Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement of multiuser diversity and multiplexing gain using multiple coherent beams

In this paper, we propose a multi-beam multiplexing scheme that can simultaneously achieve the spatial multiplexing gain, multiuser diversity gain and beamforming gain by using coherent multiple beams in the multi-user domain. Multiple beams are generated sequentially so that each beam maximizes receive signal to interference and noise power ratio (SINR) while providing zero interference toward prevously selected users. The use of multiple beams reduces the SNR of each channel (beam) in proportion to the number of beams. However, multiple beams are utilized so that the multiplexing gain is much larger than the decrease of SNR, increasing the overall system capacity. The proposed scheme is also applicable to MIMO as well as MISO schemes.
A HYBRID CONTROL ARCHITECTURE FOR MOBILE ROBOTS . CLASSIC CONTROL , BEHAVIOR BASED CONTROL , AND PETRI NETS

In this paper we propose a hybrid control architecture which combines aspects of classic control and behavior-based control. The aspects of continuous systems and discrete eventsystems are both considered. This hybrid architecture combines the advantages of behaviorbased control and classic control, allowing for a theoretical study on the stability conditions for the system. In addition, the use of Petri nets to model the discrete-event aspects enables a formal study of the conflicts between behaviors and the way to overcome them.
Option to Acquire , LBOs and Debt Ratio in a Growing Industry

In this paper, we investigate LBO in a growing industry where the target company has a growth option. Especially, we focus the bidder’s option to acquire the target company. An important setting is that the optimal timing is determined under the capital constraint. As our main results, we show that a growth option leads to delay in LBO, high leverage and low risk, and that default risk has the opposite sensitivity before/after growth.
Trainee wellness: self-critical perfectionism, self-compassion, depression, and burnout among doctoral trainees in psychology

ABSTRACT Training to become a therapist is a uniquely challenging process with frequent evaluation and balancing of multiple roles (e.g. student, teacher, researcher, counselor, and supervisee). In fact, studies report high levels of stress and depression among graduate trainees in psychology. These vulnerabilities can be exacerbated when a trainee is in high in self-critical perfectionism, a feeling that one is never good enough. The present study examines associations between self-critical perfectionism, depression, and burnout among doctoral trainees in psychology, investigating the mediating role of self-compassion. The 119 participants were all students in clinical/counseling psychology doctoral programs. Results indicated that those higher in self-critical perfectionism reported higher levels of depression and burnout. Self-compassion partially mediated the relationship between self-critical perfectionism and depression, and burnout; those higher in self-compassion experienced lower levels of depression and burnout. These results point to the importance of facilitating the development of self-compassion among doctoral trainees, specifically those who are high in self-critical perfectionism.
The role of machine learning in botnet detection

Over the past ten to fifteen years botnets have gained the attention of researchers worldwide. A great deal of effort has been given to developing systems that would efficiently and effectively detect the presence of a botnet. This unique problem saw researchers applying machine learning (ML) to solve this problem. In this paper we provide a brief overview the different machine learning (ML) methods and the part they play in botnet detection. The main aim of this paper is to clearly define the role different ML methods play in Botnet detection. A clear understanding of these roles are critical for developing effective and efficient real-time online detection approaches and more robust models.
Histogram feature deblurring

A histogram is an effective form for extracting various types of features and has been attracting keen attention in pattern recognition fields. In visual recognition, however, the histogram features suffer from smoothing due to the processes of quantizing continuous input patterns into discrete codes and pooling them. Such smoothing degrades discriminative power by blurring the distinctive features. In this paper, we propose a novel method to get rid of such blurriness and reveal the essential discriminative information from the histogram features. We first model the blurring process based on the graph Laplacian of discrete codes and then formulate a method for deblurring histogram features in a computationally efficient form which works as post-processing just after feature extraction. The proposed method is also shown to be related to unsharp masking of an image restoration technique. In the experiments on image classification using BoW histogram features, the proposed deblurring method favorably improves performance of the histogram features.
A localization algorithm for capsule endoscopy based on feature point tracking

