Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating temporal relations in clinical text: 2012 i2b2 Challenge

BACKGROUND The Sixth Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing Challenge for Clinical Records focused on the temporal relations in clinical narratives. The organizers provided the research community with a corpus of discharge summaries annotated with temporal information, to be used for the development and evaluation of temporal reasoning systems. 18 teams from around the world participated in the challenge. During the workshop, participating teams presented comprehensive reviews and analysis of their systems, and outlined future research directions suggested by the challenge contributions. METHODS The challenge evaluated systems on the information extraction tasks that targeted: (1) clinically significant events, including both clinical concepts such as problems, tests, treatments, and clinical departments, and events relevant to the patient's clinical timeline, such as admissions, transfers between departments, etc; (2) temporal expressions, referring to the dates, times, durations, or frequencies phrases in the clinical text. The values of the extracted temporal expressions had to be normalized to an ISO specification standard; and (3) temporal relations, between the clinical events and temporal expressions. Participants determined pairs of events and temporal expressions that exhibited a temporal relation, and identified the temporal relation between them. RESULTS For event detection, statistical machine learning (ML) methods consistently showed superior performance. While ML and rule based methods seemed to detect temporal expressions equally well, the best systems overwhelmingly adopted a rule based approach for value normalization. For temporal relation classification, the systems using hybrid approaches that combined ML and heuristics based methods produced the best results.
Outlier detection via a soft computing hybrid

Outlier detection has been attracting the data analysts in almost every domain for a long time now because their detection is very challenging. Outliers or novel cases need to be detected before any analysis is performed on data set. Depending upon the domain, outlier detection saves a lot of time, money or both. In this paper, we developed a novel outlier detection model using ensembling technique, in the paradigm of soft computing, which includes four algorithms, namely k-Reverse Nearest Neighbor (kRNN), Auto Associative Neural Network (AANN), Counter Propagation Auto Association Neural Network (CPAANN), and Generalized Regression Auto Association Neural network (GRAANN) as constituents. The ensemble takes the union of all the outliers found by the four techniques.
Optimum Design Approach for a Two-Phase Switched Reluctance Compressor Drive

This paper presents an optimum design approach to a two-phase switched reluctance motor (SRM) drive. The proposed drive is designed for use as a compressor drive in a small refrigerator as an alternative to the existing brushless dc motor with rare-earth magnets. In the proposed approach, three genetic algorithm loops work to optimize the lamination design and to meet the requirements for the target application while simultaneously fine-tuning the control parameters. To achieve design optimization within a realistic timescale, the repeated calculation required to obtain fitness evaluation does not use finite-element analysis but instead consists of a dynamic simulator based on an analytical expression of magnetizing curves and a geometric flux-tube-based nonlinear magnetic analysis developed specifically for this class of motor. Design results show that the proposed approach can autonomously find a feasible design solution of an SRM drive for the target application from a huge search space. The experimental studies using a two-phase 8/6 prototype manufactured in accordance with the optimized design parameters show the validity of the proposed design approach.
Sentiment Analysis using Machine Learning through Twitter Streaming API

Social media allows to share the experiences with many best suggestions and provides opportunities to share the ideas about any topics at any time. In the current trending, twitter is used to gather different kinds of information as user need and it is a social network service which enables the user for better communication and gaining of knowledge. Accurate representation of the user interactions can be done based on the facts sematic content. The pre-processed tweets which are stored in database are been identified and classified whether it relates to the user keywords related posts. The best suggestion using polarity can be predicted using the user keywords. For the interactive automatic system which predicts the tweets posted by the user this system deals with the challenges that appears during the sentimental analysis. It deals with effective study prior to the subjective information. The basic task in this is to identify the polarity of a given tweet in the sentence whether it is positive, negative or neutral. However the polarity of the tweets has been identified, it was difficult for us to check with the meaningless data. To address this challenge the extracted tweets are been pre-processed by replacing the full form instead of short term words. The better performance can be achieved using more training data. However the analysis was frequently done using the previously stored data, it was a challenging task to do it using the streaming data. There are very few works related to the sentiment analysis using online streaming data. In this paper, we propose that the sentiment analysis can be improved using the online streaming data. For online streaming data all the data related to the given topic will be collected according to the current data in the twitter. For better up-to-date analysis, the streaming data is used and can achieve better results. In contrast by conducting the continuous learning from the streaming data, this approach provides better results than the traditional way of using the training data and it achieves the overall performance and computational efficiency.
A Pathogen Type III Effector with a Novel E3 Ubiquitin Ligase Architecture

