Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and outcomes of co-infection and super-infection with SARS-CoV-2 and other pathogens: A Systematic Review and Meta-analysis

Introduction: The recovery of other respiratory viruses in patients with SARS-CoV-2 infection has been reported, either at the time of a SARS-CoV-2 infection diagnosis (co-infection) or subsequently (superinfection). However, data on the prevalence, microbiology and outcomes of co-infection and super infection are limited. The purpose of this study was to examine occurrence of respiratory co-infections and superinfections and their outcomes among patients with SARS-CoV-2 infection. Patients and Methods: We searched literature databases for studies published from October 1, 2019, through June 11, 2020. We included studies that reported clinical features and outcomes of co-infection or super-infection of SARS-CoV-2 and other pathogens in hospitalized and non-hospitalized patients. We followed PRISMA guidelines and we registered the protocol with PROSPERO as: CRD42020189763. Results: Of 1310 articles screened, 48 were included in the random effects meta-analysis. The pooled prevalence of co-infection was 12% (95% confidence interval (CI): 6%-18%, n=29, I2=98%) and that of super-infection was 14% (95% CI: 9%-21%, n=18, I2=97%). Pooled prevalence of pathogen type stratified by co- or super-infection: viral co-infections, 4% (95% CI: 2%-7%); viral super-infections, 2% (95% CI: 0%-7%); bacterial co-infections, 4% (95% CI: 1%-8%); bacterial super-infections, 6% (95% CI: 2%-11%); fungal co-infections, 4% (95% CI: 1%-8%); and fungal super-infections, 4% (95% CI: 0%-11%). Compared to those with co-infections, patients with super-infections had a higher prevalence of mechanical ventilation [21% (95% CI: 13%-31%) vs. 7% (95% CI: 2%-15%)] and greater average length of hospital stay [mean=12.5 days, standard deviation (SD) =5.3 vs. mean=10.2 days, SD= 6.7]. Conclusions: Our study showed that as many as 14% of patients with COVID-19 have super-infections and 12% have co-infections. Poor outcomes were associated with super-infections. Our findings have implications for diagnostic testing and therapeutics, particularly in the upcoming respiratory virus season in the Northern Hemisphere.
CLRG ChemNER: A Chemical Named Entity Recognizer @ ChEMU CLEF 2020

This paper describes our system developed for ChEMU @ CLEF Cheminformatics Elsevier Melbourne University lab, Named Entity Recognition (NER) task for identifying chemical compounds as well as their types in context, i.e., to assign the label of a chemical compound according to the role which the compound plays within a chemical reaction from patent documents. We have presented two systems which use Conditional random ﬁelds (CRFs) algorithms and Artiﬁcial Neural Networks (ANN). In this work we used feature set that includes linguistic, orthographical and lexical clue features. In the development of systems, we have used only the training data provided by the track organizers and no other external resources or embedding models were used. We obtained an F-score of 0.6640 using CRFs and F-Score of 0.3764 using ANN on the test data.
A Tropical Biodiversity Hotspot Under the New Threat: Discovery and DNA Barcoding of the Invasive Chinese Pond Mussel Sinanodonta Woodiana in Myanmar

A well-established invasive Sinanodonta woodiana population was discovered in a floodplain lake of the upstream section of the Irrawaddy River basin, Kachin State, Myanmar. The DNA barcoding reveals that the population belongs to the temperate invasive mtDNA lineage and represents the same cytochrome c oxidase subunit I haplotype, which has been recorded in the invasive European populations. It is the most southern location of a population appertaining to this highly invasive haplotype known to date. The actual distribution of this alien species in Myanmar is still unknown, although it appears to be rather not widespread. Its possible dispersal through the country may affect native benthos communities, which include many unique endemic taxa. However, further expansion of the temperate lineage across South East Asia is supposed to be limited due to a specific environment of tropical floodplain freshwater systems that appears too warm for this cryptic taxon.
Bursts of amino acid replacements in protein evolution