Wireless Capsule Endoscopy (WCE) has emerged as a popular non-invasive imaging tool for inspection of human Gastrointestinal (GI) tract. In order to identify the location of an anomaly or intestinal disease, the physicians need to know the exact location of the endoscopic capsule which influences the treatment plan. In this paper, we present a displacement estimation technique based on feature point tracking which utilizes the images captured by a commercial capsule, named PillCam. The proposed displacement calculation approach is tested using a virtual testbed. Results show that, with assistance of ASIFT-RANSAC algorithms, the proposed algorithm is able to estimate the linear displacement of the endoscopic capsule with an accuracy of 93.7% on average.
Research on the Impact of Mobile Communications Service Quality for Customer Loyalty

In recent years, telecom operators have to bear increasingly intense competitive pressure, so how to provide customers with better service to enhance customer satisfaction and loyalty, which has become the first one of the issues that telecom operators face. This paper use factor analysis, model hypothesis testing and other means to study on Lishui Mobile Communication Company in Zhejiang province and explore the relationship among the five dimensions of service quality, customer satisfaction and customer loyalty. Service quality not only has a direct significant positive impact on customer loyalty, but also has an indirect significant effect on customer loyalty through customer satisfaction. The result also propose the target and key area of mobile communication service quality to improve, and also give the corresponding management recommendations.
A Comparison Between Square and Hexagonal Sampling Methods for Pipeline Image Processing

The majority of machine vision systems derive their input data by digitising an image to produce a square grid of sampled points. However, other sampling techniques can represent equal picture information in a smaller number of samples, with a consequent reduction in data rate. Several workers have looked at regular hexagonal sampling of images which produces optimum data rates for a given information content. Previous work on hexagonal sampling by the authors and others, has shown that image processing operators are computationally more efficient, and as accurate, as their square counterparts. Historically, one factor which has lead to the predominance of square sampling in vision systems, is that this produces images which are more visually pleasing to human observers. This paper describes an investigation of machine vision systems performing industrial inspection tasks, which suggests that in such applications, hexagonal systems out-perform square systems. In particular hexagonal operators can follow tight curves more accurately, allowing better surface defect detection. A surprising observation of this work was that with such images, hexagonal sampling also gave images which were more visually pleasing to human operators. The paper presents a study of sampling point geometry and operator design. Details are given of an implementation of a set of hexagonal, grey-scale operators for use in pipeline or other image processing systems, and a comparison of square and hexagonal techniques has been made. Results of operations on real and simulated surface defect images are given for both sampling systems and the requirement for a defect detection figure of merit identified.
On backward stability of holomorphic dynamical systems

For a polynomial with one critical point (maybe multiple), which does not have attracting or neutral periodic orbits, we prove that the backward dynamics is stable provided the Julia set is locally connected. The latter is proved to be equivalent to the non-existence of a wandering continuum in the Julia set or to the shrinking of Yoccoz puzzle-pieces to points.
Ecological and ethical issues in virtual reality research: A call for increased scrutiny

ABSTRACT We argue that moral judgment studies currently conducted utilizing virtual reality (VR) devices must confront a dilemma as a result of how virtual environments are designed and how those environments are experienced. We first begin by describing the contexts present in paradigmatic cases of naturalistic moral judgments. We then compare these contexts to current traditional (vignette-based) and VR-based moral judgment research. We show that, contra to paradigmatic cases, vignette-based and VR-based moral judgment research often fails to accurately model the situational features of paradigmatic moral judgments. In particular, we compare and contrast six recent VR studies to support our view that only simulations high in context-realism and perspectival-fidelity can produce “virtually-real experiences. ” After analyzing the constituents of a virtually-real experience, we go on to propose guidelines for the creation of VR studies. These guidelines serve two purposes. First, we aim to increase the ecological validity of such studies to advance our understanding of moral judgments. Second, we believe that such guidelines should inform how Institutional Review Boards assess VR research. We show that our guidelines are urgently needed, given the current lax review standards in place.
Recent Patents and Discovery of Anti-inflammatory Agents from Marine Source.

