Miracle-dz/UniFAR-FTUs huggingface dataset:
THE EFFECT OF COGNITIVE REHABILITATION THERAPY ON MEMORY AND PROCESSING SPEED IN ADOLESCENTS

This research study examines the relative effectiveness of Cognitive Rehabilitation Therapy (CRT) in processing speed and working memory. These areas are targets of interest given current literature related to learning deficits. Research suggests the center of the deficit is related to delays in processing speed and working memory. CRT is one such intervention designed to overcome these deficits in students. In this study 1,277 adolescent children ages 10-19 were studied to determine if CRT had any effect on working memory and processing speed as measured by the Woodcock Johnson COG-III test. A quasiexperimental design was utilized examining scores pre-CRT and post-CRT. Based upon the results of this study, the gain in cognitive functioning as measured by working memory and processing speed in adolescents is statistically significant. These results support the use of CRT as needs based intervention in adolescents.
NOVEL SCHIFF BASES OF INDOLINE-2,3-DIONE AND NALIDIXIC ACID HYDRAZIDE: SYNTHESIS, IN VITRO ANTIMYCOBACTERIAL AND IN SILICO MYCOBACTERIUM TUBERCULOSIS (MTB) DNA GYRASE INHIBITORY ACTIVITY

A novel series of Schiff bases 5b-g was synthesized by the reaction of Nsubstitutedbenzylisatin 3b-g with nalidixic acid hydrazide 4 in order to optimize the antimycobacterial activity of our lead Schiff base 5a. Antimycobacterial (anti-mtb) activity of the synthesized hydrazones was investigated against four Mycobacterium strains: M. intercellulari, M. xenopi, M. cheleneo and M. smegmatis. It was found that parasubstitution, with electron withdrawing group, of benzyl moiety in N-benzylisatins resulted in 7 fold enhancement of the anti-mtb activity as shown with compounds 5b, 5d and 5f (MIC 0.09 g/ml) with p-chloro, fluoro and nitro substituents respectively. In silico docking study of these hydrazones with mtb-DNA gyrase revealed that there is parallelism between the antimycobacterial activity of these hydrazones and docking with the active site of the mtb-DNA gyrase B subunit.
Density regression using repulsive distributions

ABSTRACT Flexible regression is a traditional motivation for the development of non-parametric Bayesian models. A popular approach for this involves a joint model for responses and covariates, from which the desired result arises by conditioning on the covariates. Many such models involve the convolution of a continuous kernel with some discrete random probability measure defined as an infinite mixture of i.i. d. atoms. Following this strategy, we propose a flexible model that involves the concept of repulsion between atoms. We show that this results in a more parsimonious representation of the regression than the i.i. d. counterpart. The key aspect is that repulsion discourages mixture components that are near each other, thus favouring parsimony. We show that the conditional model retains the repulsive features, thus facilitating interpretation of the resulting flexible regression, and with little or no sacrifice of model fit compared to the infinite mixture case. We show the utility of the methodology by way of a small simulation study and an application to a well-known data set.
Trainability of VO2max during childhood and adolescence

Na medida em que todas as obras da UC Digitalis se encontram protegidas pelo Código do Direito de Autor e Direitos Conexos e demais legislação aplicável, toda a cópia, parcial ou total, deste documento, nos casos em que é legalmente admitida, deverá conter ou fazer-se acompanhar por este aviso. Trainability of VO2max during childhood and adolescence Autor(es): Helmantel, Adriaan J.; Elferink-Gemser, Marije T. ; Visscher, Chris Publicado por: Imprensa da Universidade de Coimbra URL persistente: URI:http://hdl.handle. net/10316.2/38873 DOI: DOI:http://dx.doi. org/10.14195/978-989-26-1171-6_10
Reframing of Classiﬁcation and Regression Tasks for Predicting the Eﬀects of Compiler Settings on Multiple Embedded Systems