Type III effectors are virulence factors of Gram-negative bacterial pathogens delivered directly into host cells by the type III secretion nanomachine where they manipulate host cell processes such as the innate immunity and gene expression. Here, we show that the novel type III effector XopL from the model plant pathogen Xanthomonas campestris pv. vesicatoria exhibits E3 ubiquitin ligase activity in vitro and in planta, induces plant cell death and subverts plant immunity. E3 ligase activity is associated with the C-terminal region of XopL, which specifically interacts with plant E2 ubiquitin conjugating enzymes and mediates formation of predominantly K11-linked polyubiquitin chains. The crystal structure of the XopL C-terminal domain revealed a single domain with a novel fold, termed XL-box, not present in any previously characterized E3 ligase. Mutation of amino acids in the central cavity of the XL-box disrupts E3 ligase activity and prevents XopL-induced plant cell death. The lack of cysteine residues in the XL-box suggests the absence of thioester-linked ubiquitin-E3 ligase intermediates and a non-catalytic mechanism for XopL-mediated ubiquitination. The crystal structure of the N-terminal region of XopL confirmed the presence of a leucine-rich repeat (LRR) domain, which may serve as a protein-protein interaction module for ubiquitination target recognition. While the E3 ligase activity is required to provoke plant cell death, suppression of PAMP responses solely depends on the N-terminal LRR domain. Taken together, the unique structural fold of the E3 ubiquitin ligase domain within the Xanthomonas XopL is unprecedented and highlights the variation in bacterial pathogen effectors mimicking this eukaryote-specific activity.
Multifunctional Bandpass Filters With Reconfigurable and Switchable Band Control

A new series of multifunctional reconfigurable bandpass filters (BPFs) and their analytical synthesis design are presented in this paper. They feature frequency-adaptive characteristics in both their passbands and notched bands, as well as switchable-notched-band capability. Their basic filter architecture is discussed and modeled in a progressive manner. The initial circuit consists of a second-order Chebyshev-type filter to which two stubs are added to increase filtering selectivity through transmission-zero (TZ) creation. Subsequently, to produce an embedded notched band, stubs acting as band-stop structures are also inserted at the input–output terminals. The multifunctional properties of this filter scheme are analyzed attending to the resonance frequencies of the introduced stubs. When these resonance frequencies are tuned while keeping a constant spacing between them, a center-frequency reconfiguration with constant bandwidth is achieved; when the resonance frequency of the notched band is shifted to the TZ positions, the notched band is switched-off. The described filter synthesis methodology is based on the constant and frequency-mapping technique, where the dual-mapping functions are controlled to obtain reconfigurable/switchable properties from a low-pass prototype. For experimental-validation purposes, two reconfigurable lumped-element filter prototypes using the proposed synthesis technique are designed, manufactured, and tested. One exhibits a simple reconfigurable passband, whereas the other one adds a controllable/switchable embedded notched band that results in a quasi-elliptic-type dual-passband response. When compared to some related prior-art designs, these filters have the advantages of explicit design process, quantified analysis, and multifunctional responses.
Robust Joint Analysis with Data Fusion in Two-Stage Quantitative Trait Genome-Wide Association Studies

Genome-wide association studies (GWASs) in identifying the disease-associated genetic variants have been proved to be a great pioneering work. Two-stage design and analysis are often adopted in GWASs. Considering the genetic model uncertainty, many robust procedures have been proposed and applied in GWASs. However, the existing approaches mostly focused on binary traits, and few work has been done on continuous (quantitative) traits, since the statistical significance of these robust tests is difficult to calculate. In this paper, we develop a powerful F-statistic-based robust joint analysis method for quantitative traits using the combined raw data from both stages in the framework of two-staged GWASs. Explicit expressions are obtained to calculate the statistical significance and power. We show using simulations that the proposed method is substantially more robust than the F-test based on the additive model when the underlying genetic model is unknown. An example for rheumatic arthritis (RA) is used for illustration.
A probability based algorithm for influence maximization in social networks

In a social network, information runs from word-of-mouth based on the relationship of the users. The influence maximization is to find a limited number of initial users (nodes) to spread the information, so that the maximum number of other users could accept the information, which is a useful technique for marketing, information monitoring and advertising in a social network. Diffusion model of social networks imitates the process of information spreading in social networks, and Independent Cascade (IC) Model and Linear Threshold (LT) Model, are well-known stochastic information influence models. In this paper, we extend the classical IC model according to the observation of users' behaviors in social networks and propose an effective influence maximization algorithm based on this extended IC model. This novel algorithm calculates the influence probability of each node in sub-graphs that other nodes can engendered to it iteratively. The simulation experiments on real social network datasets show that our algorithm is much faster than the greedy hill-climbing algorithm, while the results are very close to the greedy algorithm and out-perform the other heuristic algorithms.
The Intelligent Container - Toward Autonomous Logistic Processes

