Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor modelling for radar-based occupancy mapping

This paper addresses the issue of creating a sensor model for a new short-range 24GHz close proximity detection (CPD) radar. The CPD radar is designed to provide improved situational awareness to the driver of a large vehicle. It is able to detect light vehicles and other targets at ranges from 2.2m to 45m within an arc of 160° azimuth, but radar measurements contain data from noise and clutter which must be filtered out. Dynamic thresholds such as constant false-alarm rate (CFAR) processors [19] do not work well with short measurement vectors or targets that occupy multiple measurement bins. In this paper, a new method is used where measurements of environmental noise, clutter and targets are used to calculate the false alarm and target detection probabilities for each bin and develop a fixed detection threshold for each bin. This filter is used to construct a sensor model which maps measurement power to probability of bin occupancy, which is then used to generate an occupancy grid map of the environment from CPD radar measurements.
Fast and Accurate k-Nearest Neighbor Classification Using Prototype Selection by Clustering

Data reduction is very important especially when using the k-NN Classifier on large datasets. Many prototype selection and generation Algorithms have been proposed aiming to condense the initial training data as much as possible and keep the classification accuracy at a high level. The Prototype Selection by Clustering (PSC) algorithm is one of them and is based on a cluster generation procedure. Contrary to many other prototype selection and generation algorithms, its main goal is the fast execution of the data reduction procedure rather than high reduction rate. In this paper, we demonstrate that the reduction rate and the classification accuracy of PSC can be improved by generating a larger number of clusters. Moreover, we compare the performance of the particular algorithm with two state-of-the-art algorithms, one selection and one generation, using six real life datasets. The experimental results indicate that the classification performance of the Prototype Selection by Clustering algorithm is comparable with that of its competitors when using many clusters.
A Discriminative Approach to Grounded Spoken Language Understanding in Interactive Robotics

Spoken Language Understanding in Interactive Robotics provides computational models of human-machine communication based on the vocal input. However, robots operate in specific environments and the correct interpretation of the spoken sentences depends on the physical, cognitive and linguistic aspects triggered by the operational environment. Grounded language processing should exploit both the physical constraints of the context as well as knowledge assumptions of the robot. These include the subjective perception of the environment that explicitly affects linguistic reasoning. In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics. Empirical results achieve up to a 40% of relative error reduction.
On the Applications of the Coupled-Line Composite Right/Left-Handed Unit Cell

This paper presents two applications of the coupled-line composite right/left-handed (CL-CRLH) unit cell, compact-size rat-race hybrid, and arbitrary coupling directional coupler. Benefiting from the slow-wave effect present in both the right- and left-handed regions of the CL-CRLH unit cell, two compact-size rat-race hybrids were realized. 60% area reduction was achieved while preserving the conventional rat-race performance. The arbitrary coupling directional coupler, which operates in the stopband around the balance frequency, showed excellent in-band and out-of-band performance. To reduce its size, a slow-wave CL-CRLH unit cell was proposed. The proposed unit cell achieves a 38% size reduction. The rat-race hybrids were fabricated on microstrip technology, whereas the directional couplers were fabricated on stripline technology. Simplified circuit models for the unit cells were developed. Theoretical expectations were confirmed by electromagnetic simulations and measurements.
A Study on Triplet Repeat Expansion Disorders in Western Indian Population

Triplet repeat expansion disorders (TRED) are one of the commonest causes of hereditary neuropathy with thirty different heredity diseases due to increased number of trinucleotide repeats above certain threshold in the genomic DNA. The degree of disease aggressiveness depends on the number of repeats and resultant alteration in the expression of several genes. Moreover, mechanism of these disorders differs depending on the region of trinucleotide expansion (coding vs. non-coding regions). Present study comprises of 172 individuals suspected of triplet repeat expansion disorders such as Huntington’s disease (HD), Spinocerebellar ataxia (SCA), Friedreich’s ataxia (FA) and Myotonic dystrophy (MD) from Gujarat (Western Indian population) to know its frequency and clinical manifestations. Among them, 78 (45.34%) individuals were confirmed to have one of these four disorders. The age of onset varied from 11-45 years. SCA was the most common triplet repeat expansion disorder with a frequency of 43.60% followed by HD in 23.1%, MD in 21.8% and FA in 11.5% of patients. In addition, SCA2 was found to be the most common hereditary neuropathy in Western India.
Detection and Assessment of Ecosystem Regime Shifts from Fisher Information

