Miracle-dz/UniFAR-FTUs huggingface dataset:
The Roles of Choline in Maintaining Optimal Health

Choline is an essential nutrient involved in membrane lipid formation, neurotransmission and one carbon metabolism. Most of dietary choline is produced from phosphatidylcholine, a phospholipid abundant in eggs, meat and dairy products. Choline deficiency is implicated in neuronal development, liver disease, insulin resistance, muscle damage and choline is also vital during fetal development. The focus of this review is to shed light on the critical contributions of choline to membrane phosphatidylcholine formation and energy homeostasis. Moreover, this review will also focus on the role of choline in protecting against developmental disorders, particularly during pregnancy and fetal development while also focusing on adult onset metabolic disorders such as non-alcoholic liver disease and diabetes.
Rainfall over land from the AMSR-E

Significant improvements in the retrieval of instantaneous rain rates over land have occurred through the continued evolution of the Goddard Profiling Algorithm (GPROF), which has been used in the TRMM mission and most recently, the Aqua Advanced Microwave Scanning Radiometer (AMSR-E). At present, GPROF V6 incorporates a probability of convective rain, in conjunction with a convective and stratiform set of rain radiance vectors, which are then used to compute a final surface rain rate. Preliminary results with TMI and AMSR-E over the United States in the instantaneous scale suggest low bias errors and high correlations when compared with rain gauge adjusted radar rainfall estimates. Discussion on the use of specialized ground validation sites at Eureka, California and Iowa City, Iowa are also presented.
The Comprehensibility of Simplified English in Procedures

Anecdotal evidence suggests that using a restricted language called Simplified English (SE) to write procedural documents is the best method to accommodate specific audiences. Providing empirical data to prove or disprove this hypothesis is the point of the experiment reported here. This study examined the effect of document type (SE versus non-SE), passage (Procedure A versus Procedure B), and native language (native versus non-native English speakers) on the comprehensibility, identification of content location, and task completion time of procedure documents for airplane maintenance. This research suggests that using SE significantly improves the comprehensibility of more complex documents. Further, readers of more complex SE documents can more easily locate and identify information within the document. For the documents tested in this experiment, the SE and non-SE documents took essentially the same amount of time for subjects to read and complete the test. Finally, while the difference between native and non-native English speakers could not be tested statistically because of extremely different cell sizes, the comprehensibility and content location scores for the native and non-native speakers appear to be quite different, with the non-native speakers benefiting from SE more than the native speakers.
Analysis of high temperature burns

We have developed a finite element model to describe the thermal interaction between tissue and a high temperature material with defined properties. The model solves the thermal diffusion equation in both the tissue and the material to determine the transient temperature gradients that develop. The tissue response is dependent on both the temperature and thermal inertia of the contacting material. The transient temperature solution, obtained from the thermal analysis, is input to a damage model to calculate a quantitative value for injury as a function of time of exposure and depth into the tissue.
Proceedings of the 1st international workshop on Search and mining entity-relationship data

It is our great pleasure to welcome you to the 1st International Workshop on Search and Mining Entity-Relationship Data -- SMER 2011, held at CIKM 2011 in Glasgow on October 28, 2011. Data complexity and its diversity have been rapidly expanding over the last years. Numerous applications in various domains such as social-media, healthcare, e-commerce, and business intelligence require new methods and tools for collecting and extracting entities and their relationships from unstructured data sources to be transformed into useful knowledge. Even though lots of useful facts are being added on a daily basis on multitude web and enterprise data sources, they are still hidden behind barriers of language constraints, data heterogeneity and ambiguity, and the lack of proper query interfaces. In addition, novel search and data mining methods are required to provide expressive and powerful discovery capabilities, yet intuitive enough, for exploring the large amounts of entity-relationship data. The workshop picks up these problems in two main themes. The first is search and discovery over rich entity-relationship data and includes among others topics such as ER data collection, indexing, query languages and optimization, ranking methods, similarity and exploratory search, privacy, etc. The second theme is entity-relationship data mining methods and includes among others topics such ER data extraction, resolution, representation, fusion, ER graph mining, etc. The workshop main objective is to serve as an open forum for discussing the new research challenges in search and mining of large scale ER data extracted from multitude of unstructured and semi-structured data sources, driven by recent industry trends and requirements in various domains and increasing academic interest. The workshop will bring together researchers from different communities working on similar problems in the context of ER and other semantic data, allowing for cross-fertilization between areas. During the workshop, we will identify common problems and their various solution approaches in DB, KM, and IR. The workshop will consist of three parts: Keynote talks by Michael Witbrock (Cycorp), Krisztian Balog (Norwegian University of Science and Technology), and David Carmel (IBM Research) with valuable insights from both research and industry, three research talks and three posters, selected by the program committee from a total of 10 papers, with topics spanning from entity extraction methods to entity search and privacy, and an open panel for discussing open issues and advances in ER search and data mining, serving as an open stage for interdisciplinary collaboration for facing the emerging research challenges.
Robust co-evolutionary design of SISO Smith predictor PID controllers

