expanding or shrinking the weight manifolds containing suitable controllers for each individual task, thus increasing or decreasing their probability of overlap across tasks, and thus reducing or inducing the potential for catastrophic forgetting.
Similarity Driven Approximation for Text Analytics

Text analytics has become an important part of business intelligence as enterprises increasingly seek to extract insights for decision making from text data sets. Processing large text data sets can be computationally expensive, however, especially if it involves sophisticated algorithms. This challenge is exacerbated when it is desirable to run different types of queries against a data set, making it expensive to build multiple indices to speed up query processing. In this paper, we propose and evaluate a framework called EmApprox that uses approximation to speed up the processing of a wide range of queries over large text data sets. The key insight is that different types of queries can be approximated by processing subsets of data that are most similar to the queries. EmApprox builds a general index for a data set by learning a natural language processing model, producing a set of highly compressed vectors representing words and subcollections of documents. Then, at query processing time, EmApprox uses the index to guide sampling of the data set, with the probability of selecting each subcollection of documents being proportional to its {\em similarity} to the query as computed using the vector representations. We have implemented a prototype of EmApprox as an extension of the Apache Spark system, and used it to approximate three types of queries: aggregation, information retrieval, and recommendation. Experimental results show that EmApprox's similarity-guided sampling achieves much better accuracy than random sampling. Further, EmApprox can achieve significant speedups if users can tolerate small amounts of inaccuracies. For example, when sampling at 10\%, EmApprox speeds up a set of queries counting phrase occurrences by almost 10x while achieving estimated relative errors of less than 22\% for 90\% of the queries.
Ultra-high frequency magnetic resonance through strain-spin coupling in perpendicular magnetic multi-layers.

The interaction between strain and spin has received intensive attention in the scientific community due to its abundant physical phenomena and huge technological impact. Until now, there is no experimental report on ultra-high frequency magnetic resonance through the strain-spin coupling for any technologically relevant perpendicular magnetic material. Here we report the experimental detection of the acoustic strain waves that have a response time on the order of 10 picoseconds in perpendicular magnetic [Co/Pd]n multilayers via a femtosecond laser pulse excitation. Through direct measurements of acoustic strain waves, we observe an ultra-high frequency magnetic resonance up to 60 GHz in [Co/Pd]n multilayers. We further report a theoretical model of the strain-spin interaction. Our model reveals that the energy could be transferred efficiently from the strain to the spins and well explains the existence of a steady resonance state through exciting the spin system. The physical origins of the resonance between strain waves and magnetic precession and the requested conditions for obtaining magnetic resonance within thin magnetic films have also been discussed after thorough analysis. These combined results point out a potential pathway to enable an extremely high frequency (EHF) magnetic resonance through the strain-spin coupling.
Production and constraints for a massive dark photon at electron-positron colliders

Dark sector may couple to the Standard Model via one or more mediator particles. We discuss two types of mediators: the dark photon and the dark scalar mediator . The total cross-sections and various differential distributions of the processes and ( and b quarks) are discussed. We focus on the study of the invisible due to the cleaner background at future colliders. It is found that the kinematic distributions of the two-jet system could be used to identify (or exclude) the dark photon and the dark scalar mediator, as well as to distinguish between them. We further study the possibility of a search for dark photons at a future CEPC experiment with GeV and 240 GeV. With CEPC running at 91.2 GeV, it would be possible to perform a decisive measurement of the dark photon (20 GeV 60 GeV) in less than one operating year. The lower limits of the integrated luminosity for the significance 2 , 3 and 5 are presented.
Attention Network Robustification for Person ReID

The task of person re-identification (ReID) has attracted growing attention in recent years with improving performance but lack of focus on real-world applications. Most state of the art methods use large pre-trained models, e.g., ResNet50 (~25M parameters), as their backbone, which makes it tedious to explore different architecture modifications. In this study, we focus on small-sized randomly initialized models which enable us to easily introduce network and training modifications suitable for person ReID public datasets and real-world setups. We show the robustness of our network and training improvements by outperforming state of the art results in terms of rank-1 accuracy and mAP on Market1501 (96.2, 89.7) and DukeMTMC (89.8, 80.3) with only 6.4M parameters and without using re-ranking. Finally, we show the applicability of the proposed ReID network for multi-object tracking.
Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is a demand for an autonomous cinematographer that can reason about both geometry and scene context in real‐time. Existing approaches do not address all aspects of this problem; they either require high‐precision motion‐capture systems or global positioning system tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow fixed artistic guidelines specified before the flight. In this study, we address the problem in its entirety and propose a complete system for real‐time aerial cinematography that for the first time combines: (a) vision‐based target estimation; (b) 3D signed‐distance mapping for occlusion estimation; (c) efficient trajectory optimization for long time‐horizon camera motion; and (d) learning‐based artistic shot selection. We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in‐depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at https://youtu.be/ookhHnqmlaU.
The Fast Radio Burst Luminosity Function and Death Line in the Low-twist Magnetar Model

