Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of time varying transmission rates on the coupled dynamics of epidemic and awareness over a multiplex network.

A non-linear stochastic model is presented to study the effect of time variation of transmission rates on the co-evolution of epidemics and its corresponding awareness over a two layered multiplex network. In the model, the infection transmission rate of a given node in the epidemic layer depends upon its awareness probability in the awareness layer. Similarly, the infection information transmission rate of a node in the awareness layer depends upon its infection probability in the epidemic layer. The spread of disease resulting from physical contacts is described in terms of a Susceptible Infected Susceptible process over the epidemic layer and the spread of information about the disease outbreak is described in terms of an Unaware Aware Unaware process over the virtual interaction mediated awareness layer. The time variation of the transmission rates and the resulting co-evolution of these mutually competing processes are studied in terms of a network topology dependent parameter ( α ). Using a second order linear theory, it is shown that in the continuous time limit, the co-evolution of these processes can be described in terms of damped and driven harmonic oscillator equations. From the results of a Monte-Carlo simulation, it is shown that for a suitable choice of the parameter ( α ) , the two processes can either exhibit sustained oscillatory or damped dynamics. The damped dynamics corresponds to the endemic state. Furthermore, for the case of an endemic state, it is shown that the inclusion of the awareness layer significantly lowers the disease transmission rate and reduces the size of the epidemic. The infection probability of the nodes in the endemic state is found to have a dependence on both the transmission rates and on their absolute degrees in each of the network layers and on the relative differences between their degrees in the respective layers.
Suitability Analysis of Wrist-Worn Sensors for Implementing Pedestrian Dead Reckoning Systems

Wearable devices seem an ideal option for implementing pedestrian dead-reckoning (PDR) systems and, eventually, providing seamless location-based services. Among the different available wearable devices, wrist-worn devices, such as smartwatches, have great potential due to their featuring more and more sensors and their convenience for the users. However, the low number of wrist-worn PDR systems proposed in the scientific literature reveals the intrinsic difficulty of estimating the position from the upper limbs. Before starting to design new algorithms for improving wrist-worn PDRs, it was considered necessary to make a deep analysis of the characteristics of the different signals generated in a wrist-worn sensor when a person walks, and identify how they affect the implementation of an inertial PDR system for that body location. After this analysis task, it seems feasible to implement wrist-worn PDR systems thanks to the presence of clear step/stride patterns in the signals coming from different types of wrist-worn sensors. However, these patterns show a non-negligible variability dependent on the different motion modes, walking speed and the way each user moves their upper limbs while walking, indicating that adaptive algorithms and prior calibration stages are necessary.
Algorithmic Aspects of Parallel Query Processing

In the last decade we have witnessed a growing interest in process- ing large data sets on large-scale distributed clusters. A big part of the complex data analysis pipelines performed by these systems consists of a sequence of relatively simple query operations, such as joining two or more tables, or sorting. This tutorial discusses several recent algorithmic developments for data processing in such large distributed clusters. It uses as a model of computation the Massively Parallel Computation (MPC) model, a simplification of the BSP model, where the only cost is given by the amount of communication and the number of communication rounds. Based on the MPC model, we study and analyze several algorithms for three core data processing tasks: multiway join queries, sorting and matrix multiplication. We discuss the common algorithmic techniques across all tasks, relate the algorithms to what is used in practical systems, and finally present open problems for future research.
Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere

Multichannel blind deconvolution is the problem of recovering an unknown signal $f$ and multiple unknown channels $x_i$ from convolutional measurements $y_i=x_i \circledast f$ ($i=1,2,\dots,N$). We consider the case where the $x_i$'s are sparse, and convolution with $f$ is invertible. Our nonconvex optimization formulation solves for a filter $h$ on the unit sphere that produces sparse output $y_i\circledast h$. Under some technical assumptions, we show that all local minima of the objective function correspond to the inverse filter of $f$ up to an inherent sign and shift ambiguity, and all saddle points have strictly negative curvatures. This geometric structure allows successful recovery of $f$ and $x_i$ using a simple manifold gradient descent algorithm with random initialization. Our theoretical findings are complemented by numerical experiments, which demonstrate superior performance of the proposed approach over the previous methods.
Full reference quality assessment of downsized images

