Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine grain scheduler for shared-memory multiprocessor systems

The Tatung fine grain scheduler (TFGS), which works on machine instruction level for multiprocessor systems, is described. The object of TFGS is to minimise the total execution time of an application program that is to be executed on a shared memory multiprocessor system. An application program is compiled to generate intermediate code. This code is then represented by a data/control dependence graph, a branch nest tree and a priority list. The data dependence between operations, the pipeline effect of each processing element, and branches in the application programs are considered when TFGS does the scheduling task. The multiprocessor system is assumed to be interconnected by a shared memory. The hardware support of shared memory is designed. To process branches and loops within the application program, a status recording mechanism is proposed. The hardware has been designed and simulated. TFGS has been implemented, and some application programs have been used as the testing inputs. The results are very encouraging.
The Callerya Group redefined and Tribe Wisterieae (Fabaceae) emended based on morphology and data from nuclear and chloroplast DNA sequences

Abstract The Tribe Wisterieae (Zhu 1994), founded on the single genus Wisteria, is emended and recircumscribed based on morphology and data from nuclear ITS and ndhJ-trnF, matK and rbcL chloroplast DNA sequences. This newly enlarged tribe comprises 36 species and 9 infraspecific taxa within 13 described genera. Six genera are new, two are reinstated and five were previously placed in Tribe Millettieae. The genus Adinobotrys is also reinstated comprising two species including the new combination A.vastus. Other reinstated genera include Whitfordiodendron, with four species, and Padbruggea, with three species, including the reinstatement of P.filipes and the new combination P.filipesvar. tomentosa. The existing genera Afgekia, Callerya, Endosamara (with the new combination E.racemosavar.pallida), Sarcodum and Wisteria, with the new combinations W.frutescenssubsp. macrostachya are evaluated. The new genera comprise three Australasian species in Austrocallerya: A.australis, A.megasperma and A.pilipes; Wisteriopsis with five species from east Asia has six new combinations: W.japonica, W.kiangsiensis, W.championii, W.eurybotrya, W.reticulata and W.reticulatavar. stenophylla. Two species comprise the new Thai genus Kanburia: K.tenasserimensis and K.chlorantha. Nanhaia comprises the two species: N.fordii and N.speciosa and the monotypic genera Sigmoidala and Serawaia are based respectively on the species S.kityana and S.strobilifera. Lectotypes are designated for the names Adinobotrysfilipes, A.myrianthus, Millettiabonatiana, Millettiabracteosa, Millettiachampionii, Millettiacinerea, Millettiadielsiana, Millettiakityana, M.maingayi, Millettianitida, Millettiaoocarpa, Millettiapurpurea, M.reticulata, M.reticulatavar. stenophylla, Padbruggeadasyphylla, Pterocarpusaustralis, Robiniaracemosa, Whitfordiodendronscandens, W.sumatranum and Wisteriapallida. A neotype is designated for the name Millettialeiogyna.
Genetic Variants in Circadian Rhythm Genes and Self-Reported Sleep Quality in Women with Breast Cancer

Introduction: Women diagnosed with breast cancer (BC) are at increased risk of sleep deficiency. Approximately 30–60% of these women report poor sleep during and following surgery, chemotherapy, radiation therapy, and anti-estrogen therapy. The purpose of this study was to examine the relationship between genetic variation in circadian rhythm genes and self-reported sleep quality in women with BC. Methods: This cross-sectional study recruited women with a first diagnosis of breast cancer at five sites in Nebraska and South Dakota. Sixty women were included in the study. Twenty-six circadian genes were selected for exome sequencing using the Nextera Rapid Capture Expanded Exome kit. 414 variants had a minor allele frequency of ≥5% and were included in the exploratory analysis. The association between Pittsburgh Sleep Quality Index (PSQI) score and genetic variants was determined by two-sample t-test or ANOVA. Results: Twenty-five variants were associated with the PSQI score at p < 0.10, of which 19 were significant at p<0.05, although the associations did not reach statistical significance after adjustment for multiple comparisons. Variants associated with PSQI were from genes CSNK1D & E, SKP1, BHLHE40 & 41, NPAS2, ARNTL, MYRIP, KLHL30, TIMELESS, FBXL3, CUL1, PER1&2, RORB. Two genetic variants were synonymous or missense variants in the BHLHE40 and TIMELESS genes, respectively. Conclusions: These exploratory results demonstrate an association of genetic variants in circadian rhythm pathways with self-reported sleep in women with BC. Testing this association is warranted in a larger replication population.
Efficient Computational Workload Distribution on Heterogeneous GPUs

