Miracle-dz/UniFAR-FTUs huggingface dataset:
CASE STUDY: Effect of Supplemental β-Carotene on Yield of Milk and Milk Components and on Reproduction of Dairy Cows

A well-managed, high-producing commercial dairy herd was used to test the impact of supplementing β-carotene (425 mg/d per cow) to lactating multiparous Holstein cows with normally low serum β-carotene (<3 µg/mL). Milk production was not affected by treatment (43.83 and 43.65 kg/d for the β-carotene and control diets). There was no difference because of treatment in overall 3.5% FCM (43.23 vs. 42.24 kg/d for the β-carotene and control diets) but early-lactation cows (0 to 100 DIM) and mature cows (3+ lactation) tended to produce more 3.5% FCM when supplemented with β-carotene (P < 0.10). Milk fat percentage was higher (P < 0.05) with supplemental β-carotene (3.28 and 3.18% for β-carotene and control cows), especially in early-lactation cows and mature cows. Overall milk fat yield (kg/d) was unaffected by treatment but early-lactation and mature cows supplemented with β-carotene tended to produce more milk fat than control cows. Milk true protein percentage and yield were not affected by treatment. Milk urea N levels were higher for cows supplemented with β-carotene (16.06 vs. 15.72 mg/dL for β-carotene-supplemented and control cows; P < 0.05). Somatic cell count was not affected by treatment. Overall pregnancy rate was unaffected by treatment, but after 105 d of β-carotene supplementation, pregnancy rate was 22% for β-carotene-supplemented cows compared with 11% for control cows. β-Carotene supplementation increased percentage of milk fat and tended to increase 3.5% FCM and milk fat yield in early-lactation and mature cows.
Estimation of rotary inverted pendulum by using the unscented Kalman filter - estimation of the initial state -

This paper proposes a continuous-time model estimation method by using the unscented Kalman filter (UKF) from the sampled I/O data, in which the initial state as well as the plant parameter are estimated. The initial state is estimated based on a backward system of the plant. The effectiveness is demonstrated by using the rotary inverted pendulum (RIP) to estimate the initial state and the parameter of a continuous-time nonlinear system.
Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms

Midline shift (MLS) of the brain is an important feature that can be measured using various imaging modalities including X-ray, ultrasound, computed tomography, and magnetic resonance imaging. Shift of midline intracranial structures helps diagnosing intracranial lesions, especially traumatic brain injury, stroke, brain tumor, and abscess. Being a sign of increased intracranial pressure, MLS is also an indicator of reduced brain perfusion caused by an intracranial mass or mass effect. We review studies that used the MLS to predict outcomes of patients with intracranial mass. In some studies, the MLS was also correlated to clinical features. Automated MLS measurement algorithms have significant potentials for assisting human experts in evaluating brain images. In symmetry-based algorithms, the deformed midline is detected and its distance from the ideal midline taken as the MLS. In landmark-based ones, MLS was measured following identification of specific anatomical landmarks. To validate these algorithms, measurements using these algorithms were compared to MLS measurements made by human experts. In addition to measuring the MLS on a given imaging study, there were newer applications of MLS that included comparing multiple MLS measurement before and after treatment and developing additional features to indicate mass effect. Suggestions for future research are provided.
TASS: A Naive-Bayes strategy for sentiment analysis on Spanish tweets