BACKGROUND Inflammation has become pathology in the majority of the prevalent diseases such as diabetes, atherosclerosis, epilepsy and neurodegenerative disorders. Anti-inflammatory drugs work wonder in all these conditions, where the patient has become refractory to standard treatment. However, available anti-inflammatory agents have side effects associated with chronic use, thus if we could develop safe and efficacious molecules, quality of health care provided will improve. Since plant sources have been extensively explored, the focus needs to be shifted on the alternative natural sources of anti-inflammatory agents. Water bodies especially the sea and ocean are under investigation to find agents which can tackle inflammation. OBJECTIVE This article reviews anti-inflammatory agents obtained from five types of marine organisms namely microalgae, sea cucumber, mussels, sponges and corals. METHODS A literature search was conducted using PubMed/Science Direct with keywords marine organisms, inflammation, marine sponges, sea cucumber, mussels, corals and microalgae. Patents were searched using the key terms inflammation, marine agents from www.google. com/patents, www.uspto. gov, http://espacenet.com, www.freepatentsonline. com, www.wipo. int/pctdb/en/searchsimp. jsp and www.freshpatents. com. RESULTS Literature and current patents have revealed applications of anti-inflammatory agents from marine organisms in pharmaceuticals and cosmeceuticals. These agents are used to treat inflammatory disorders ranging from minor allergy to chronic conditions like rheumatoid arthritis. Marine waste is also a valuable resource for nutraceuticals and anti-inflammatory agents. CONCLUSION The findings reveal that marine organisms could be a promising source of novel antiinflammatory agents. However, further investigations are suggested for the isolation and identification of bioactive, exploring the mechanism of action and evaluating the efficacy in various inflammatory conditions.
A Continuous-time Fixed-lag Smoother Converging in Finite Time

In this paper, we propose a new fixed-lag smoother that estimates the fixed-delayed state for a continuous-time stochastic system. The estimation error variance of the proposed smoother is minimized under the constraint that the estimated state converges to the real state exactly in finite time after noises or uncertainties disappear. For numerical computing, the proposed smoother is represented in a differential form. In order to achieve the convergence in finite time, any additional processes such as batch processing and sampling data through discrete-time techniques are not required. A numerical example is presented to illustrate the finite time convergence of the proposed smoother in comparison with the asymptotic convergence of the conventional Kalman smoothers.
CAPSU: A completely automated method for carotid plaques segmentation in ultrasound images

The accurate carotid plaques segmentation (CPs-S) is essential in the ultrasound (US)-guided carotid atherosclerosis diagnosis. In this work, we propose CAPSU: a completely automated method for CPs-S in US images. The core of the method is an innovative initialization procedure that exploits carotid wall motion analysis to automatically initialize a level-set segmentation algorithm. A strain analysis is also used to improve the initialization procedure. CAPSU performance with and without the strain analysis were evaluated on US data from 8 patients and compared with manual contouring from an expert sonographer. Results show the CAPSU effectiveness as accurate and reliable tool for fully automated CPs-S in US images.
Prescribing under the Influence: The Business of Breastmilk Substitutes

This study draws on a general theoretical framework comprising of a decision maker (a doctor), perceived moral intensity of the issue (breastfeeding substitute prescription), and the situational environment (hospital policy, pharma company promotions, and mother’s beliefs regarding breastfeeding) to explain the physician’s role and influence on mothers’ infant feeding choices when prescribing infant formula in Kuwait, Middle East. Moral intensity is an issue-contingent model that suggests ethical decisions vary in terms of how much a moral imperative is present in a situation. The moral intensity of the issue is assessed using six components. Path Least Squares results indicate the following moral intensity components have significant impact on prescription behavior: magnitude of consequences, probability of effect, and temporal immediacy. Company promotion and hospital policy also significantly influence doctor’s prescription of infant formula. Doctors appear to disengage from the consequences of over prescribing infant formula.
Bayesian non‐parametric hidden Markov models with applications in genomics

Summary. We propose a flexible non‐parametric specification of the emission distribution in hidden Markov models and we introduce a novel methodology for carrying out the computations. Whereas current approaches use a finite mixture model, we argue in favour of an infinite mixture model given by a mixture of Dirichlet processes. The computational framework is based on auxiliary variable representations of the Dirichlet process and consists of a forward–backward Gibbs sampling algorithm of similar complexity to that used in the analysis of parametric hidden Markov models. The algorithm involves analytic marginalizations of latent variables to improve the mixing, facilitated by exchangeability properties of the Dirichlet process that we uncover in the paper. A by‐product of this work is an efficient Gibbs sampler for learning Dirichlet process hierarchical models. We test the Monte Carlo algorithm proposed against a wide variety of alternatives and find significant advantages. We also investigate by simulations the sensitivity of the proposed model to prior specification and data‐generating mechanisms. We apply our methodology to the analysis of genomic copy number variation. Analysing various real data sets we find significantly more accurate inference compared with state of the art hidden Markov models which use finite mixture emission distributions.
Detection of periodic problems using non-parametric density estimation