Resizing image processing tools are in vogue nowadays due to various practical reasons. The images are resized into different resolutions and scales. The quality of the image may get affected by resizing the original image. In this paper, quality of downsized images is evaluated. The proposed metric is full reference and based on edge detection, sharpness and contrast of images. Moreover, due to the limited availability of databases with perceived image quality score of resized images, the proposed metric is compared with recent no reference image quality assessment techniques (as they are size independent) BRISQUE and NIQE. Experimental results show that the proposed metric is consistent with the perceived quality and outperform the aforesaid no reference techniques.
Soft paternalism, merit goods, and normative individualism

Paternalism is an attempt to influence individuals’ decisions for their own benefit, even if there are no third parties involved. This seems to contradict normative individualism, which provides the general orientation to our modern democracies. Soft or libertarian paternalism accepts the necessity of paternalism due to the existence of behavioural anomalies, but intends to apply only such measures that do not restrict the decision leeway of individuals. Nevertheless, the same objections that can be raised against its strong version can also be raised against soft paternalism. On the other hand, as soon as we accept that human beings are able to reflect not only about their actions but also about the preferences guiding their actions, there is no longer a necessary contradiction between paternalism and normative individualism. As far as we know today, the possibilities to successfully apply soft paternalistic measures are rather limited. On the other hand, while some criticisms are justified, others largely overshoot the mark and seem to be at least partly ideologically motivated.
ELISA Development for Serum Hemeoxygenase-1 and Its Application to Patients with Acute Respiratory Distress Syndrome

Background Hemeoxygenase-1 (HO-1) is an essential enzyme in heme catabolism and has been proposed as a biomarker of lung disease prognosis. We modified a commercial HO-1 enzyme-linked immunosorbent assay (ELISA) kit to achieve higher sensitivity and evaluated if serum HO-1 could be a biomarker to predict the prognosis of acute respiratory distress syndrome (ARDS) patients. Methods Serum samples were collected from 15 healthy volunteers to validate the modified ELISA. In the 22 patients with ARDS who were enrolled, serum HO-1 was measured upon diagnosis (D0) and at 7 days after diagnosis (D7). Results The serum HO-1 concentration could be measured in all healthy volunteers. The intra- and interassay tests and the percentage recovery test were acceptable. Compared with normal control subjects, patients with ARDS had significantly higher D0 HO-1 concentrations (75.4 ng/mL versus 31.7 ng/mL, P < 0.001). The 28-day survival was significantly better in patients with low D0 HO-1 (<75.8 ng/mL) than in those with high D0 HO-1 (≥75.8 ng/mL) (mortality rate: 18% versus 73%, P=0.016). Nonsurvivors had significantly higher D0 and D7 HO-1 concentrations than survivors (P < 0.05). Conclusion Serum HO-1 may be a useful biomarker to predict the prognosis of patients with ARDS.
Clients’ Perceptions of HIV/AIDS Supportive Counseling in Botswana: A Qualitative Study

Introduction Clients’ Perceptions of HIV/AIDS Supportive Counseling in Botswana: A Qualitative Study Stockton R*1, Paul T2, Mokalake E2, Morran DK3, Powless MD4, Goldberg D4, Li S4, Blackwell N4, and Sharma R4 1Department of Counseling and Educational Psychology, Indiana University, Bloomington, USA 2Institute for Development Management, Botswana, Africa 3Indiana University-Purdue University Indianapolis, USA 4Indiana University, USA *Corresponding author: Stockton R, Department of Counseling and Educational Psychology, Indiana University, Bloomington, IN 47405, E-mail: stocktor@indiana.edu
Susceptibility to misdiagnosis of adversarial images by deep learning based retinal image analysis algorithms

