Miracle-dz/UniFAR-FTUs huggingface dataset:
LANGUAGE INDEPENDENT NAMED ENTITY RECOGNITION

The role of Internet in personal, economic and political advancement is growing in a fast pace. By the turn of century, data on web reaches to petabytes or exabytes or may even scale up-to unimaginable quantities. Extraction of precise and structured information from such large amounts of unstructured or semi-structured data is the major concern of web known as Information Extraction. Named entity recognition (NER) (also known as entity identification and entity extraction) is one of the important subtask of information extraction that seeks to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, locations, monetary values, percentages, expressions of times, etc. NER has many applications in NLP, for e.g., in data classification, question answering, cross language information access, machine translation system, query processing, etc. Recognizing Named Entities (NEs) in English has reached accuracies nearing to 98%. For English, many cues aid to know the structure of language (one such important cue in identifying NEs is capitalization) which made the accuracies to be high. Whereas in Indian languages, there are no such cues available and moreover each Indian language differ from the other in grammatical structure. Hence, developing a language independent NER is a challenging task. Previous works includes developing an NER system using language dependent tools such as POS Tagger, dictionaries, Chunk Tagger, gazetteer lists, etc., or they have used linguistic experts to manually tag the training and testing data or linguistic experts used to generate rules for recognizing NEs. Language Independent approaches include supervised machine learning techniques such as CRF, HMM, MEMM, SVM, etc. These techniques need High amounts of manually tagged data which is again a point of concern. Some of the other approaches include exploiting the external knowledge such as Wikipedia. But, in those methods the utilization of Wikipedia is not complete. Hence, the main objective of this work is to build a language independent NER system without any manual intervention and without any usage of language dependent tools. The approach specified throughout the work, includes language independent methods to identify, extract and recognize the NEs. Identification of NEs is done using an External Knowledge namely
MicroRNA Machinery Genes as Novel Biomarkers for Cancer

MicroRNAs (miRNAs) directly and indirectly affect tumorigenesis. To be able to perform their myriad roles, miRNA machinery genes, such as Drosha, DGCR8, Dicer1, XPO5, TRBP, and AGO2, must generate precise miRNAs. These genes have specific expression patterns, protein-binding partners, and biochemical capabilities in different cancers. Our preliminary analysis of data from The Cancer Genome Atlas consortium on multiple types of cancer revealed significant alterations in these miRNA machinery genes. Here, we review their biological structures and functions with an eye toward understanding how they could serve as cancer biomarkers.
Three-dimensional real-time in vivo magnetic particle imaging

Magnetic particle imaging (MPI) is a new tomographic imaging method potentially capable of rapid 3D dynamic imaging of magnetic tracer materials. Until now, only dynamic 2D phantom experiments with high tracer concentrations have been demonstrated. In this letter, first in vivo 3D real-time MPI scans are presented revealing details of a beating mouse heart using a clinically approved concentration of a commercially available MRI contrast agent. A temporal resolution of 21.5 ms is achieved at a 3D field of view of 20.4 × 12 × 16.8 mm3 with a spatial resolution sufficient to resolve all heart chambers. With these abilities, MPI has taken a huge step toward medical application.
A graphene-based affinity glucose nanosensor

Affinity glucose sensors based on equilibrium binding can offer improved stability and accuracy for continuous glucose monitoring in diabetes care. This paper presents a graphene-based affinity nanosensor for glucose measurements. The nanosensor exploits the affinity binding of a novel, surface-immobilized synthetic polymer with glucose. The binding induces changes in the bulk electrical properties of graphene, which are measured for sensitive glucose detection in a highly miniaturized device without the use of physical barriers commonly employed by existing affinity glucose sensors.
Novel therapeutic strategies targeting vascular endothelium in essential hypertension

Importance of the field: Several studies have demonstrated the high prevalence of hypertension and the crucial role of the association between endothelial function and hypertension. Thus, in depth investigation of the pathophysiological mechanisms linking endothelial dysfunction and hypertension, as well as evaluation of the efficacy of therapeutic approaches targeting vascular endothelium in states of essential hypertension seems to be of great interest. Areas covered in this review: The association of essential hypertension and endothelial function are discussed in depth. In addition to the classical anti-hypertensive agents, agents such as statins, tetrahydrobiopterin, l-arginine, antioxidants, sildenafil, third generation beta blockers and NO-release related agents have been found to be beneficial by improving endothelial dysfunction in essential hypertension. What the reader will gain: Important aspects regarding the association of hypertension and endothelial dysfunction will be highlighted. In addition, classical and novel agents especially, will be reported thoroughly according to their effects on endothelial function in hypertension. Take home message: There is a strong bidirectional association between essential hypertension and endothelial dysfunction. Moreover, novel agents appear to be beneficial and promising in improving endothelial function in states of hypertension. However, more studies are required to evaluate their role, as the literature lacks large scale studies.
Recent progress in local and global traversability for planetary rovers