Recently, heterogeneous system architectures are becoming a mainstream for achieving high performance and power efficiency. In particular, many-core graphics processing units (GPUs) have started to play an important role for computing in heterogeneous architectures. However, for application designers, computational workload still needs to be distributed among heterogeneous GPUs manually and remains inefficient. In this work, we propose a MINLP-based method for efficient workload distribution among GPUs by considering the capabilities of GPUs for various applications. Experimental results demonstrate the performance of our proposed method.
Nomogram to predict the prognosis of parotid gland mucoepidermoid carcinoma: a population-based study of 1306 cases

Background Mucoepidermoid carcinoma (MEC) is a common cancer in the oral salivary gland malignancy, which mainly occurs in the parotid gland. The aim of this study is to identify independent prognostic factors and establish a nomogram model for parotid gland mucoepidermoid carcinoma (P-MEC) patients using the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database. Method Patients with P-MEC were selected from between 2004 and 2015. The overall survival (OS) and cancer-specific survival (CSS) rates were estimated using the Kaplan-Meier method with the log-rank test. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify the independent prognostic factors. Results A total of 1,306 patients with P-MEC were enrolled. Age, grade, T stage, N stage, M stage, chemotherapy, and surgery type were independent prognostic factors for OS and CSS. A nomogram for OS was formulated based on these independent prognostic factors and validated using an internal bootstrap resampling approach, which showed that the nomogram exhibited a sufficient level of discrimination according to the C-index (0.877, 95% CI [0.855–0.898]). Conclusion Several prognostic factors for P-MEC were identified. The nomogram developed in this study accurately predicted the 5- and 10-year OS rates of American patients with P-MEC based on individual characteristics. Risk stratification using the survival nomogram can optimize individual therapies and follow-up.
Implementation of FPGA Building Platform as a Cloud Service

The building environment for field-programmable gate array (FPGA) development has a number of requirements such as using high-end computers for faster hardware compilation and installing the various versions of FPGA computer-aided design (CAD) tools specified by different projects while at the same avoiding conflicts between these tools. These requirements make it difficult to enable FPGA development on a wide range of devices, which is critical for developers from different application domains if they are to leverage FPGAs to their full extent. In this work, we propose an FPGA project building platform that reduces hardware costs by sharing highperformance servers between developers and enables a variety of FPGA CAD tools to be available in one platform by using container technology. The evaluation of the proposed platform showed that it provides shared and clean building environments without having significant performance overhead.
Extending an assistance robot with the ability to play board games

This paper presents an interactive game system integrated in a mobile robot. Our proposal enables assistant robots to provide cognitive and entertainment games to elders with the aim of improving skills like memory, reflexes, or hand-eye coordination, which are jeopardised in aged people. To gain in user acceptance, the game is virtually recreated by projecting the game board over a table by means of a small projector mounted on the robot. The user can naturally interact with the projected board while him/her movements are detected by the perception system of the robot. To validate the proposed system, a practical case is presented in a laboratory environment.
mROS: A Lightweight Runtime Environment for Robot Software Components onto Embedded Devices

