Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Hardware Aspects of Multiantenna Algorithms

The field of wireless communication is growing rapidly, with new requirements for the next generation of mobile and wireless communications technology. In order to achieve the capacities needed for future wireless systems, the design and implementation of advanced communications techniques such as multiantenna systems is required. These systems are realized by computationally complex algorithms, requiring new digital hardware architectures to be developed. The development of efficient and scalable hardware building blocks for realization of multiantenna algorithms is the focus of this thesis. The first part of the thesis deals with the implementation of complex valued division. Two architectures implementing a numerically robust algorithm for computing complex valued division with standard arithmetic units are presented. The first architecture is based on a parallel computation scheme offering high throughput rate and low latency, while the second architecture is based on a resource conservative time-multiplexed computation scheme offering good throughput rate. The two implementations are compared to an implementation of a CORDIC based complex valued division. The second part of the thesis discusses implementation aspects of fundamental matrix operations found in many multiantenna algorithms. Four matrix operations were implemented; triangular matrix inversion, QR-decomposition, matrix inversion, and singular value decomposition. Matrix operations are usually implemented using large arrays of processors, which are difficult to scale and consume a lot of resources. In this thesis a method based on the data flow was applied to map the algorithms to scalable linear arrays. An even more resource conservative design based on a single processing element was also derived. All the architectures are capable of handling complex valued data necessary for the implementation of communication algorithms. In the third part of the thesis, developed building blocks are used to implement the Capon beamformer algorithm. Two architectures are presented; the first architecture is based on a linear data flow, while the second architecture utilizes the single processing element architecture. The Capon beamformer implementation is going to be used in a channel sounder to determine the direction-of-arrival of impinging signals. Therefore it was important to derive and implement flexible and scalable architectures to be able to adapt to different measuring scenarios. The linear flow architecture was implemented and tested with measured data from the channel sounder. By analyzing each block in the design, a minimum wordlength design could be derived. The fourth part of the thesis presents a design methodology for hardware implementation on FPGA. (Less)
An Adaptive Markov Chain Monte Carlo Algorithm for TSP

Traveling salesman problem is widely utilized as the typical issue for algorithm performance research because of its important engineering and theoretical value. An adaptive Markov Chain Monte Carlo algorithm is employed in resolving TSP for the purpose of ameliorating the temperature management problems in the original Metropolis algorithm. In order to get the balance between the runtime and route distance accuracy, the annealing parameter is no longer fixed in advance, but optimized by an adaptive sampler with simple expression and fast convergence. The simulation results shows that the adaptive algorithm has more powerful capacity of finding global solution and stability compared with those of the original Metropolis algorithm.
Predictive power control for dynamic state estimation over wireless sensor networks with relays

We present a predictive power controller for state estimation of a stationary ARMA process over a wireless sensor network (WSN), consisting of sensor nodes, relays, and a single gateway (GW). The state estimate is formed centrally at the GW by using packets received from sensors and relays. The latter perform network coding of sensor measurements. Communication from sensors and relays to the GW is over a fading channel. Packet loss probabilities depend upon the time-varying channel gains and the transmission powers used. To achieve an optimal trade-off between state estimation quality and energy expenditure, in our approach the GW decides upon the in general time-varying transmission powers of sensors and relays. This decision process is carried out on-line and adapts to changing channel conditions by using elements of stochastic model predictive control. Simulations on measured channel data illustrate the performance achieved by the proposed controller.
Will P2P users cooperate with ISPs? A word-of-mouth communication approach

Researchers who address the problem of application-level traffic optimization often consider only the simplest comparison scenario: (a) the case before the option to cooperate and optimize the service is enabled, and (b) the case in which all clients try to optimize the traffic. In this paper, the situations that a user may not wish to cooperate or that a user does not perceive the optimization possibility to be valuable enough to cooperate are taken into account. An analytical model for estimating the popularity of the cooperate-to-optimize option is derived. A method for calculating the time-dependent value of the predicted popularity is presented. The model is verified by simulation experiments.
A robust approach to fault tolerant controller design based on GIMC structure for non-minimum phase systems

This paper represents a robust scheme for designing fault tolerant controller (FTC) based on Generalized Internal Model Control structure, and extends it to non-minimum phase LTI systems. In this scheme, first the nominal controller is designed to have a closed-loop stability and acceptable performance, and then by correcting the nominal controller based on the idea of GIMC, a robustification signal is imposed to compensate the actuator faults. The proposed scheme, deals with nonminimum phase systems by utilizing the idea of transmission zero-assignment. It will be shown that the problem of zero-assignment problem for state-accessible systems is equivalent to a pole-placement problem with state feedback on a reduced-order subspace, which always has a solution. The advantages of the proposed control scheme have been verified by the simulation on the quadruple-tank process subjected to the actuator fault.
CRP Henri Tudor at TREC 2014: Combining Search Results for Clinical Decision Support

