Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid ocular responses are a robust marker for bottom-up driven auditory salience

Despite the prevalent use of alerting sounds in alarms and human-machine interface systems, we have only a rudimentary understanding of what determines auditory salience - the automatic attraction of attention by sound - and the brain mechanisms which underlie this process. A major roadblock to understanding has been the lack of a robust, objective means of quantifying sound-driven attentional capture. Here we demonstrate that microsaccade inhibition – an oculomotor response arising within 300ms of sound onset - is a robust marker for the salience of brief sounds. Specifically, we show that a ‘crowd sourced’ (N=911) subjective salience ranking correlated robustly with the superior colliculus (SC) mediated ocular freezing response measured in naïve, passively listening participants (replicated in two groups of N=15 each). More salient sounds evoked earlier microsaccadic inhibition, consistent with a faster orienting response. These results establish that microsaccade-indexed activity within the SC is a practical objective measure for auditory salience. Significance statement Microsaccades are small, rapid, fixational eye movements, measurable with sensitive eye-tracking equipment. We reveal a novel, robust link between microsaccade dynamics and the subjective salience of brief sounds (salience ranking obtained from a large number of participants in an online experiment): Within 300 ms of sound onset, the eyes of naïve, passively listening participants demonstrate different microsaccade patterns as a function of the sound’s perceptual salience. These results position the Superior Colliculus (the generator of microsaccades) as an important brain area to investigate in the context of a putative multi-modal salience-hub and establish a robust objective means for quantifying auditory salience, critical in the development of sound-based human machine interfaces.
Building a Corpus for Vietnamese Text Readability Assessment in The Literature Domain

Text readability is a measure of how easy or difficult it is to read a text. This readability factor plays a crucial role in the processes of drafting and comprehending the texts, affecting the choice of proper texts for reading. Studies on the readability of text have started since the late nineteenth century and there have been many practical applications. However, these studies are mainly performed in English and other popular languages. In Vietnamese, the study of the text readability is still relatively untapped and has only received attention in recent years in the process of improving the curriculum and teaching methods. Recent studies on the readability of text in Vietnamese language are still limited, the main reason was largely due to the lack of text resources, which are corpora graded accordingly to difficulty levels. Therefore, in this study, we focused on building a corpus for assessing the readability of Vietnamese texts in the literature domain through the process of collecting, processing and evaluating documents. The result is that we have built up a corpus of 1,825 Vietnamese texts, divided into four levels of difficulty (Very easy, Easy, Medium and Difficult). Experiments with the existing Vietnamese readability assessment methods show that the built corpus is reliable and usable for further research on the text readability.
Profitability and Water Productivity of Small Scale Irrigation Schemes in Northern Ghana

The Savelugu-Nanton District of Northern Ghana is a beneficiary of irrigation projects mostly on small scale basis schemes. Poor data situation due to inadequate appraisal of these schemes results in difficulty to track their progress and impacts, which threatens their sustainability. This study was conducted to assess the profitability and productivity of the Libga and Bunglung small scale irrigation schemes in the District between 2013 and 2015. Sixty households were selected using random sampling techniques. Production data, costs, yield and soil data were gathered using structured questionnaires and field measurements. Data on traditional rainfed systems were gathered from secondary information. The results indicated that yields of rice were greater in Bunglung than in Libga scheme but both schemes had greater yields than rainfed systems, resulting in greater profits under irrigation. However, yields of pepper were greater in Libga than in Bunglung. Crop water productivity (CWP) in terms of harvested weight of rice was 0.50 and 0.58 kilogram per cubic meter in Libga and Bunglung respectively while CWPs in terms of gross value of harvested rice were 0.38 and 0.41 Ghana cedis per cubic meter respectively. For pepper, the CWPs were 0.74 and 0.64 kilogram per cubic meter in terms of crop weight in Libga and Bunglung respectively while CWPs in terms of gross value were 1.23 and 1.07 Ghana cedis per cubic meter respectively. Irrigation improved farmers’ incomes, however, pepper production was more profitable than rice production at both schemes. More investments by farmers are important to achieving maximum yields.
Causal Models for Real Time Bidding with Repeated User Interactions