Evolution can occur both gradually and through alternating episodes of stasis and rapid changes. However, the prevalence and magnitude of fluctuations of the rate of evolution remains obscure. Detecting a rapid burst of changes requires a detailed record of past evolution, so that events that occurred within a short time interval can be identified. Here, we use the phylogenies of the Baikal Lake amphipods and of Catarrhini, which contain very short internal edges facilitating this task. We detect 6 radical bursts of evolution of individual proteins during such short time periods, each involving between 6 and 38 amino acid substitutions. These bursts were extremely unlikely to have occurred neutrally, and were apparently caused by positive selection. On average, in the course of a time interval required for one synonymous substitution per site, a protein undergoes a strong burst of rapid evolution with probability at least ~0.01.
A Fast, Scalable, Universal Approach For Distributed Data Reductions

In the current era of Big Data, data engineering has transformed into an essential field of study across many branches of science. Advancements in Artificial Intelligence (AI) have broadened the scope of data engineering and opened up new applications in both enterprise and research communities. Reductions/ aggregations are an integral functionality in these applications. They are traditionally aimed at generating meaningful information on large data-sets, and today, they are used for engineering more effective features for complex AI models. Reductions are usually carried out on top of data abstractions such as tables/ arrays and are combined with other operations such as grouping of values. There are frameworks that excel in the said domains individually. But, we believe that there is an essential requirement for a data analytics tool that can universally integrate with existing frameworks, and thereby increase the productivity and efficiency of the entire data analytics pipeline. Cylon endeavors to fulfill this void. In this paper, we present Cylon's fast and scalable aggregation operations implemented on top of a distributed in-memory table structure that universally integrates with existing frameworks.
Nuclear liquid-gas phase transition with machine learning

Machine-learning techniques have shown their capability for studying phase transitions in condensed matter physics. Here, we employ machine-learning techniques to study the nuclear liquid-gas phase transition. We adopt an unsupervised learning and classify the liquid and gas phases of nuclei directly from the final-state raw experimental data of heavy-ion reactions. Based on a confusion scheme which combines the supervised and unsupervised learning, we obtain the limiting temperature of the nuclear liquid-gas phase transition. Its value 9.24±0.04MeV is consistent with that obtained by the traditional caloric curve method. Our study explores the paradigm of combining machine-learning techniques with heavy-ion experimental data, and it is also instructive for studying the phase transition of other uncontrollable systems, such as QCD matter.
Genetic load and mutational meltdown in cancer cell populations

Large and non-recombining genomes are prone to accumulating deleterious mutations faster than natural selection can purge (Muller’s ratchet). A possible consequence would then be the extinction of small populations. Relative to most single-cell organisms, cancer cells, with large and non-recombining genomes, could be particularly susceptible to such “mutational meltdown”. Curiously, deleterious mutations in cancer cells are rarely noticed despite the strong signals in cancer genome sequences. Here, by monitoring single-cell clones from HeLa cell lines, we characterize deleterious mutations that retard cell proliferation. The main mutational events are copy number variations (CNVs), which happen at an extraordinarily high rate of 0.29 events per cell division. The average fitness reduction, estimated to be 18% per mutation, is also very high. HeLa cell populations therefore have very substantial genetic load and, at this level, natural population would likely experience mutational meltdown. We suspect that HeLa cell populations may avoid extinction only after the population size becomes large. Because CNVs are common in most cell lines and cancer tissues, the observations hint at cancer cells’ vulnerability, which could be exploited by therapeutic strategies.
Training Generative Adversarial Networks by Solving Ordinary Differential Equations

