Miracle-dz/UniFAR-FTUs huggingface dataset:
“A Zone of Indistinction” – A Critique of Giorgio Agamben’s Concept of Biopolitics

Until recently, the term “biopolitics” as developed by Michel Foucault was unknown beyond a group of experts and scholars. As Foucault understood it, the term designates what “brought life and its mechanisms into the realm of explicit calculations and made knowledgepower an agent of transformation of human life” (Foucault 1979: 143). He distinguished historically and analytically between two dimensions of this “power to life”, namely between the disciplining of the individual body, on the one hand, and the social regulation of the body of the population, on the other. According to Foucault, biopolitics marks the threshold of political modernity since it places life at the center of political order. In this theoretical perspective, there is an intimate link between the constitution of a capitalist society and the birth of biopolitics: “Society’s control over individuals was accomplished not only through consciousness or ideology but also in the body and with the body. For capitalist society, it was biopolitics, the biological, the corporal, that mattered more than anything else” (Foucault 2000: 137). Furthermore, the introduction of the concept of biopolitics by Foucault marks a theoretical critique of the “juridico-discursive” model of power (Foucault 1979: 82). In this model, power is assumed to be exercised as interdiction and repression in a framework of law and legality resting ultimately on the problem of sovereignty. In contrast, Foucault uses the notion of biopolitics to stress the productive capacity of power that cannot be reduced to the ancient sovereign “right of death”. While sovereignty mainly operated as a “subtraction mechanism” that seized hold of life in order to suppress it, the new life-administering power is dedicated to inciting, reinforcing, monitoring and optimizing the forces under its control (see Foucault 1979). Today, the term “biopolitics” is used more and more frequently in scientific literature and journalistic texts. Mostly it is employed as a neutral notion or a general category to point out the social and political implications of biotechological interventions. This technology centred approach ignores the historical and critical dimension of the Foucauldian notion, how technological developments are embedded in more global economic strategies and political
Distilling Knowledge from a Multi-scale Deep CNN Ensemble for Robust and Light-weight Acoustic Modeling

This paper presents our work on constructing a multi-scale deep convolutional neural network (CNN) ensemble for robust and light-weight acoustic modeling. Several VGG nets are used that differ solely in the kernel size of the convolutional layers. The ensemble serves as teacher for distilling knowledge into a much simpler student CNN. We compare the performance of the distilled CNN model with the results of system combination. We show that the knowledge distillation from a multi-scale ensemble yields equal performance with the best conventional combination methods, with a much simpler system architecture and decoding pipeline.
Optimal Reactive Power Dispatch for Voltage Instability Alleviation Using Evolutionary Programming Technique

In the current power system planning and operation, a lot of concern has been shown in the security of the system and stability analysis. The load sharing or generation scheduling patterns have resulted in heavy flows which lead to greater losses; intimidate stability, security and eventually making certain generation patterns undesirable. With increased loading of existing power transmission systems, the voltage instability problem and voltage collapse have also become a major anxiety in power system planning and operation. Although voltage stability is more dependent on the reactive power sources or voltage profile in the system, it is also a function of real power flows. Voltage instability can be alleviated by performing reactive power dispatch in power transmission system. Reactive power dispatch problem embraced the voltage instability alleviation and minimisation of real power transmission losses while satisfying the equality and inequality constraints. This paper presents the optimal reactive power dispatch as a reactive power planning approach for alleviating voltage instability problem based on Evolutionary Programming (EP) optimisation technique. A line-based voltage stability index was chosen as the basis of the optimisation process, while voltage instability alleviation was chosen as the objective function. Extensive testing of EP was conducted and results are included to demonstrate the effectiveness of the proposed technique. From the study, it was revealed that the proposed EP optimisation technique has successfully alleviated the voltage instability condition and reduced the total loss, while improving the voltage in the system within minimal iteration number. Key-Words: Reactive power dispatch, Voltage instability alleviation, Evolutionary Programming, Objective function.
Protein Kinase A Is Central for Forward Transport of Two-pore Domain Potassium Channels K2P3.1 and K2P9.1*