Genetic algorithms are proposed as a new and novel technique to solve the problem of designing a robust Smith predictor PID controller for a plant with model uncertainties, capable of dealing with time-delays in an exact manner. The evolutionary scheme used, involves generating two separate populations, one representing the controller and the other the plant. The controller population is then co-evolved against a population of plants covering the plant uncertainty search space, such that the controller can control all the plants effectively. A time-domain cost function subjected to a frequency-domain vector margin stability constraint, is then deployed in order to obtain a robust controller design. This evolutionary approach is illustrated by evolving a Smith predictor PID controller for a linear plant which has a set of prescribed model uncertainties, and compares the results with the ones of robust PID control for the same uncertainty operating envelope.
Business Process Improvement Using Adjustable Parameters on Simulation–A Case Study in Restaurant Business

The business process plays an important role in an organization in achieving its goals. An organization makes a profit or loss depending on effective management procedures. Thus, in response to changes inside and outside the organization it needs to improve and optimize its processes for survival and competitive advantage in the market. Business Process Improvement (BPI) is a way to improve the existing process by using process redesign heuristics. In this paper, the adaptation of BPI methodology is used with redesign heuristics and integrated simulation to measure the influencing factors. An effective number of parameters for improving the performance characteristics of this process are proposed by considering those which affect the slow service response time and impact on queue times. The cycle time, the time taken to complete the task, the cost of operation, and the queue time, are shown in the test results of the simulation process.
Robust Header Compression Protocol Enhancements for Voice Multicast over Mobile Ad-Hoc Networks

Robust Header Compression (RoHC) is a protocol to compress the headers in IP packets to reduce traffic. Particularly, in VolP (Voice over IP) packets, the whole 40 bytes of RTP header section can potentially be compressed to as small as 1 byte. In Mobile Ad-hoc Networks (MANETs), the underlying wireless links between the compressor and the decompressor are often not stable. Consequently the context between the compressor and decompressor may be lost during communication. This makes the header compression ineffective. This paper proposes a technique that leverages the inherent broadcasting characteristics of MANETs that allows mobiles to reinstate a new context by sniffing the wireless medium and capturing packets from different compressors. The proposed method improves the robustness of the protocol while retaining all other benefits of the protocol. The viability and the performance of the approach are demonstrated using an NS-2.
N ON-STATIONARY MATHEMATICAL MODEL OF INDUSTRIAL DIESEL FUEL HYDROTREATING PROCESS

Catalytic hydrotreating is widely used in secondary petroleum refining industry. The main purpose of this process is to significantly lower sulfur content in the petroleum products. Currently, there is a lack of industrially applicable mathematical models of diesel hydrotreating, especially the models which take into account the reaction kinetics and catalyst deactivation. In this paper, research is focused at processing of experimental data from laboratory equipment and industrial hydrotreating unit and developing mathematical model, which adequately describe industrial data of hydrotreating industrial unit with the use of current view of chemical mechanism and catalyst deactivation.
Building Efficient Neuromorphic Networks in Hardware with Mixed Signal Techniques and Emerging Technologies

In recent years, neuromorphic architectures have been an increasingly effective tool used to solve big data problems. Hardware neural networks have not been able to fully exploit the power efficient properties of the neural paradigm, however, due to limitations in standard CMOS. One of the largest challenges is the quadratic scaling of the synapses in a neural network. There has been some work in using post CMOS technology as synapses to overcome this limitation, but systems to date have not been scalable due to the design of their neurons. This dissertation aims to design and build scalable neural network architectures that can use emerging resistive memory technology as synapses. Using analog computing techniques to build networks is promising, especially due to the development of dense, CMOS compatible analog resistive memories. Building functional analog networks in advanced technology nodes, however, is challenging due to the relatively poor performance of analog components in these nodes. This work explores oscillatory neural networks (ONNs), which use phase as the analog state variable instead of voltage or current, reducing the number of traditional analog components required and making the networks better-suited for advanced nodes. This thesis develops additional ONN theory with regard to hardware networks, since previous work did not consider the effect of transmission delay on network dynamics. Transmission delay is proven to cause desynchronization in unmodified ONNs, and the theoretical analysis suggests ways to build networks which do synchronize. Conclusions from the theoretical development are used to build a PLL-based ONN in hardware. The PLL-based ONN is more energy efficient than comparable systems implemented in digital CMOS, although the neuron area is somewhat larger. The measurement of the PLL-based ONN also reveals additional poorly-studied facets of ONN dynamics. Using the knowledge gained from the PLL-based ONN, a larger, PLL-free ONN is built in the same technology. Removing the PLL in each neuron reduces the power and area consumption without sacrificing any functionality. This dissertation demonstrates that ONNs are well-suited to take advantage of emerging resistive memory technology to build efficient hardware neural networks.
Hybrid Credit Scoring Algorithm using PCA and SVM Submitted

