Miracle-dz/UniFAR-FTUs huggingface dataset:
Full-Length 16S rRNA and ITS Gene Sequencing Revealed Rich Microbial Flora in Roots of Cycas spp. in China

Cycads have developed a complex root system categorized either as normal or coralloid roots. Past literatures revealed that a great diversity of key microbes is associated with these roots. This recent study aims to comprehensively determine the diversity and community structure of bacteria and fungi associated with the roots of two Cycas spp. endemic to China, Cycas debaoensis Zhong & Chen and Cycas fairylakea D.Y. Wang using high-throughput amplicon sequencing of the full-length 16S rRNA (V1-V9 hypervariable) and short fragment ITS region. The total DNA from 12 root samples were extracted, amplified, sequenced, and analyzed. Resulting sequences were clustered into 61 bacteria and 2128 fungal OTUs. Analysis of community structure revealed that the coralloid roots were dominated mostly by the nitrogen-fixer Nostocaceae but also contain other non-diazotrophic bacteria. The sequencing of entire 16S rRNA gene identified four different strains of cyanobacteria under the heterocystous genera Nostoc and Desmonostoc. Meanwhile, the top bacterial families in normal roots were Xanthobacteraceae, Burkholderiaceae, and Bacillaceae. Moreover, a diverse fungal community was also found in the roots of cycads and the predominating families were Ophiocordycipitaceae, Nectriaceae, Bionectriaceae, and Trichocomaceae. Our results demonstrated that bacterial diversity in normal roots of C. fairylakea is higher in richness and abundance than C. debaoensis. On the other hand, a slight difference, albeit insignificant, was noted for the diversity of fungi among root types and host species as the number of shared taxa is relatively high (67%). Our results suggested that diverse microbes are present in roots of cycads which potentially interact together to support cycads survival. Our study provided additional knowledge on the microbial diversity and composition in cycads and thus expanding our current knowledge on cycad-microbe association. Our study also considered the possible impact of ex situ conservation on cyanobiont community of cycads.
Knowledge, Attitude and Practice Towards COVID-19 in Ethiopia: A Systematic Review; 2020

Background Novel coronavirus disease is a very fast spreading respiratory disease. This disease is becoming a public health emergency across the world. Despite many efforts by countries, organizations, and institutions to prevent and control, the pandemic remains a world health danger. Ethiopia is one of the countries which is severely affected by the pandemic. However, a systematic review of the knowledge, attitude, and practice about coronavirus disease in Ethiopia has not previously done. Thus, the main aim of this systematic review is to investigate the status of people’s knowledge, attitude, and practice about coronavirus disease preventive measures in Ethiopia in 2020. Methods Data were extracted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies were accessed through electronic web-based search from PubMed, Cochrane Library, Google, Google Scholar, and Embase. All statistical analyses were done using Stata version 11 software with a random-effects model. Results A total of 11 studies with 3818 participants were included in this systematic review and the overall estimated status of the knowledge, attitude, and practice about coronavirus in Ethiopia are 61.78%, 72.39%, and 52.83%, respectively. According to region subgroup analysis, the highest estimated statuses of the knowledge, attitude, and practice are 74% in Tigray, 86.13% in Southern nations, nationalities, and Peoples’ of the region, and 72% in Tigray region, respectively. Subgroup analysis was done based on the focused group and way of questionnaire administration. Conclusion This systematic review revealed people’s knowledge, attitude, and practice about coronavirus disease preventive measures were generally more than 50%. However, government and every individual should address those behaviors stipulated by the World Health Organization and Center for Communicable Disease and Control guidelines to prevent coronavirus disease.
Expanding boundaries of Gap Safe screening

