Miracle-dz/UniFAR-FTUs huggingface dataset:
FUNDUS IMAGE CLASSIFICATION USING HYBRIDIZED GLCM FEATURES AND WAVELET FEATURES

We find the usefulness of computers in every field including medical field. Scanning the affected part has become a standard study. Diagnosing a disease at the right time, i.e. early detection, from the study of images enables the physician to take right decision and provide proper treatment to the patient. With the alarming growth of population, it is difficult for every individual patient to get a second opinion from medical expert. In these situations, computer-aided automatic diagnosis system will be much helpful. Diabetic retinopathy is a disorder that arises from increase in blood glucose level. Based on the severity, it has been distinguished into four stages. Diagnosing diabetic retinopathy at an early stage from retinal images and providing proper treatment will save the patient from severe vision loss. The proposed method adopts hybridized GLCM features and wavelet features to classify the fundus images according to the severity of the disease. The method is tested with fundus images collected from Indian Diabetic Retinopathy Dataset.
Optimal Fiscal Policy in a Model with Uninsurable Idiosyncratic Shocks (cid:3)

In a standard incomplete markets model a Ramsey planner chooses time-varying paths of proportional capital and labor income taxes, lump-sum transfers (or taxes), and government debt. Distortive taxes reduce the variance cross-sectionally and over time of after-tax income, improving welfare for redistributive and insurance motives, which we quantify. Optimal capital income tax is higher than labor income tax in the long run; it provides insurance more eﬃciently. The government accumulates assets providing redistribution via general equilibrium price eﬀects. The planners degree of inequality aversion only aﬀects policy in the short run. Ignoring transition leads to signiﬁcant welfare losses.
Application of Norm Concepts to Evaluation of Multiconductor Transmission Line Parameters

Recent developments in the field of ultrafast approximate methods for solution of large-scale problems concentrate on bounding of levels for the solutions of multiconductor transmission line equations [1,2] and State Space ODEs [6]. Here we apply an approach, which is similar to those in these references, to the solution of linear operator equations. Numerical solution of electromagnetic coupling involve approximation of linear operator by matrices and the solution of resulting linear matrix equation. Fortunately, in many cases an exact solution of the matrix equation need not be found. Rather it is sufficient simply to estimate an appropriate norm of the solution vector. This is done by obtaining the associated norms of the matrix blocks and solving the resulting linear matrix equation which has a much smaller dimension. This procedure has a cost of in the worst case, where M is the dimension of the original equation, N is the number of interacting conductors and typically . Numerical experiments showed that computational time can be reduced by several orders of magnitude, preserving reasonable accuracy of estimation. We provide an example for evaluation of a microstrip MTL parameters (capacitance matrix). Copyright (c) Nick Soveiko, 1995-96 Application of Norm Concepts to Evaluation of Multiconductor Transmission Line Parameters 1. Background For many practical purposes, phenomena of electromagnetic coupling is described by coupling coefficients, i.e. matrices of inductances, capacitances and resistances, which in general case are dependent on frequency, propagation mode and geometrical coordinates in 3-d space. The coupling coefficients are traditionally evaluated upon the results of solution of linear operator equations [4]:
Application of UAS for Monitoring of Forest Ecosystems

In the last couple of years, there have been a great number of articles that cover and emphasize the advantages and possibilities that UAS (Unmanned Air System) offers in forest ecosystem research. In the available research, alongside UAS, the importance of developing sensors that are designed to be used with UAV (Unamnned Air Vehicle), a flight programming software and UAS collected data processing software have been pointed out. With the widespread use of high-precision sensors and accompanying software in forestry, it is possible to obtain accurate data in a short time that replaces long-term manpower in the field with equal or in some cases, such as windthrow calculation or wildlife counting, greater accuracy. The former practice of manual imagery processing is being partly replaced with automated approaches. The paper analyses studies that deal with some form of application of UAS in forestry, e.g. forest inventory, forest operations, ecological monitoring, forest pests and forest fires, and wildlife monitoring. In the forest inventory, a large number of studies deal with the possibilities of applying UAS in mapping vegetation and individual trees, morphological research of individual parts of trees, surface analysis, etc. The use of remote and proximal sensing technologies in forest engineering has mainly been focused on defining surface roughness and topology, road geometry, planning and maintenance, ground-based and cable-based harvesting and soil characteristics and displacement. Wildfire monitoring already relies heavily on the use of UAS and thermal cameras in operations, and it is similar to the mapping of windthrow or directions of the spread of certain insects important for forestry. In wildlife research, numerous studies deal with abundance research of individual terrestrial birds and mammals using UAS thermal imagery. With some drawbacks such as wildlife disturbance or limited UAV range, common to most of the processed studies are positive attitudes regarding the application of UAS in forestry sensing and monitoring, which is slowly becoming a common operative practice, with the scientists’ focus being on developing automated approaches in UAS imagery processing. Reducing the error by improving the technological characteristics of the sensors will in the long run reduce the number of people required to collect data important for forestry, reduce risks and in some cases increase accuracy.
TEMPERATURE CONTROL OF CSTR A COMPARATIVE APPROACH