Deep learning algorithms, typically implemented as Convolutional Neural Networks (CNNs), in recent years have gained traction in medical image analysis. The majority of CNNs employed in retinal image diagnosis applications are image-based; wherein input is the retinal image and output is the classification/diagnosis, resulting in a blackbox like algorithm. In contrast, hybrid lesion-based algorithms employ multiple CNN-based detectors to categorically detect various lesions in the image, and final diagnosis is computed from combination of detector outputs. Such algorithms are more physiologically plausible and provides explainability of the final prediction through intermediate detector outputs. Both classes of algorithms have reported equal diagnostic performance and outperform clinical experts in detecting referable diabetic retinopathy (rDR). However, CNNs are sensitive to adversarial images where a limited number of pixels are modified by a fraction of intensity while preserving global image context; leading to CNN misclassification. We compared diagnostic accuracy of the two classes of algorithms on adversarial images generated from rDR retinal images and results show that image-based CNNs are significantly more susceptible to adversarial images than hybrid lesion-based algorithms.
ASSESSING THE PHONETIC LEVEL IN L2 VOWELS PRODUCTION WITH THE VISUVO SOFTWARE: CASE OF CZECH LEARNERS ACQUIRING CONTRASTS BETWEEN FRENCH MID VOWELS e/ɛ, ø/œ AND o/ɔ

The VisuVo software is designed to visually compare formants of vowels as uttered by different speakers, in isolation and in different contexts or prosodic positions. Both researchers and teachers can use the software. VisuVo aims to evaluate the initial phonetic level of a learner and to trace the progress in L2 vowels realisation compared to a set of pre-recorded references. VisuVo has an interactive and simple interface to handle. Its features and output graphics are illustrated by a study of contrasts between the French mid vowels e/ɛ, o/œ and o/ɔ produced in isolation and in symmetrical contexts /p, t, k, R/ by 10 native non-southern French speakers representing the reference, and 10 Czech learners of French to be tested.
Improved Approximation for Node-Disjoint Paths in Grids with Sources on the Boundary

We study the classical Node-Disjoint Paths (NDP) problem: given an undirected $n$-vertex graph G, together with a set {(s_1,t_1),. ,(s_k,t_k)} of pairs of its vertices, called source-destination, or demand pairs, find a maximum-cardinality set of mutually node-disjoint paths that connect the demand pairs. The best current approximation for the problem is achieved by a simple greedy $O(\sqrt{n})$-approximation algorithm. A special case of the problem called NDP-Grid, where the underlying graph is a grid, has been studied extensively. The best current approximation algorithm for NDP-Grid achieves an $\tilde{O}(n^{1/4})$-approximation factor. On the negative side, a recent result by the authors shows that NDP is hard to approximate to within factor $2^{\Omega(\sqrt{\log n})}$, even if the underlying graph is a sub-graph of a grid, and all source vertices lie on the grid boundary. In a follow-up work, the authors further show that NDP-Grid is hard to approximate to within factor $\Omega(2^{\log^{1-\epsilon}n})$ for any constant $\epsilon$ under standard complexity assumptions, and to within factor $n^{\Omega(1/(\log\log n)^2)}$ under randomized ETH. In this paper we study NDP-Grid, where all source vertices {s_1,. ,s_k} appear on the grid boundary. Our main result is an efficient randomized $2^{O(\sqrt{\log n} \cdot \log\log n)}$-approximation algorithm for this problem. We generalize this result to instances where the source vertices lie within a prescribed distance from the grid boundary. Much of the work on approximation algorithms for NDP relies on the multicommodity flow relaxation of the problem, which is known to have an $\Omega(\sqrt n)$ integrality gap, even in grid graphs. Our work departs from this paradigm, and uses a (completely different) linear program only to select the pairs to be routed, while the routing itself is computed by other methods.
Bf3R at SemEval-2018 Task 7: Evaluating Two Relation Extraction Tools for Finding Semantic Relations in Biomedical Abstracts

Automatic extraction of semantic relations from text can support finding relevant information from scientific publications. We describe our participation in Task 7 of SemEval-2018 for which we experimented with two relations extraction tools - jSRE and TEES - for the extraction and classification of six relation types. The results we obtained with TEES were significantly superior than those with jSRE (33.4% vs. 30.09% and 20.3% vs. 16%). Additionally, we utilized the model trained with TEES for extracting semantic relations from biomedical abstracts, for which we present a preliminary evaluation.
Structural studies and molecular dynamics simulations suggest a processive mechanism of exolytic lytic transglycosylase from Campylobacter jejuni