The instability of Generative Adversarial Network (GAN) training has frequently been attributed to gradient descent. Consequently, recent methods have aimed to tailor the models and training procedures to stabilise the discrete updates. In contrast, we study the continuous-time dynamics induced by GAN training. Both theory and toy experiments suggest that these dynamics are in fact surprisingly stable. From this perspective, we hypothesise that instabilities in training GANs arise from the integration error in discretising the continuous dynamics. We experimentally verify that well-known ODE solvers (such as Runge-Kutta) can stabilise training - when combined with a regulariser that controls the integration error. Our approach represents a radical departure from previous methods which typically use adaptive optimisation and stabilisation techniques that constrain the functional space (e.g. Spectral Normalisation). Evaluation on CIFAR-10 and ImageNet shows that our method outperforms several strong baselines, demonstrating its efficacy.
Predicting Classification Accuracy when Adding New Unobserved Classes

Multiclass classifiers are often designed and evaluated only on a sample from the classes on which they will eventually be applied. Hence, their final accuracy remains unknown. In this work we study how a classifier's performance over the initial class sample can be used to extrapolate its expected accuracy on a larger, unobserved set of classes. For this, we define a measure of separation between correct and incorrect classes that is independent of the number of classes: the reversed ROC (rROC), which is obtained by replacing the roles of classes and data-points in the common ROC. We show that the classification accuracy is a function of the rROC in multiclass classifiers, for which the learned representation of data from the initial class sample remains unchanged when new classes are added. Using these results we formulate a robust neural-network-based algorithm, CleaneX, which learns to estimate the accuracy of such classifiers on arbitrarily large sets of classes. Our method achieves remarkably better predictions than current state-of-the-art methods on both simulations and real datasets of object detection, face recognition, and brain decoding.
Controlling COVID-19: Labor structure is more important than lockdown policy

Countries and cities around the world have resorted to unprecedented mobility restrictions to combat COVID 19-transmission. Here we exploit a natural experiment whereby Colombian cities implemented varied lockdown policies based on ID number and gender to analyse the impact of these policies on urban mobility. Using mobile phone data, we find that the severity of local lockdown rules, measured in the number of days citizens are allowed to go out, does not correlate with mobility reduction. Instead, we find that larger, wealthier cities with a more formalized and complex industrial structure experienced greater reductions in mobility. Commuters are more likely to stay home when their work is located in wealthy or commercially/industrially formalized neighbourhoods. Hence, our results indicate that cities' employment characteristics and workfrom home capabilities are the primary determinants of mobility reduction.
Parameterized neural ordinary differential equations: applications to computational physics problems

This work proposes an extension of neural ordinary differential equations (NODEs) by introducing an additional set of ODE input parameters to NODEs. This extension allows NODEs to learn multiple dynamics specified by the input parameter instances. Our extension is inspired by the concept of parameterized ODEs, which are widely investigated in computational science and engineering contexts, where characteristics of the governing equations vary over the input parameters. We apply the proposed parameterized NODEs (PNODEs) for learning latent dynamics of complex dynamical processes that arise in computational physics, which is an essential component for enabling rapid numerical simulations for time-critical physics applications. For this, we propose an encoder–decoder-type framework, which models latent dynamics as PNODEs. We demonstrate the effectiveness of PNODEs on benchmark problems from computational physics.
Soergel bimodules and matrix factorizations.

We establish an isomorphism between the Khovanov-Rozansky triply graded link homology and the geometric triply graded homology due to the authors. Hence we provide an interpretation of the Khovanov-Rozansky homology of the closure of a braid $\beta$ as the space of derived sections of a $\mathbb{C}^*\times \mathbb{C}^*$- equivariant sheaf $Tr(\beta)$ on the Hilbert scheme $Hilb_n(\mathbb{C}^2)$, thus proving a version of Gorsky-Negut-Rasmussen conjecture \cite{GorskyNegutRasmussen16}. As a consequence we prove that Khovanov-Rozansky homology of knots satisfies the $q\to t/q$ symmetry conjectured by Dunfield-Gukov-Rasmussen \cite{DunfieldGukovRasmussen06}. We also apply our main result to compute the Khovanov-Rozansky homology of torus links.
Unsupervised Domain Adaptation for Visual Navigation