A large portion of online advertising displays are sold through an auction mechanism called Real Time Bidding (RTB). Each auction corresponds to a display opportunity, for which the competing advertisers need to precisely estimate the economical value in order to bid accordingly. This estimate is typically taken as the advertiser's payoff for the target event -- such as a purchase on the merchant website attributed to this display -- times this event estimated probability. However, this greedy approach is too naive when several displays are shown to the same user. The purpose of the present paper is to discuss how such an estimation should be made when a user has already been shown one or more displays. Intuitively, while a user is more likely to make a purchase if the number of displays increases, the marginal effect of each display is expected to be decreasing. In this work, we first frame this bidding problem with repeated user interactions by using causal models to value each display individually. Then, based on this approach, we introduce a simple rule to improve the value estimate. This change shows both interesting qualitative properties that follow our previous intuition as well as quantitative improvements on a public data set and online in a production environment.
Preliminary pharmacological evaluation of Manilkara zapota stems bark extract for ulcerative colitis in rats

Inflammatory bowel disease (IBD), which comprises Crohn disease and ulcerative colitis, characterizes a group of chronic diseases illustrated GIT. The chief reason of IBD are not well implicit, but inequities in proinflammatory cytokines like TNF-α, IFN-γ, IL-1, IL-6, and IL-12 and anti-inflammatory cytokines as well as IL-4, IL-10, and IL-11 are consideration to participate an innermost character in mediating and modulating inflammation (Jump and Levine, 2004). Ulcerative colitis (UC) is a rectal and colonic mucosal chronic, idiopathic, inflammatory bowel disease (IBD). It is characterized by colonic inflammation, most likely due to the infiltration of polymorphonuclear cells, lymphocytes, monocytes and plasma cells, accompanied by oxygen-free radicals, which ultimately leads to mucosal alteration and ulceration (Cho ., 2007). et al
Multi‐objective optimal provision of fast frequency response from EV clusters

: Declining levels of rotational inertia in modern power systems prompt transmission system operators (TSOs) to develop novel ways of maintaining the balance between generation and demand. Services such as fast frequency response (FFR) can help the TSO achieve this balance. The growing penetration of electric vehicles (EVs) promotes the provision of FFR from clusters of EVs. Fast charging stations are more geared towards destination charging, whereas slow charging stations are more attractive as providers of FFR, given the longer connection times. In this study, the provision of FFR from EV clusters is formulated as a multi-objective optimisation problem with network security constraints and two minimisation objectives, i.e. the maximum frequency deviation following a disturbance and the energy provided by public EV charging stations. A methodology was developed to solve the optimisation problem with a variant of the non-dominated sorting genetic algorithm. This methodology allows the decision-maker to consider trade-offs among the objectives, leading to a more informed decision. An enhanced frequency-responsive aggregate model of an EV cluster was developed to study the provision of FFR in a multi-area power system. A reduced model of the Nordic power system was used to illustrate the performance of the proposed methodology.
Robust temperature control of thermostatic oven based on adaptive and fuzzy algorithm

The authors propose a simple robust temperature control system for a thermostatic oven based on a fuzzy I-P controller and a fuzzy passive adaptive compensator. The proposed control algorithm suppresses the state error caused by dynamics variation, disturbances, and dead time. Numerical results that confirm the validity of the proposed robust temperature control of a thermostatic oven are presented. <<ETX>>
Sociomaterial struggle: An ethnographic analysis of power, discourse, and materiality in a working class unemployment support organization

Using the ontological lens of sociomateriality with the theoretical notion of struggle, this critical ethnography explores interactions within an unemployment support organization run by white collar workers who train working class populations for skilled blue collar occupations. Results illustrate how sociomaterial struggle is enacted in two ways: struggle against embodied others and struggle against discursive forces. The first theme, struggle for control, occurs between blue collar trainees and white collar trainers. Second, the struggle for upward mobility, occurs when trainers and trainees struggled alongside one another in collaboration toward the goal of upward mobility. Participants' struggles organize in a complex combination of materiality and discourse throughout this unemployment support organization, mirroring social class relations in the United States.
Performance analysis of local exit for distributed deep neural networks over cloud and edge computing

In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to end‐device service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation‐intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge‐computing environment. Our test results show that a single‐exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi‐exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single‐exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.
Efficient WENOCU4 scheme with three different adaptive switches