Ecosystem regime shifts, which are long-term system reorganizations, have profound implications for sustainability. There is a great need for indicators of regime shifts, particularly methods that are applicable to data from real systems. We have developed a form of Fisher information that measures dynamic order in complex systems. Here we propose the use of Fisher information as a means of: (1) detecting dynamic regime shifts in ecosystems, and (2) assessing the quality of the shift in terms of intensity and pervasiveness. Intensity is reflected by the degree of change in dynamic order, as determined by Fisher information, and pervasiveness is a reflection of how many observable variables are affected by the change. We present a new robust methodology to calculate Fisher information from time series field data. We demonstrate the use of Fisher information to detect regime shifts on a model for a shallow lake. Next, we use Fisher information to analyze marine ecosystem response to physical changes using real time-series data of a coastal marine ecosystem, the North Pacific Ocean.
Unwanted Message FiltrationFrom Online Social Network (OSN)

The best entertainment for the younger generation now is given in the form of Social Networking sites. The Online Social Networks (OSN) mainly helps an individual to connect with their friends, family and the society online in order to gather and share new experiences with others. Now- a-days, the OSNs are facing the problem of the people posting the indecent messages on any individual's wall which annoys other people on seeing them. In order to filter those unbearable messages a system called Machine Learning is introduced. The aim of the present work is therefore to propose and experimentally evaluate an automated system, called Filtered Wall (FW), able to filter unwanted messages from OSN user walls.
Biotechnological Products and Process Engineering

The astaxanthin dideoxyglycoside biosynthesis pathway in Sphingomonas sp. PB304 Abstract A major carotenoid in Sphingomonas sp. PB304, originally isolated from a river in Daejon City, South Korea, was identified as astaxanthin dideoxyglycoside. Gene clusters encoding the astaxanthin dideoxyglycoside biosynthetic enzymes were identified by screening Sphingomonas sp. PB304 fosmid libraries using degenerate probes that harbor highly conserved sequences from the Sphigomonas elodea-derived crtI and Nostoc sp. PCC 7120-dervied crtW genes. Selected positive gene clusters were fully sequenced and annotated, revealing genes encoding six putative carotenogenic enzymes: phytoene synthase (CrtB), phytoene desaturase (CrtI), lyco-pene cyclase (CrtY), carotene hydroxylase (CrtZ), carotene ketolase (CrtW), and glycosyltransferase (CrtX). All of the carotenogenic enzymes, except for CrtX, were functional in the recombinant host Escherichia coli expressing synthetic carotenogenic modules from Pantoea agglomerans. CrtX did not take up UDP-glucose or GDP-fucose as sugar sub-strates during the in vitro reaction. Although no direct experimental evidence was obtained for the function of Sphingomonas sp. PB304 CrtX, it can be categorized as a putative deoxyglycosyltransferase based on the presence of astaxanthin dideoxyglycoside in Sphingomonas sp. PB304, a putative corresponding gene in the carotenoid biosynthetic gene cluster, and high amino acid sequence homology to the existing glycosyltransferases. Therefore, we propose that astaxanthin dideoxyglycoside can be synthesized in Sphingomonas sp. PB304 via sequential reactions of six pathway enzymes, including CrtX on the phytoene intermediate.
Subthreshold diode micropulse photocoagulation for the treatment of clinically significant diabetic macular oedema

Aim: To report the visual and clinical outcomes of a pilot study of subthreshold diode micropulse (SDM) laser photocoagulation for clinically significant diabetic macular oedema (CSMO). Methods: The results of infrared (810 nm) SDM laser photocoagulation for CSMO were retrospectively reviewed in 95 eyes of 69 consecutive patients with mild to moderate non-proliferative diabetic retinopathy. The same laser parameters were used for each patient. Only the number of laser applications varied between patients, depending on their macular findings. Primary outcome measures were Snellen visual acuity, fluorescein angiographic leakage, and CSMO status. Results: Visual acuity was stable or improved in 85% of treated eyes, with a mean follow up of 12.2 months (range 3–29 months). CSMO decreased in 96% and resolved in 79% of treated eyes. No adverse laser events occurred. No laser lesions were detectable ophthalmoscopically or angiographically after treatment, consistent with calculations based on ANSI Z136.1 laser safety standards suggestive of only histologically detectable tissue effects at the laser exposure levels. No laser scarring was observed during the follow up period. Conclusion: Subthreshold diode micropulse laser photocoagulation minimises chorioretinal damage in the management of CSMO and demonstrates a beneficial effect on visual acuity and CSMO resolution. Prospective studies are needed to fully evaluate this technique.
Methodological and Statistical Challenges in the Study of How Anxiety and Stress Impair Working Memory Performance

This case study presents an overview of the major challenges and successes encountered assessing whether working memory performance was impaired in healthy individuals with high levels of anxiety when their cortisol levels increased after acute stress. The study presented used an experimental design and quantitative data analysis using a moderated mediation model. In the case study that follows, we describe anxiety disorders, stress and the resulting physiological responses, and how anxiety and stress can impair working memory performance. In addition, we reflect on the use of healthy college students in research that has clinical implications, how to design a study that uses an experiment stressor, and the potential pitfalls of salivary cortisol collection and analysis. We also discuss the changes we would implement if we were to conduct similar research in the future. This case study highlights the use of an experimental design and statistical modeling to answer interesting questions about human behavior.
Exploring a Structured Definition for Learner-Centered Design