This paper presents the rst participation of theLuxembourgish Public Research Center HenriTudor in the TREC 2014 Clinical Decision Sup-port (CDS) Track. At the Resource Centrefor Healthcare Technologies (SANTEC) depart-ment, we focus our research activities on health-care technologies. Our mission consists primarilyin improving healthcare by developing methods,tools, services and solutions that can be appliedby healthcare professionals, patients and citizenson a daily basis.In this research work, we present an approachto combining search results using data fusiontechniques. The focus of the 2014 Clinical Deci-sion Support Track was the retrieval of relevantbiomedical articles for answering generic clinicalquestions about medical records. Each questionconsists of a case report and one of three genericclinical question types, such as \What is the pa-tient’s diagnosis? ". Retrieved articles are judgedrelevant if they provide information of the spec-i ed type that is relevant to the given case.The remainder of this report is organized asfollows. Section 2 gives a brief description aboutthe CDS Task. Section 3 describes our method-ology for combining search results. Our submit-ted runs and the ocial results of TREC CDSTrack are described in Section 4. Finally, Section5 draws our conclusions and outlining directionsfor future work.
A Foundational Approach to Mining Itemset Utilities from Databases

Most approaches to mining association rules implicitly consider the utilities of the itemsets to be equal. We assume that the utilities of itemsets may differ, and identify the high utility itemsets based on information in the transaction database and external information about utilities. Our theoretical analysis of the resulting problem lays the foundation for future utility mining algorithms.
Insulin-Producing Cells Derived from Human Embryonic Stem Cells: Comparison of Definitive Endoderm- and Nestin-Positive Progenitor-Based Differentiation Strategies

Human embryonic stem cells (hESCs) are pluripotent and capable of undergoing multilineage differentiation into highly specialized cells including pancreatic islet cells. Thus, they represent a novel alternative source for targeted therapies and regenerative medicine for diabetes. Significant progress has been made in differentiating hESCs toward pancreatic lineages. One approach is based on the similarities of pancreatic β cell and neuroepithelial development. Nestin-positive cells are selected as pancreatic β cell precursors and further differentiated to secrete insulin. The other approach is based on our knowledge of developmental biology in which the differentiation protocol sequentially reproduces the individual steps that are known in normal β cell ontogenesis during fetal pancreatic development. In the present study, the hESC cell line PKU1.1 was induced to differentiate into insulin-producing cells (IPCs) using both protocols. The differentiation process was dynamically investigated and the similarities and differences between both strategies were explored. Our results show that IPCs can be successfully induced with both differentiation strategies. The resulting IPCs from both protocols shared many similar features with pancreatic islet cells, but not mature, functional β cells. However, these differently-derived IPC cell types displayed specific morphologies and different expression levels of pancreatic islet development-related markers. These data not only broaden our outlook on hESC differentiation into IPCs, but also extend the full potential of these processes for regenerative medicine in diabetes.
Replicability of Effect when Transferring a Supportive Programme for Parents Exposed to Intimate Partner Violence and Their Children from the US to Sweden

ABSTRACT Transferring an evidence-based parenting programme for parents exposed to intimate partner violence (IPV) and their children with emotional and behavioural problems reveals the extent to which cultural and social aspects can interfere with the programme’s effectiveness. Feasibility studies are of value in such circumstances, and the aim of the present feasibility study was to explore, on a small scale and in its natural context, whether the effects of the parenting programme, Project Support, were replicable when transferred to another country. In this study, the programme, which was originally designed for parents exposed to IPV and their children who had developed psychological symptoms in the United States, was evaluated in an equivalent population receiving Swedish social services. Parents (n = 35) self-assessed their parenting capacity and their children’s (n = 35) psychological symptoms. The results indicate that the parents improved their parenting capacity, and feelings of helplessness and fear regarding parenting their children decreased. Those feelings were also associated with the children’s psychological symptoms. The promising results are similar to the findings of previous research from the US, and further implementation and evaluation of Project Support in Sweden are indicated.
Caenorhabditis elegans reveals a FxNPxY-independent low-density lipoprotein receptor internalization mechanism mediated by epsin1