Compiler settings can have a signiﬁcant impact on the performance of software but the task of ﬁnding eﬀective conﬁgurations is time-consuming due to the large number of optimizations available and complex interactions between them. Furthermore, eﬀective conﬁgura-tions are dependent on the target program and architecture. Previous work used prior knowledge about the performance of similar training programs in order to predict good conﬁgurations but these methods required retraining for each new architecture. In this paper we identify interesting classiﬁcation and regression tasks for evaluating the performance of compiler conﬁgurations on two embedded system architectures. We show how a model learned for one architecture is not directly applicable to the other architecture and we discuss potential ideas for modeling the shift between architectures which in future could allow for the reuse of a single model on many platforms rather than retraining for each new system.
Effect of Subacute Dose of Mitragyna Speciosa Korth Crude Extract in Female Sprague Dawley Rats

Mitragyna speciosa Korth (MS) leaves are widely used as a traditional remedy. The main aim of the present study was to observe the subacute toxicity of MS crude extract on the liver, kidney and uterus in female Sprague Dawley rats. Thirty two rats (150-200g) were randomly divided into four groups: control acute (CA); control subacute (CS); experimental acute (EA) and experimental subacute (ES). CA and CS groups were only given 15% Tween-80. Group EA rats were administered single oral dose of 1000mg/kg MS extract for 14 days. Group ES rats were administered repeated dose of 500 mg/kg MS methanol for 28 days. Liver of ES group showed severe sinusoidal congestion with enlarged hepatocytes and numerous vacuolation compared to EA group. The lining of epithelial cells of uterine tissue in ES group showed more vacuolated cells with increasing in height. No changes were observed in kidney with both doses. 
Image segmentation by graph partitioning

In this paper we propose an hybrid method for the image segmentation which combines the edge-based, region-based and the morphological techniques in conjunction through the spectral based clustering approach. An initial partitioning of the image into atomic regions is set by applying a watershed method to the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation process which produces the final segmentation. We have applied our approach on several images of the Berkeley Segmentation Dataset. The results reveal the accuracy of the propose method.
Antibody Drug Conjugates: A New Era in Personalized Medicine and Targeted Cancer Therapy

The challenges in discovery and development of small molecule drug has led to the emergence of antibody drug conjugates (ADCs) as an alternative for targeted cancer therapy. Over the past two decades, several antigens that have selective over expression on cancer cell surface have been identified and they have been exploited for tumor specific cargo delivery. Specific antibodies can be engineered to function as small molecule targeting ligand that can bind tightly to tumor cell’s over expressing the target antigen. The potential of ADCsrepresentsa novel class of cancer cell specific antigen targeted chemotherapy agent. ADCsthus represent a significant step forward in the war against cancer and personalized medicine.
Audio Event Detection for In-Home Care

One in eight persons attending hospital following an accident in the home, are aged 65 and over. This number will increase with the demographic change and the desire of the elderly to remain in their own homes. Hence, the demand for unobtrusive monitoring systems based on autonomous sensor technologies will increase. Such systems can limit observation by a human operator to cases when there is evidence of an incident, by responding to events in a ‘human-like’ fashion. To do so, the system must pick up situations that would also attract human attention, including situations it has not encountered before.
Intelligent Multimedia Mind Maps to Support Media Pre-Production

To date, there are almost no tools that support the elaboration and research of project ideas in media pre- production. The typical tools that are being used are merely a browser and a simple text editor. Therefore, it is our goal to improve this pre-production process by structuring the multimedia and accompanying annotations found by the creator, by providing functionality that makes it easier to find appropriate multimedia in a more efficient way, and by providing the possibility to work together. To achieve these goals, intelligent multimedia mind maps are introduced. These mind maps offer the possibility to structure your multimedia information and accompanying annotations by creating relations between the multimedia. By automatic connecting to external sources, the user can rapidly search different information sources without visiting them one by one. Furthermore, the content that is added to the mind map is analyzed and enriched; these enrichments are then used to give the user extra recommendations based on the content of the current mind map. Subsequently, an architecture for these needs has been designed and implemented as an architectural concept. Finally, this architectural concept is evaluated positively by several people that are active in the media production industry.
GEEK: Incremental Graph-based Entity Disambiguation