The bacterial soluble lytic transglycosylase (LT) breaks down the peptidoglycan (PG) layer during processes such as cell division. We present here crystal structures of the soluble LT Cj0843 from Campylobacter jejuni with and without bulgecin A inhibitor in the active site. Cj0843 has a doughnut shape similar but not identical to that of E. coli SLT70. The C-terminal catalytic domain is preceded by an L-domain, a large helical U-domain, a flexible linker, and a small N-terminal NU-domain. The flexible linker allows the NU-domain to reach over and complete the circular shape, using residues conserved in the Epsilonproteobacteria LT family. The inner surface of the Cj0843 doughnut is mostly positively charged including a pocket that has 8 Arg/Lys residues. Molecular dynamics simulations with PG strands revealed a potential functional role for this pocket in anchoring the negatively charged terminal tetrapeptide of the PG during several steps in the reaction including homing and aligning the PG strand for exolytic cleavage, and subsequent ratcheting of the PG strand to enhance processivity in degrading PG strands.
The Effects of Dabigatran and Rivaroxaban on Markers of Polymorphonuclear Leukocyte Activation

Dabigatran is an oral direct thrombin inhibitor, and rivaroxaban, a factor Xa inhibitor. Dabigatran has been implicated in the etiology of acute coronary syndromes and as these occur following inflammatory changes in the endothelium, we investigated the inflammatory potential of these agents in vitro. In order to do so, polymorphonuclear leukocytes (PMNL) were isolated from heparinized venous blood from non-smoking, healthy adults and exposed to dabigatran or rivaroxaban (0.5–10 µM). Generation of reactive oxygen species (ROS), elastase release, cytosolic Ca2+ fluxes, neutrophil extracellular trap (NET) formation and cell viability were measured using chemiluminescence, spectrophotometric and flow cytometric procedures respectively. However, with the exception of modest inhibitory effects on elastase release, neither agent at concentrations of up to 10 µM affected these markers of PMNL activation. Although no pro-inflammatory effects of dabigatran nor any difference between the two test agents were detected in vitro, the existence of a pro-inflammatory mechanism involving the generation of thrombin during dabigatran therapy cannot be fully excluded.
Multiple instance learning of deep convolutional neural networks for breast histopathology whole slide classification

Whole slide histopathology images are the digitised version of physical slides that are acquired by stitching multiple overlapping image patches acquired at different optical magnifications. Typically sized at 2 GB per slide, computer aided analysis for whole slide pathology classification is a challenging task. Since local image patches generally provide more discriminative information as compared to the whole slide, challenges are associated with handling pathological heteregeneity varying across the whole slide. Given this challenge to classify the slide pathology using local image patches, in this paper we propose a multiple instance learning (MIL) framework for convolutional neural network (CNN). We introduce a new pooling layer that helps to aggregate most informative features from patches constituting a whole slide, without necessitating inter-patch overlap or global slide coverage. This helps our method to jointly learn to discover informative features locally as well as learn the classification margin globally; without the explicit need for individually annotating each local image patch in the training data. We have experimentally evaluated performance using a patient level 5-folded cross-validation with 58 malignant and 24 benign cases of breast tumors to obtain best performance of 89.52%, 89.06%, 88.84%, 87.67% accuracy at 40×, 100×, 200 × and 400× magnifications respectively while processing each slide in under 40 ms.
Peripheral Nerve Injury-Induced Astrocyte Activation in Spinal Ventral Horn Contributes to Nerve Regeneration