We explore the burst energy distribution of fast radio bursts (FRBs) in the low-twist magnetar model of Wadiasingh & Timokhin (WT19). Motivated by the power-law fluence distributions of FRB 121102, we propose an elementary model for the FRB luminosity function of individual repeaters with an inversion protocol that directly relates the power-law distribution index of magnetar short burst fluences to that for FRBs. The protocol indicates that the FRB energy scales virtually linearly with crust/field dislocation amplitude, if magnetar short bursts prevail in the magnetoelastic regime. Charge starvation in the magnetosphere during bursts (required in WT19) for individual repeaters implies the predicted burst fluence distribution is narrow, ≲3 decades for yielding strains and oscillation frequencies feasible in magnetar crusts. Requiring magnetic confinement and charge starvation, we obtain a death line for FRBs, which segregates magnetars from the normal pulsar population, suggesting only the former will host recurrent FRBs. We convolve the burst energy distribution for individual magnetars to define the distribution of luminosities in evolved magnetar populations. The broken power-law luminosity function’s low-energy character depends on the population model, while the high-energy index traces that of individual repeaters. Independent of the evolved population, the broken power-law isotropic-equivalent energy/luminosity function peaks at ∼1037–1040 erg with a low-energy cutoff at ∼1037 erg. Lastly, we consider the local fluence distribution of FRBs and find that it can constrain the subset of FRB-producing magnetar progenitors. Our model suggests that improvements in sensitivity may reveal a flattening of the global FRB fluence distribution and saturation in FRB rates.
Quantitative level lowering for Galois representations

We use Galois cohomology methods to produce optimal mod pd level lowering congruences to a p ‐adic Galois representation that we construct as a well‐chosen lift of a given residual mod p representation. Using our explicit Galois cohomology methods, for F a number field, ΓF its absolute Galois group and G a reductive group, k a finite field and a suitable representation ρ¯:ΓF→G(k) , ramified at a finite set of primes S , we construct under favorable conditions lifts ρ , {ρq} of ρ¯ to G(W(k)) for q∈Q with Q a finite set of places of F . The lifts {ρq} have the following properties: ρ:ΓF→G(W(k)) is ramified precisely at S∪Q ; for q∈Q , ρq:GF→G(W(k)) is unramified outside S∪Q∖{q} and ρ and ρq are congruent mod pd if ρ mod pd is unramified at q . Furthermore, the Galois representations {ρq} are ‘independent’.
Machine-learning computation of distance modulus for local galaxies

Context. Quickly growing computing facilities and an increasing number of extragalactic observations encourage the application of data-driven approaches to uncover hidden relations from astronomical data. In this work we raise the problem of distance reconstruction for a large number of galaxies from available extensive observations. Aims. We propose a new data-driven approach for computing distance moduli for local galaxies based on the machine-learning regression as an alternative to physically oriented methods. We use key observable parameters for a large number of galaxies as input explanatory variables for training: magnitudes in U, B, I, and K bands, corresponding colour indices, surface brightness, angular size, radial velocity, and coordinates. Methods. We performed detailed tests of the five machine-learning regression techniques for inference of m−M: linear, polynomial, k-nearest neighbours, gradient boosting, and artificial neural network regression. As a test set we selected 91 760 galaxies at z < 0.2 from the NASA/IPAC extragalactic database with distance moduli measured by different independent redshift methods. Results. We find that the most effective and precise is the neural network regression model with two hidden layers. The obtained root–mean–square error of 0.35 mag, which corresponds to a relative error of 16%, does not depend on the distance to galaxy and is comparable with methods based on the Tully–Fisher and Fundamental Plane relations. The proposed model shows a 0.44 mag (20%) error in the case of spectroscopic redshift absence and is complementary to existing photometric redshift methodologies. Our approach has great potential for obtaining distance moduli for around 250 000 galaxies at z < 0.2 for which the above-mentioned parameters are already observed.