Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutritional route in oesophageal resection trial II (NUTRIENT II): study protocol for a multicentre open-label randomised controlled trial

Introduction Early start of an oral diet is safe and beneficial in most types of gastrointestinal surgery and is a crucial part of fast track or enhanced recovery protocols. However, the feasibility and safety of oral intake directly following oesophagectomy remain unclear. The aim of this study is to investigate the effects of early versus delayed start of oral intake on postoperative recovery following oesophagectomy. Methods and analysis This is an open-label multicentre randomised controlled trial. Patients undergoing elective minimally invasive or hybrid oesophagectomy for cancer are eligible. Further inclusion criteria are intrathoracic anastomosis, written informed consent and age 18 years or older. Inability for oral intake, inability to place a feeding jejunostomy, inability to provide written consent, swallowing disorder, achalasia, Karnofsky Performance Status <80 and malnutrition are exclusion criteria. Patients will be randomised using online randomisation software. The intervention group (direct oral feeding) will receive a liquid oral diet for 2 weeks with gradually expanding daily maximums. The control group (delayed oral feeding) will receive enteral feeding via a jejunostomy during 5 days and then start the same liquid oral diet. The primary outcome measure is functional recovery. Secondary outcome measures are 30-day surgical complications; nutritional status; need for artificial nutrition; need for additional interventions; health-related quality of life. We aim to recruit 148 patients. Statistical analysis will be performed according to an intention to treat principle. Results are presented as risk ratios with corresponding 95% CIs. A two-tailed p<0.05 is considered statistically significant. Ethics and dissemination Our study protocol has received ethical approval from the Medical research Ethics Committees United (MEC-U). This study is conducted according to the principles of Good Clinical Practice. Verbal and written informed consent is required before randomisation. All data will be collected using an online database with adequate security measures. Trial registration numbers NCT02378948 and Dutch trial registry: NTR4972; Pre-results.
Shrinking Number of Clusters by Multi-Dimensional Scaling

— Clustering is to divide given data and then, automatically find out the meanings hidden in the data. It analyzes data, which are difficult for people to check in detail, and then, makes several clusters consisting of data with similar characteristics. Clustering, which is used in various fields, is automatically done without human interference, but the number of clusters should be decided by men in advance. The number of clusters is a very important element because the result of clustering can be different, depending on the number of clusters. Therefore, this paper proposed a method of deciding the number of clusters, which is projecting the center of a cluster on the two-dimensional plane by use of Multi-Dimensional Scaling, and then, combining the clusters. As a result of experimenting this method with real data, it was found that clustering performance became better.
A novel predistorter structure for memory nonlinear distortion in wideband OFDM systems

To solve the problem of memory nonlinear distortion in wideband OFDM system, a novel Frequency Domain Predistorter (FDPD) structure is proposed. The Predistorter which based on 2-dimension look-up table method is used in frequency domain. It can compensate the memory nonlinear distortion accurately, but the system performance is restricted by the size of lookup table. Then the Filter Look-Up Table (FLUT) with FDPD, which combined the FDPD with FLUT, is proposed to improve the BER performance and convergence speed. The simulation results indicate that the novel structure can decrease the BER in the receiver and increase the convergence speed.
Parallelization of searching and mining time series data using Dynamic Time Warping

Among the various algorithms present for data mining, the UCR Dynamic Time Warping (DTW) suite provided a solution to search and mine large data sets of time series data more efficiently as compared to the previously existing method of using Euclidean Distance. The UCR DTW algorithm was developed for a single CPU core. In this paper, we consider 2 methods of parallelizing the DTW algorithm. First, we consider a multi-core implementation, followed by a cluster implementation using Spark. From the multi-core implementation, we achieve nearly linear speedup. In the Spark implementation, we find that a straightforward implementation of DTW does not perform well. This is because; a major step in DTW is parallel computation of a lower bound. This paradigm is not supported well by Spark, which supports (i) broadcast variables that are broadcasts of read-only variables (ii) accumulation variables that represent distributed sums. We show how to compute distributed lower bounds efficiently in Spark and achieve nearly linear speedup with DTW in a Spark computation as well.
CS838-1 Advanced NLP: Automatic Summarization

Extractive methods work by selecting a subset of existing words, phrases, or sentences in the original text to form the summary. In contrast, abstractive methods build an internal semantic representation and then use natural language generation techniques to create a summary that is closer to what a human might generate. Such a summary might contain words not explicitly present in the original. The state-of-the-art abstractive methods are still quite weak, so most research has focused on extractive methods, and this is what we will cover.
Elasticity Based Scheduling Heuristic Algorithm for Cloud Environments