Most of the industrial processes including the chemical process industry are nonlinear in nature, but still control practitioners have been using linear control techniques to control such systems. In this paper the design of a nonlinear feedback controller is analyzed for temperature control of continuous stirred tank reactors (CSTR) which have strong nonlinearities. The reaction temperature is the most important process parameter in CSTR operation process, and the control of reaction temperature quality is closely related to the production efficiency and economic benefit. Here Particle Swarm Optimization (PSO) algorithm based PID controller tuning is used for the temperature control of CSTR, The performance is analyzed for the controllers designed by using PID, PID-GA and PID-PSO methods, identifying a suitable controller and enhanced the system performance. The Integral Square Error (ISE) criterion is used to guide PSO algorithm to search the controller parameters like Kp, Ki , Kd . A comprehensive simulation is carried out with PID and GA controller Structures. The simulation results show that the PSO based PID controller tuning approach provides better performance compared to other conventional PID tuning methods.
Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis.

BACKGROUND Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. METHODS Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. RESULTS Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC). CONCLUSIONS These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.
Weighing Risk Factors for Adolescent Victimization in the Context of Romantic Relationship Initiation

Research has paid little attention to the link between the characteristics of the relational context where adolescents are likely to initiate their romantic relationships and teen dating violence (TDV). Hence, the findings are still scattered. This study examined different risks in the female teenagers’ relational context (peer group characteristics, participants’ risky activities, and pressure to start dating) and their TDV victimization, which had not been previously studied in the Spanish population. The moderating role of parental monitoring strategies was also analyzed. Participants were 1,248 Spanish female teenagers who completed measures of the aforementioned factors. Highly victimized girls reported having more deviant and older male peers, receiving more pressure to start dating, and using more alcohol and drugs than participants with low victimization did. High parental monitoring was only effective to prevent TDV victimization in low-risk relational contexts. The findings extend prior research by providing evidence of the risk of pressure to start dating and low effectiveness of parental monitoring against high-risk peers. They also highlight the need to reduce specific risks of TDV in the adolescent relational context.
Recognizing and Managing the Deteriorating Patient: the Role of Rapid Response Systems, Critical Care Outreach Nurse and Medical Emergency Team

The last decades have witnessed the emergence of a new strategy to identify and respond to clinical deterioration in acute care hospitals, the RRS)/MET (Jones et al., 2009; Jones et al., 2015). This concern emerged due to the increased acuity of ward patients [Armitage et al., 2007; Australian Commission on Safety and Quality in Health Care (ACSQHC), 2012], the limited beds in ICU and limited critical care resources in ward services. Critical care outreach nurses have played a pivotal role in the development, operation and management of the RRS (Niven et al., 2014; Stelfox et al., 2014) whether they are medicalor nurse-led (Devita et al., 2006). The MET has become an accreditation requirement in some countries and is a highly recommended strategy for recognising and responding to clinical deterioration in acute care hospitals (ACSQHC, 2012). Critical care nurses' contribution to the management of clinical deterioration is noteworthy. Outreach nurses in the UK, liaison nurses in Australia or ICU liaison nurses “enfermeros de vinculación con la UCI” in Argentina are clinical experts, who use advanced assessment, technical, teaching and communication skills to both assist in the care of complex patients and support nurses who are providing care to these patients (Alberto et al., 2014; Chaboyer et al., 2004; Green & Edmonds, 2004; Williams et al., 2012). Patients discharged from ICU are also vulnerable to clinical deterioration as they are recovering from a critical illness (Stelfox et al., 2014) requiring clinical expert surveillance to identify early signs of clinical deterioration and trigger a response mechanism (Eliott et al., 2012; Priestley et al., 2004).
Inter-Peer Group Status and School Bullying: The Case of Middle-School Students in Japan