Advances in visual navigation methods have led to intelligent embodied navigation agents capable of learning meaningful representations from raw RGB images and perform a wide variety of tasks involving structural and semantic reasoning. However, most learning-based navigation policies are trained and tested in simulation environments. In order for these policies to be practically useful, they need to be transferred to the real-world. In this paper, we propose an unsupervised domain adaptation method for visual navigation. Our method translates the images in the target domain to the source domain such that the translation is consistent with the representations learned by the navigation policy. The proposed method outperforms several baselines across two different navigation tasks in simulation. We further show that our method can be used to transfer the navigation policies learned in simulation to the real world.
The Population Genetic Structure of Quadrula aurea (Bivalvia: Unionidae), A Threatened Freshwater Mussel in Central Texas

The anthropogenic alteration of riverine ecosystems has led to declines in the abundance and diversity of freshwater mussels (Bivalvia: Unionoida) worldwide. Central Texas is home to a diverse freshwater mussel fauna including three candidates for federal listing under the Endangered Species Act. Surveys conducted over the last few decades suggest many of the endemic freshwater mussel species in Texas exist in small isolated populations that may be vulnerable to the deleterious effects of genetic diversity loss. Microsatellite primers from two closely related species were used to identify a set of genetic markers that functioned in the Golden Orb (Quadrula aurea). Microsatellite markers were then applied to document the population genetic structure of Q. aurea within and among three connected river drainages in southeastern Texas. Gene flow within existing Q. aurea populations appears high indicating little potential for genetic issues stemming from isolation and inbreeding. Two weakly divergent admixed populations were identified occupying the San Antonio and Guadalupe/San Marcos rivers. Population genetic structure was related to river basin affiliation, but results for environmental factors were unresolved. Current effective population size estimates are large for the Guadalupe/San Marcos drainage and moderately large for the San Antonio drainage and there is no clear genetic evidence of contemporary population declines. Transport in the glochidial phase by a highly mobile host fish, the channel catfish (Ictalurus punctatus), may provide a mechanism for maintaining connectivity among spatially discrete mussel beds and deserves further study. Information on the occurrence and habitat associations of Q. aurea and two other threatened freshwater mussel species was documented. Quantification of the population genetic structure for Q. aurea provides important information needed for the management of this species, a baseline for understanding future changes, and insight into the factors that shape the population genetic structure of other threatened unionids in Texas.
Replay and Synthetic Speech Detection with Res2Net Architecture

Existing approaches for replay and synthetic speech detection still lack generalizability to unseen spoofing attacks. This work proposes to leverage a novel model structure, so-called Res2Net, to improve the anti-spoofing countermeasure’s generalizability. Res2Net mainly modifies the ResNet block to enable multiple feature scales. Specifically, it splits the feature maps within one block into multiple channel groups and designs a residual-like connection across different channel groups. Such connection increases the possible receptive fields, resulting in multiple feature scales. This multiple scaling mechanism significantly improves the countermeasure’s generalizability to unseen spoofing attacks. It also decreases the model size compared to ResNet-based models. Experimental results show that the Res2Net model consistently outperforms ResNet34 and ResNet50 by a large margin in both physical access (PA) and logical access (LA) of the ASVspoof 2019 corpus. Moreover, integration with the squeeze-and-excitation (SE) block can further enhance performance. For feature engineering, we investigate the gen-eralizability of Res2Net combined with different acoustic features, and observe that the constant-Q transform (CQT) achieves the most promising performance in both PA and LA scenarios. Our best single system outperforms other state-of-the-art single systems in both PA and LA of the ASVspoof 2019 corpus.
Batch Reinforcement Learning With a Nonparametric Off-Policy Policy Gradient