espanolEn este articulo, se describe la estrategia que subyace al sistema presentado por nuestro grupo para la tarea de analisis de sentimiento en el TASS 2013. El sistema se basa principalmente en un clasificador Naive-Bayes orientado a la deteccion de la polaridad en tweets escritos en espanol. Los experimentos realizados han mostrado que los mejores resultados se han alcanzado utilizando clasificadores binarios que distinguen apenas entre dos categorias de polaridad: positivo y negativo. Para poder identificar mas niveles de subjetividad, hemos incorporado al sistema umbrales de separacion con los que distinguir valores de polaridad fuertes, medios y debiles o neutros. Ademas, para poder detectar si un tweet tiene o no tiene polaridad, el sistema incorpora tambien una regla basica basada en la busqueda de palabras con polaridad dentro del texto analizado. Los resultados de la evaluacion muestran valores razonablemente altos (cerca del 67% de precision) cuando el sistema se aplica para detectar cuatro categorias de sentimiento. EnglishThis article describes the strategy underlying the system presented by our team for the sentiment analysis task at TASS 2013. The system is mainly based on a naive-bayes classifier for detecting the polarity of Spanish tweets. The experiments have shown that the best performance is achieved by using a binary classifier distinguishing between just two sharp polarity categories: positive and negative. To identify more polarity levels, the system is provided with experimentally set thresholds for detecting strong, average, and weak (or neutral) values. In addition, in order to detect tweets with and without polarity, the system makes use of a very basic rule that searchs for polarity words within the analysed text. Evaluation results show a good performance of the system (about 67% accuracy) when it is used to detect four sentiment categories.
Array of Antennas Design for the Source Stirring Technique

The paper exhibits an array design for the source stirring technique in reverberation chambers. The development is pursued in two steps devoted to achieve a configuration of antennas that, fed by an electronic switching network, are able to provide the required statistics. In particular, the last step investigates the possibility to feed two antennas simultaneously.
stimation of rice grain yield from dual-polarization Radarsat-2 SAR ata by integrating a rice canopy scattering model and a genetic lgorithm

Fast and accurate estimation of rice yield plays a role in forecasting rice productivity for ensuring regional or national food security. Microwave synthetic aperture radar (SAR) data has been proved to have a great potential for rice monitoring and parameters retrieval. In this study, a rice canopy scattering model (RCSM) was revised and then was applied to simulate the backscatter of rice canopy. The combination of RCSM and genetic algorithm (GA) was proposed for retrieving two important rice parameters relating to grain yield, ear length and ear number density, from a C-band, dual-polarization (HH and HV) Radarsat2 SAR data. The stability of retrieved results of GA inversion was also evaluated by changing various parameter configurations. Results show that RCSM can effectively simulate backscattering coefficients of rice canopy at HH and HV mode with an error of <1 dB. Reasonable selection of GA’s parameters is essential for stability and efficiency of rice parameter retrieval. Two rice parameters are retrieved by the proposed RCSM-GA techice ear nology with better accuracy. The rice ear length are estimated with error of <1.5 cm, and ear number density with error of <23 #/m2. Rice grain yields are effectively estimated and mapped by the retrieved ear length and number density via a simple yield regression equation. This study further illustrates the capability of C-band Radarsat-2 SAR data on retrieval of rice ear parameters and the practicability of radar remote sensing technology for operational yield estimation.
The Design, Implementation, and Evaluation of a Pointing Device For a Wearable Computer

U.S. Air Force special tactics operators at times use small wearable computers (SWCs) for mission objectives. The primary pointing device of a SWC is either a touchpad or trackpoint, which is embedded into the chassis of the SWC. In situations where the user cannot directly interact with these pointing devices, the utility of the SWC is decreased. We developed a pointing device called the G3 that can be used for SWCs used by operators. The device utilizes gyroscopic sensors attached to the user’s index ﬁnger to move the computer cursor according to the angular velocity of his ﬁnger. We showed that, as measured by Fitts’ law, the overall performance and accuracy of the G3 was better than that of the touchpad and trackpoint. These ﬁndings suggest that the G3 can adequately be used with SWCs. Additionally, we investigated the G3 ’s utility as a control device for operating micro remotely piloted aircrafts
Tuning Range and Efficiency Optimization of a Frequency-Reconfigurable Patch Antenna