Sparse optimization problems are ubiquitous in many fields such as statistics, signal/image processing and machine learning. This has led to the birth of many iterative algorithms to solve them. A powerful strategy to boost the performance of these algorithms is known as safe screening: it allows the early identification of zero coordinates in the solution, which can then be eliminated to reduce the problem's size and accelerate convergence. In this work, we extend the existing Gap Safe screening framework by relaxing the global strong-concavity assumption on the dual cost function. Instead, we exploit local regularity properties, that is, strong concavity on well-chosen subsets of the domain. The non-negativity constraint is also integrated to the existing framework. Besides making safe screening possible to a broader class of functions that includes beta-divergences (e.g., the Kullback-Leibler divergence), the proposed approach also improves upon the existing Gap Safe screening rules on previously applicable cases (e.g., logistic regression). The proposed general framework is exemplified by some notable particular cases: logistic function, beta = 1.5 and Kullback-Leibler divergences. Finally, we showcase the effectiveness of the proposed screening rules with different solvers (coordinate descent, multiplicative-update and proximal gradient algorithms) and different data sets (binary classification, hyperspectral and count data).
Using Prior Knowledge to Guide BERT’s Attention in Semantic Textual Matching Tasks

We study the problem of incorporating prior knowledge into a deep Transformer-based model, i.e., Bidirectional Encoder Representations from Transformers (BERT), to enhance its performance on semantic textual matching tasks. By probing and analyzing what BERT has already known when solving this task, we obtain better understanding of what task-specific knowledge BERT needs the most and where it is most needed. The analysis further motivates us to take a different approach than most existing works. Instead of using prior knowledge to create a new training task for fine-tuning BERT, we directly inject knowledge into BERT’s multi-head attention mechanism. This leads us to a simple yet effective approach that enjoys fast training stage as it saves the model from training on additional data or tasks other than the main task. Extensive experiments demonstrate that the proposed knowledge-enhanced BERT is able to consistently improve semantic textual matching performance over the original BERT model, and the performance benefit is most salient when training data is scarce.
Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval

The ad-hoc retrieval task is to rank related documents given a query and a document collection. A series of deep learning based approaches have been proposed to solve such problem and gained lots of attention. However, we argue that they are inherently based on local word sequences, ignoring the subtle long-distance document-level word relationships. To solve the problem, we explicitly model the document-level word relationship through the graph structure, capturing the subtle information via graph neural networks. In addition, due to the complexity and scale of the document collections, it is considerable to explore the different grain-sized hierarchical matching signals at a more general level. Therefore, we propose a Graph-based Hierarchical Relevance Matching model (GHRM) for ad-hoc retrieval, by which we can capture the subtle and general hierarchical matching signals simultaneously. We validate the effects of GHRM over two representative ad-hoc retrieval benchmarks, the comprehensive experiments and results demonstrate its superiority over state-of-the-art methods.
Nuclear Magnetic Resonance Diffusometry of Linear and Branched Wormlike Micelles.

Diffusion studies using nuclear magnetic resonance (NMR) spectroscopy were conducted on two model surfactant solutions of cetyltrimethylammonium bromide/sodium salicylate (CTAB/NaSal) and cetylpyridinium chloride/sodium salicylate (CPCl/NaSal). By increasing the salt-to-surfactant concentration ratio, these systems display two peaks in the zero-shear viscosity and relaxation time, which are indicative of transitions from linear to branched micellar networks. The goal of this work is to assess the sensitivity of NMR diffusometry to different types of micellar microstructures and identify the mechanism(s) of surfactant self-diffusion in micellar solutions. At low salt-to-surfactant concentration ratios, for which wormlike micelles are linear, the surfactant self-diffusion is best described by a mean squared displacement, Z2, that varies as Z2 ∝ Tdiff0.5, where Tdiff is the diffusion time. As the salt concentration increases to establish branched micelles, Z2 ∝ Tdiff, indicating a Brownian-like self-diffusion of surfactant molecules in branched micelles. This result indicates that NMR diffusometry is capable of differentiating various types of micellar microstructures. In addition, the self-diffusion coefficient of the surfactant molecules in linear and branched micelles are determined, for the first time, by comparing the existing restricted diffusion models and are shown to be much slower than the diffusion of proton molecules in the bulk. Moreover, in linear and moderately branched wormlike micelles, the dominant mechanism of surfactant self-diffusion is through the curvilinear diffusion of the surfactant molecules along the contour length of the micelles, whereas in the branched micelles, before the second viscosity maxima, the surfactant self-diffusion could arise from a combination of micellar breakage, exchange between micelles and/or the bulk.
Inverse Gaussian Process regression for likelihood-free inference

