Miracle-dz/UniFAR-FTUs huggingface dataset:
Antibacterial Activity of Beetroot Peel and Whole Radish Extract by Modified Well Diffusion Assay

ISSN: 2319-7706 Volume 9 Number 1 (2020) Journal homepage: http://www.ijcmas. com Screening of antimicrobial compound from waste and low cost plant material would be better alternative material for synthetic food preservatives. Hence, in the present study, beetroot peel and whole radish was chosen. Beet root peel extract (BPE) and Whole radish extract (WRE) was prepared using water and screened for the antibacterial compound against various food borne pathogen using modified well diffusion assay. Then the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) level was identified for the various food pathogens. Results showed that direct crude extract of both beetroot peel and radish having the antibacterial activity against Staphylococcus aureus, Escherichia coli, Salmonella typhi and Vibrio cholera. WRE was more effective than the BPE. It is concluded that antibacterial activity of the plant derived material is truly depends upon the extraction method and the type of solvent used for extraction. Two to four fold higher concentration of water based extraction is needed to get the effect of fat solvent based extraction. BPE and WRE can be a promising source of antimicrobial agents to control the specific food borne pathogens. The modified well diffusion assay can be used for the initial screening of large number of unfiltered the plant extract and combined method of well diffusion assay with MIC would be more suitable procedure to identify the exact antibacterial activity of the crude unfiltered plant materials. K e y w o r d s
A combined in vivo and in vitro approach to assess supraspinatus activation and tissue responses to arm elevation demands

Rotator cuff pathology typically originates in the supraspinatus tendon, but uncertainty exists on how combinations of posture and load intensity affect the intact, functional supraspinatus unit. The purpose of this study was expose the supraspinatus tendon to mechanical loading scenarios emulative of muscle activation and postural conditions measured in vivo and document its responses. Right shoulders from 48 Sprague-Dawley rats were placed into one of eight testing groups combining glenohumeral elevation angles (0/30/60/75°) and a high or low load intensity for 1500 cycles at 0.25 Hz using a custom mounting apparatus attached to a tensile testing system. Load intensities were determined from in vivo muscular activation levels measured in Chapter III and scaled to the animal model. Mechanical variables examined included maximum and minimum displacement, tangent stiffness and hysteresis. Localized surface stretch ratios were calculated based on virtual tracking points. A three-way interaction between elevation angle, load magnitude and cycle number occurred for tangent stiffness, with increasing angles, loads and cycles increasing stiffness by up to 49% in some scenarios. Longitudinal stretch ratios increased with increasing elevation angles, load intensities and cycle numbers, and differences existed between the articular and bursal sides of the tendon. Complex interactions between angle, load and cycle number suggest higher abduction angles, increased load magnitude and subsequent cycles increase mechanical responses within the tendon.
Distance Guided Channel Weighting for Semantic Segmentation

Recent works have achieved great success in improving the performance of multiple computer vision tasks by capturing features with a high channel number utilizing deep neural networks. However, many channels of extracted features are not discriminative and contain a lot of redundant information. In this paper, we address above issue by introducing the Distance Guided Channel Weighting (DGCW) Module. The DGCW module is constructed in a pixel-wise context extraction manner, which enhances the discriminativeness of features by weighting different channels of each pixel's feature vector when modeling its relationship with other pixels. It can make full use of the high-discriminative information while ignore the low-discriminative information containing in feature maps, as well as capture the long-range dependencies. Furthermore, by incorporating the DGCW module with a baseline segmentation network, we propose the Distance Guided Channel Weighting Network (DGCWNet). We conduct extensive experiments to demonstrate the effectiveness of DGCWNet. In particular, it achieves 81.6% mIoU on Cityscapes with only fine annotated data for training, and also gains satisfactory performance on another two semantic segmentation datasets, i.e. Pascal Context and ADE20K. Code will be available soon at this https URL.
Task-Level Curriculum Learning for Non-Autoregressive Neural Machine Translation

Non-autoregressive translation (NAT) achieves faster inference speed but at the cost of worse accuracy compared with autoregressive translation (AT). Since AT and NAT can share model structure and AT is an easier task than NAT due to the explicit dependency on previous target-side tokens, a natural idea is to gradually shift the model training from the easier AT task to the harder NAT task. To smooth the shift from AT training to NAT training, in this paper, we introduce semi-autoregressive translation (SAT) as intermediate tasks. SAT contains a hyperparameter k, and each k value defines a SAT task with different degrees of parallelism. Specially, SAT covers AT and NAT as its special cases: it reduces to AT when k=1 and to NAT when k=N (N is the length of target sentence). We design curriculum schedules to gradually shift k from 1 to N, with different pacing functions and number of tasks trained at the same time. We called our method as task-level curriculum learning for NAT (TCL-NAT). Experiments on IWSLT14 De-En, IWSLT16 En-De, WMT14 En-De and De-En datasets show that TCL-NAT achieves significant accuracy improvements over previous NAT baselines and reduces the performance gap between NAT and AT models to 1-2 BLEU points, demonstrating the effectiveness of our proposed method.
Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator

Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the vast majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real time for early brain injury diagnosis. However, training such models requires large number of kinematic measurements, and therefore data augmentation is required given the limited on-field measured data available. In this study we present a principal component analysis-based method that emulates an empirical low-rank substitution for head impact kinematics, while requiring low computational cost. In characterizing our existing data set of 537 head impacts, each consisting of 6 degrees of freedom measurements, we found that only a few modes, e.g., 15 in the case of angular velocity, is sufficient for accurate reconstruction of the entire data set. Furthermore, these modes are predominantly low frequency since over 70% of the angular velocity response can be captured by modes that have frequencies under 40 Hz. We compared our proposed method against existing impact parametrization methods and showed significantly better performance in injury prediction using a range of kinematic-based metrics—such as head injury criterion (HIC), rotational injury criterion (RIC), and brain injury metric (BrIC)—and brain tissue deformation-based metrics—such as brain angle metric (BAM), maximum principal strain (MPS), and axonal fiber strains (FS). In all cases, our approach reproduced injury metrics similar to the ground truth measurements with no significant difference, whereas the existing methods obtained significantly different (p < 0.01) values as well as substantial differences in injury classification sensitivity and specificity. This emulator will enable us to provide the necessary data augmentation to build a head impact kinematic data set of any size. The emulator and corresponding examples are available on our website1.
High-latitude emission from the structured jet of γ-ray bursts observed off-axis

The X-ray emission of γ-ray burst (GRBs) is often characterized by an initial steep decay followed by a nearly constant emission phase (so-called “plateau”) which can extend up to thousands of seconds. While the steep decay is usually interpreted as the tail of the prompt γ-ray flash, the long-lasting plateau is commonly associated to the emission from the external shock sustained by energy injection from a long-lasting central engine. A recent study proposed an alternative interpretation, ascribing both the steep decay and the plateau to high-latitude emission (HLE) from a “structured jet” whose energy and bulk Lorentz factor depend on the angular distance from the jet symmetry axis. In this work we expand on this idea and explore more realistic conditions: (a) the finite duration of the prompt emission, (b) the angular dependence of the optical depth, and (c) the dependence of the light curve on the observer viewing angle. We find that, when viewed highly off-axis, the structured jet HLE light curve is smoothly decaying with no clear distinction between the steep and flat phases, as opposed to the on-axis case. For a realistic choice of physical parameters, the effects of a latitude-dependent Thomson opacity and finite duration of the emission have a marginal effect on the overall light-curve evolution. We discuss the possible HLE of GW170817, showing that the emission would have faded away long before the first Swift-XRT observations. Finally, we discuss the prospects for the detection of HLE from off-axis GRBs by present and future wide-field X-ray telescopes and X-ray surveys, such as eROSITA and the mission concept THESEUS.
EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association

Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By exploiting the nature of different statistics, our method can effectively aggregate the information of different measurements, and thus significantly improve the robustness and accuracy of data association. We then present an accurate object pose estimation framework, in which an outliers-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of pose estimation results. Furthermore, we build a SLAM system that can generate semi-dense or lightweight object-oriented maps with a monocular camera. Extensive experiments are conducted on three publicly available datasets and a real scenario. The results show that our approach significantly outperforms state-of-the-art techniques in accuracy and robustness. The source code is available on https://github.com/yanmin-wu/EAO-SLAM.
How to read faces without looking at them

Face reading is the most intuitive aspect of emotion recognition. Unfortunately, digital analysis of facial expression requires digitally recording personal faces. As emotional analysis is particularly required in a more poised scenario, capturing faces becomes a gross violation of privacy. In this paper, we use the concept of compressive analysis to conceptualise a system which compressively acquires faces in order to ascertain unusable reconstruction, while allowing for acceptable (and adjustable) accuracy in inference.
Condition numbers for the truncated total least squares problem and their estimations