Although classical WENOCU schemes can achieve high-order accuracy by introducing a moderate constant parameter C to increase the contribution of optimal weights, they exhibit distinct numerical dissipation in smooth regions. This study presents an extension of our previous research which confirmed that adaptively adjusting parameter C can indeed overcome the inadequacy of the usage of a constant small value. Cmin is applied near a discontinuity while Cmax is used elsewhere and they are switched according to the variation of the local flow-field property. This study provides the reference values of the adaptive parameter C of WENOCU4 and systematically evaluates the comprehensive performance of three different switches (labeled as the binary, continuous, and hyperbolic tangent switches, respectively) based on an optimized efficient WENOCU4 scheme (labeled as EWENOCU4). Varieties of 1D scalar equations, empirical dispersion relation analysis, and multi-dimensional benchmark cases of Euler equations are analyzed. Generally, the dissipation and dispersion properties of these three switches are similar. Especially, employing the binary switch, EWENOCU4 achieves the best comprehensive properties. Specifically, the binary switch can efficiently filter more misidentifications in smooth regions than others do, particularly for the cases of 1D scalar equations and Euler equations. Also, the computational efficiency of the binary switch is superior to that of the hyperbolic tangent switch. Moreover, the optimized scheme exhibits high-resolution spectral properties in the wavenumber space. Therefore, employing the binary switch is a more cost-effective improvement for schemes and is particularly suitable for the simulation of complex shock/turbulence interaction. This study provides useful guidance for the reference values of parameter C and the evaluation of adaptive switches.
Nonparametric variational learning of multivariate beta mixture models in medical applications

Clustering as an essential technique has matured into a capable solution to address the gap between the growing availability of data and deriving the knowledge from them. In this paper, we propose a novel clustering method “variational learning of infinite multivariate Beta mixture models. ” The motivation behind proposing this technique is the flexibility of mixture models to fit the data. This approach has the capability to infer the model complexity and estimate model parameters from the observed data automatically. Moreover, as a label‐free method, it could also address the problem of high costs of medical data labeling, which can be undertaken just by medical experts. The performance of the model is evaluated on real medical applications and compared with other similar alternatives. We demonstrate the ability of our proposed method to outperform widely used methods in the field as it has been shown in experimental results.
MDR Genes are Created and Transmitted in Plasmids and Chromosomes to Keep Normal Intestinal Microbiota Alive against High Dose Antibiotics- A Hypothesis

Copyright: © 2017 Chakraborty AK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. *Corresponding author: Asit Kumar Chakraborty, Department of Biochemistry and Biotechnology, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore-721102, West Bengal, India, Tel: 91+9339609268; E-mail: chakraakc@gmail.com
Multiply accumulate operations in memristor crossbar arrays for analog computing

Memristors are now becoming a prominent candidate to serve as the building blocks of non-von Neumann in-memory computing architectures. By mapping analog numerical matrices into memristor crossbar arrays, efficient multiply accumulate operations can be performed in a massively parallel fashion using the physics mechanisms of Ohm’s law and Kirchhoff’s law. In this brief review, we present the recent progress in two niche applications: neural network accelerators and numerical computing units, mainly focusing on the advances in hardware demonstrations. The former one is regarded as soft computing since it can tolerant some degree of the device and array imperfections. The acceleration of multiple layer perceptrons, convolutional neural networks, generative adversarial networks, and long short-term memory neural networks are described. The latter one is hard computing because the solving of numerical problems requires high-precision devices. Several breakthroughs in memristive equation solvers with improved computation accuracies are highlighted. Besides, other nonvolatile devices with the capability of analog computing are also briefly introduced. Finally, we conclude the review with discussions on the challenges and opportunities for future research toward realizing memristive analog computing machines.
A tags’ arrival rate estimation method using weighted grey model(1,1) and sliding window in mobile radio frequency identification systems

In order to guarantee the tag identification accuracy and efficiency in mobile radio frequency identification system, it is necessary to estimate the tags’ arrival rate before performing identification. This research aims to develop a novel estimation method based on improved grey model(1,1) and sliding window mechanism. By establishing tags’ dynamic arrival model, this article emphasizes the importance of tags’ arrival rate estimation in mobile radio frequency identification system. Using sliding window mechanism and weighted coefficients method, weighted grey model(1,1) with sliding window (WGMSW(1,1)) is proposed based on traditional grey model(1,1). For experimental verification, three kinds of data are used as original data in WGMSW(1,1). The experimental results show that the proposed method has lower estimation error rate, lower computation complexity, and high system stability.
Digital Storytelling Beyond Flagship Projects: Exploring Multimedia Work Routines in Higher Education Practical Training

