Miracle-dz/UniFAR-FTUs huggingface dataset:
23 Selection of species and sampling areas: the importance to inference

Inductive inference, the process of drawing general conclusions from specifi c observations, is fundamental to the scientifi c method. Platt (1964) termed conclusions obtained through rigorous application of the scientifi c method as “strong inference” and noted the following basic steps: generating alternative hypotheses; devising experiments, the results of which will exclude one or more hypotheses; conducting the experiment to get a “clean result”; and repeating the process with revision based on the information obtained. Every student is exposed to these basics in introductory courses, and a considerable proportion of a modern graduate education in the sciences is devoted to acquiring the analytic (statistical) skills necessary to apply the scientifi c method. Not even considering the fi eld of mathematical statistics or applied statistics in disciplines such as social sciences, library shelves groan under the weight of texts on applied statistics, ranging from introductory (Hayek and Buzas 1997) to advanced (Williams et al. 2002), for conducting research in ecology, and new works are published every year. Much effort is currently devoted to the mechanisms of analysis and the issues involved in choosing among statistical methods; specifi cally, traditional hypothesis testing versus information-theoretic or Bayesian approaches (Hobbs and Hilborn 2006). This chapter does not address these topics, but instead discusses some of the issues related to selection of study sites and species necessary to obtain a “clean result. ” Regardless of the method of analysis, successful inference relies on correctly designed data collection, meaning that the observations represent the population of interest. As Anderson (2008, p. 7) puts it, valid
An Improved Robust Sparse Coding for Face Recognition with Disguise

Robust vision-based face recognition is one of most challenging tasks for robots. Recently the sparse representation-based classification (SRC) has been proposed to solve the problem. All training samples without disguise are used to compose an over-complete dictionary, and the testing sample with disguise is represented by the dictionary with a sparse coding coefficients plus an error. The coding residuals between the sample and each class of training samples are measured and the minimum of them is the identified class to which the sample belongs. The robust sparse coding (RSC) seeks for the MLE (maximum likelihood estimation) solution of the sparse coding problem, so it is more robust to disguise. However, the iteratively algorithm to solve RSC is high time consuming. In this paper, we propose an improved robust sparse coding (iRSC) algorithm for practical application conditions. During iterations, the dictionary is reduced by eliminating the objects with larger coding residuals. The over-complete property of dictionary is not affected. Experiments on AR face database demonstrate that the coding is sparser and the efficiency is higher in iRSC.
A Secure Mobile VPN Scheme for UMTS

In this paper, a mobile Virtual Private Network (VPN) scheme for the Universal Mobile Telecommunication System (UMTS) is proposed and analyzed from a security point of view. The proposed scheme improves the level of protection for data transfer that is currently supported in UMTS and facilitates the realization of Mobile Internet. The security functionality, which is based on the IPsec framework, is integrated to the mobile network infrastructure so as to eliminate the potential computational overhead on mobile devices. The proposed VPN scheme operates transparently to the mobile subscribers’ movement. Moreover, the required enhancements can be integrated in the existing network infrastructure, and, therefore, the proposed security scheme can be employed as an add-on feature to the UMTS standard. Thus, data communications over the UMTS network may experience security threats. The reason is that there is a lack of a general-purpose, application independent security mechanism that provides advanced security services to user data traffic according to the particular end-user needs, inside and outside the mobile core network [2]. To address this, the current article proposes and analyses a secure mobile VPN scheme for the UMTS network architecture. The proposed security scheme integrates the VPN functionality into the mobile network infrastructure, minimizing the administrative and the computational overheads for end-users. It utilizes the encryption mechanism over the radio interface and deploys an IPsec-based [3] VPN over the UMTS backbone and the pubic Internet. For VPN initialization and key agreement procedures, an IKE protocol proxy scheme [4] is employed, which enables the mobile user to establish a VPN, while outsourcing complex key negotiation to the network infrastructure. The proposed scheme operates transparently to the mobile subscribers’ movement. Moreover, the required enhancements can be integrated in the existing network infrastructure, and, therefore, the proposed security scheme can be employed as an add-on feature to the UMTS standard.
Nanoparticle (NP) Delivery of Chemotherapy Drugs to Prostate Cancer Patients