A genome-wide RNA interference screen using Caenorhabditis elegans LRP-1/megalin as a model for LDLR transport was employed to identify factors critical to LDLR uptake. We provide evidence that epsin1 promotes LDLR internalization via a FxNPxY-independent pathway. We complement C. elegans in vivo approaches with loss-of-function and biochemical analyses, using mammalian cell culture systems to evaluate epsin1’s mode of action in LDLR endocytosis.
Mobiscan3D: A Low Cost Framework for Real Time Dense 3D Reconstruction on Mobile Devices

In this paper we propose a computationally inexpensive framework for dense 3D reconstruction on smart device platforms leveraging the feed from motion sensors. In contrast to other methods, we solely rely on the motion sensors to compute pair wise epipolar relationships. In particular, camera positions are obtained only through noisy mobile sensor data which is further optimized globally using iterative reweighted least squares. Rotations are also obtained using mobile sensors. Our method of obtaining camera motion reduce the processing time of the entire pipeline. We further use pair wise epipolar relationships along with normalized cross correlation and gradient information in a pair of images to obtain dense correspondences. The calibrations and correspondences are accurate enough for triangulation which in turn serve as a good initializer for final global bundle adjustment in near real time. Experimental results show that our method works reliably well for both indoor and outdoor scenes of different size and shapes without even utilizing mobile GPU.
Weeds Forage Potential for Ruminant Feeding: Chemical Composition and Kinetics of Rumen Degradation

Introduction Systems of ruminant production in the Andean highlands of Ecuador are dominated by monoculture grasses, where producers do not allow association with other plants such as weeds. These systems have small areas of land and the peasants would be wasting feed resources (association) with high nutritional content and secondary compounds that may be beneficial for feeding ruminants improving the nutritional value of grassland. In this context, Castelán et al. (2003) mention that the use of weeds in ruminant nutrition by small producers in temperate areas of Mexico, it becomes the main natural forage resource for livestock to feed round the year, as there great availability. For farmers with limited financial resources this practice is very common, because it represents feeding efficiency with low production costs, because they use non-conventional feed resources. Weed species such as Sicyos deppei G. , Jaltomata procumbens (Cav.), Drymaria laxiflora Benth y Lopezia racemosa Cav. contain high levels of ruminal fermentation and high crude protein content, as well as low levels of fiber (Castelán et al., 2003). Under this background, the objective of this research is to determine the chemical composition and in situ ruminal degradation of weeds with forage potential that exist in the grasslands of the Andean highlands of Ecuador.
A Methodology for Statistical Matching with Fuzzy Logic

The Analysis of data often requires information that is not available from a single source, but from multiple sources. Statistical matching procedures are methods that help to merge information from different sources into a single data set. Traditionally, statistical matching is done on the basis of computed distances between selected variables found in all data sets. Situations where no decision can be made in traditional statistical matching, e.g., in the case of identical distances, cause problems. We present a methodology for statistical matching with fuzzy logic which solves these problems. After a short introduction, the basics of traditional statistical matching are presented. The description of the theory of statistical fuzzy matching follows thereafter. The paper concludes with a short example.
Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation

The paper describes the engineering and design of a doubly fed induction generator (DFIG), using back-to-back PWM voltage-source converters in the rotor circuit. A vector-control scheme for the supply-side PWM converter results in independent control of active and reactive power drawn the supply, while ensuring sinusoidal supply currents. Vector control of the rotor-connected converter provides for wide speed-range operation; the vector scheme is embedded in control loops which enable optimal speed tracking for maximum energy capture from the wind. An experimental rig, which represents a 7.5 kW variable speed wind-energy generation system is described, and experimental results are given that illustrate the excellent performance characteristics of the system. The paper considers a grid-connected system; a further paper will describe a stand-alone system.
CASK: Open-Source Custom Architectures for Sparse Kernels

Sparse matrix vector multiplication (SpMV) is an important kernel in many scientific applications. To improve the performance and applicability of FPGA based SpMV, we propose an approach for exploiting properties of the input matrix to generate optimised custom architectures. The architectures generated by our approach are between 3.8 to 48 times faster than the worst case architectures for each matrix, showing the benefits of instance specific design for SpMV.
Towards a container-based architecture for multi-tenant SaaS applications

SaaS providers continuously aim to optimize the cost-efficiency, scalability and trustworthiness of their offerings. Traditionally, these concerns have been addressed by application-level middleware platforms that implement a multi-tenant architecture. However, the recent uprise and industry adoption of container technology such as Docker and Kubernetes, exactly for the purpose of improving the cost-efficiency, elasticity and resilience of cloud native services, triggers the unanswered question whether and how container technology may affect such multi-tenant architectures. To answer this question, we outline our ideas on a container-based multi-tenant architecture for SaaS applications. Subsequently, we make an assessment of the technical Strengths, Weaknesses, Opportunities, and Threats (SWOT) which should be taken into account by a SaaS provider when considering the adoption of such container-based architecture.
An Evaluation of SingleTapBraille Keyboard: A Text Entry Method that Utilizes Braille Patterns on Touchscreen Devices