In this demonstration a research prototype for autonomous local control of logistic processes in the transportation domain is pre- sented. The aim of autonomy in logistics is to gain flexibility and robust- ness in process execution as well as increasing promptness and reducing human efforts in the coordination of a logistic process. This is enabled by state-of-the-art technologies from computer science and electrical engi- neering. The intelligent container and associated stationary systems form a demonstration platform that provides a distributed infrastructure for autonomous process monitoring, control, and coordination systems.
A new tone conversion method for Mandarin by an adaptive linear prediction analysis

Conventional frame-based linear prediction coding(LPC) does not always de-convovle the speech signal into the vocal tract response and the voice source excitation signal exactly. In order to get better results, an adaptive linear prediction coding analysis(ALPC) method is proposed in this paper. The vocal tract responses obtained from ALPC analysis are used to synthesize a vowel syllable that has a different tone. During the synthesizing procedure, the voice source excitations are superseded by the improved LF-4 model, and the pitch-synchronized synthesizing method is used. The hearing experiments indicate that the synthesized speech by ALPC analysis, which has a different tone from the original one, has a high intelligibility and a better voice quality than that by the traditional LPC analysis method.
Flow-Based P2P Network Traffic Classification Using Machine Learning

With the introduction of new and new services in the market every day, the internet is growing rapidly. The network traffic generated by these network protocols and applications needs to be categorised which is an important task of network management. Among these, p2p has the largest share of the bandwidth. This great demand in the bandwidth has increased the importance of network traffic engineering. So, in order to meet the current demand and develop new architectures which help in improving the network performance, a broad understanding of the network traffic properties is required. The flow based methods classify p2p and non-p2p traffic using the characteristics of flows on the internet. In this paper, Naïve Bayes estimator is used to categorize the traffic into p2p and non-p2p. Our results show that with the right set of features and good training data, high level of accuracy is achievable with the simplest of Naïve Bayes estimator.
A Novel Hybrid Approach of Bayesian Theory and Neural Networks for Video Image Segmentation

Video image segmentation is essential for image analysis and the target recognition. In this study, a Bayesian theory and neural networks based image processing method was applied to video image segmentation. Firstly, a neural network with an incremental input node was designed for approximating to the posterior probability, which avoided the difficulty of estimation of class-conditional probability and could be applied to the occasions when prior probability changed. Secondly, the location information in the estimation of prior probability played a role in inhibiting the over-segmentation, and made the classifier more robust and flexible. Finally, a variable-step algorithm using the "Center of gravity" as the starting point for moving target diffused searching was developed. This algorithm could not only reduce noise, but also avoided the classification of each pixel in every video image, which facilitated to improve the performance of real-time.
Sleep among bereaved caregivers of patients admitted to hospice: a 1-year longitudinal pilot study

Objectives This pilot study aimed to describe the sleep of partners and other family caregivers prior to and in the first year after a hospice patient's death. The study also evaluated the feasibility of the study protocol and determined the effect sizes in preparation for a full-scale study. Design The pilot study used a longitudinal, descriptive and comparative design. Setting and participants Participants included primary family caregivers of patients admitted to a hospice in Oslo, Norway. Primary outcome Caregiver sleep was measured subjectively with the Pittsburgh Sleep Quality Index (PSQI) and objectively using wrist actigraphy for 4 nights and 3 days at three different times: during the hospice stay, and at 6 and 12 months after the patient's death. Results 16 family caregivers (10 partners and 6 other family members) completed the 1-year study protocol. Overall, sleep quality and quantity were stable over time and at each assessment, approximately half of the sample had poor sleep quality, both by self-report and objective measures. However, the sleep trajectories differed significantly over time, with older caregivers (≥65 years) having significantly longer sleep durations than younger caregivers (<65 years). Furthermore, sleep quality also differed over time depending on the caregiver's relationship to the patient, with partner caregivers having significantly worse sleep quality than other family caregivers. Conclusions Caring for a dying family member is known to interfere with sleep, yet little is known about bereaved caregivers. The results of this pilot study demonstrate the feasibility of the longitudinal study protocol and indicate that sleep problems are common for caregivers and continue into the bereavement period, particularly for partner caregivers. The caregiver's relationship to the patient may be an important factor to consider in future studies.
Adaptive equalization and data recovery in a dual-mode (PAM2/4) serial link transceiver

To achieve high bit rates link designers are using more sophisticated communication techniques, often turning to 4PAM transmission or decision-feedback equalization (DFE). Interestingly, with only minor modification the same hardware needed to implement a 4PAM system can be used to implement a loop-unrolled single-tap DFE receiver. To get the maximum performance from either technique, the link has to be tuned to match the specific channel it is driving. Adaptive equalization using data based update filtering allows continuous updates while minimizing the required sampler front-end hardware and significantly reduces the cost of implementation in multi-level signaling schemes. A transceiver chip was designed and fabricated in 0.13 /spl mu/m CMOS process to investigate dual-mode operation and the modifications of the standard adaptive algorithms necessary to operate in high-speed link environments.
Design Tradeoffs for Data Deduplication Performance in Backup Workloads