Controlled drug delivery systems have attracted diverse research interests over the years [1-5]. This system is a useful mechanism in prostate cancer treatment. Designing nanoparticles that selectively recognize and kill prostate cancer cells in the body, remains an innovative concept [6-8]. The knowledge has motivated researchers to build nanoparticles with unique physicochemical properties such as, size, shape and surface chemistry. These nanoparticles are programmed with a multitude of biological and medical functions [9,10] that are vital for effective treatment of prostate cancer.
3D ARCHITECTURAL VISUALIZATION USING UDK AS AN INTERACTIVE TOOL

3D visualization in the field of architecture is an emerging discipline that has had a powerful impact on how architects present their work and communicate ideas to the client. However, the potential for what 3D software can do is still being realized in the architectural design process. This project proposes an alternative approach to the current design process by presenting a system that will develop a stronger relationship between the client and the architectural designer. By drawing from aspects in other fields of 3D design, specifically video-games, the project lays the framework for a system to be used by architects to immerse their clients in a proposed architecture and encourage interaction with this virtual environment. The project uses programs such as 3Ds Max, Photoshop, and Flash, to develop content for an environment realized in the Unreal Engine using the Unreal Development Kit (UDK). 3 ACKNOWLEDGMENTS
Distributed Iterative Interference Cancellation and MapReduce Paradigm for Cooperative Network

In this paper, we propose an iterative interference cancellation scheme based on nested lattice to combat the inter-cell interference for cooperative cellular networks. The achievable rate region of the proposed scheme under a two-cell and two-user model is obtained. In this simplified model, each decoder separately decodes its corresponding user message, and sends the result to the other decoder for interference cancellation. The interference can be mitigated gradually by the step-by-step iterative decoding. Once the message from one user is successively decoded, the interference to the other receiver can be totally canceled. For more general multi-cell and multi-user networks, a sufficient condition of convergence of the proposed scheme is derived. In addition, we find that the modern cloud computing paradigm, MapReduce, is very suitable for our distributed scheme. This means that the proposed scheme can be utilized as joint decoder in Cloud Ratio Access Network (CRAN).
Machine vision methods for autonomous micro‐robotic systems

Purpose – To develop customised machine vision methods for closed‐loop micro‐robotic control systems. The micro‐robots have applications in areas that require micro‐manipulation and micro‐assembly in the micron and sub‐micron range.Design/methodology/approach – Several novel techniques have been developed to perform calibration, object recognition and object tracking in real‐time under a customised high‐magnification camera system. These new methods combine statistical, neural and morphological approaches.Findings – An in‐depth view of the machine vision sub‐system that was designed for the European MiCRoN project (project no. IST‐2001‐33567) is provided. The issue of cooperation arises when several robots with a variety of on‐board tools are placed in the working environment. By combining multiple vision methods, the information obtained can be used effectively to guide the robots in achieving the pre‐planned tasks.Research limitations/implications – Some of these techniques were developed for micro‐visi.
Network tomography from an operator perspective

Network tomography is the study of a network's traffic characteristics using measures. This subject has already been addressed by a whole community of researchers, especially to answer the need for knowledge of residential Internet traffic that ISPs have to carry. One of the main aspects of the Internet is that it evolves very quickly, so that there is a never ending need for Internet measurements. In this work, we address the issue of residential Internet measure from two different perspectives: passive measurements and active measurements. In the first part of this thesis, we passively collect and analyse statistics of residential users' connections spanning over a whole week. We use this data to update and deepen our knowledge of Internet residential traffic. Then, we use clustering methods to form groups of users according to the application they use. This shows how the vast majority of customers are now using the Internet mainly for Web browsing and watching video Streaming. This data is also used to evaluate new opportunities for managing the traffic of a local ADSL platform. As the main part of the traffic is video streaming, we use multiple snapshots of packet captures of this traffic over a period of many years to accurately understand its evolution. Moreover we analyse and correlate its performance, defined out of quality of service indicators, to the behavior of the users of this service. In the second part of this thesis, we take advantage of this knowledge to design a new tool for actively probing the quality of experience of video streaming sites. We have modeled the playback of streaming videos so that we are able to figure out its quality as perceived by the users. With this tool, we can understand the impact of the video server selection and the DNS servers on the user's perception of the video quality. Moreover the ability to perform the experiments on different ISPs allows us to further dig into the delivery policies of video streaming sites.
Digital control with improved performance for boost power factor correction circuits