This letter studies the design tradeoffs for a classical frequency-reconfigurable microstrip patch antenna using tuning varactors. A solid framework to maximize the tuning range and efficiency of the antenna is proposed and experimentally validated. The method can be generalized to different antennas using varactors as tuning mechanism. The study shows the importance in simultaneously choosing the number of tuning devices as well as, importantly, their distances to obtain the appropriate capacitance range for an optimal design.
Energy Efficiency Optimization For Wireless Powered Sensor Networks With Nonorthogonal Multiple Access

This article studies a wireless powered sensor network (WPSN), where sensors harvest energy from a hybrid access point (H-AP) and then transmit information to the H-AP via nonorthogonal multiple access (NOMA) scheme. The energy efficiency (EE) maximization problem for the NOMA-based WPSN is, for the first time, formulated as a nonlinear fractional programming, which is difficult to solve for global optimality due to the lack of convexity. To this end, we propose two important properties for the optimal solution to the EE maximization problem, based on which a particle-swarm-optimization-based solution algorithm is proposed. Simulations demonstrate the fast convergence and stability of the proposed algorithm.
Query Feature Pool Late Fusion & Ranking Result ... Feedback Feature Pool Late Fusion & Ranking Result

In this paper we present an approach to recognize individual persons in aerial video data. We start from a baseline approach that matches a number of color and texture image features in order to find possible matches to a query person track. This approach is improved by incorporating operator feedback in three ways. First, features are being weighted based on their degree of agreement (correlation) to the operator feedback. Second, a classifier is trained based on the operator feedback. This classifier learns to distinguish the query person from others. Third, we use feedback from past queries to further restrict the search space and improve feature selection as well as provide additional classifier training data. Finally we also investigate active learning strategies to select those results that the operator should label in order to gain the highest possible improvement of accuracy in the next iteration. We evaluate our approach on a publicly available dataset and demonstrate improvements over the baseline as well as over a previous work.
MyPlace LOCATOR : FLEXIBLE SHARING OF LOCATION INFORMATION TECHNICAL REPORT 606

Pervasive computing research makes it feasible to model people’s location. However, there are challenges in effective sharing of location information, with support for user control, enabling people to choose the location information available, at the same time, providing information that is useful. We describe our exploration of these issues, in our MyPlace Locator, an interface to people’s location within a closed community. First, we surveyed how people use MyPlace Locator. Then we created an interface for controlling personalised displays of location information and we studied its use. We conclude that the preferences people chose for releasing their own location information were generally consistent with support for the uses valued by the participants in our use study: to facilitate meeting people and contacting them, for awareness of others and to check the accuracy of their own information.
Training convolutional networks with weight-wise adaptive learning rates

Current state–of–the–art Deep Learning classiﬁcation with Convolutional Neural Networks achieves very impressive results, which are, in some cases, close to human level performance. However, training these methods to their optimal performance requires very long training periods, usually by applying the Stochastic Gradient Descent method. We show that by applying more modern methods, which involve adapting a diﬀerent learning rate for each weight rather than using a single, global, learning rate for the entire network, we are able to reach close to state–of–the–art performance on the same architectures, and improve the training time and accuracy.
Statistical Calculations in Far-advanced Glaucomatous Visual Field Loss