As computers become more powerful and prevalent, more researchers are exploring computer tools to help novices gain an understanding into new work domains. The development of such tools involves what has been termed a “learner-centered design” process. But while there is an intuitive notion of what is meant by learner-centered design, there is no formal definition. Here we present our definition of learner-centered design, a definition formed by extending the definition for the traditional “user-centered design” approach. Specifically, we define “learnercentered design” in terms of its target audience, the problems it addresses, and the underlying approach for addressing those problems. From a software development viewpoint, by presenting a more formal definition, we hope to not only better describe what we mean by learner-centered software, but also provide a base by which we can explore more structured design methods for developing and implementing effective learner-centered software.
Use of a Dual Reporter Plasmid to Demonstrate Bactofection with an Attenuated AroA- Derivative of Pasteurella multocida B:2

A reporter plasmid pSRG has been developed which expresses red fluorescent protein (RFP) from a constitutive prokaryotic promoter within Pasteurella multocida B:2 and green fluorescent protein (GFP) from a constitutive eukaryotic promoter within mammalian cells. This construct has been used to determine the location and viability of the bacteria when moving from the extracellular environment into the intracellular compartment of mammalian cells. Invasion assays with embryonic bovine lung (EBL) cells and an attenuated AroA- derivative of Pasteurella multocida B:2 (strain JRMT12), harbouring the plasmid pSRG, showed that RFP-expressing bacteria could be detected intracellularly at 3 h post-invasion. At this stage, some EBL cells harbouring RFP-expressing bacteria were observed to express GFP simultaneously, indicating release of the plasmid into the intracellular environment. At 5 h post-invasion, more EBL cells were expressing GFP, while still harbouring RFP-expressing bacteria. Concurrently, some EBL cells were shown to express only GFP, indicating loss of viable bacteria within these cells. These experiments proved the functionality of the pSRG dual reporter system and the potential of P. multocida B:2 JRMT12 for bactofection and delivery of a DNA vaccine.
Differences in the Mathematics–Vocabulary Knowledge of Fifth–Grade Students with and without Learning Difficulties

The purpose of this pilot study was to explore the impact of mathematics and reading learning difficulties on the mathematics–vocabulary understanding of fifth–grade students. Students (n = 114) completed three measures: mathematics computation, general vocabulary, and mathematics vocabulary. Based on performance on the mathematics computation and general vocabulary measures, students were categorized with no learning difficulty (i.e., typical), mathematics difficulty without reading difficulty (MD–only), reading difficulty without mathematics difficulty (RD–only), and combined mathematics and reading difficulties (MDRD). On the mathematics–vocabulary measure, students with MD–only or RD–only scored significantly lower than typical students, and students with MDRD demonstrated significantly lower performance than students with MD–only or RD–only.
A QoS-aware AIMD protocol for time-sensitive applications in wired/wireless networks

A TCP-friendly additive increase and multiplicative decrease (AIMD) protocol is proposed to support time-sensitive applications in hybrid wired/wireless networks. By analyzing the performance of AIMD-controlled flows in hybrid networks, we propose a cross-layer procedure to select the AIMD protocol parameters with consideration of wireless link characteristics and application QoS requirements, in terms of delay, loss, and throughput. Since the cross-layer interaction only exchanges parameters among the application, the transport layer protocol, and the link layer protocol, our approach preserves the end-to-end semantics of the transport protocol and the layered structure of the Internet, and it is applicable to supporting various multimedia applications over heterogeneous wireless links. With appropriate parameters, AIMD-controlled flows can fairly share network resources with TCP flows, efficiently utilize wireless resources, and statistically guarantee end-to-end delay for time-sensitive applications. Extensive simulations are performed to validate the analytical results, evaluate the protocol performance, and demonstrate that the AIMD protocol can outperform the unresponsive UDP protocol when transporting multimedia traffic in hybrid networks. With satisfactory QoS, end systems have more incentives to voluntarily regulate multimedia traffic with an AIMD-based congestion controller, which is vital for network stability, integrity, and future proliferation.
SLAW: Self-Similar Least-Action Human Walk