A significant number of chronic GSM quality faults cause chronic quality alarms, which happen periodically, repeat often and disappear after a short time. There are a huge number of quality alarms and alarm quality experts who are responsible for the fault management try to determine chronic quality faults by examining quality alarms. The task of examining the history of a huge number of alarms and determining the chronic quality faults is a very time consuming and expensive task. In this study, a new non-parametric density estimation approach is proposed to determine quality faults which cause periodic alarms. We use the alarm history to do a nonparametric density estimation using histograms, based on the source, type and reoccurrence information for each alarm. These histograms were examined by the experts and they labeled the alarm histograms based on whether they point to chronic quality problems or not. We built a model to determine chronic quality problems and learned the parameters of this model using the alarm histogram data labeled by the experts. The learned model had a very good performance in determining the chronic quality faults.
Controlling Soil-Transmitted Helminthiasis in Pre-School-Age Children through Preventive Chemotherapy

Pre-school age children account for 10%–20% of the 2 billion people worldwide who are infected with soil-transmitted helminths (STHs): Ascaris lumbricoides (roundworm), Trichuris trichiura (whipworm), and Ancylostoma duodenale/Necator americanus (hookworms). Through a systematic review of the published literature and using information collated at World Health Organization headquarters, this paper summarizes the available evidence to support the recommendation that pre-school children should be included in regular deworming programmes. The first section describes the burden of STH disease in this age group, followed by a summary of how infection impacts iron status, growth, vitamin A status, and cognitive development and how STHs may exacerbate other high mortality infections. The second section explores the safety of the drugs themselves, given alone or co-administered, drug efficacy, and the importance of safe administration. The third section provides country-based evidence to demonstrate improved health outcomes after STH treatment. The final section provides country experiences in scaling up coverage of pre-school children by using other large scale public health interventions, including vitamin A programmes, immunization campaigns, and Child Health days. The paper concludes with a number of open research questions and a summary of some of the operational challenges that still need to be addressed.
Cross Lingual Information Retrieval Using Data Mining Methods

One of the challenges in cross lingual information retrieval is the retrieval of relevant information for a query expressed in a native language. While retrieval of relevant documents is slightly easier, analyzing the relevance of the retrieved documents and the presentation of the results to the users are non-trivial tasks. A method for information retrieval for a query expressed in a native language is presented in this paper. It uses insights from data mining and intelligent search for formulating the query and parsing the results. It also uses heuristic methods for the categorization of documents in terms of relevance. Our approach compliments the search engine’s inbuilt methods for identifying and displaying the results of queries. A prototype has been developed for analyzing Tamil-English corpora. The initial results have shown that this approach is suitable for on the fly retrieval of documents.
A new method to measure local oxygen consumption in human skeletal muscle during dynamic exercise using near-infrared spectroscopy

Near infrared spectroscopy (NIRS) can readily report on changes in blood volume and oxygenation. However, it has proved more problematic to measure real-time changes in blood flow and oxygen consumption. Here we report the development of a novel method using NIRS to measure local oxygen consumption in human muscle. The method utilizes the blood volume changes induced by the muscle pump during rhythmically contracting exercising skeletal muscle. We found that the saturation of the blood during the contraction phase was lower than that during the relaxation phase. The calculated oxygen drop was then divided by the contraction time to generate a value for the muscle oxygen consumption in the optical region of interest. As a test we measured the muscle oxygen consumption in the human vastus lateralis during exercise on a cycle ergometer by 11 trained male athletes (32 ± 11 years old) at 40% and 110% peak aerobic power. We saw an increase from 13.78 µmol 100 g−1 min−1 to 19.72 µmol 100 g−1 min−1 with the increase in power. The measurements are theoretically exempt from usual NIRS confounders such as myoglobin and adipose tissue and could provide a useful tool for studying human physiology.