Acid-sensitive two-pore domain potassium channels (K2P3.1 and K2P9.1) play key roles in both physiological and pathophysiological mechanisms, the most fundamental of which is control of resting membrane potential of cells in which they are expressed. These background “leak” channels are constitutively active once expressed at the plasma membrane, and hence tight control of their targeting and surface expression is fundamental to the regulation of K+ flux and cell excitability. The chaperone protein, 14-3-3, binds to a critical phosphorylated serine in the channel C termini of K2P3.1 and K2P9.1 (Ser393 and Ser373, respectively) and overcomes retention in the endoplasmic reticulum by βCOP. We sought to identify the kinase responsible for phosphorylation of the terminal serine in human and rat variants of K2P3.1 and K2P9.1. Adopting a bioinformatic approach, three candidate protein kinases were identified: cAMP-dependent protein kinase, ribosomal S6 kinase, and protein kinase C. In vitro phosphorylation assays were utilized to determine the ability of the candidate kinases to phosphorylate the channel C termini. Electrophysiological measurements of human K2P3.1 transiently expressed in HEK293 cells and cell surface assays of GFP-tagged K2P3.1 and K2P9.1 enabled the determination of the functional implications of phosphorylation by specific kinases. All of our findings support the conclusion that cAMP-dependent protein kinase is responsible for the phosphorylation of the terminal serine in both K2P3.1 and K2P9.1.
Atherosclerosis precursors in Finnish children and adolescents. IX. Socioeconomic status and risk factors of coronary heart disease

ABSTRACT. Relationships between parental Socioeconomic status and CHD risk factors were examined in the Finnish Multicentre Study on Atherosclerosis Precursors comprising a material of 3,596 study subjects aged 3–18 years in five university cities and 12 rural communes. The work is based on the hypothesis that Socioeconomic status has associations with important lifestyle factors related to the evolution of CHD risk factors. Although there is some indication that parental status variables correlate with CHD risk factors, the majority of the data point to the conclusion that Socioeconomic status indicators have little relevance for children's CHD risk factor levels. The main exceptions to the above stated were the lower P/S ratio of the diet in farmers’children compared to the other Socioeconomic groups in all age cohorts, and the higher serum total and LDL‐cholesterol levels in the farmers’children as compared to the others in some age cohorts. The explanation to these findings is the dietary practice in farmers’households, full milk and butter being favoured instead of other milk types and vegetable margarine, respectively. Our findings illustrate the importance of the families’dietary habits with regard to certain CHD risk factors.
The thickness imaging of channels using multiple-frequency components analysis

Blending of different frequency components of seismic traces is a common way to estimate the relative time thickness of the formation. Red, blue, and green (RGB) color blending is one of the most popular blending models in analyzing multiple seismic attributes. Geologists and geophysicist interpreters typically associate low-frequency components (formations with the largest thickness value) with a red color, medium-frequency components (formations with a medium thickness value) with a green color, and high-frequency components (formations with the smallest thickness value) with a blue color for the thickness estimation of thin beds using frequency components. However, we found that the same result of RGB blending may come from different sets of three frequency components. As a result, the same blended color may correspond to several different time thicknesses. It is also very difficult to interpret the corresponding thickness of the blended colors such as white and yellow. To avoid the ambiguity of time-thickness estimation using RGB blending, we have estimated the time thickness of the thin beds using all of the frequency components in a user-defined frequency band instead of only three frequency components. Our workflow begins with the normal seismic spectral decomposition. Considering that the different reflectivity pairs with a different time thickness have a different amplitude spectrum, we then use the self-organizing map to cluster the decomposed amplitude spectra of seismic traces. We finally assign each cluster with a relative thickness by comparing the clustered results with well logs.
Is the hypothesis that the commonest fetal heart rate decelerations are caused by peripheral chemoreflex due to fetal hypoxaemia correct?

A thought-provoking review by a group of experts proposes that head compression causing fetal heart rate (FHR) decelerations in labour is a myth (Lear et al. 2016). It also concludes that all FHR decelerations are due to fetal hypoxaemia and hence their time relationship to contractions is of no relevance. This is not just of academic interest because when a nonacidaemic fetus develops serious acidaemia during labour it invariably displays FHR decelerations reflecting its deterioration. Hence, meaningful cardiotocography (CTG) interpretation is not possible
A novel approach for research paper abstracts summarization using cluster based sentence extraction

This paper presents a part of research work to develop a method for automatic summarization of sets of research paper abstracts of desired subject in a specific area that may be retrieved by a digital library system or search engine in response to a user query. Many digital libraries or online services provide research papers published in journals, conferences or workshops for reference purpose to the user. Research papers contain a wealth of high quality information by specifying research objectives, research methods, evaluation of research objectives, research results and concluding remarks. However, a research paper abstract is relatively long and browsing too many of such research paper abstracts results in information overload. Therefore, it would be helpful to summarize a set of research paper abstracts to assist users in grasping the main ideas on a desired topic in a specific area.
Practical issues of ontology design, evaluation and reuse: a case study in chemical and process engineering