Data deduplication has become a standard component in modern backup systems. In order to understand the fundamental tradeoffs in each of its design choices (such as prefetching and sampling), we disassemble data deduplication into a large N-dimensional parameter space. Each point in the space is of various parameter settings, and performs a tradeoff among backup and restore performance, memory footprint, and storage cost. Existing and potential solutions can be considered as specific points in the space. Then, we propose a general-purpose framework to evaluate various deduplication solutions in the space. Given that no single solution is perfect in all metrics, our goal is to find some reasonable solutions that have sustained backup performance and perform a suitable tradeoff between deduplication ratio, memory footprints, and restore performance. Our findings from extensive experiments using real-world workloads provide a detailed guide to make efficient design decisions according to the desired tradeoff.
Bronchiectasis is associated with human T-lymphotropic virus 1 infection in an Indigenous Australian population.

BACKGROUND Recent studies suggest that infection with human T-lymphotropic virus 1 (HTLV-1) might be associated with bronchiectasis among Indigenous Australians. The present study compared the clinical characteristics and outcomes of bronchiectasis in this population, according to HTLV-1 serologic status. METHODS We performed a retrospective cohort study of Indigenous adults with bronchiectasis and known HTLV-1 serologic status admitted to Alice Springs Hospital, central Australia, from January 2000 through December 2006. RESULTS Among 89 Indigenous adults whose HTLV-1 serologic status was confirmed, 52 (58.4%) were HTLV-1 seropositive. Differences between HTLV-1-seropositive and HTLV-1-seronegative groups were apparent in childhood presentations and adult outcomes. Among adults, an increasing number of bronchiectatic lobes (univariable odds ratio [OR], 1.51; 95% confidence interval [CI]; 1.03-2.20; P = .033) and the presence of ground-glass opacities at chest high-resolution computed tomography (univariable OR, 8.54; 95% CI, 1.04-70.03; P = .046) predicted HTLV-1 infection. Cor pumonale (HTLV-1-positive group, 10/52; HTLV-1-negative group, 1/37; P = .023) was more frequent among HTLV-1-seropositive adults, who also experienced a higher disease-specific mortality (univariable OR, 5.78; 95% CI, 1.17-26.75; P = .028). Only HTLV-1-seropositive patients were admitted specifically for the treatment of infected skin lesions, and this finding predicted death (multivariable OR, 6.77; 95% CI, 1.46-31.34; P = .014). Overall mortality was high; 34.2% of the cohort died at a median age of 42.5 years. CONCLUSIONS HTLV-1 infection contributes to the risk of developing bronchiectasis and worsens outcomes among Indigenous Australians.
Fingerprint Classification by Ridgeline and Singular Point Analysis

Fingerprint classification is an important indexing method for any large scale fingerprint recognition system or database as a method for reducing the number of fingerprints that need to be searched. Fingerprints are generally classified into broad categories based on global features. This paper describes a new fingerprint classification algorithm that uses some curve features of ridgelines. The algorithm mainly uses the total direction change of the ridgelines to classify the ridgelines. Using these information, classification is performed. The algorithm was tested on NIST-4 database and got a good accuracy for the 6 classes.
Reproductive efficiency of nellore (Bos indicus) cows subject to both ftai and homeopathic supplementation

The aim of this work was to determine the effect of homeopathic supplementation on both ovarian dynamics and conception rate in Nellore cows subjected to fixed-time artificial insemination (FTAI). Cows (n = 150) were randomly distributed to the control (CG) and the homeopathy group (HG). The HG cows were supplemented with Pró-cio in the mineral salt for 60 days and both experimental groups were further subjected to FTAI. Cows were evaluated for ovarian dynamics (n = 16), progesterone (P4) concentration (n = 16), and conception rates (n = 150). Ovarian dynamics determined by ultrasonography and showed similar findings for CG and HG, respectively. Thus follicular diameter (8.7 ± 1.0 mm vs. 10.0 ± 0.8 mm), mean pre-ovulatory follicle volume (0.46 ± 0.15 mL vs. 0.61 ± 0.12 mL), and mean follicular growth (3.65 ± 1.41 mm vs. 4.60 ± 1.21 mm) did not differ between groups. Moreover, corpus luteum diameter was similar between groups (CG: 16.28 ± 0.7 mm vs. HG: 15.6 ± 0.8 mm; P > 0.05), although P4 levels did differ (CG: 2.55 ± 0.85 ng mL vs. HG: 6.52 ± 1.19 ng mL; P < 0.05). The conception rate after FTAI was not affected by homeopathic supplementation (CG: 74.67 %, and did HG: 77.33 %; P > 0.05). In conclusion, the homeopathic supplementation Pró-cio increases P4 concentrations but does improve the reproductive efficiency of Nellore cows subject to FTAI.