Off-policy reinforcement learning (RL) holds the promise of better data efficiency as it allows sample reuse and potentially enables safe interaction with the environment. Current off-policy policy gradient methods either suffer from high bias or high variance, delivering often unreliable estimates. The price of inefficiency becomes evident in real-world scenarios such as interaction-driven robot learning, where the success of RL has been rather limited, and a very high sample cost hinders straightforward application. In this paper, we propose a nonparametric Bellman equation, which can be solved in closed form. The solution is differentiable w.r. t the policy parameters and gives access to an estimation of the policy gradient. In this way, we avoid the high variance of importance sampling approaches, and the high bias of semi-gradient methods. We empirically analyze the quality of our gradient estimate against state-of-the-art methods, and show that it outperforms the baselines in terms of sample efficiency on classical control tasks.
Frequency-Undersampled Short-Time Fourier Transform

The short-time Fourier transform (STFT) usually computes the same number of frequency components as the frame length while overlapping adjacent time frames by more than half. As a result, the number of components of a spectrogram matrix becomes more than twice the signal length, and hence STFT is hardly used for signal compression. In addition, even if we modify the spectrogram into a desired one by spectrogram-based signal processing, it is re-changed during the inversion as long as it is outside the range of STFT. In this paper, to reduce the number of components of a spectrogram while maintaining the analytical ability, we propose the frequency-undersampled STFT (FUSTFT), which computes only half the frequency components. We also present the inversions with and without the periodic condition, including their different properties. In simple numerical examples of audio signals, we confirm the validity of FUSTFT and the inversions.
Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowds

We present a novel Deep Reinforcement Learning (DRL) based policy for mobile robot navigation in dynamic environments that computes dynamically feasible and spatially aware robot velocities. Our method addresses two primary issues associated with the Dynamic Window Approach (DWA) and DRL-based navigation policies and solves them by using the benefits of one method to fix the issues of the other. The issues are: 1. DWA not utilizing the time evolution of the environment while choosing velocities from the dynamically feasible velocity set leading to sub-optimal dynamic collision avoidance behaviors, and 2. DRL-based navigation policies computing velocities that often violate the dynamics constraints such as the non-holonomic and acceleration constraints of the robot. Our DRL-based method generates velocities that are dynamically feasible while accounting for the motion of the obstacles in the environment. This is done by embedding the changes in the environment's state in a novel observation space and a reward function formulation that reinforces spatially aware obstacle avoidance maneuvers. We evaluate our method in realistic 3-D simulation and on a real differential drive robot in challenging indoor scenarios with crowds of varying densities. We make comparisons with traditional and current state-of-the-art collision avoidance methods and observe significant improvements in terms of collision rate, number of dynamics constraint violations and smoothness. We also conduct ablation studies to highlight the advantages and explain the rationale behind our observation space construction, reward structure and network architecture.
Second-Order Unsupervised Neural Dependency Parsing

Most of the unsupervised dependency parsers are based on first-order probabilistic generative models that only consider local parent-child information. Inspired by second-order supervised dependency parsing, we proposed a second-order extension of unsupervised neural dependency models that incorporate grandparent-child or sibling information. We also propose a novel design of the neural parameterization and optimization methods of the dependency models. In second-order models, the number of grammar rules grows cubically with the increase of vocabulary size, making it difficult to train lexicalized models that may contain thousands of words. To circumvent this problem while still benefiting from both second-order parsing and lexicalization, we use the agreement-based learning framework to jointly train a second-order unlexicalized model and a first-order lexicalized model. Experiments on multiple datasets show the effectiveness of our second-order models compared with recent state-of-the-art methods. Our joint model achieves a 10% improvement over the previous state-of-the-art parser on the full WSJ test set.
The Volctrans Machine Translation System for WMT20

This paper describes our submission systems for VolcTrans for WMT20 shared news translation task. We participated in 8 translation directions. Our basic systems are based on Transformer (CITATION), into which we also employed new architectures (bigger or deeper Transformers, dynamic convolution). The final systems include text pre-process, subword(a.k.a. BPE(CITATION)), baseline model training, iterative back-translation, model ensemble, knowledge distillation and multilingual pre-training.