The aim of the current study was to investigate the relationship between school bullying and inter-peer group status among Japanese middle-school students. A total of 2294, 7th–9th grade Japanese middle-school students participated in this study. Using a self-report questionnaire, we measured their interand intra-peer group status, bullying behavior, victimization, coping strategy, and current victimization status. The results showed that, compared to those in the higher-status groups, students in lower-status peer groups tended to be bullied and experienced prolonged victimization. These findings suggest that a lower peer-group status predisposes students to victimization.
Target selection method on the occluded and distant object in handheld augmented reality

Existing interaction techniques within handheld augmented reality (AR) have frequently used touchscreen input (pure two-dimensional (2D) pointing and clicking) from the handheld device's display for target selection on the virtual object. However, performing accurate target selection on a distant target object becomes challenging as the target object will appear smaller when the distance increases. Aside from that, the difficulty increases in performing target selection when another virtual object obscures the distant virtual object. Therefore, this study aims to present a target selection method to perform the target selection. We enable the raycasting technique with real hand gesture for the target selection method on the occluded and distant object in handheld AR. The leap motion device is mounted at the back of the handheld device to track the real hand gesture. The markerless tracking technology of simultaneous localization and mapping (SLAM) is implemented to enable the AR environment. Based on the results, the aim of this study was achieved.
Learning intrinsic excitability in medium spiny neurons [version 1; peer review: 2 approved with reservations]

We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal activation function. We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal variability on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how variability and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic variability determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction. Open Peer Review
Design and Implementation of Online Vehicular Traffic Monitoring System Using Convolutional Neural Network (CNN)

The paper presents the design and implementation of Online Vehicular Monitoring System using Convolutional Neural Network (CNN), Traffic violations are unlawful activities that occur while an individual is operating a motor vehicle on the road. Traffic offenses are usually ruled by state motor vehicle codes that outline offenses ranging from minor infractions to severe violations. The proposed system was developed using Java for systems interfaces and programming of the system, MySQL database as its backend which serves as the database to the proposed system. Different traffic offence dataset (videos) were collected from the internet for both testing and performance evaluation. These datasets (videos) serves as input to the system. The system preprocesses the dataset to form video frames using the Fast Fourier Transform (FFT), these frames then passes through the Convolutional Neural Network (CNN) which then detect the vehicle plate number, segments it and classify the type of traffic offence committed by the offender. The proposed system captured four different types of traffic offences, these offences are the “Red-Light violation”, “Route Offences”, “Road Marking Offences”, and “Wrongful Overtaking Offences”. The output of the system will ease the job of the traffic officers in term of billing offenders appropriately for the type of traffic offence committed and safe keeping of offence data collected.
In vitro Studies on the Mechanism of Pyrethroid Resistance in the German Cockroach

A strain (R) of German cockroach originally collected in Ginza, Tokyo, and selected with permethrin in our laboratory showed cross resistance to various pyrethroids and DDT, and moderate resistance to organophosphate and carbamate insecticides as compared to a susceptible (S) strain. In vitro studies on the degradation of 14C-fenvalerate by subcellular fractions suggested that the major activity was in the soluble fraction and the microsomes, but no significant difference was found in either of these fractions between the S and R strains. Little, if any, difference in the synergistic effect of metabolic inhibitors, DEF and PBO, on the fenvalerate toxicity to the R and S strains supported the conclusion that metabolism is not an important factor for the pyrethroid resistance in this strain. Although in the R strain the content of cytochrome P450 in the microsomes was 2. 6-fold larger than in the S strain, it may be due to cytochrome P450 species which are not involved in fenvalerate degradation. Electrophysiological studies on the exposed abdominal nerve cord clearly demonstrated that the nerve system of the R strain was much less sensitive than that of the S strain to all the pyrethroids tested and DDT, indicating that the so-called KDR was a predominant factor for the pyrethroid resistance in this strain.
Chapter 7 Static Detection of Malware