Credit Scoring is the use of statistical models to transform relevant data into numerical measures that guide the management and decision makers to accept/reject, pricing, pay/no pay, collections and other credit decisions. In this research study a new principal component analysis-support vector machine hybrid (PCA-SVM) algorithm is proposed for credit scoring. This algorithm is consists of two basic steps, during first step feature selection is employed using PCA later during second step SVM is employed as a classifier. The efficiency and effectiveness of PCA-SVM is evaluated on UK credit scoring dataset. It is observed that PCA-SVM with reduced features perform better than the SVM standalone with full features.
Time and frequency synchronization for OFDM using PN-sequence preambles

Fast and reliable time and frequency synchronization is crucial for packet-based orthogonal frequency division multiplex (OFDM) systems. In this paper we compare and analyze preambles for OFDM systems based on repeated OFDM data symbols or repeated short pseudonoise sequences (PN-sequences). We make an analytical evaluation for AWGN channels and simulate the performance in oneand two-tap Rayleigh fading channels. The PN-based preamble gives better detection properties in terms of lower false detection probability and lower probability of missing the synchronization signal. The PN-based preamble has low peak-to-average power ratio and it makes it possible to use an ADC with one-bit quantization in stand-by mode. For frequency offset estimation both preambles give similar performance, but the PN-based preamble allows for a greater reduction in stand-by mode power consumption.
Deep Learning Implementation for Comparison of User Reviews and Ratings

Sentiment Analysis is the task of identifying and classifying the sentiment expressed in a piece of text as of positive or negative sentiment and has wide application in E-Commerce. In present time, most e-commerce websites have product review sections, which can be used to identify customer satisfaction/dissatisfaction for their product. In E-COMMERCE websites such as Amazon.com, E-bay.com etc, consumers can submit their reviews along with a specific polarity rating (e.g. 1 to 5 stars at Amazon.com). There is a possibility of mismatch between review submitted and polarity of rating. For Amazon.com, a customer can submit a strongly positive review but give it a low rating. The objective of this thesis is to develop a web-service application which can be used to tackle this situation. We will perform Sentiment Analysis using Deep Learning on Amazon.com product review data. Product reviews will be converted to vectors using “PARAGRAPH VECTOR” which will later be used to train a Recurrent Neural Network with Gated Recurrent Unit. Our model will incorporate both semantic relationship of review text as well as product information. We have also developed an application in Python, that will predict rating score for the submitted review using the trained model. If there is a mismatch between predicted rating score and submitted rating score, a warning/info will be provided.
A Bassline Generation System Based on Sequence-to-Sequence Learning

This paper presents a detailed explanation of a system generating basslines that are stylistically and rhythmically in-terlocked with a provided audio drum loop. The proposed system is based on a natural language processing technique: word-based sequence-to-sequence learning. The word-based sequence-to-sequence learning method proposed in this paper is comprised of recurrent neural networks composed of LSTM units. The novelty of the proposed method lies in the fact that the system is not reliant on a voice-by-voice transcription of drums; instead, in this method, a drum representation is used as an input sequence from which a translated bassline is obtained at the output. The drum representation consists of ﬁxed size sequences of onsets detected from a 2-bar audio drum loop in eight diﬀerent frequency bands. The basslines generated by this method consist of pitched notes with diﬀerent duration. The proposed sys-tem was trained on two distinct datasets compiled for this project by the authors. Each dataset contains a variety of 2-bar drum loops with annotated basslines from two different styles of dance music: House and Soca. A listening experiment designed based on the system revealed that the proposed system is capable of generating basslines that are interesting and are well rhythmically interlocked with the drum loops from which they were generated.
Daunorubicin can eliminate iPS-derived cancer stem cells via ICAD/CAD-independent DNA fragmentation