Cloud computing environments mainly focus on the delivery of resources, platforms, and applications as services to users over the Internet. Cloud promises users access to as many resources as they need, making use of an elastic provisioning of resources. The cloud technology has gained popularity in recent years as the new paradigm in the IT industry. The number of users of Cloud services has been increasing steadily, so the need for efficient task scheduling is crucial for maintaining performance. In this particular case, a scheduler is responsible for assigning tasks to virtual machines efficiently, it is expected to adapt to changes along with defined demand. In this paper, we suggest an elastic scheduler that is able to alter its focus based on the current requirements demanded by the cloud service provider and the user of those services. The Elasticity Based Scheduling Heuristic (EBSH) suggested is measured against the bio-inspired optimization algorithms such as Ant Colony Optimization (ACO) and Honey Bee Optimization (HBO). Also, a networking algorithm is used in this study, namely Random Biased Sampling (RBS). The presented EBSH shows superior performance because of its ability to adapt to changes.
CoBib: An Architecture for a Collaborative Database

A game of chance apparatus includes a rotatable symbol substrate driven relative to a marker by an electric motor. Teeth are mounted coaxially with the symbol substrate and have a tooth repetition equal to that of the symbols. The relative motion between the symbol substrate and the marker can be started or stopped by an electromagnet, the marker and the symbol substrate being lockable into predetermined relative positions by means of the teeth. To reduce operational energy consumption and impact stresses, two annular electromagnets are mounted concentrically to each other and coaxially with the shaft of the symbol substrate. The magnetizable marker is freely displaceable between the poles of the electromagnets. Rings which cannot be magnetized are mounted at a distance from each of the electromagnets between their poles and the associated ends of the marker. Both rings are so shaped and mounted that the marker rests against the ring associated with the energized electromagnet and upon contact with a ring engages the teeth.
DnaSP v5: a software for comprehensive analysis of DNA polymorphism data

MOTIVATION DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser. AVAILABILITY Freely available to academic users from: (http://www.ub.edu/dnasp).
Explore MicroRNA Precursors through a High-Throughput Computational Prediction

Yu-Ching Chang Shih-Je Lan YuChingChang@itri.org. tw 890348@itri.org. tw Yu-Yu Lin Cheng-Tao Wu YuYuLin@itri.org. tw jdwu1@itri.org. tw 1 Bioinformatics Department, Molecular Biomedical Technology Division, Biomedical Engineering Research Laboratories (BEL), Industrial Technology Research Institute (ITRI), Hsinchu 310, Taiwan, R.O. C. 2 Institute of Bioinformatics, National Yang-Ming University, Taipei 112, Taiwan, R.O. C. * These authors contributed equally. Correspondence: Cheng-Tao Wu. E-mail:jdwu1@itri.org. tw
Building Bayesian Inference Graphs for Healthcare Statistic Evidence

Healthcare is a complex process. It is difficult to choose an effective strategy from numerous possible treatment courses. Whether a healthcare strategy is good or bad? The statistic evidence of instances can tell the truth. Recently, many models of machine learning can handle the static data sets well. They usually use classification methods for disease diagnosis, which relates features to diseases. However, few data sets comprise healthcare processes, and few models relate healthcare actions to healthcare results. We propose a Bayesian inference graph for acquiring the experience of experts and the healthcare statistic evidence. We use a set of states to represent the physical condition of a person, and use a set of actions to represent the healthcare methods. Our aim is to build a probabilistic inference graph of each state transition, which shows the probability of a state transition through a certain action. The inference graph, like the experience of human beings, can be enriched. It begins from the prior experience, and then it will increase its knowledge by increasing evidential instances.
Color preserving HDR fusion for dynamic scenes

We present a novel algorithm to efﬁciently generate high quality high dynamic range (HDR) images. Our method is based on the idea of expanding the dynamic range of a reference image at granularity of tiles. In each tile, we use data from a single exposure, but different tiles can come from different exposures. We show that this approach is not only efﬁcient and robust against camera and object movement, but also improves the color quality of the resulting HDR images. We compare our method against the commonly used HDR generation algorithms.
Optimizing the shortest path query on large-scale dynamic directed graph