Ontologies are a useful tool for knowledge representation, sharing and reuse. Their potential has been recognised in a variety of domains and the number of publicly available ontologies, more than 10,000 as shown by dedicated search engines (Swoogle 2009), is a good indicator of the scale of development and use of ontologies across disciplines. Developing an ontology is a time consuming process which requires a high level of domain specific expertise. Ontologies are by definition designed to be shared and reused. Ontology reuse is a paramount activity for knowledge engineers who, in turn, are expected to reduce the cost of development and to promote interoperability between applications. This is further amplified by the fact that many of existing ontologies cover complementary and/or overlapping domains. The aim of this work is to investigate the practical aspects of ontology reuse and development and devise a framework for these tasks with a focus on the domain of Process Systems Engineering. This is achieved by investigating a number of practical aspects of reusing ontologies with a focus in the domain of Process Systems Engineering, during the process of developing a full-scale ontology for the domain of Industrial Symbiosis – the eSymbiosis ontology. The practical aspects of reusing ontologies have been addressed throughout the lifecycle of ontology development. The proposed methodology – called MetROn – is focusing on reusing ontologies as well as making an ontology reusable. Also, a high level framework for evaluating existing ontology for potential reuse has also been devised. Both the proposed methodology and the proposed framework for ontology evaluation have been validated with the case study of eSymbiosis ontology.
Review and meta‐analysis of the epidemiology of congenital cytomegalovirus (CMV) infection

We reviewed studies that reported results of systematic cytomegalovirus (CMV) screening on fetuses and/or live‐born infants. The overall birth prevalence of congenital CMV infection was 0.64%, but varied considerably among different study populations. About 11% of live‐born infants with congenital CMV infection were symptomatic, but the inter‐study differences in definitions of symptomatic cases limit the interpretation of these data. Non‐white race, low socioeconomic status (SES), premature birth, and neonatal intensive care unit admittance were risk factors for congenital CMV infection. Birth prevalence increased with maternal CMV seroprevalence. Maternal seroprevalence accounted for 29% of the variance in birth prevalence between study populations. Maternal seroprevalence and birth prevalence were both higher in study populations that were ascertained at birth rather than in the prenatal period. Thus, timing of ascertainment should be considered when interpreting birth prevalence estimates. Birth prevalence was inversely correlated with mean maternal age, but this relationship was not significant when controlling for maternal seroprevalence. The rate of transmission to infants born to mothers who had a primary infection or a recurrent infection during pregnancy was 32% and 1.4%, respectively. Possible maternal primary infections (i.e. seropositive mother with CMV IgM) resulted in congenital infections about 20% of the time, but are likely to represent a mixture of primary and recurrent infections. In summary, CMV is a common congenital infection worldwide that can lead to permanent disabilities. There is an urgent need for interventions that can reduce the substantial burden of this often overlooked disease. Copyright © 2007 John Wiley & Sons, Ltd.
Development of low-cost embedded-based electrooculogram blink pulse classifier for drowsiness detection system

This paper discusses the development of a low-cost embedded-based electrooculogram (EOG) blink pulse classifier. A signal conditioning circuit from a single quad operational amplifier (Op-Amp) and an Arduino based on the ATmega32u4 AVR 8-bit microcontroller board comprised the major components of the embedded-based classifier. The evaluation of the nearest neighbor algorithm classifier resulted to an accuracy of 87.14%, precision of 93.33% and sensitivity of 80.00%. Further, based on the participants who evaluated the drowsiness detection system the results were 3.38 and 4.13 with verbal interpretations of comfortable and very convenient respectively.
3-D Modeling of Pyroelectric Sensor Arrays Part I: Multiphysics Finite-Element Simulation

When modeling pyroelectric sensors, not all relevant and coupled physical phenomena could be considered contemporaneously up to now. We describe a method to examine problems with coupled thermal, electrical, and mechanical fields using finite-element software. Modeling the complex interaction between the physical fields is realized by implementing a two-step approach that takes advantage of the internal computational routines of finite-element programs. The method will be exemplified on a standard pyroelectric sensor array, whose efficient 3-D modeling and simulation is demonstrated using a public accessible commercial finite element software.
Ultrasonic Air Bubble Detection Employing Signal Processing Techniques

An air bubble detection system is presented, suitable for medication drug delivery system that must avoid air bubble beyond certain limits. The suggested approach is based on transmission of an acoustic (ultrasonic) wave through the active detection area of the fluid media and analysis of its impact on the acoustic wave. Physical operation principle and design considerations are provided as well as the cross correlation based signal processing procedures. Experimental results are provided, showing good agreement with the theory
Fos Family Members Induce Cell Cycle Entry by Activating Cyclin D1