Autonomous planetary rovers operating in vast unknown environments must operate efficiently because of size, power and computing limitations. Recently, we have developed a rover capable of efficient obstacle avoidance and path planning. The rover uses binocular stereo vision to sense potentially cluttered outdoor environments. Navigation is performed by a combination of several modules that each "vote" for the next best action for the robot to execute. The key distinction of our system is that it produces globally intelligent behavior with a small computational resource - all processing and decision making are done on a single processor. These algorithms have been tested on our outdoor prototype rover, Bullwinkle, and have recently driven the rover 100 m at a speed of 15 cm/sec. In this paper we report on the extension on the systems that we have previously developed that were necessary to achieve autonomous navigation in this domain.
Semantically enriching text representation model for document clustering

This paper presents a novel text space model that represents textual documents for document clustering, which contains the 'concept' space independently of the 'document' and 'term' spaces. The text model described here represents documents as matrices (i.e., 2nd-order tensors), and a document corpus is represented as a 3rd-order tensor. For this, it is necessary to produce the concept vector for each term that occurs in a given document, which is related to word sense disambiguation. As an external knowledge source for concept weighting, we employ the Wikipedia encyclopedia.
Occupancy-Based Frequent Pattern Mining*

Frequent pattern mining is an important data mining problem with many broad applications. Most studies in this field use support (frequency) to measure the popularity of a pattern, namely the fraction of transactions or sequences that include the pattern in a data set. In this study, we introduce a new interesting measure, namely occupancy, to measure the completeness of a pattern in its supporting transactions or sequences. This is motivated by some real-world pattern recommendation applications in which an interesting pattern should not only be frequent, but also occupies a large portion of its supporting transactions or sequences. With the definition of occupancy we call a pattern dominant if its occupancy value is above a user-specified threshold. Then, our task is to identify the qualified patterns which are both dominant and frequent. Also, we formulate the problem of mining top-k qualified patterns, that is, finding k qualified patterns with maximum values on a user-defined function of support and occupancy, for example, weighted sum of support and occupancy. The challenge to these tasks is that the value of occupancy does not change monotonically when more items are appended to a given pattern. Therefore, we propose a general algorithm called DOFRA (DOminant and FRequent pattern mining Algorithm) for mining these qualified patterns, which explores the upper bound properties on occupancy to drastically reduce the search process. Finally, we show the effectiveness of DOFRA in two real-world applications and also demonstrate the efficiency of DOFRA on several real and large synthetic datasets.
Applications of Unconstrained Minimization Methods to the Dynamic Analysis of Air-Lubricated Bearings

This study deals with the procedure to estimate the stiffness and damping of air-lubricated bearings under dynamic conditions. Unconstrained minimization methods were used in the nonlinear least squares (NLS) analysis to obtain the dynamic parameters of simulated noisy data with small signal-to-noise ratios (SNR). In the analysis the contaminating noise, either uniform or normally distributed noise, was scaled to give a known SNR and added to a simulated noise-free data. Even with poor initial-guess in the NLS as demonstrated in this study, both simplex and Powell's methods reach minimal variance with far fewer searching steps than those of the lattice and Hooke's methods. In the example of experimental study, the effect of an arbitrary data truncation point on the parameter estimation can be minimized in the fitting process by adding additional parameter-phase angle. The result shows good agreement between experimentally determined stiffness and theoretical prediction. This study provides a general procedure for dynamic analysis of air-lubricated bearings from noisy experimental measurements. Presented as a Society of Tribologists and Lubrication Engineers Paper at the ASME/STLE Tribology Conference in Seattle, Washington, October 1–4, 2000
Learning Radio Resource Management in 5G Networks: Framework, Opportunities and Challenges

In the fifth generation (5G) of mobile broadband systems, Radio Resources Management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and with the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean 5G RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture relies on a single general-purpose learning framework conceived for RRM directly using the data gathered in the network. The complexity of RRM is shifted to the design of the framework, whilst the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios and future directions on applications of machine learning to RRM are discussed.
Applications of Functionalized Carbon Nanotubes for the Therapy and Diagnosis of Cancer