The Robot Operating System (ROS) has attracted attention as a design platform for robot software development. One of the problems of ROS is that it is necessary to adopt high-performance and power-hunger devices as ROS requires a Linux environment for operation. This paper proposes a novel solution called mROS, which is a lightweight runtime environment of ROS nodes, to execute robot software components on mid-range embedded devices. mROS consists of a real-time OS and TCP/IP protocol stack to provide a tiny ROS communication library. It provides connectivity from the edge node to the host and other nodes through the native ROS protocol. Experimental results confirmed that mROS meets the performance requirement for practical applications. We further conducted a case study to validate the portability of mROS from ROS nodes. Our work would contribute to the power minimization and real-time performance enhancement of mobile robot systems.
Autonomous driving on a direct perception system with deep recurrent layers

Controlling a car in traffic is a complex task that is hard to treat with classical algorithms. With the fast development of computer vision techniques, deep artificial neural networks have been use on this task with the use of a frontal camera on a car. Few research projects with these techniques, however, go beyond the perception task and are tested on a driving environment. The use of recurrent neural networks combined with convolutional neural networks has achieved good results in analysis of videos and image streams because it can capture temporal features. We achieve a better performance with the use of recurrent layers combined with a convolutional network in a autonomous driving system based on direct perception, which is composed by two steps: the perception of indicators on the image from the car frontal camera and the control of the car that directly uses these indicators to drive the car on a simulator. Compared to the original work we achieved a percentual decrease of the mean absolute error on the indicators perception task of 36%, with the use of a deeper convolutional architecture with residual connections, and 48% when adding recurrent layers to this network. In the autonomous driving test on the simulator we found that the best system was composed by an hybrid of these two networks, using recurrent layers only for part of the indicators. The system developed on this project, PyDeepDriving, is an extension of the original system from the Princeton Vision & Robotics Group, DeepDriving. We open sourced it to facilitate the research of autonomous driving systems based on direct perception.
Development HealthCare System of Smart Hospital Based on UML and XML

The convergence of information technology systems in health care system building is causing us to look at more effective integration of technologies. Facing increased competition, tighter spaces, staff retention and reduced reimbursement, today’s traditional hospitals are looking at strategic ways to use technology to manage their systems called smart hospital. The concept of the smart hospital is a useful system for any hospital; about adding intelligence to the traditional hospital system by covering all resources and locations with patient information. Patient’s information is an important component of the patient privacy in any health care system that is based on the overall quality of each patient in the health care system. The main commitment for any health care system is to improve the quality of the patient and privacy of patient’s information. Today, there is a need of such computer environment where treatment to patients can be given on the basis of his/her previous medical history at the time of emergency at any time, on any place and anywhere. Pervasive and ubiquitous environment and UML (unified modeling language) can bring the boon in this field. For this it's needed to develop the ubiquitous health care computing environment using the UML with traditional hospital environment. This paper is based on the ubiquitous and pervasive computing environment based on UML and XML(The Extensible Markup Language) technology, in which these problems has been tried to improve traditional hospital system into smart hospital in the near future. The key solution of the smart hospital is online identification of all patients, doctors, nurses, staff, medical equipments, medications, blood bags, surgical tools, blankets, sheets, hospital rooms, etc. In this paper efforts is channeled into improving the knowledge-base ontological description for smart hospital system by using UML and XML technology, Our knowledge is represented in XML format from UML modeling(class diagram). Our smart hospital provides access to its system by using a smart card. Finally, the former try to improve health care delivery through development and management of acute care hospital designed; both physically and operationally, for more efficiency and increased patients safety. Keywords: UML; Smart Hospital (SH); Ontology; XML; health care system.
Teaching Requestive Downgraders in L2: How Effective are Input-Based and Output-Based Tasks?