A document may include mentions of people, locations, organizations, films, product brands and other kinds of entities. Such mentions are often ambiguous, with no obvious way for a machine to map them to real world entities, due to reasons like homonymy and polysemy. The process of recognizing suchmentions in unstructured texts and disambiguating them by mapping them to entities stored in a knowledge base is known as Named Entity Recognition and Disambiguation (NERD) or Entity Linking. In this paper, we introduce GEEK (Graphical Entity Extraction Kit), a NERD system that extracts named entities in text and links them to a knowledge base using a graph-based method, taking into account measures of entity commonness, relatedness, and contextual similarity. All relevant data is retrieved at runtime using public RESTful APIs. GEEK tries to push the performance limits of a straightforward disambiguation method, that doesn’t require arduous training or a complex mathematical foundation.
Limited benefit of joint estimation in multi‐agent iterative learning

This paper studies iterative learning control (ILC) in a multi‐agent framework, wherein a group of agents simultaneously and repeatedly perform the same task. Assuming similarity between the agents, we investigate whether exchanging information between the agents improves an individual's learning performance. That is, does an individual agent benefit from the experience of the other agents? We consider the multi‐agent iterative learning problem as a two‐step process of: first, estimating the repetitive disturbance of each agent; and second, correcting for it. We present a comparison of an agent's disturbance estimate in the case of (I) independent estimation, where each agent has access only to its own measurement, and (II) joint estimation, where information of all agents is globally accessible. When the agents are identical and noise comes from measurement only, joint estimation yields a noticeable improvement in performance. However, when process noise is encountered or when the agents have an individual disturbance component, the benefit of joint estimation is negligible.
RNN as a Multivariate Arrival Process Model: Modeling and Predicting Taxi Trips

— Understanding time-related geospatial data is a very interesting and challenging task, but is necessary to support an efficient and intelligent urban environment. In this paper we model taxi trips in New York City from historical data and by using a recurrent neural network. The goal of the model is to gain a better understanding of the spatial-temporal patterns related to taxi travel in New York City, so that we can better predict demand. This paper uses the taxi domain to explore the usage of different neural network models for this type of prediction task. In particular, three types of recurrent neural network cells are evaluated: vanilla cells, Long Short Term Memory cells, and Neural Turing Machine cells. The internal states of these models are also examined, during the learning process, to better understand the key features of the different cell structures.
MATERNAL ADIPOSITY, INSULIN SENSITIVITY AND INFLAMMATION, AND INFANT ADIPOSITY: ASSOCIATIONS WITH OFFSPRING AUTONOMIC NERVOUS SYSTEM DEVELOPMENT

MATERNAL ADIPOSITY, INSULIN SENSITIVITY AND INFLAMMATION, AND INFANT ADIPOSITY: ASSOCIATIONS WITH OFFSPRING AUTONOMIC NERVOUS SYSTEM DEVELOPMENT By Mira Dewi M.D., University of Indonesia, 2003 M.S., Bogor Agricultural University, 2008 Submitted to the graduate degree program in Medical Nutrition Science and the Graduate Faculty of the University of Kansas Medical Center in partial fulfil of the requirement for the degree of Doctor of Philosophy.
Improving Network Connectivity and Robustness Using Trusted Nodes With Application to Resilient Consensus

To observe and control a networked system, especially in failure-prone circumstances, it is imperative that the underlying network structure be robust against node or link failures. A common approach for increasing network robustness is redundancy: deploying additional nodes and establishing new links between nodes, which could be prohibitively expensive. This paper addresses the problem of improving structural robustness of networks without adding extra links. The main idea is to ensure that a small subset of nodes, referred to as the trusted nodes, remains intact and functions correctly at all times. We extend two fundamental metrics of structural robustness with the notion of trusted nodes, network connectivity, and $r$-robustness, and then show that by controlling the number and location of trusted nodes, any desired connectivity and robustness can be achieved without adding extra links. We study the complexity of finding trusted nodes and construction of robust networks with trusted nodes. Finally, we present a resilient consensus algorithm with trusted nodes and show that, unlike existing algorithms, resilient consensus is possible in sparse networks containing few trusted nodes.
Genetic Diversity in Centella asiatica ( L . ) Urb . , a Memory-Enhancing Neutraceutical Herb , using RAPD Markers