Carbon nanotubes (CNTs) are attractive nanostructures that serve as multifunctional transporters in biomedical applications, especially in the field of cancer therapy and diagnosis. Owing to their easily tunable nature and remarkable properties, numerous functionalizations and treatments of CNTs have been attempted for their utilization as hybrid nano-carriers in the delivery of various anticancer drugs, genes, proteins, and immunotherapeutic molecules. In this review, we discuss the current advances in the applications of CNT-based novel delivery systems with an emphasis on the various functionalizations of CNTs. We also highlight recent findings that demonstrate their important roles in cancer imaging applications, demonstrating their potential as unique agents with high-level ultrasonic emission, strong Raman scattering resonance, and magnetic properties.
The role of prediction in social neuroscience

Research has shown that the brain is constantly making predictions about future events. Theories of prediction in perception, action and learning suggest that the brain serves to reduce the discrepancies between expectation and actual experience, i.e., by reducing the prediction error. Forward models of action and perception propose the generation of a predictive internal representation of the expected sensory outcome, which is matched to the actual sensory feedback. Shared neural representations have been found when experiencing one's own and observing other's actions, rewards, errors, and emotions such as fear and pain. These general principles of the “predictive brain” are well established and have already begun to be applied to social aspects of cognition. The application and relevance of these predictive principles to social cognition are discussed in this article. Evidence is presented to argue that simple non-social cognitive processes can be extended to explain complex cognitive processes required for social interaction, with common neural activity seen for both social and non-social cognitions. A number of studies are included which demonstrate that bottom-up sensory input and top-down expectancies can be modulated by social information. The concept of competing social forward models and a partially distinct category of social prediction errors are introduced. The evolutionary implications of a “social predictive brain” are also mentioned, along with the implications on psychopathology. The review presents a number of testable hypotheses and novel comparisons that aim to stimulate further discussion and integration between currently disparate fields of research, with regard to computational models, behavioral and neurophysiological data. This promotes a relatively new platform for inquiry in social neuroscience with implications in social learning, theory of mind, empathy, the evolution of the social brain, and potential strategies for treating social cognitive deficits.
Humanoid Robot Control Using Spiking Neural Networks

In recent years there has been an increasing amount of interest in spiking neural networks, which are more biologically realistic than rate based models. Whilst a lot of simulation work has been carried out on spiking neural networks, relatively little research has been done on the use of spiking neural networks to control robots in an environment. The main obstacle to the development of embodied spiking neural networks is the lack of an easy way to link spiking neural network simulators to a robot body in an environment. To address this issue we have developed iSpike: an open source software interface between a humanoid robot, the iCub, and a spiking neuron simulator NeMo. iSpike performs encoding of sensory stimulus received from the iCub into patterns of neuron spikes used by the simulator. The spike output from the simulator is decoded into motor commands for the robot to perform. In our implementation we attempt to mimic the visual, proprioceptive and motor control pathways of the human body. The implementation has been tested with the iCub virtual simulator, the test demonstrates the possibility of real-time control and monitoring of the virtual iCub robot using NeMo. Performance tests show that motor control and proprioceptive pathway performance is efficient enough for real-time application. iSpike is currently available at http://ispike.sf. net.
Hospital waiting time: the forgotten premise of healthcare service delivery?

PURPOSE This is a national study which aims to determine the average waiting time in Malaysian public hospitals and to gauge the level of patient satisfaction with the waiting time. It also aims to identify factors perceived by healthcare providers which contribute to the waiting time problem. DESIGN/METHODOLOGY/APPROACH Self-administered questionnaires were the main method of data collection. Two sets of questionnaires were used. The first set solicited information from patients on their waiting time expereince. The second set elucidated information from hospital employees on the possible causes of lengthy waiting time. The questionnaires were administered in 21 public hospitals throughout all 13 states in Malaysia. A total of 13,000 responses were analysed for the patient survey and almost 3,000 were analysed for the employee survey. FINDINGS The findings indicate that on average, patients wait for more than two hours from registration to getting the prescription slip, while the contact time with medical personnel is only on average 15 minutes. Employee surveys on factors contributing to the lengthy waiting time indicate employee attitude and work process, heavy workload, management and supervision problems, and inadequate facilities to be among the contributory factors to the waiting time problem. SOCIAL IMPLICATIONS Public healthcare in Malaysia is in a state of "excess demand", where demand for subsidised healthcare far outstrips supply, due to the large fee differential between public and private healthcare services. There is a need for hospital managers to reduce the boredom faced by patients while waiting, and to address the waiting time problem in a more scientific manner, as has been carried out in other countries through simulation and modelling techniques. ORIGINALITY/VALUE Healthcare organisations are keen to address their waiting time problem. However, not much research has been carried out in this area. The study thus fills the lacuna in waiting time studies in healthcare organisations.
Monte Carlo simulation on sequence statistical structures of gradient copolymers