This paper presents our approach in order to optimize the shortest path query on a large-scale directed, dynamic graph such as a social network. For this kind of graph, edges can be dynamically added or removed while a user asks to determine the shortest path between two vertices. To solve this problem, we propose a strategic solution based on (i) an appropriate data structure, (ii) the optimized update actions (insertions and deletions) and (iii) by improving the performance of query processing by both reducing the searching space and computing in multithreaded parallel. Thus, graph is globally organized by the adjacent lists in order to improve the cache hit ratio and the update action performance. The reduction of searching space is based on the way of calculating the potential enqueued vertices. Cilkplus is chosen to parallelize the consecutive queries. Our strategy was validated by the datasets from SigMod Contest 2016 and SNAP DataSet Collection with the good experimental results.
NMOS-based integrated modular bypass for use in solar systems (NIMBUS): intelligent bypass for reducing partial shading power loss in solar panel applications

NMOS-based Integrated Modular Bypass for Use in Solar systems (NIMBUS) is designed as a replacement for the traditional bypass diode, used in common solar panels. Because of the series connection between the individual solar cells, the power output of a photovoltaic (PV) panel will drop disproportionally under partial shading. Currently, this is solved by dividing the PV panel into substrings, each with a diode bypass placed in parallel. This allows an alternative current path. However, the diodes still have a significant voltage drop (about 350 mV), and due to the fairly large currents in a panel, the diodes are dissipating power that we would rather see at the output of the panel. The NIMBUS chip, being a low-voltage-drop switch, aims to replace these diodes and, thus, reduce that power loss. NIMBUS is a smart bypass: a completely stand-alone system that detects the failing of one or more cells and activates when necessary. It is designed for a 100-mV voltage drop under a 5-A load current. When two or more NIMBUS chips are placed in parallel, an internal synchronization circuit ensures proper operation to provide for larger load currents. This paper will elaborate on the operation, design and implementation of the NIMBUS chip, as well as on the first measurements.
Secure Access Mechanism for Cloud Storage

Emerging storage cloud systems provide continuously available and highly scalable storage services to millions of geographically distributed clients. A secure access control mechanism is a crucial prerequisite for allowing clients to entrust their data to such cloud services. The seamlessly unlimited scale of the cloud and the new usage scenarios that accompany it pose new challenges in the design of such access control systems. In this paper we present a capability-based access control model and architecture appropriate for cloud storage systems that is secure, flexible, and scalable. We introduce new functionalities such as a flexible and dynamic description of resources; an advanced delegation mechanism and support for auditability, accountability and access confinement. The paper details the secure access model, shows how it fits in a scalable storage cloud architecture, and analyzes its security and performance.
Stationary frame voltage harmonic controller for standalone power generation

The paper presents a new control scheme for voltage control of voltage source inverter (VSI) with LC output filter. Proposed high performance control scheme with use of stationary regulators can be used in UPS or standalone power generation where sine wave output voltage is to be maintained. The proposed controller is able to compensate voltage distortion from unbalanced and nonlinear loads, thus controlling negative and positive voltage sequence and its harmonics. For the control purpose only voltage measurement in required. Besides, the controller can be further used in distributed power generation where the parallel operation is desired. In order to improve the parallel operation and sharing any kind of distorting loads, the proposed controller allows adjusting the gain of selected harmonics required for proper harmonic sharing. The proposed scheme is fully appropriate for digital implementation of UPS systems where high quality AC output voltage is in importance. The diagram block of the proposed controller is shown together with some simulation and experimental results. Presented controller shows outstanding performance under nonlinear and unbalanced loads which uses selective harmonic compensators.
Pedestrian detection and direction estimation by cascade detector with multi-classifiers utilizing feature interaction descriptor

This paper proposes a pedestrian detection and direction estimation method by the cascade approach with multiclassifiers using the Feature Interaction Descriptor (FIND). FIND describes the high-level properties of an object's appearance by computing pair-wise interactions of adjacent regionlevel features. To perform efficient and accurate detection using FIND, we employ the cascade approach with multiclassifiers specialized in both the direction of a pedestrian and the distance of the pedestrian from a camera. Using this framework, the developed system can improve the detection performance and provide information of the direction of a pedestrian simultaneously. The experimental results show that superior detection performance and direction estimation results were obtained by our method.
Testing and Validation of Large Scale Collaborative Robotics Projects Lessons from the GUARDIANS experience

The article describes the goals, some major implementation steps and the testing activities of a large collaborative robotics project GUARDIANS funded by the European Union (EU) under the 6’th Framework Program (FP6) as a Specific Targeted Research Project (STREP). The main testing and demonstration results are presented and a discussion about the effect of the testing activities in the final results is made.
Right harmonic spectrum for the back-electromotive force of an n-phase synchronous motor