Many empirical studies of human walks have reported that there exist fundamental statistical features commonly appearing in mobility traces taken in various mobility settings. These include: 1) heavy-tail flight and pause-time distributions; 2) heterogeneously bounded mobility areas of individuals; and 3) truncated power-law intercontact times. This paper reports two additional such features: a) The destinations of people (or we say waypoints) are dispersed in a self-similar manner; and b) people are more likely to choose a destination closer to its current waypoint. These features are known to be influential to the performance of human-assisted mobility networks. The main contribution of this paper is to present a mobility model called Self-similar Least-Action Walk (SLAW) that can produce synthetic mobility traces containing all the five statistical features in various mobility settings including user-created virtual ones for which no empirical information is available. Creating synthetic traces for virtual environments is important for the performance evaluation of mobile networks as network designers test their networks in many diverse network settings. A performance study of mobile routing protocols on top of synthetic traces created by SLAW shows that SLAW brings out the unique performance features of various routing protocols.
Conjugate gradient-like algorithms for solving nonsymmetric linear systems

This paper presents a unified formulation of a class of the conjugate gradient-like algorithms for solving nonsymmetric linear systems. The common framework is the Petrov- Galerkin method on Krylov subspaces. We discuss some practical points concerning the methods and point out some of the interrelations between them. 1. Introduction. In the recent few years, a large number of generalizations of the conjugate gradient and conjugate residual methods, which are very successful in solving symmetric positive-definite linear systems, have been proposed for solving nonsymmetric linear systems (3), (6), (5), (7), (12). In this paper we present an abstract framework which includes most of these methods and many new ones. Our goal is to understand the relationships among the methods and to synthesize. Consider the general linear system:
Mining Software Evolution to Predict Refactoring

Can we predict locations of future refactoring based on the development history? In an empirical study of open source projects we found that attributes of software evolution data can be used to predict the need for refactoring in the following two months of development. Information systems utilized in software projects provide a broad range of data for decision support. Versioning systems log each activity during the development, which we use to extract data mining features such as growth measures, relationships between classes, the number of authors working on a particular piece of code, etc. We use this information as input into classification algorithms to create prediction models for future refactoring activities. Different state-of-the-art classifiers are investigated such as decision trees, logistic model trees, prepositional rule learners, and nearest neighbor algorithms. With both high precision and high recall we can assess the refactoring proneness of object-oriented systems. Although we investigate different domains, we discovered critical factors within the development life cycle leading to refactoring, which are common among all studied projects.
A Correlation-Breaking Interleaving of Polar Codes

It's known that the bit errors of polar codes with successive cancellation (SC) decoding are coupled. However, existing concatenation schemes of polar codes with other error correction codes rarely take this coupling effect into consideration. To achieve a better BER performance, a concatenation scheme, which divides all $N_l$ bits in a LDPC block into $N_l$ polar blocks to completely de-correlate the possible coupled errors, is first proposed. This interleaving is called the blind interleaving (BI) and can keep the simple SC polar decoding while achieving a better BER performance than the state-of-the-art (SOA) concatenation of polar codes with LDPC codes. For better balance of performance and complexity, a novel interleaving scheme, named the correlation-breaking interleaving (CBI), is proposed by breaking the correlation of the errors among the correlated bits of polar codes. This CBI scheme 1) achieves a comparable BER performance as the BI scheme with a smaller memory size and a shorter turnaround time; 2) and enjoys a performance robustness with reduced block lengths. Numerical results have shown that CBI with small block lengths achieves almost the same performance at BER=$10^{-4}$ compared with CBI with block lengths $8$ times larger.
Improved prediction of MHC class I and II epitopes using a novel Gibbs sampling approach

Motivation: Prediction of which peptides that will bind a specific MHC complex constitutes an important step in identifying potential T-cell epitopes suitable as vaccine candidates. MHC class II binding peptides have a broad length distribution complicating binding such predictions. Thus, identifying the correct alignment is a crucial part of identifying the core of an MHC class II binding motif. In this context, we whish to describe a novel Gibbs motif sampler method ideally suited to recognize such weak sequence motifs. The method is based on the Gibbs sampling method previously described by Lawrence et al. (1993), and it incorporates novel features optimized for the task of recognizing the binding motif of MHC class I and II. The method locates the binding motif in a set of sequences and characterizes the motif in terms of a weight-matrix. Subsequently, the weight matrix can be applied to effectively identify potential MHC binding peptides and to guide the process of rational vaccine design. Results : We apply the motif sampler method to the complex problem of MHC class II binding. The input to the method is amino acid peptide sequences extracted from the public databases of SYFPEITHI and MHCPEP, and known to bind to the MHC class II complex HLA-DR4(B1*0401). Prior identification of information-rich (anchor) positions in the binding motif is shown to improve the predictive performance of the Gibbs sampler. Similarly a consensus solution obtained from an ensemble average over suboptimal solutions is shown to outperform the use of a single optimal solution. In a large-scale benchmark calculation the performance is quantified using ROC curve plots and we make a detailed comparison of the performance to that of both the TEPITOPE method and a weight matrix derived using the conventional alignment algorithm of ClustalW. The calculation demonstrates that the predictive performance of the Gibbs sampler is higher than that of ClustalW and in most cases also higher than that of the TEPITOPE method.