In this paper, an approach for the design of a digital controller for a PFC pre-regulator is proposed. The controller is modified to account for large control periods and computational delays, and can therefore be implemented on processors with few available computational resources. Results show that, even with a very low control rate, system specifications can be met using the proposed technique.
Evaluating tisagenlecleucel and its potential in the treatment of relapsed or refractory diffuse large B cell lymphoma: evidence to date

Abstract Chimeric antigen receptor (CAR) T cells have changed the treatment landscape of relapsed or refractory diffuse large B cell lymphoma. This review focuses on the biology of tisagenlecleucel and the clinical data that support its use in this setting. In addition, we discuss how it compares to other CAR T products, the financial implications for payers, and ongoing trials.
A discrete binary version of the particle swarm algorithm

The particle swarm algorithm adjusts the trajectories of a population of "particles" through a problem space on the basis of information about each particle's previous best performance and the best previous performance of its neighbors. Previous versions of the particle swarm have operated in continuous space, where trajectories are defined as changes in position on some number of dimensions. The paper reports a reworking of the algorithm to operate on discrete binary variables. In the binary version, trajectories are changes in the probability that a coordinate will take on a zero or one value. Examples, applications, and issues are discussed.
Position-based routing in ad hoc wireless networks

In ad hoc networks, autonomous nodes collaborate to route information through the network. Commonly, nodes are end-systems and routers at the same time. In cases in which nodes have a notion of their geographic position, position-based routing protocols can be used; these protocols' properties of statelessness and fast adaptability to changes in the topology match very well with the characteristics of ad hoc networks.Position-based routing does not require the maintenance of routes; instead, forwarding decisions are made locally, based only on the node's own position, the positions of its neighbors, and the position of the destination. The routing algorithms are complemented by location services through which a node can obtain the position of a packet's destination.We will introduce the main components of position-based routing, discuss position-based routing algorithms as well as location services, and present an application scenario where position-based routing can be used for intervehicle communication.
PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and Radial Basis Neural Network (RBNN). The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.
Text search engine for digitized historical book

There’s need to digitalize numerous historical books and texts and make it possible to read them electronically. Also it is often wanted to preserve their original appearance, not just the text itself. For these operations there is a need for systems, which understand the books and text as they are and are able to distinguish the text information from other context. Traditional optical character recognition systems perform well when processing modern printed text, but they might face problems with old handwritten texts. These types of texts need to be analyzed with systems, which can analyse and segment the text areas well from other irrelevant information. That is why it is important, that the document image segmentation works well. This thesis focuses on manual rectification, automatic segmentation and text line search on document images in Orationes project. When the document images are segmented and text lines found, information from XML transcript is used to find characters and words from the segmented document images. Search engine was developed with with Python programmin language. Python was chosen to ensure high platform independency.
Comparison of Task-oriented Balance Training on Stable and Unstable Surfaces for Fall Risk, Balance, and Gait Abilities of Patients with Stroke

PURPOSE: The purpose of this study was to compare task-oriented balance training on stable and unstable surfaces in terms of the fall risk, balance, and gait abilities in patients with stroke. METHODS: Twenty patients with stroke were divided randomly into a stable surface group (SSG, N=10) or unstable surface group (USG, N=10). The participants in the SSG and USG performed task-oriented balance training on stable and unstable surfaces, respectively. All participants were evaluated using the Tetrax, Berg balance scale (BBS), and 10-meter walking test (10MWT) before and after the
The Long-Term Health-Related Outcomes of Breast Ironing in Cameroon

The Long-Term Health-Related Outcomes of Breast Ironing in Cameroon by Nkwelle, Norbert Nicholas Njume MPH Walden University, 2010 MBA Greenwich University, 2004 BA University of Buea, 1997 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Public Health
Application of forecast combination in volatility modelling