ABSTRACT Research on longform multimedia storytelling has mainly focused on the practices of large-scale media outlets which have the necessary means to produce resource-intensive multimodal stories. This paper investigates how digital storytelling production can be managed with limited resources in smaller, more inexperienced teams in order to be applicable beyond flagship projects. It thereby explores the (new) skills future journalists need to acquire within their education in order to become proficient in multimedia work routines. Following the team-based learning approach (TBL), 41 early-career journalists produced multimedia stories in higher education practical training in a simulated low-budget editorial office. By conducting a quantitative survey among all participants and a qualitative content analysis of one guided interview per team (n = 7), results show that working according to individual skills, maintaining a strong group accountability and staying flexible were important success factors under these circumstances. The largest obstacles were the conceptualization of complex projects under timed conditions as well as the disproportionately high communication efforts. In order for the production of multimedia stories to become more efficient and have greater applicability to smaller media outlets, the implementation of both project management and communication skills in higher education practical and editorial training are recommended.
Is zooplankton an indicator of the water trophic level in dam reservoirs?

Abstract The trophic state of ten dam reservoirs was assessed using a zooplankton community. In order to determine the trophic state of dam reservoirs, we used indices calculated on the basis of density and species structure of Rotifera and Crustacea communities. Samples were collected once during summer 2012 in ten dam reservoirs. The largest numbers of zooplankton taxa were found in a lowland high meso-eutrophic dam reservoir (Koronowski) and the lowest ones in a submontane low eutrophic dam reservoir (Lubachowski). The trophic state was determined for the investigated dam reservoirs. In the case of the Rotifera community, the percentage of the tecta form in the Keratella cochlearis population and the proportion of high-trophic species in the total species density were the best indices to describe the water trophic status. In the case of the crustacean community, the best indices were the density of Crustacea and the biomass of Cyclopoida. The high value of all indices calculated on the basis of density and species structure of Rotifera and Crustacea was determined for Zygmunt August Lake, whose trophic status was defined as highly eutrophic to polytrophic. Our study has shown that zooplankton could be a good ecosystem indicator of the water trophic level in dam reservoirs.
AirSurf-Lettuce: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning

Aerial imagery is regularly used by farmers and growers to monitor crops during the growing season. To extract meaningful phenotypic information from large-scale aerial images collected regularly from the field, high-throughput analytic solutions are required, which not only produce high-quality measures of key crop traits, but also support agricultural practitioners to make reliable management decisions of their crops. Here, we report AirSurf-Lettuce, an automated and open-source aerial image analysis platform that combines modern computer vision, up-to-date machine learning, and modular software engineering to measure yield-related phenotypes of millions of lettuces across the field. Utilising ultra-large normalized difference vegetation index (NDVI) images acquired by fixed-wing light aircrafts together with a deep-learning classifier trained with over 100,000 labelled lettuce signals, the platform is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution in the field, based on which global positioning system (GPS) tagged harvest regions can be derived to enable growers and farmers’ precise harvest strategies and marketability estimates before the harvest.
Biochemical and Molecular Evaluation of the Plant Ecballium elaterium Extract Effects on Escherichia coli

Aims: This study was conducted to evaluate the genotoxic effects of fruit and leaf ethanolic extracts of Ecaballium elateruim on clinical and reference strains of E. coli (E. coli ATCC 25922). Methodology: The genotoxic effects of fruit and leaf ethanolic extracts were determined by using enterobacterial repetitive intergenic consensus (ERIC-PCR) and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Results: The results showed an alteration in DNA and protein profiles of both tested Escherichia coli strains treated with fruit and leaf extracts compared with untreated control. The alterations ranged between decreased or increased intensity of some bands, absence or appearance of new amplified fragments. Moreover, increased concentrations of E. elaterium extracts and increased time intervals seems to yield a more profound increase in total protein concentrations in both tested E. coli strains. Original Research Article Abu-Hijleh et al.; JABB, 19(2): 1-11, 2018; Article no.JABB. 43882 2 Conclusions: Such findings strongly indicate the genotoxic effects of E. elaterium extracts on both E. coli strains. The results draw attention to the unsafe, improper use of E. elaterium extracts in folkloric medicine and point out the capability of using E. elaterium to treat E. coli infections. Future studies should be needed to find out the exact mechanisms responsible for the observed genotoxicity.