Accumulating evidences suggest that peripheral nerve injury (PNI) may initiate astrocytic responses in the central nervous system (CNS). However, the response of astrocytes in the spinal ventral horn and its potential role in nerve regeneration after PNI remain unclear. Herein, we firstly illustrated that astrocytes in the spinal ventral horn were dramatically activated in the early stage following sciatic nerve injury, and these profiles were eliminated in the chronic stage. Additionally, we found that the expression of neurotrophins, including brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF), and neurotrophin-3 (NT-3), also accompanied with astrocyte activation. In comparison with the irreversible transected subjects, astrocyte activation and the neurotrophic upregulation in the early stage were more drastic in case the transected nerve was rebridged immediately after injury. Furthermore, administering fluorocitrate to inhibit astrocyte activation resulted in decreased neurotrophin expression in the spinal ventral horn and delayed axonal regeneration in the nerve as well as motor function recovery. Overall, the present study indicates that peripheral nerve injury can initiate astrocyte activation accompanied with neurotrophin upregulation in the spinal ventral horn. The above responses mainly occur in the early stage of PNI and may contribute to nerve regeneration and motor function recovery.
Local Identifiability of $\ell_1$-minimization Dictionary Learning: a Sufficient and Almost Necessary Condition

We study the theoretical properties of learning a dictionary from $N$ signals $\mathbf x_i\in \mathbb R^K$ for $i=1,. ,N$ via $l_1$-minimization. We assume that $\mathbf x_i$'s are $i.i. d. $ random linear combinations of the $K$ columns from a complete (i.e., square and invertible) reference dictionary $\mathbf D_0 \in \mathbb R^{K\times K}$. Here, the random linear coefficients are generated from either the $s$-sparse Gaussian model or the Bernoulli-Gaussian model. First, for the population case, we establish a sufficient and almost necessary condition for the reference dictionary $\mathbf D_0$ to be locally identifiable, i.e., a local minimum of the expected $l_1$-norm objective function. Our condition covers both sparse and dense cases of the random linear coefficients and significantly improves the sufficient condition by Gribonval and Schnass (2010). In addition, we show that for a complete $\mu$-coherent reference dictionary, i.e., a dictionary with absolute pairwise column inner-product at most $\mu\in[0,1)$, local identifiability holds even when the random linear coefficient vector has up to $O(\mu^{-2})$ nonzeros on average. Moreover, our local identifiability results also translate to the finite sample case with high probability provided that the number of signals $N$ scales as $O(K\log K)$.
Acute toxicity of double-walled carbon nanotubes to three aquatic organisms

This study investigated the toxicity of double walled carbon nanotubes (DWCNTs) to three aquatic organisms, namely, Pseudokirchneriella subcapitata, Daphnia pulex, and Poecilia reticulata under the influence of exposure media properties specifically the ionic strength and organic matter represented by humic acid. Results indicated that ionic strength enhanced DWCNTs agglomeration whilst humic acid stabilized the CNTs and in turn inhibited the formation of aggregates. LC50s for D. pulex were higher at 2.81 and 4.45 mg/L for pristine and oxidised DWCNTs, respectively; however, P. reticulata had lower values of 113.64 mg/L and 214.0 mg/L for the same CNTs correspondingly. P. subcapitata had EC50s of 17.95 mg/L and 10.93 mg/L for the pristine and oxidised DWCNTs, respectively. In the presence of humic acid high DWCNTs acute toxicity towards D. pulex and P. reticulata was observed but ionic strength led to opposite effect irrespective of DWCNTs form. Both humic acid and ionic strength shielded the P. subcapitata from toxic effects of DWCNTs. Overall, our findings suggest that the toxicity of DWCNTs in the aquatic systems (i) will be dependent on media properties and (ii) is likely to proceed at different rates to organisms at different trophic levels.
Evaluation of machine learning models for employee churn prediction

Employees are the valuable assets of any organization. But if they quit jobs unexpectedly, it may incur huge cost to any organization. Because new hiring will consume not only money and time but also the freshly hired employees take time to make the respective organization profitable. Hence in this paper we try to build a model which will predict employee churn rate based on HR analytics dataset obtained from Kaggle website. To show the relation between attributes, the correlation matrix and heatmap is generated. In the experimental part, the histogram is generated, which shows the contrast between left employees vs. salary, department, satisfaction level, etc. For prediction purpose, we use five different machine learning algorithms such as linear support vector machine, C 5.0 Decision Tree classifier, Random Forest, k-nearest neighbor and Naïve Bayes classifier. This paper proposes the reasons which optimize the employee attrition in any organization.