It is well known in the forecasting literature that combining forecasts from different models can often lead to superior forecast performance, at least in the Mean Squares Error (MSE) sense. It has also been noted that combining forecasts by simple averaging often performs better than more sophisticated weighting schemes, although simple averages tend to ignore correlations between forecast errors. However, it is unclear whether these stylized facts hold under different forecast criteria. This is particularly important when evaluating the performance of Value-at-Risk forecasts where MSE is not an appropriate measure. In practice the VaR performance is measured against the Back-testing procedure as outlined in Basel Accord. Given the role of VaR in risk management, it would be important to investigate if forecast combination provided any benefit in forecasting VaR. An interesting implication of this study is that, if forecast combination does in fact provide superior VaR forecasts over individual models, then it also provides a convenience way to combine qualitative forecasts (from expert opinion) and quantitative forecasts (from quantitative models). The combination of qualitative and quantitative forecasts may in fact, enhance the forecast accuracy of VaR further. The aim of this paper is to provide an empirical evaluation of forecast combination for Value-at-Risk. Value-at-Risk forecasts based on four different volatility models, namely, EGARCH, IGARCH, Stochastic Volatility, will be constructed and combined. The forecast performance of the combined forecasts will be compared to the forecast performances of each of the individual models. Two weighting schemes are being considered in this paper, namely, simple weighted average and Quantile Regression (QR). The empirical performances of these forecasts will be based on the percentages of violation as proposed in the Basel Accord with two sets of daily data, namely FTSE and S&P 500, for the period 3 January 1996 to 3 August 2010. The results show that, overall, (i) forecast combination performed better than individual models and (ii) simple weighted average performed better than QR. These results are consistent with the stylised findings in the forecast combination literature. Thus, the paper provided empirical evidence supporting the use of forecast combination in forecasting VaR thresholds.
Harms of treatment with cholesterol-lowering drugs in children: A systematic review of the published literature

3 Introduction 4 Background 5 Dyslipidemia – What is it? 5 Why are dyslipidemias medically relevant? 5 How is dyslipidemia defined? 6 Current recommendations for treatment 9 When is treatment recommended? 10 Harms associated with medications 12 Dyslipidemia in children/adolescents 13 Dyslipidemia in children defined 16 Treatment of dyslipidemias in children 17 Current recommendations for treatment in children 18 Focused clinical question 22 Methods 23 Population 23 Interventions 23 Outcomes 24 Time frame 24 Publication Types 25 Inclusion/Exclusion Criteria 25 Search Strategy 27 Quality Criteria 29 Article Selection 30 Results 31 Search Results 31 Article details and demographics 33 Harms Data 37 Conclusions/Discussion 43 References 50 Appendix 1 57 Appendix 2 64
Physically Dense Server Architectures

Physically Dense Server Architectures by Anthony Thomas Gutierrez Chair: Trevor N. Mudge Distributed, in-memory key-value stores have emerged as one of today’s most important data center workloads. Being critical for the scalability of modern web services, vast resources are dedicated solely to key-value stores in order to ensure that quality of service guarantees are met. These resources include: many server racks to store terabytes—possibly petabytes—of key-value data, the power necessary to run all of the machines, networking equipment and bandwidth, and the data center warehouses used to house the racks. There is, however, a mismatch between the key-value store software and the commodity servers on which it is run, leading to inefficient use of resources. The primary cause of this inefficiency is the overhead incurred from processing individual network packets, which typically carry small payloads of less than a few kilobytes, and require minimal compute resources. Thus, one of the key challenges as we enter the exascale era is how to best adjust to the paradigm shift from compute-centric data centers, to storage-centric data centers. This dissertation presents a hardware/software solution that addresses the inefficiency issues present in the modern data centers on which key-value stores are
Online optimization for the locomotion of Roombots structures

This semester project was done at the BioRobotic Labotatory of the Swiss Federal Institute of Technology in Lausanne. It focuses on the locomotion of Roombots, a Modular Robot designed at the same laboratory. Since such robots can have quite unexpected structure, one of the main challenge concerns the locomotion of such robots, which contains quite a lot of degrees of freedom. Central Pattern Generators, which are biologicallyinspired models responsible for the generation of rythmic movements in animals, are used to control the gait of a given structure. The main challenge is to find a set of parameter for this CPG, which can produce a locomotion pattern. For this purpose, the use of optimization methods, like Particle Swarm Optimization or Powell’s method, allows us to explore a wide number of gaits in order to find a set of parameters that maximized the speed of our robot. The reseach group on Roombots has already made an offline optimization on Webots R ©, leading to a fast theoretical gait which proved to be quite unefficient when tested on the real hardware. The main objective of this project is to make an online optimization process, i.e. using the physical robot to evaluate the quality of a gait. The laboratory already has some experiments of such an experiment with other, simpler modular robots. For this work, we only consider a simple modular structure made of two modules. In this document, we describe the methodology of this project and the different task I did and difficulties I encountered in order to perform an online optimization. Then, we present the experiments we were able to do within the given time, and discuss the gaits exhibited by the optimization process.