ABSTRACT Expression of the fos family of transcription factors is stimulated by growth factors that induce quiescent cells to reenter the cell cycle, but the cellular targets of the Fos family that regulate cell cycle reentry have not been identified. To address this issue, mice that lack two members of the fos family, c-fos and fosB, were derived. ThefosB−/− c-fos−/−mice are similar in phenotype to c-fos−/−mice but are 30% smaller. This decrease in size is consistent with an abnormality in cell proliferation. Fibroblasts derived fromfosB−/− c-fos−/−mice were found to have a defect in proliferation that results at least in part from a failure to induce cyclin D1 following serum-stimulated cell cycle reentry. Although definitive evidence that c-Fos and FosB directly induce cyclin D1 transcription will require further analysis, these findings raise the possibility that c-Fos and FosB are either direct or indirect transcriptional regulators of the cyclin D1 gene and may function as a critical link between serum stimulation and cell cycle progression.
Delay Prediction Based on Deep Stacked Autoencoder Networks

Accurate and timely flight delay information is essential for overall coordination of airports, airlines and air traffic management. However, because of the complexity of factors influencing flight delay, limitations like weak generalization ability, narrow application scope and unsatisfying prediction accuracy, could be found in existing flight delay prediction technology. Thus, a new method based on deep stacked autoencoders networks is proposed to predict flight delay in a future period, which totally considers its relationship with time and space and obtains information from high-dimensional data. A stacked autoencoder is adopted to train networks, deriving the characteristics of flight delay information from massive data by unsupervised learning and optimizing all the networks' parameters with backpropagation method. Future flight delay situation is predicted based on real data which was obtained from flight delay information of American airports announced in FAA website. The algorithm model reveals the evolution rule of flight delay in space-time variation and proves to be effective and superior after being compared with the performance of traditional neural network. Results from plenty of experiments have implicated that the prediction accuracy with deep stacked autoencoders is above 90%, which exceeds main delay prediction method over 5%, proving the efficiency of this method.
A Cost Efficient Approach to Correct OCR Errors in Large Document Collections

Word error rate of an OCR is often higher than its character error rate. This is especially true when OCRs are designed by recognizing characters. High word accuracies are critical for many practical applications like content creation and text-to-speech systems. In order to detect and correct the misrecognised words, it is common for an OCR to employ a post-processor module to improve the word accuracy. However, conventional approaches to post-processing like looking up a dictionary or using a statistical language model (SLM), are still limited. In many such scenarios, it is often required to remove the outstanding errors manually. We observe that the traditional post-processing schemes look at error words sequentially, since OCRs process documents one at a time. We propose a cost-efficient model to address the error words in batches rather than correcting them individually. We exploit the fact that a collection of documents (eg. a book), unlike a single document, has a structure leading to repetition of words. Such words, if efficiently grouped together and corrected together, can lead to a significant reduction in the effort. Error correction can be fully automatic or with a human in the loop. We compare the performance of our method with various baseline approaches including the case where all the errors are removed by a human. We demonstrate the efficacy of our solution empirically by reporting more than 70% reduction in the human effort with near perfect error correction. We validate our method on books in both English and Hind
FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT Applications

Fire disasters typically result in lot of loss to life and property. It is therefore imperative that precise, fast, and possibly portable solutions to detect fire be made readily available to the masses at reasonable prices. There have been several research attempts to design effective and appropriately priced fire detection systems with varying degrees of success. However, most of them demonstrate a trade-off between performance and model size (which decides the model's ability to be installed on portable devices). The work presented in this paper is an attempt to deal with both the performance and model size issues in one design. Toward that end, a `designed-from-scratch' neural network, named FireNet, is proposed which is worthy on both the counts: (i) it has better performance than existing counterparts, and (ii) it is lightweight enough to be deploy-able on embedded platforms like Raspberry Pi. Performance evaluations on a standard dataset, as well as our own newly introduced custom-compiled fire dataset, are extremely encouraging.
Dynamic Nonparametric Edge-Clustering Model for Time-Evolving Sparse Networks

Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner. Such behavior can be well-captured by structured, nonparametric edge-exchangeable graphs. However, such exchangeable models necessarily ignore temporal dynamics in the network. We propose a dynamic nonparametric model for interaction graphs that combine the sparsity of the exchangeable models with dynamic clustering patterns that tend to reinforce recent behavioral patterns. We show that our method yields improved held-out likelihood over stationary variants, and impressive predictive performance against a range of state-of-the-art dynamic interaction graph models.