Abstract The simulation of controlled/living radical copolymerization (CRcP) is conducted using the method of Monte Carlo. Target Markov chains are obtained by calculating transfer probabilities instantly. The statistical results which are all the functions of conversion are obtained, including cumulative composition, segment length distribution, dyad and triad concentrations. The effect of reaction parameters on gradient structures is investigated, including initiation rates, monomer feed ratio and reactivity ratios. The results show that reactivity ratios, monomer feed ratio and conversion are more important parameters that can affect gradient structure than initiation rates. Initiation rates can cause difference in sequence structures at early stage of copolymerization and affect cumulative compositions continuously to a moderate degree. Monomer feed ratio mainly affects the fore-head of gradient copolymers while conversion simply affects the rear-end of gradient copolymers. The results of segment length distribution show that with the increase of difference in reactivity ratios, the distribution changes from Flory distribution which indicates random structure to Poisson distribution which indicates block composition
Radiotherapy-Associated Long-term Modification of Expression of the Inflammatory Biomarker Genes ARG1, BCL2L1, and MYC

Ionizing radiation (IR) exposure of cells in vitro and in vivo triggers a complex cellular response among which modifications of gene expression have been consistently reported. Nevertheless, little is currently known about the transcriptionally responsive genes which play a role in the inflammation response. In order to improve our understanding of such transcriptional response to radiation in vivo, we simultaneously monitored the expression of 249 genes associated with the inflammation response over the course of the radiotherapy treatment in blood of patients treated for endometrial or head and neck cancer. We have identified genes whose transcriptional expression is either upregulated (ARG1, BCL2L1) or downregulated (MYC) several fold in vivo. These modifications were consistently detected across patients and further confirmed by quantitative real-time polymerase chain reaction (QRT-PCR); they were specifically significant toward the end of the radiotherapy treatment, 5 weeks following the first radiation fraction and more pronounced in endometrial patients (respectively, 2.9, 4.1, and 1.8 times). Importantly, in an attempt to correlate expression levels with normal tissue reaction to IR, we also identified three other genes CD40, OAS2, and CXCR1 whose expression level fluctuations during radiotherapy were more pronounced in patients developing late normal tissue responses to curative radiotherapy after the end of the radiotherapy treatment. Overall, we identified inflammation-associated genes which are promising biomarkers of IR exposure and susceptibility to radiation-induced toxicity.
Speaker Identification by Joint Statistical Characterization in the Log Gabor Wavelet Domain

Real world Speaker Identification (SI) application differs from ideal or laboratory conditions causing perturbations that leads to a mismatch between the training and testing environment and degrade the performance drastically. Many strategies have been adopted to cope with acoustical degradation; wavelet based Bayesian marginal model is one of them. But Bayesian marginal models cannot model the inter-scale statistical dependencies of different wavelet scales. Simple nonlinear estimators for wavelet based denoising assume that the wavelet coefficients in different scales are independent in nature. However wavelet coefficients have significant inter-scale dependency. This paper enhances this inter-scale dependency property by a Circularly Symmetric Probability Density Function (CS-PDF) related to the family of Spherically Invariant Random Processes (SIRPs) in Log Gabor Wavelet (LGW) domain and corresponding joint shrinkage estimator is derived by Maximum a Posteriori (MAP) estimator. A framework is proposed based on these to denoise speech signal for automatic speaker identification problems. The robustness of the proposed framework is tested for Text Independent Speaker Identification application on 100 speakers of POLYCOST and 100 speakers of YOHO speech database in three different noise environments. Experimental results show that the proposed estimator yields a higher improvement in identification accuracy compared to other estimators on popular Gaussian Mixture Model (GMM) based speaker model and Mel-Frequency Cepstral Coefficient (MFCC) features. Keywords—Speaker Identification, Log Gabor Wavelet, Bayesian Bivariate Estimator, Circularly Symmetric Probability Density Function, SIRP.