In this work we consider Bayesian inference problems with intractable likelihood functions. We present a method to compute an approximate of the posterior with a limited number of model simulations. The method features an inverse Gaussian Process regression (IGPR), i.e., one from the output of a simulation model to the input of it. Within the method, we provide an adaptive algorithm with a tempering procedure to construct the approximations of the marginal posterior distributions. With examples we demonstrate that IGPR has a competitive performance compared to some commonly used algorithms, especially in terms of statistical stability and computational efficiency, while the price to pay is that it can only compute a weighted Gaussian approximation of the marginal posteriors.
Zoomorphic Gestures for Communicating Cobot States

Communicating the robot state is vital to creating an efficient and trustworthy collaboration between humans and collaborative robots (cobots). Standard approaches for Robot-to-human communication face difficulties in industry settings, e.g., because of high noise levels or certain visibility requirements. Therefore, this letter presents zoomorphic gestures based on dog body language as a possible alternative for communicating the state of appearance-constrained cobots. For this purpose, we conduct a visual communication benchmark comparing zoomorphic gestures, abstract gestures, and light displays. We investigate the modalities regarding intuitive understanding, user experience, and user preference. In a first user study <inline-formula><tex-math notation="LaTeX">$(n = 93)$</tex-math></inline-formula>, we evaluate our proposed design guidelines for all visual modalities. A second user study <inline-formula><tex-math notation="LaTeX">$(n = 214)$</tex-math></inline-formula> constituting the benchmark indicates that intuitive understanding and user experience are highest for both gesture-based modalities. Furthermore, zoomorphic gestures are considerably preferred over other modalities. These findings indicate that zoomorphic gestures with their playful nature are especially suitable for novel users and may decrease initial inhibitions.
SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences

Large labeled data sets are one of the essential basics of modern deep learning techniques. Therefore, there is an increasing need for tools that allow to label large amounts of data as intuitively as possible. In this paper, we introduce SALT, a tool to semi-automatically annotate RGB-D video sequences to generate 3D bounding boxes for full six Degrees of Freedom (DoF) object poses, as well as pixel-level instance segmentation masks for both RGB and depth. Besides bounding box propagation through various interpolation techniques, as well as algorithmically guided instance segmentation, our pipeline also provides built-in pre-processing functionalities to facilitate the data set creation process. By making full use of SALT, annotation time can be reduced by a factor of up to 33.95 for bounding box creation and 8.55 for RGB segmentation without compromising the quality of the automatically generated ground truth.
GroupifyVAE: from Group-based Definition to VAE-based Unsupervised Representation Disentanglement

The key idea of the state-of-the-art VAE-based un-supervised representation disentanglement meth-ods is to minimize the total correlation of the latent variable distributions. However, it has been proved that VAE-based unsupervised dis-entanglement can not be achieved without introducing other inductive bias. In this paper, we address VAE-based unsupervised disentan-glement by leveraging the constraints derived from the Group Theory based deﬁnition as the non-probabilistic inductive bias. More speciﬁ-cally, inspired by the nth dihedral group (the permutation group for regular polygons), we pro-pose a speciﬁc form of the deﬁnition and prove its two equivalent conditions: isomorphism and “the constancy of permutations”. We further provide an implementation of isomorphism based on two Group constraints: the Abel constraint for the exchangeability and Order constraint for the cyclicity. We then convert them into a self-supervised training loss that can be incorporated into VAE-based models to bridge their gaps from the Group Theory based deﬁnition. We train 1800 models covering the most prominent VAE-based models on ﬁve datasets to verify the effectiveness of our method. Compared to the original models, the Groupidied VAEs consistently achieve better mean performance with smaller variances, and make meaningful dimensions controllable. Project page at https://github. com/ThomasMrY/Groupified-VAE .
Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval

Differential Privacy offers strong guarantees such as immutable privacy under post processing. Thus it is often looked to as a solution to learning on scattered and isolated data. This work focuses on supervised manifold learning, a paradigm that can generate ﬁne-tuned manifolds for a target use case. Our contributions are two fold. 1) We present a novel differentially private method PrivateMail for supervised manifold learning, the ﬁrst of its kind to our knowledge. 2) We provide a novel private geometric embedding scheme for our experimental use case. We experiment on private "content based image retrieval" - embedding and querying the nearest neighbors of images in a private manner - and show extensive privacy-utility tradeoff results, as well as the computational efﬁciency and practicality of our methods.
Decaying Clipping Range in Proximal Policy Optimization

Proximal Policy Optimization (PPO) is among the most widely used algorithms in reinforcement learning, which achieves state-of-the-art performance in many challenging problems. The keys to its success are the reliable policy updates through the clipping mechanism and the multiple epochs of minibatch updates. The aim of this research is to give new simple but effective alternatives to the former. For this, we propose linearly and exponentially decaying clipping range approaches throughout the training. With these, we would like to provide higher exploration at the beginning and stronger restrictions at the end of the learning phase. We investigate their performance in several classical control and locomotive robotic environments. During the analysis, we found that they influence the achieved rewards and are effective alternatives to the constant clipping method in many reinforcement learning tasks.
Analyzing Cross Validation in Compressed Sensing with Mixed Gaussian And Impulse Measurement Noise With L1 Errors

Compressed sensing (CS) involves sampling signals at rates less than their Nyquist rates and attempting to reconstruct them after sample acquisition. Most such algorithms have parameters, for example the regularization parameter in LASSO, which need to be chosen carefully for optimal performance. These parameters can be chosen based on assumptions on the noise level or signal sparsity, but this knowledge may often be unavailable. In such cases, cross validation (CV) can be used to choose these parameters in a purely data-driven fashion. Previous work analyzing the use of CV in CS has been based on the $\ell_{2}$ cross-validation error with Gaussian measurement noise. But it is well known that the $\ell_{2}$ error is not robust to impulse noise and provides a poor estimate of the recovery error, failing to choose the best parameter. Here we propose using the $\ell_{1}-\mathbf{CV}$ error which provides substantial performance benefits given impulse measurement noise. Most importantly, we provide a detailed theoretical analysis and error bounds for the use of $\ell_{1}-\mathbf{CV}$ error in CS reconstruction. We show that with high probability, choosing the parameter that yields the minimum $\ell_{1}-\mathbf{CV}$ error is equivalent to choosing the minimum recovery error (which is not observable in practice). To our best knowledge, this is the first paper which theoretically analyzes $\ell_{1}$ -based CV in CS.
Recurrent Model Predictive Control

This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems. Unlike traditional Model Predictive Control (MPC) algorithms, it can make full use of the current computing resources and adaptively select the longest model prediction horizon. Our algorithm employs a recurrent function to approximate the optimal policy, which maps the system states and reference values directly to the control inputs. The number of prediction steps is equal to the number of recurrent cycles of the learned policy function. With an arbitrary initial policy function, the proposed RMPC algorithm can converge to the optimal policy by directly minimizing the designed loss function. We further prove the convergence and optimality of the RMPC algorithm thorough Bellman optimality principle, and demonstrate its generality and efficiency using two numerical examples.
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown

Air pollution has a wide range of implications on agriculture, economy, road accidents, and health. In this paper, we use novel deep learning methods for short-term (multi-step-ahead) air-quality prediction in selected parts of Delhi, India. Our deep learning methods comprise of long short-term memory (LSTM) network models which also include some recent versions such as bidirectional-LSTM and encoder-decoder LSTM models. We use a multivariate time series approach that attempts to predict air quality for 10 prediction horizons covering total of 80 hours and provide a long-term (one month ahead) forecast with uncertainties quantified. Our results show that the multivariate bidirectional-LSTM model provides best predictions despite COVID-19 impact on the air-quality during full and partial lockdown periods. The effect of COVID-19 on the air quality has been significant during full lockdown;however, there was unprecedented growth of poor air quality afterwards.
Jointly Learning Clinical Entities and Relations with Contextual Language Models and Explicit Context

Introduction Electronic health records contain a wealth of information about a patient’s health. However, much of this information is stored as unstructured narrative text. Accessing this information requires natural language processing (NLP) techniques that can extract knowledge from the text. Named Entity Recognition (NER) and Relation Extraction (RE) automatically identify mentions of salient entities and the relationships between them in text, making them accessible to computerized systems. This makes NER and RE vital components of many applications, such as information retrieval, question answering, and clinical decision support. NER and RE are traditionally performed sequentially, where entities are first identified through NER and the relations between the predicted entities are inferred by RE. Recently, deep learning systems have shown great success in both tasks. However, they have drawbacks when applied sequentially. (1) Sequential application does not allow the two tasks to inform each other. (2) It is computationally wasteful since NER and RE duplicate some of their processing. (3) Errors in NER directly limit performance in RE. Multi-task Learning (MTL) architectures address these limitations (1) by allowing the two tasks to be learned jointly (i.e., simultaneously) and to inform each other, (2) by eliminating redundant work, and (3) by eliminating cascading errors. MTL approaches have shown great success for joint NER/RE in the clinical domain. ​1,2,3 ​Past joint NER/RE methods use contextual language models to build representations of relations using only the entities and do not explicitly include their context. This results in significant reliance on the language models to encode relation information. We hypothesize that explicit integration of contextual information into an MTL framework would emphasize the significance of context for boosting performance in joint NER/RE. We create an MTL architecture that uses a novel average pooling operation on the word embeddings before, between, and after both entities, in addition to both entities, providing additional context to the RE classifier. Combining this idea with MTL creates a model capable of accurately predicting both named entities and the relationships between them, all while using additional context for each relationship inference.
Trapping of a run-and-tumble particle in an inhomogeneous domain: the weak noise limit

A one-dimensional run-and-tumble particle (RTP) switches randomly between a left and right moving state of constant speed $v$. This type of motion arises in a wide range of applications in cell biology, including the unbiased growth and shrinkage of microtubules or cytonemes, the bidirectional motion of molecular motors, and the"run-and-tumble"motion of bacteria such as {\em E. coli}. RTPs are also of more general interest within the non-equilibrium statistical physics community, both at the single particle level and at the interacting population level, where it provides a simple example of active matter. In this paper we use asymptotic methods to calculate the mean first passage time (MFPT) for a one-dimensional RTP to escape an effective trapping potential generated by space-dependent switching rates. Such methods are part of a more general framework for studying metastability in so-called piecewise deterministic Markov processes (PDMPs), which include the RTP as a special case.
Evaluating Contextualized Language Models for Hungarian

We present an extended comparison of contextualized language models for Hungarian. We compare huBERT, a Hungarian model against 4 multilingual models including the multilingual BERT model. We evaluate these models through three tasks, morphological probing, POS tagging and NER. We find that huBERT works better than the other models, often by a large margin, particularly near the global optimum (typically at the middle layers). We also find that huBERT tends to generate fewer subwords for one word and that using the last subword for token-level tasks is generally a better choice than using the first one.