Aim: To identify a drug that can effectively eliminate these cancer stem cells (CSCs) and determine its mode of action. Methods: CSCs were obtained from mouse induced pluripotent stem cells (miPSCs) using cancer cell-conditioned media. Drug screening was performed on these cells or after transplantation into mice. Apoptosis was analyzed by flow cytometry and western blotting. Results: Drug screening studies showed that daunorubicin, a topoisomerase II inhibitor, is specifically cytotoxic to miPS-CSCs. Daunorubicin-induced apoptosis was found to be associated with p53 accumulation, activation of the caspase cascade, and oligonucleosomal DNA fragmentation. Treatment with the caspase inhibitor abolished daunorubicin-induced DNA fragmentation and was therefore considered to act downstream of caspase activation. This was also suppressed by treatment with a Ca2+-specific chelator, which suggested that CAD endonuclease does not contribute. Moreover, no obvious ICAD reduction/degradation was detected. Conclusion: Daunorubicin effectively eliminated CSCs, which are dependent on the p53/caspase signaling cascade. The current findings provided the basis for further studies on CSC-targeted drugs for the development of cancer treatment strategies.
Cooperation between man and robot: interface and safety

The question of robot safety become increasingly important as future robots will be required to share their working area with humans in order to achieve a fruitful cooperation. The robot's contribution could be restricted to the laborious and repetitive work, whereas the human acts as a supervisor and handles exceptions. A new safety system has been developed which allows such a close interaction. One module has the task to localize people inside the working area and provide the robot with an appropriate behavior pattern in order to avoid situations endangering the operator. This module uses a camera and computer vision techniques to obtain the location of the operator. The other module of the safety system has to prevent a robot runaway, which can be defined as an unexpected robot motion due to a failure of robot hardware or software. Speech recognition is used as interface between operator and robot. A dedicated robot-control structure has been implemented permitting a fast and safe intervention of the operator by means of the speech system. <<ETX>>
Performance of hard decision detection for impulse radio

Impulse radio (IR) is an ultra-wide bandwidth, time-hopping, code division multiple access (CDMA) technology that possesses unique characteristics that make it a promising candidate for future tactical military radio networks. In this paper, we present an analysis of the bit error performance of IR using hard decision detection and soft decision detection techniques in an environment with dense multiple access interference and large near-far ratios, including severe interference from several local transmitters and a large number of distant interferers and thermal noise. The results show that hard decision detection performs better than soft decision detection in the presence of several large interferers. We discuss the tradeoffs between hard decision detection and soft decision detection, and we also compare the performance of hard decision detection and soft decision detection with blanking. In the soft decision detection with blanking scheme, the input signal is blanked during any interval when an interfering pulse overlaps the desired pulse. This blanking scheme requires knowledge of the time-hopping pattern of the interfering users and a corresponding increase in the complexity needed to achieve synchronization. Our analysis shows that the performance of the hard decision detection receiver is nearly equivalent to the performance of the soft decision detection receiver with blanking without an increase in complexity.
CDP: A multithreaded implementation of a network communication protocol on the Cyclops-64 multithreaded architecture

A trend of emerging large-scale multi-core chip design is to employ multithreaded architectures such as the IBM Cyclops-64 (C64) chip that integrates large number of hardware thread units, main memory banks and communication hardwares on a single chip. A cellular supercomputer is being developed based on a 3D connection of the C64 chips. This paper introduces our design, implementation, and evaluation of the Cyclops Datagram Protocol (CDP) for the IBM C64 multithreaded architecture and the C64 supercomputer system. CDP is inspired by the TCP/IP protocol and its design is very simple and compact. The implementation of CDP leverages the abundant hardware thread-level parallelism provided by the C64 multithreaded architecture. The main contributions of this paper are: (1) We have completed a design and implementation of CDP that is used as the fundamental communication infrastructure for the C64 supercomputer system. It connects the C64 back-end to the front-end and forms a global uniform namespace for all nodes in the heterogeneous C64 system; (2) On a multithreaded architecture like C64, the CDP design and implementation effectively exploit the massive thread-level parallelism provided on the C64 hardware, achieving good performance scalability; (3) CDP is quite efficient. Its Pthread version can achieve around 90% channel capacity on the Gigabit Ethernet, even it is running at the user-level on a single processor machine; (4) Extensive application test cases are passed and no reliability problems have been reported.
Sorting by Block Moves

The research in this thesis is focused on the problem of Block Sorting, which has applications in Computational Biology and in Optical Character Recognition (OCR). A block in a permutation is a maximal sequence of consecutive elements that are also consecutive in the identity permutation. BLOCK SORTING is the process of transforming an arbitrary permutation to the identity permutation through a sequence of block moves. Given an arbitrary permutation π and an integer m, the Block Sorting Problem, or the problem of deciding whether the transformation can be accomplished in at most m block moves has been shown to be NP-hard. After being known to be 3-approximable for over a decade, block sorting has been researched extensively and now there are several 2-approximation algorithms for its solution. This work introduces new structures on a permutation, which are called runs and ordered pairs, and are used to develop two new approximation algorithms. Both the new algorithms are 2-approximation algorithms, yielding the approximation ratio equal to the current best. This work also includes an analysis of both the new algorithms showing they are 2-approximation algorithms.