The present study is the first report of genetic diversity analysis of Centella asiatica (L.) Urb, a medicinal and aromatic plant collected from various locations of Andhra Pradesh, India. Though there are several reports on diversity analysis of C. asiatica using RAPD analysis the data presented in this article is the first report on diversity studied in accessions collected from Andhra Pradesh State. It addresses the determination of genetic variations using a few visual morphological parameters and by RAPD markers. We collected sixteen accessions which demonstrated clear morphological variations and used nine to test for molecular variation. Wide phenotypic variations were observed. Molecular analyses revealed that out of the 30 primers screened, the 16 that were selected for data analysis generated a total of 137 scorable polymorphic markers out of 156 total number of markers. An 87% polymorphism was observed. Cluster analysis based on Dice’s coefficient showed two major groups indicating that in cross-pollinated plants, high levels of differentiation among accessions exists. The grouping of these accessions was independent of the geographical distance. Hence the results of the present study can be seen as a starting point for future research on the population and evolutionary genetics of this species and understanding such variation would facilitate their use in various conservation management practices, and for generating an elite variety from which the production of secondary metabolites can be enhanced. _____________________________________________________________________________________________________________
MAPLE: Mobility support using asymmetric transmit power in low-power and lossy networks

With the proliferation of emerging Internet of Things (IoT) devices and applications, mobility is becoming an integral part of low-power and lossy networks (LLNs). However, most LLN protocols have not yet focused on the support for mobility with an excuse of resource constraints. Some work that do provide mobility support fail to consider radio duty-cycling, control overhead, or memory usage, which are critical on resource-limited lowpower devices. In this paper, we introduce MAPLE, an asymmetric transmit power-based routing architecture that leverages a single resource-rich LLN border router. MAPLE supports mobility in duty-cycled LLNs using received signal strength indicator (RSSI) gradient field-based routing. High-power transmission of the gateway not only allows LLN endpoints to be synchronized for low duty-cycle operation, but also establishes an RSSI gradient field which can be exploited for opportunistic routing without a need for any neighbor or routing table. This eliminates the scalability problem due to memory limitation, and provides a responsive routing metric without control overhead. MAPLE also addresses the RSSI local maximum problem through local adaptation. We implement MAPLE on a low-power embedded platform, and evaluate through experimental measurements on a real multihop LLN testbed consisting of 31 low-power ZigBee nodes and 1 high-power gateway. We show that MAPLE improves the performance of mobile devices in LLN by 27.2%/55.7% and 17.9% in terms of both uplink/downlink reliability and energy efficiency, respectively.
The impact of human resource competencies of front line employees on tourist arrivals of unclassified hotels in Western province , Sri Lanka

The prime objective of this study is to examine the impact of Human Resource (HR) competencies of front line employees on tourist arrivals of unclassified hotels in western province in Sri Lanka. Thus, a comprehensive conceptual model was developed and tested on a survey strategy and 131 frontline employees were taken as the potential respondents for this research. The findings revealed that there is significant positive impact of HR competencies of front line employees on tourist arrivals of unclassified hotels in western province in Sri Lanka. Interpersonal skills and communication skills were only identified as the significant dimensions in HR competencies. This study limits only to five unclassified hotels in western province in Sri Lanka and was carried out only as a cross-sectional study. The study has highlighted the importance for managers in developing interpersonal and communication skills of front line employees to promote tourist arrivals and highlighted the necessity of skill assessment of front line employees to identify skill gap existing in them.
Fine mesquite pod meal on performance, palatability and feed preference in lambs

