Miracle-dz/UniFAR-FTUs huggingface dataset:
Presence‐only modelling using MAXENT: when can we trust the inferences?

Recently, interest in species distribution modelling has increased following the development of new methods for the analysis of presence‐only data and the deployment of these methods in user‐friendly and powerful computer programs. However, reliable inference from these powerful tools requires that several assumptions be met, including the assumptions that observed presences are the consequence of random or representative sampling and that detectability during sampling does not vary with the covariates that determine occurrence probability. Based on our interactions with researchers using these tools, we hypothesized that many presence‐only studies were ignoring important assumptions of presence‐only modelling. We tested this hypothesis by reviewing 108 articles published between 2008 and 2012 that used the MAXENT algorithm to analyse empirical (i.e. not simulated) data. We chose to focus on these articles because MAXENT has been the most popular algorithm in recent years for analysing presence‐only data. Many articles (87%) were based on data that were likely to suffer from sample selection bias; however, methods to control for sample selection bias were rarely used. In addition, many analyses (36%) discarded absence information by analysing presence–absence data in a presence‐only framework, and few articles (14%) mentioned detection probability. We conclude that there are many misconceptions concerning the use of presence‐only models, including the misunderstanding that MAXENT, and other presence‐only methods, relieve users from the constraints of survey design. In the process of our literature review, we became aware of other factors that raised concerns about the validity of study conclusions. In particular, we observed that 83% of articles studies focused exclusively on model output (i.e. maps) without providing readers with any means to critically examine modelled relationships and that MAXENT's logistic output was frequently (54% of articles) and incorrectly interpreted as occurrence probability. We conclude with a series of recommendations foremost that researchers analyse data in a presence–absence framework whenever possible, because fewer assumptions are required and inferences can be made about clearly defined parameters such as occurrence probability.
Uso de tecnologías 3D en la digitalización y difusión de documentos de alto valor patrimonial

We analyze the limitations of two-dimensional (2D) raster digital images for graphic representation of documents with volume or three-dimensional (3D) elements, proposing a visual model based on 3D techniques that complements the visual information conveyed by the 2D raster images that are usually offered in digital archives or library services. The proposed model concurrently applies stereoscopic photography techniques, reflectance transformation imaging (RTI) and 3D models obtained by structure for motion (SfM) procedures.
3D Scientific Visualization with Blender

This is the first book written on using Blender (an open source visualization suite widely used in the entertainment and gaming industries) for scientific visualization. It is a practical and interesting introduction to Blender for understanding key parts of 3D rendering and animation that pertain to the sciences via step-by-step guided tutorials. 3D Scientific Visualization with Blender takes you through an understanding of 3D graphics and modelling for different visualization scenarios in the physical sciences.
Essays on Networks: Theory and Applications

textabstractNetworks have proven to be a useful representation of various systems. Social and economic interactions, biological and ecological systems, the internet can be understood better if modelled as networks. Intuitively, a network describes a collection of nodes and the links between them. The notion of nodes is fairly general, they may be individuals or firms or countries. A link between two nodes represents a direct relation between them; for instance, a link could be a friendship tie between people, a research and development agreement between firms, or, in the context of countries, a link may be a mutual defense pact. The study of networks spans across disciplines. A central field in sociology and studied in depth by mathematicians over the past fifty years, networks have recently received extensive attention in statistical physics, computer science, business strategy and organization theory. While economists have occasionally showed interest in networks, a literature has emerged only in the last decade. This thesis brings two contributions. On the one hand, it aims to enhance the role of network theories in solving economic issues, by proposing a network representation of financial systems. On the other hand, it aims to enrich the set of methods used to analyze networks, by introducing economic microfoundations to a preferential attachment model of network formation.
Antenna Miniaturization Using Fractals (RESEARCH NOTE)

Antenna Miniaturization Using Fractals Richa Garg, AP ECE Deptt. , richa.jan11@gmail.com, J.C. D.C.O.E. , Sirsa ABSTRACTThe use of fractal geometry in designing antenna has been a resent topic of interest. Fractal shape has their own unique characteristics that the improved antenna performance can be achieved without degrading the antenna properties. Modern telecommunication system requires antenna having wider bandwidth and reduced size. Fractal antennas have become a hot topic of interest for the antenna designers because of their unique features like compact size, multi band operation etc. Now a days, the size of electronic system has decreased drastically whereas functionality has increased. The antennas have not experienced that evolution. Wireless communications have progressed very rapidly in recent years, and many mobile units are becoming smaller and smaller. To meet the miniaturization requirement, the antennas employed in mobile terminals must have their dimensions reduced accordingly. Several researchers studied and suggested the incorporation of fractal antenna is advantageous as the space filling properties of antenna leads to the miniaturization of the antenna. Fractals have non-integral dimensions and their space filling capability could be used for miniaturizing antenna size. Fractal antennas have improved Impedance, improved SWR (standing wave ratio) performance on a reduced physical area when compared to non fractal Euclidean geometries. In many cases, the use of fractal element antennas can simplify circuit design. Often fractal antenna, do not require any matching components to achieve multiband or broadband performance. Perturbation could be applied to shape of fractal antenna to make it to resonate at different frequency. Another beneficial factor of fractal antennas is that they are in form of a PCB.The objective here is to give the description of designing and simulation of a microstrip patch Fractal Antenna . The behavior and properties of this antenna are investigated and compared with the simple microstrip patch antenna. Different iterations were considered to miniaturize the size of antenna without degrading the antenna performances. In theoretical design procedure, numerical simulation was performed using software Ansoft HFSS v 11.
A Comparative Study of Machine Learning and Evolutionary Computation Approaches for Protein Secondary Structure Classification