In this paper, we present explicit expressions for the mixed and componentwise condition numbers of the truncated total least squares (TTLS) solution of Ax≈b under the genericity condition, where A is a m × n real data matrix and b is a real m‐vector. Moreover, we reveal that normwise, componentwise, and mixed condition numbers for the TTLS problem can recover the previous corresponding counterparts for the total least squares (TLS) problem when the truncated level of the TTLS problem is n. When A is a structured matrix, the structured perturbations for the structured truncated TLS (STTLS) problem are investigated and the corresponding explicit expressions for the structured normwise, componentwise, and mixed condition numbers for the STTLS problem are obtained. Furthermore, the relationships between the structured and unstructured normwise, componentwise, and mixed condition numbers for the STTLS problem are studied. We devise reliable condition estimation algorithms for the TTLS problem by utilizing small‐sample statistical condition estimation techniques. The proposed condition estimation algorithms employ the singular value decomposition (SVD) of the augmented matrix [Ab] to reduce the computational complexity, where both unstructured and structured normwise, mixed, and componentwise condition estimations are considered. The proposed condition estimation algorithms can be integrated into the SVD‐based direct solver for the small and medium size TTLS problem to give the error estimation for the numerical TTLS solution. Numerical experiments are reported to illustrate the reliability of the proposed condition estimation algorithms.
Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies

Consider the situation where a data analyst wishes to carry out an analysis on a given dataset. It is widely recognized that most of the analyst's time will be taken up with \emph{data engineering} tasks such as acquiring, understanding, cleaning and preparing the data. In this paper we provide a description and classification of such tasks into high-levels groups, namely data organization, data quality and feature engineering. We also make available four datasets and example analyses that exhibit a wide variety of these problems, to help encourage the development of tools and techniques to help reduce this burden and push forward research towards the automation or semi-automation of the data engineering process.
Finding Planted Cliques in Sublinear Time

We study the planted clique problem in which a clique of size $k$ is planted in an Erdős-Renyi graph of size $n$ and one wants to recover this planted clique. For $k=\Omega(\sqrt{n})$, polynomial time algorithms can find the planted clique. The fastest such algorithms run in time linear $O(n^2)$ (or nearly linear) in the size of the input [FR10,DGGP14,DM15a]. In this work, we develop sublinear time algorithms that find the planted clique when $k=\omega(\sqrt{n \log \log n})$. Our algorithms can recover the clique in time $\widetilde{O}\left(n+(\frac{n}{k})^{3}\right)=\widetilde{O}\left(n^{\frac{3}{2}}\right)$ when $k=\Omega(\sqrt{n\log n})$, and in time $\widetilde{O}\left(n^2/\exp{\left(\frac{k^2}{24n}\right)}\right)$ for $\omega(\sqrt{n\log \log n})=k=o(\sqrt{n\log{n}})$. An ${\Omega}(n)$ running time lower bound for the planted clique recovery problem follows easily from the results of [RS19] and therefore our recovery algorithms are optimal whenever $k = \Omega(n^{\frac{2}{3}})$. As the lower bound of [RS19] builds on purely information theoretic arguments, it cannot provide a detection lower bound stronger than $\widetilde{\Omega}(\frac{n^2}{k^2})$. Since our algorithms for $k = \Omega(\sqrt{n \log n})$ run in time $\widetilde{O}\left(\frac{n^3}{k^3} + n\right)$, we show stronger lower bounds based on computational hardness assumptions. With a slightly different notion of the planted clique problem we show that the Planted Clique Conjecture implies the following. A natural family of non-adaptive algorithms---which includes our algorithms for clique detection---cannot reliably solve the planted clique detection problem in time $O\left( \frac{n^{3-\delta}}{k^3}\right)$ for any constant $\delta>0$. Thus we provide evidence that if detecting small cliques is hard, it is also likely that detecting large cliques is not \textit{too} easy.
GASP XXVII: Gas-phase Metallicity Scaling Relations in Disk Galaxies with and without Ram Pressure Stripping

Exploiting the data from the GAs Stripping Phenomena in galaxies with MUSE (GASP) survey, we study the gas-phase metallicity scaling relations of a sample of 29 cluster galaxies undergoing ram pressure stripping and of a reference sample of (16 cluster and 16 field) galaxies with no significant signs of gas disturbance. We adopt the pyqz code to infer the mean gas metallicity at the effective radius and achieve a well-defined mass–metallicity relation (MZR) in the stellar mass range with a scatter of 0.12 dex. At any given mass, reference cluster and stripping galaxies have similar metallicities, while the field galaxies with M⋆ < 1010.25 M⊙ show on average lower gas metallicity than galaxies in clusters. Our results indicate that at the effective radius, the chemical properties of the stripping galaxies are independent of the ram pressure stripping mechanism. Nonetheless, at the lowest masses, we detect four stripping galaxies well above the common MZR that suggest a more complex scenario. Overall, we find signs of an anticorrelation between the metallicity and both the star formation rate and the galaxy size, in agreement with previous studies. No significant trends are instead found with the halo mass, clustercentric distance, and local galaxy density in clusters. In conclusion, we advise a more detailed analysis of the spatially resolved gas metallicity maps of the galaxies, able to highlight effects of gas redistribution inside the disk due to ram pressure stripping.
Heterogeneous Graph Neural Networks for Extractive Document Summarization