Objective: In this study, the capability of statistical analysis indices to characterize static automated visual fields (VFs) accurately in cases of far-advanced glaucoma was assessed. Design: Retrospective observational case series. Participants: Sixteen eyes of 15 patients with end-stage glaucoma and evidence of collapse of VF statistical analysis indices were included in the study. Methods: End-stage glaucoma was defined as vertical cup-to-disc ratio of 0.9 or more, mean deviation less than 24 dB and with only a central or temporal island remaining in the VF gray scale. Collapse of statistical indices was defined as any of the following: pattern deviation probability plot without a single VF location showing P 0.5%; corrected pattern standard deviation (CPSD) and pattern standard deviation (PSD) probability less than 5% or within normal limits (WNL); short-term fluctuation (SF) probability WNL; glaucoma hemifield test (GHT) not outside normal limits (ONL); or presence of a low patient reliability comment triggered by 40% or more false-negative (FN) responses. Main Outcome Measures: Visual field statistical indices. Results: Of the 16 VFs showing misleading statistical calculations, 9 of 16 eyes had a normal pattern deviation probability plot. The PSD, SF, and CPSD parameters were normal or barely outside the normal range in 4 of 16, 10 of 16, and 5 of 16 eyes, respectively. The GHT was ONL in 7 of 13 eyes, borderline with generalized reduction of sensitivity (GRS) in three eyes, and only GRS in two additional eyes. Low patient reliability was triggered because of an FN score of 40% or more in 10 of 16 eyes. Conclusions: Statistical indices are crucial for the interpretation of automated static VFs. However, in end-stage glaucomatous VF loss, both summary statistical indices and reliability indices may not detect abnormality, thus misleading the casual observer. Ophthalmology 2003;110:196–200 © 2003 by the American Academy of Ophthalmology.
Mikko Title : Automatic Speech Recognition for Northern Sámi with comparison to other Uralic Languages Year : 2016 Version : Post print

Speech technology applications for major languages are becoming widely available, but for many other languages there is no commercial interest in developing speech technology. As the lack of technology and applications will threaten the existence of these languages, it is important to study how to create speech recognizers with minimal effort and low resources. As a test case, we have developed a Large Vocabulary Continuous Speech Recognizer for Northern Sámi, an Finno-Ugric language that has little resources for speech technology available. Using only limited audio data, 2.5 hours, and the Northern Sámi Wikipedia for the language model we achieved 7.6% Letter Error Rate (LER). With a language model based on a higher quality language corpus we achieved 4.2% LER. To put this in perspective we also trained systems in other, better-resourced, Finno-Ugric languages (Finnish and Estonian) with the same amount of data and compared those to state-of-the-art systems in those languages.
Heterogeneous population dynamical model: a filtering problem

We consider a heterogeneous population of identical particles divided into a finite number of classes according to their level of health. The partition can change over time, and a suitable exchangeability assumption is made to allow for having identical items of different types. The partition is not observed; we only observe the cardinality of a particular class. We discuss the problem of finding the conditional distribution of particle lifetimes, given such observations, using stochastic filtering techniques. In particular, a discrete-time approximation is given.
Peritoneal Dialysis versus Hemodialysis 3 Selection Criteria and Methods

Canada. Rapid responses are based on a limited literature search and are not comprehensive, systematic review s. The intent is to provide a list of sources of the best evidence on the topic that CADTH could identify using all reasonable efforts w ithin the time allow ed. Rapid responses should be considered along w ith other types of information and health care considerations. The information included in this response is not intended to replace professional medical advice, nor should it be construed as a recommendation for or against the use of a particular health technology. Readers are also cautioned that a lack of good quality evidence does not necessarily mean a lack of effectiveness particularly in the case of new and emerging health technologies, for w hich little information can be found, but w hich may in future prove to be effective. While CADTH has taken care in the preparation of the report to ensure that its contents are accurate, complete and up to date, CADTH does not make any guarantee to that ef fect. CADTH is not liable for any loss or damages resulting from use of the information in the report.
Generation Dispatch with Reserve Margin Constraints Using Linear Programming

The optimal allocation of system generation to the individual units of a power system is normally done on a marginal cost basis. However, system regulation and reserve requirements' may not allow the operation of the system at this point forcing the operator to nranually move to non-optimal operation. ' In this paper the cost-optimal generation allocation problem with reserve margins and other constraints is formulated as a linear programming problem. This formulation is shown to have a structure that can be decomposed into a set of smaller linear programming subproblems using the Dantzig-Wolfe method. Some unique aspects of the computer program developed to solve this problem are described together with some results using a medium-size power system. Although the program was developed to determine the cost of providing a certain level of regulation or reserve margin from off-line studies, it appears that the computer time and storage requirements are such that real-time dispatching with such a program may be feasible in a modern control center
neurons to vertebrate neural crest Evolutionary conservation of cell migration genes: from nematode

data Supplementary http://www.genesdev. org/cgi/content/full/21/4/391/DC1 "Supplemental Research Data" References http://www.genesdev. org/cgi/content/full/21/4/391#References This article cites 27 articles, 14 of which can be accessed free at: service Email alerting click here top right corner of the article or Receive free email alerts when new articles cite this article sign up in the box at the
ZJU Participation in TAC 2016 EDL task