This paper provides an evaluation of the SingleTapBraille keyboard, designed to assist people with no or low vision in using touchscreen smartphones. This application allows blind users to input characters based on braille patterns. To assess SingleTapBraille, this study compares its performance with that of the commonly used QWERTY keyboard. We conducted an evaluation study with 7 blind participants to examine the performance of both keyboards on Android platforms. Overall, participants were able to quickly adjust to SingleTapBraille and type on touchscreen devices using their knowledge of Braille patterns within fifteen to twenty minutes of introduction to the system. The SingleTapBraille keyboard was better than the QWERTY keyboard in terms of both speed and accuracy, indicating that SingleTapBraille represents an improvement over existing alternatives in making touchscreen keyboards more accessible for blind users. Based on the evaluation results and the feedback of our participants, we discuss the strengths and weaknesses of previous keyboards that have been used by participants, as well as those of SingleTapBraille. In doing so, we consider possible design improvements for the future development of accessible keyboards for blind users.
In search of intelligent genes: The cartesian genetic programming computational neuron (CGPCN)

Biological neurons are extremely complex cells whose morphology grows and changes in response to the external environment. Yet, artificial neural networks (ANNs) have represented neurons as simple computational devices. It has been evident for a long time that ANNs have learning abilities that are insignificant compared with some of the simplest biological brains. We argue that we understand enough neuroscience to create much more sophisticated models. In this paper, we report on our attempts to do this.We identify and evolve seven programs that together represents a neuron which grows post evolution into a complete 'neurological' system. The network that occurs by running the programs has a highly dynamic morphology in which neurons grow, and die, and neurite branches together with synaptic connections form and change. We have evaluated the capability of these networks for playing the game of checkers. Our method has no board evaluation function, no explicit learning rules and no human expertise at playing checkers is used. The learning abilities of these networks are encoded at a genetic level rather than at the phenotype level of neural connections.
Extracting Timelines from News Content

Natural Language Processing (NLP) has been an intensively studied subject of Arti cial Intelligence for decades. There are many powerful techniques available nowadays that boost the computer's ability to process human languages, especially in text format. These techniques are commonly applied in machine translation, search engines, querying and information retrieval systems etc. However, in some application areas, there is still great potential of enhancing NLP development through the use of machine learning techniques. This report relates to a new developing topic of recent years extracting times and events from news articles. By following the TimeML annotating speci cation, I developed a Java artifact, which is trained and evaluated on tagged news articles from TimeBank corpus. This is done either through a 'Dictionary Lookup' baseline algorithm or variants of Naive Bayes algorithm. A comparison between these is presented in the experiment section. The results show that, by applying Tokenization, Part-of-Speech Tagging, self-prediction using TimeML tagging and other techniques, one variant of the Naive Bayes algorithm gives signi cantly better performance in this classi cation task.
A markovian signature-based approach to IP traffic classification

In this paper we present a real-time automatic process to traffic classification and to the detection of abnormal behaviors in IP traffic. The proposed method aims to detect anomalies in the traffic associated to a particular service, or to automatically recognize the service associated to a given sequence of packets at the transport layer. Service classification is becoming a central issue because of the emergence of new services (P2P, VoIP, Streaming video, etc.) which raises new challenges in resource reservation, pricing, network monitoring, etc. In order to identify a specific signature to an application, we first of all model the sequence of its packets at the transport layer by means of a first order Markov chain. Then, we decide which service should be associated to any new sequence by means of standard decision techniques (Maximum Likelihood criterion, Neyman-Pearson test). The evaluation of our automatic recognition procedure using live GPRS Orange France traffic traces demonstrates the feasibility and the excellent performance of this approach.
Automatic Detection of Instruments in Laparoscopic Images: A First Step Towards High-level Command of Robotic Endoscopic Holders

The tracking of surgical instruments o fers interesting possibilities for the development of high-level commands for robotic camera holders in laparoscopic surgery. We have developed a new method to detect instruments in laparoscopic images which uses information on the 3D position of the insertion point of an instrument into the abdominal cavity. This information strongly constrains the search for the instrument in each endoscopic image. Hence, the instrument can be detected in near real-time using shape considerations. Early results on laparoscopic images show that the method is rapid and robust in the presence of partial occlusion and smoke. Our first experiment on a cadaver validates our approach and shows encouraging results.