This paper deals with a vector control of n-phase permanent magnet synchronous machine. To use control algorithms already developed for sine-wave 3-phase machines, the spectrum of back electromotive force (EMF) must contain only odd 2k+1 harmonics which verify the following inequality, 1 /spl les/ 2k+1 < n. In an experimental vector control of a 5-phase drive, two usual algorithms of sine-wave 3-phase machine are thus used to supply a machine with trapezoidal waveform back EMF. In this case, the first and third harmonics are used to produce torque: the other harmonics, and particularly the 7/sup th/ one, induce effects as torque ripples and parasitic currents.
Asymptotic Optimal Strategy for Portfolio Optimization in a Slowly Varying Stochastic Environment

In this paper, we study the portfolio optimization problem with general utility functions and when the return and volatility of underlying asset are slowly varying. An asymptotic optimal strategy is provided within a specific class of admissible controls under this problem setup. Specifically, we first establish a rigorous first order approximation of the value function associated to a fixed zeroth order suboptimal trading strategy, which is given by the heuristic argument in [J.-P. Fouque, R. Sircar and T. Zariphopoulou, {\it Mathematical Finance}, 2016]. Then, we show that this zeroth order suboptimal strategy is asymptotically optimal in a specific family of admissible trading strategies. Finally, we show that our assumptions are satisfied by a particular fully solvable model.
Large-scale screening of transcription factor–promoter interactions in spruce reveals a transcriptional network involved in vascular development

This research aimed to investigate the role of diverse transcription factors (TFs) and to delineate gene regulatory networks directly in conifers at a relatively high-throughput level. The approach integrated sequence analyses, transcript profiling, and development of a conifer-specific activation assay. Transcript accumulation profiles of 102 TFs and potential target genes were clustered to identify groups of coordinately expressed genes. Several different patterns of transcript accumulation were observed by profiling in nine different organs and tissues: 27 genes were preferential to secondary xylem both in stems and roots, and other genes were preferential to phelloderm and periderm or were more ubiquitous. A robust system has been established as a screening approach to define which TFs have the ability to regulate a given promoter in planta. Trans-activation or repression effects were observed in 30% of TF–candidate gene promoter combinations. As a proof of concept, phylogenetic analysis and expression and trans-activation data were used to demonstrate that two spruce NAC-domain proteins most likely play key roles in secondary vascular growth as observed in other plant species. This study tested many TFs from diverse families in a conifer tree species, which broadens the knowledge of promoter–TF interactions in wood development and enables comparisons of gene regulatory networks found in angiosperms and gymnosperms.
Electrocardiogram Signal Compression with Reconstruction via Radial Basis Function Interpolation Based on the Vertex

Patients with heart disease need long-term monitoring of the electrocardiogram (ECG) signal using a portable electrocardiograph. This trend requires the miniaturization of data storage and faster transmission to medical doctors for diagnosis. The ECG signal needs to be utilized for efficient storage, processing and transmission, and its data must contain the important components for diagnosis, such as the P wave, QRS-complex, and T wave. In this study, we select the vertex which has a larger curvature value than the threshold value for compression. Then, we reconstruct the compressed signal using by radial basis function interpolation. This technique guarantees a lower percentage of root mean square difference with respect to the extracted sample points and preserves all the important features of the ECG signal. Its effectiveness has been demonstrated in the experiment using the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital arrhythmia database.
High-Order Numerical Methods for Wave Equations with van der Pol Type Boundary Conditions

We develop high-order numerical methods for solving wave equations with van der Pol type nonlinear boundary conditions. Based on the wave reflection on the boundaries, we first solve the corresponding Riemann invariants by constructing two iterative mappings, and then, regarding the regularity of boundary conditions, propose two different high-order numerical approaches to the system. When the degree of regularity is high, we establish a sixth-order finite difference scheme. While for a low degree of regularity, we provide another method by utilizing the high-order GaussKronrod quadrature rule. Numerical experiments are performed to illustrate the proposed approaches.
A convergent adaptive wavelet-Rothe method for elastoplastic hardening

The Arabian Journal for Science and Engineering, Volume 29, Number 2C. December 2004 17 †To whom correspondence should be addressed. A CONVERGENT ADAPTIVE WAVELET–ROTHE METHOD FOR ELASTOPLASTIC HARDENING* Giovanni Naldi Universita di Milano, Dipartimento di Matematica Via Saldini 50, I-20133 Milano, Italy e-mail: giovanni.naldi@mat.unimi. it Karsten Urban† Universitat Ulm, Abteilung Numerik, D-89069 Ulm, Germany e-mail: karsten.urban@mathematik.uni-ulm.de Paolo Venini Universita di Pavia, Dipartimento die Meccanica Strutturale Via Ferrata 1, I-27100 Pavia, Italy e-mail: venini@cpmec.unipv. it