We participated in the Trilingual Entity Discovery and Linking (EDL) task at TAC KBP 2016 under the team name of WednesdayGO. This year, we made some improvements to our last year’s system to achieve better overall EDL performance. We also tried a neural network approach for entity discovery, though in our experiments it performs slightly worse than the traditional approach we used. In this paper, we will mainly introduce the modifications we made to our last year’s system and the neural network approach for entity discovery. We will also demonstrate the results of some experiments we conducted.
Improving Cognitive Impairment of Elderly by an Educational Program for Their Caregivers

Cognitive impairment is associated with increased risk for progression to dementia and functional disability. The aim of this study was to examine the effectiveness of an educational program for the caregivers of elderly persons in improving their cognitive impairment. This quasi-experimental design with pre-post assessment was conducted at four geriatric care homes in Cairo governorate on 34 elderly persons having cognitive impairment (Mini-Mental State Examination [MMSE] score 19 to 24). The researchers prepared the training program for the caregivers. It included knowledge about dementia, instructions to deal with the elderly persons, and a practical part focused on memory aids. The effectiveness of the program was measured by comparing the weekly morning and evening MMSE scores, and comparing the baseline (pre-test) and 8-week (post-test) MMSE scores. The data was from August 2012 to April 2013. The results showed statistically significant improvements in elderly’s MMSE scores (p<0.001). The total MMSE score improved by 3.50 points. All elderly had cognitive impairment before the intervention; this decreased to 29.4% after the intervention (p<0.001). The improvement was markedly higher in the morning measurements compared with the evening ones. In multivariate analysis, the intervention was a statistically significant independent predictor of the improvement in the total MMSE score, in addition to the educational level. In conclusion, training the caregivers of elderly people in mental stimulation and memory aids can improve the cognitive impairment among these persons. The findings corroborate the need for developing intervention programs that equip the caregivers of elderly people with non-pharmacological management strategies that enable them to improve their cognitive impairment. Further research is needed to investigate the effectiveness of memory aids in improving the memory of elderly at different stages of dementia.
Hardware Accelerated Rendering of Unprocessed Point Clouds

In this paper we present a method for rendering unprocessed point clouds using commodity hardware. A point cloud is a set of coordinates which are interpreted as points in space. No assumptions have to be made for the point clouds, and therefore the point clouds do not have to be preprocessed. The method includes view-frustum culling and a level-of-detail (LOD) algorithm which does not need any additional geometry to the original point cloud. It renders always faster than the simple usage of vertex buffer objects (VBOs). Data inside VBOs reside in graphics card memory and can therefore be accessed very fast by the GPU.
Entropy optimized image fusion: Using particle swarm technology and discrete wavelet transform

This paper proposes a method of image fusion whose input consists of multiple images with selective information which can be combined to produce a single substantive, complete image. Through this paper, we discuss enhanced image fusion using a Particle Swarm Optimization (PSO) based framework in the DWT domain. The Dual Tree Discrete Wavelet Transform (DTDWT) method used for decomposition of the source images in this paper is advantageous due to its increased shift invariance and an orientation selectivity property in comparison to the DWT method. In this paper, an optimized weighted averaging rule is applied for fusing the wavelet coefficients. An objective function, which is optimized based on entropy, forms the basis for the discovery of ideal weights for image fusion in the Particle Swarm Optimization method used. The weights thus calculated form the basis for the image fusion and the resultant is obtained by applying an inverse DTDWT. The fused image obtained as the end result is weighed up against other existing techniques like DWT, averaging, simple minimum and PCA using entropy as the basis.