The present study examines the impact of focused tasks on the development of Iranian EFL learners’ pragmatic competence. To this end, we compared the effectiveness of the dictogloss (DIG) as an output-based task and the consciousness raising (CR) as an input-based task in teaching English requestive downgraders. Prior to the experiment, 147 Iranian EFL learners participated in the study to develop the instruments. Also, 43 American native English speakers provided the
Deep soil inventories reveal that impacts of cover crops and compost on soil carbon sequestration differ in surface and subsurface soils

Increasing soil organic carbon (SOC) via organic inputs is a key strategy for increasing long‐term soil C storage and improving the climate change mitigation and adaptation potential of agricultural systems. A long‐term trial in California's Mediterranean climate revealed impacts of management on SOC in maize‐tomato and wheat–fallow cropping systems. SOC was measured at the initiation of the experiment and at year 19, at five depth increments down to 2 m, taking into account changes in bulk density. Across the entire 2 m profile, SOC in the wheat–fallow systems did not change with the addition of N fertilizer, winter cover crops (WCC), or irrigation alone and decreased by 5.6% with no inputs. There was some evidence of soil C gains at depth with both N fertilizer and irrigation, though high variation precluded detection of significant changes. In maize‒tomato rotations, SOC increased by 12.6% (21.8 Mg C/ha) with both WCC and composted poultry manure inputs, across the 2 m profile. The addition of WCC to a conventionally managed system increased SOC stocks by 3.5% (1.44 Mg C/ha) in the 0–30 cm layer, but decreased by 10.8% (14.86 Mg C/ha) in the 30–200 cm layer, resulting in overall losses of 13.4 Mg C/ha. If we only measured soil C in the top 30 cm, we would have assumed an increase in total soil C increased with WCC alone, whereas in reality significant losses in SOC occurred when considering the 2 m soil profile. Ignoring the subsoil carbon dynamics in deeper layers of soil fails to recognize potential opportunities for soil C sequestration, and may lead to false conclusions about the impact of management practices on C sequestration.
Control-theoretic bandwidth-on-demand protocol for satellite networks

In this paper, a control-theoretic DAMA (Demand Assignment Multiple Access) protocol for ATM-based geostationary (GEO) satellite networks is introduced The proposed scheme, developed for the European Union, project "GOECAST" (Information Society and Technology (IST), programme), deals with the lower priority traffic categories, which have to adapt their transmission rates to the traffic conditions. The novelty of this paper consists of the use of control theory concepts to model the satellite system and to generate the bandwidth requests. The proposed scheme is stable, avoids wasting the assigned capacity and is capable of dealing with congestions. Software simulations have been performed with the OPNET tool, in order to test the effectiveness of the proposed protocol.
Reinforcement distribution for fuzzy classifiers: a methodology to extend crisp algorithms

Fuzzy classifier systems (FCS) implement a mapping from real numbers to real numbers, through fuzzy interpretation of input and output. Reinforcement learning (RL) algorithms can be successfully applied to develop learning FCS analogously to what can be done with learning classifier systems (LCS). The author motivates this approach and presents a methodology to extend straightforwardly reinforcement distribution algorithms originally designed for crisp input and output to fully exploit the features of FCS.
A multidimensional approach to aligned sentences in translated text

Using unsupervised clustering techniques this study explores sentence alignment patterns in a parallel corpus of Norwegian source texts and Spanish translations, the NSPC (Hareide and Hofland 2012). The results show that three strategies with respect to sentence alignment dominate: one to one correspondence, merging two sentences into one, and removing sentences altogether (omission). The strategies are intricately correlated with the variables translator , author , and genre . However, we show how visualization techniques for cluster analyses offer a possibility for teasing apart these interactions as well as their relative importance. Our results indicate that non-fiction texts allow translators more freedom with respect to the treatment of sentences than do texts that are written by professional authors of fiction. The style of the author appears to play only a secondary role, but is especially important in fiction. Keywords: corpus based translation, cluster analysis, parallel corpora, corpus alignment, unidirectional bilingual corpus
Desaturation during Physical Exercise in COPD Patients – a Stable-over-time Phenomenon