As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships. In this paper, we present a heterogeneous graph-based neural network for extractive summarization (HETERSUMGRAPH), which contains semantic nodes of different granularity levels apart from sentences. These additional nodes act as the intermediary between sentences and enrich the cross-sentence relations. Besides, our graph structure is flexible in natural extension from a single-document setting to multi-document via introducing document nodes. To our knowledge, we are the first one to introduce different types of nodes into graph-based neural networks for extractive document summarization and perform a comprehensive qualitative analysis to investigate their benefits. The code will be released on Github.
Duality and hidden symmetry in transport models

A large family of diffusive models of transport that has been considered in the past years admits a transformation into the same model in contact with an equilibrium bath. This mapping holds at the full dynamical level, and is independent of dimension or topology. It provides a good opportunity to discuss questions of time reversal in out of equilibrium contexts. In particular, thanks to the mapping one may define the free-energy in the non-equilibrium states very naturally as the (usual) free energy of the mapped system.
Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization

Recently, variational auto-encoder (VAE) based approaches have made impressive progress on improving the diversity of generated responses. However, these methods usually suffer the cost of decreased relevance accompanied by diversity improvements. In this paper, we propose a novel multimodal response generation framework with exemplar augmentation and curriculum optimization to enhance relevance and diversity of generated responses. First, unlike existing VAE-based models that usually approximate a simple Gaussian posterior distribution, we present a Gaussian mixture posterior distribution (i.e, multimodal) to further boost response diversity, which helps capture complex semantics of responses. Then, to ensure that relevance does not decrease while diversity increases, we fully exploit similar examples (exemplars) retrieved from the training data into posterior distribution modeling to augment response relevance. Furthermore, to facilitate the convergence of Gaussian mixture prior and posterior distributions, we devise a curriculum optimization strategy to progressively train the model under multiple training criteria from easy to hard. Experimental results on widely used SwitchBoard and DailyDialog datasets demonstrate that our model achieves significant improvements compared to strong baselines in terms of diversity and relevance.
Development in astronomy in Ethiopia and East-Africa through nuclear activity in galaxies

Abstract In this paper we summarise the research that is currently going on in Ethiopia and East-Africa in extragalactic astronomy and physics of active galaxies and active galactic nuclei (AGN). The study is focused on some of the still open questions such as: what are the stellar ages and populations of ultra hard X-ray detected AGN and connection between AGN and their host galaxies? , what are the properties of AGN in galaxy clusters and the role that environment has in triggering nuclear activity? , what are the morphological properties of AGN and how precisely we can deal with morphological classification of active galaxies? , what are the properties of galaxies in the green valley and the role of AGN in galaxy evolution? , and what are the properties of radio-loud and radio-quiet quasars (QSO) and dichotomy between the two? Each of these questions has been developed under one specific project that will be briefly introduced. These projects involve 6 PhD and 3 MSc students and collaborations between Ethiopia, Rwanda, South Africa, Uganda, Tanzania, Spain, Italy, and Chile. With all projects we aim: first, to contribute to our general knowledge about AGN, and second, to contribute to the development in astronomy and science in Ethiopia and East-Africa.
Learning and Testing Junta Distributions with Subcube Conditioning

We study the problems of learning and testing junta distributions on $\{-1,1\}^n$ with respect to the uniform distribution, where a distribution $p$ is a $k$-junta if its probability mass function $p(x)$ depends on a subset of at most $k$ variables. The main contribution is an algorithm for finding relevant coordinates in a $k$-junta distribution with subcube conditioning [BC18, CCKLW20]. We give two applications: 1. An algorithm for learning $k$-junta distributions with $\tilde{O}(k/\epsilon^2) \log n + O(2^k/\epsilon^2)$ subcube conditioning queries, and 2. An algorithm for testing $k$-junta distributions with $\tilde{O}((k + \sqrt{n})/\epsilon^2)$ subcube conditioning queries. All our algorithms are optimal up to poly-logarithmic factors. Our results show that subcube conditioning, as a natural model for accessing high-dimensional distributions, enables significant savings in learning and testing junta distributions compared to the standard sampling model. This addresses an open question posed by Aliakbarpour, Blais, and Rubinfeld [ABR17].