We evaluated the effect of inclusion of fine mesquite pod meal (FPM) in the diet on performance, preference and palatability of diets in feedlot lambs. Five diets were formulated with increasing levels of FPM (0, 1.5, 3.0, 4.5 and 6.0%). For performance evaluation, the design was completely randomized with five treatments and seven replications. For palatability and preference analysis, the same design was used, but the treatments consisted of diets with 0 and 3% FPM and fourteen replications. The palatability was assessed considering the amount of feed consumed within 30 min., and the preference, considering the feed intake after 24 hours. FPM had no influence on weight gain, but caused a reduction in intake and an increase in dry matter digestibility for inclusion level above 2.4%. The greatest contribution of FPM inclusion is due to the increase in neutral detergent fiber digestibility. The preference and palatability in feedlot lambs is not affected by the inclusion of 3% FPM.
Document Image Processing

The Special Issue “Document Image Processing” in the Journal of Imaging aims at presenting approaches which contribute to access the content of document images. These approaches are related to low level tasks such as image preprocessing, skew/slant corrections, binarization and document segmentation, as well as high level tasks such as OCR, handwriting recognition, word spotting or script identification. This special issue brings together 12 papers that discuss such approaches. The first three articles deal with historical document preprocessing. The work by Hanif et al. [1] aims at removing bleed-through using a non-linear model, and at reconstructing the background by an inpainting approach based on non-local patch similarity. The paper by Almeida et al. [2] proposes a new binarization approach that includes a decision-based process for finding the best threshold for each RGB channel. In the paper by Kavallieratou et al. [3], a segmentation-free approach based on the Wigner-Ville distribution is used to detect the slant of a document and correct it. Once a document image is preprocessed, a next step described in the paper by Ghosh et al. [4] consists in separating text components from non-text ones, using a classifier based on LBP features. Following steps may consist in recognizing text components or searching from word queries. In the paper by Nashwan et al. [5] a holistic-based approach for the recognition of printed Arabic words is proposed, coupled with an efficient dictionary reduction. In the work by Nagendar et al. [6] it is shown that using a query specific fast Dynamic Time Warping distance, improves the Direct Query Classifier (DQC) word spotting system. Deep neural network-based approaches are now widely used in the domain of document image processing, especially for the recognition of textual elements. The following papers also follow this trend. In the work by Jangid and Srivastava [7], deep convolutional networks trained layer-wise, are applied to the recognition of Devanagari characters. The paper by Kesiman et al. [8] is dedicated to southeast Asian scripts written on palm leafs. Character and word images are recognized by CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks), respectively. Several binarization and text-line segmentation approaches are also benchmarked on these specific documents. The work by Granell et al. [9] describes an efficient text-line recognition system, based on CNN and stacks of RNNs, that has been developed for the recognition of historical Spanish documents. These documents include out-of-vocabulary ancient words which are handled by a language model based on sub-lexical units. Annotated datasets are necessary to train systems or to evaluate the various tasks related to document image processing. In several papers published in this special issue, new datasets are released as well as open-source tools that are able to generate synthetic images. A dataset of indic
Performance of a laser frequency comb calibration system with a high-resolution solar echelle spectrograph

Laser frequency combs (LFC) provide a direct link between the radio frequency (RF) and the optical frequency regime. The comb-like spectrum of an LFC is formed by exact equidistant laser modes, whose absolute optical frequencies are controlled by RF-references such as atomic clocks or GPS receivers. While nowadays LFCs are routinely used in metrological and spectroscopic fields, their application in astronomy was delayed until recently when systems became available with a mode spacing and wavelength coverage suitable for calibration of astronomical spectrographs. We developed a LFC based calibration system for the high-resolution echelle spectrograph at the German Vacuum Tower Telescope (VTT), located at the Teide observatory, Tenerife, Canary Islands. To characterize the calibration performance of the instrument, we use an all-fiber setup where sunlight and calibration light are fed to the spectrograph by the same single-mode fiber, eliminating systematic effects related to variable grating illumination.