Abstract Introduction: Exercise-induced desaturation is a common finding in patients with moderate and severe COPD. It is an important marker in the course of disease that has a prognostic value for mortality risk. Aim: To monitor over time COPD patients with and without desaturation during 6-minute walking test (6MWT) and to assess the stability of that phenomenon. Materials and methods: A 6MWT was administered to 70 patients with COPD which ranged in severity from stage 2A to stage 4D (GOLD 2011); the patients had a mean age of 64.5±10.1, mean pack-years - 38.8±21, FEV1% = 46.4%±15.7%, FVC% = 73.7%±1.3%, MRC = 2.31±0.84, CAT = 20.8±6.6. Oxygen saturation was monitored during the test; indications for desaturation were a decrease of SpO2 by ≥4% and a fall in SpO2 to ≤88% for at least 3 min. The patients were followed-up for mean 40.9±22.3 months and tests were repeated. Results: Patients were divided into two groups based on the decrease in SpO2: Group A included patients with desaturation (n=35) and Group B – patients with no desaturation (n=35). In 66 of the patients the desaturation profile was stable over time. Only two patients, who did not desaturated at baseline, experienced desaturation in the follow-up 6MWT and another two patients, who desaturated at baseline, did not have it later in the follow-up. Conclusion: Desaturation is a phenomenon that is persistent over time. Based on the results, it could be concluded that exercise-induced desaturation is a major marker of a particular COPD phenotype.
Intermodular connection suitable for module addition

We propose a new intermodular connections model of multimodular associative neural networks /spl nu/-connection, which is suitable for module addition. A module of multimodular associative neural networks called MuNet, which can memorize and associate the patterns combined complexly, was presented by Ohsumi et al. (1993). However, in the case that some modules are added, MuNet needs to relearn all connection weights between modules, and has high computational complexity to relearn. Thus we propose the /spl nu/-connection which needs to learn the only connection weights between existing and additional modules. Our model needs low computational complexity and its additional learning is faster than relearning all patterns. We use the merits of RBF (Radial Basis Function) nets, and improve learning speed and other properties.
Research on fuzzy modeling forecasting method based on stochastic neural network

In this article, a novel modeling forecasting method based on the combination of the Modified Takagi and Sugeno (MTS) fuzzy model and the stochastic neural network is presented. Expectation–Maximization (EM) algorithm is put forward to calculate the parameters of neural network structure and its weights. Theoretical analysis and prediction examples all show that the technique has strong universalized capabilities and the methods are effective.
Geno2proteo, a Tool for Batch Retrieval of DNA and Protein Sequences from Any Genomic or Protein Regions

Abstract The interconversion of sequences that constitute the genome and the proteome is becoming increasingly important due to the generation of large amounts of DNA sequence data. Following mapping of DNA segments to the genome, one fundamentally important task is to find the amino acid sequences which are coded within a list of genomic sections. Conversely, given a series of protein segments, an important task is to find the genomic loci which code for a list of protein regions. To perform these tasks on a region by region basis is extremely laborious when a large number of regions are being studied. We have therefore implemented an R package geno2proteo which performs the two mapping tasks and subsequent sequence retrieval in a batch fashion. In order to make the tool more accessible to users, we have created a web interface of the R package which allows the users to perform the mapping tasks by going to the web page http://sharrocksresources.manchester. ac.uk/tofigaps and using the web service.
How Can we Study Cardiopulmonary Resuscitation and Cardiac Arrest in Animals: A Review

Animal research is essential to propose new therapeutic approaches limiting the sequels of cardiac arrest in human and veterinary medicine. The ultimate goal is to improve the long term prognosis and neurological recovery. For such investigations, the use of animal models seems unavoidable as cardiac arrest provokes multiple visceral consequences as well as inflammatory response and coagulopathy. These models allow not only a better understanding of the pathophysiology but also the investigation of new treatment strategies to improve basic life support efficiency or prevent multi-organ failure. In this review, we are summarizing the characteristics of the main animal models used in resuscitation sciences. The choice of the species and methods of evaluation of cardiac arrest is crucial. These studies often concern human medicine but they can also address fundamental questions for veterinary research as they are mostly based on animal research.