In the search for research fields that can shed light on our issue of checking a piece of equipment for unwanted functionality, staticmalware detection stands out as themost obvious candidate. Malware detection is as old as malware itself and its main goal is to discover if maliciously behaving code has been introduced into an otherwise clean system by a third party. In this chapter, we consider techniques that are static, in the sense that they are based on investigating the code rather than a running system. We will return to dynamic methods in a later chapter. Our point of view is somewhat different from that of classic malware detection, in that we do not assume that we have a clean system to begin with. Our intention is to shed light on the implications of this difference to understand the extent to which the successes of static malware detection can transfer to our case.
Cholangiocyte‐Derived Exosomal Long Noncoding RNA H19 Promotes Hepatic Stellate Cell Activation and Cholestatic Liver Fibrosis

Activation of hepatic stellate cells (HSCs) represents the primary driving force to promote the progression of chronic cholestatic liver diseases. We previously reported that cholangiocyte‐derived exosomal long noncoding RNA‐H19 (lncRNA‐H19) plays a critical role in promoting cholestatic liver injury. However, it remains unclear whether cholangiocyte‐derived lncRNA‐H19 regulates HSC activation, which is the major focus of this study. Both bile duct ligation (BDL) and Mdr2 knockout (Mdr2‐/‐) mouse models were used. Wild‐type and H19maternalΔExon1/+ (H19KO) mice were subjected to BDL. Mdr2‐/‐H19maternalΔExon1/+ (DKO) mice were generated. Exosomes isolated from cultured mouse and human cholangiocytes or mouse serum were used for in vivo transplantation and in vitro studies. Fluorescence‐labeled exosomes and flow cytometry were used to monitor exosome uptake by hepatic cells. Collagen gel contraction and bromodeoxyuridine assays were used to determine the effect of exosomal‐H19 on HSC activation and proliferation. Mouse and human primary sclerosing cholangitis (PSC)/primary biliary cholangitis (PBC) liver samples were analyzed by real‐time PCR, western blot analysis, histology, and immunohistochemistry. The results demonstrated that hepatic H19 level was closely correlated with the severity of liver fibrosis in both mouse models and human patients with PSC and PBC. H19 deficiency significantly protected mice from liver fibrosis in BDL and Mdr2‐/‐ mice. Transplanted cholangiocyte‐derived H19‐enriched exosomes were rapidly and preferentially taken up by HSCs and HSC‐derived fibroblasts, and promoted liver fibrosis in BDL‐H19KO mice and DKO mice. H19‐enriched exosomes enhanced transdifferentiation of cultured mouse primary HSCs and promoted proliferation and matrix formation in HSC‐derived fibroblasts. Conclusion: Cholangiocyte‐derived exosomal H19 plays a critical role in the progression of cholestatic liver fibrosis by promoting HSC differentiation and activation and represents a potential diagnostic biomarker and therapeutic target for cholangiopathies.
Thai weaving pattern classification using convolutional neural networks

The creation of Thai weaving patterns based on reproduction of original patterns with adapted patterns inspired by designer is a way to reserve Thai textiles. Although weaving pattern designs are useful for promoting Thai textiles such as in casual attires, preserving original Thai patterns is still needed. This work aimed to classify the images of original Thai ethnic and adapted weaving patterns. In this paper, we trained 28 original Thai ethnic patterns using the convolutional neural network. We cropped and preprocessed the weaving images to a binary format. The data augmentation method was also used to increase the number of weaving patterns for training the convolutional neural network. The model was tested in the real world by using test patterns from Google images and gave the results of 0.90, 0.92, and 0.90 for precision, recall, and F1 score, respectively.
The Magellanic Stream: New Observations

Abstract New HI observations of the Magellanic Stream have been made using the Parkes 64 m telescope. These observations highlight in detail its complicated structure and uncover new features of the Stream. The extreme velocity clouds (EVCs) are morphologically linked to the Stream indicating that these HI clouds are very likely truly part of the Stream. Also it is suggested that many of the high velocity clouds seen around the Stream are indeed part of the Magellanic Stream itself, and that the stream consists of many different velocity components in bulk motion.