Proteins are essential to life and they have countless biological functions. Proteins are synthesized in the ribosome of cells following a template given by the messenger RNA (mRNA). During the synthesis, the protein folds into a unique three-dimensional structure, known as native conformation. This process is called protein folding. The biological function of a protein depends on its three-dimensional conformation, which in turn, is a function of its primary and secondary structures. It is known that ill-formed proteins can be completely inactive or even harmful to the organism. Several diseases are believed to result from the accumulation of ill-formed proteins, such as Alzheimer’s disease, cystic fibrosis, Huntington’s disease and some types of cancer. Therefore, acquiring knowledge about the secondary structure of proteins is an important issue, since such knowledge can lead to important medical and biochemical advancements and even to the development of new drugs with specific functionality. A possible way to infer the full structure of an unknown protein is to identify potential secondary structures in it. However, the pattern formation rules of secondary structure of proteins are still not known precisely. This paper aims at applying Machine Learning and Evolutionary Computation methods to define suitable classifiers for predicting the secondary structure of proteins, starting from their primary structure (that is, their linear sequence of amino acids). The organization of this paper is as follows: in Section 2 we introduce some basic concepts and some important aspects of molecular biology, computational methods for classification tasks and the protein classification problem. Next, in Sections 3 and 4, we present, respectively, a review of the machine learning and evolutionary computation methods used in this work. In Section 6, we describe the methodology applied to develop the comparison of different classification algorithms. Next, Section 7, the computational experiments and results are detailed. Finally, in the last Section 8, discussion about results, conclusions and future directions are pointed out. 12
PERFORMANCE OF SENSITIZING RULES ON SHEWHART CONTROL CHARTS WITH AUTOCORRELATED DATA

Sensitizing Rules are commonly applied to Shewhart Charts to increase their effectiveness in detecting shifts in the mean that may otherwise go unnoticed by the usual "out-of-control" signals. The purpose of this paper is to demonstrate how well these rules actually perform when the data exhibit autocorrelation compared to non-correlated data. Since most control chart data are collected as time series, it is of interest to examine the performance of Shewhart's Chart using data generated from typical time series models. In this paper, measurements arising from autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA) processes are examined using Shewhart Control Charts in conjunction with several sensitizing rules. The results indicate that the rules work well when there are strong autocorrelative relationships, but are not as effective in recognizing small to moderate levels of correlation. We conclude with the recommendation to practitioners that they use a more definitive measure of autocorrelation such as the Sample Autocorrelation Function correlogram to detect dependency.
3.1.2 Machine Learning technologies for the Requirements Analysis in Complex Systems

Requirement Analysis is a relevant application area for a variety of Semantic Technologies related to the extraction, disambiguation and exploitation of the knowledge derived from technical requirement documents. Most methods rely on shallow language processing technologies for the automatic extraction of core concepts (e.g. components/devices, their parts and functionalities) and norms (e.g. constraints on the use of components). Few works have been devoted to study paraphrasing (i.e. textual equivalence between requirement definitions) for consistency checking and redundancy elimination. We propose here a distributional method to train a kernel‐based learning algorithm (i.e. SVM), as a cost‐effective approach to validate requirements from text in support of Requirement Analysis in the design of a Complex Systems, i.e. Naval Combat Systems. These latter are complex systems based on software components able to manage all the Combat System Equipment in different mission scenarios. We will describe the application of Recognition of Textual Entailment (RTE) techniques based on data‐driven learning methods to this scenario. While modeling the asymmetric logical relation of entailment between two textual descriptions (i.e. an hypothesis and its thesis), RTE can be here applied to the validation step of compositions between system functionalities described in the requirement specification texts. Early evidences are here discussed, as a proof of the strong applicability of the method to the general case and to other realistic scenarios in System Engineering.
Ensuring robust transactional processing in Silverlight database applications

In this paper a compact, robust alternative to the WCF is proposed. Its purpose is to remedy the shortcomings of the WCF infrastructure. Firstly, WCF channels close immediately after browser exits, compromising session management and transactional processing. Secondly, WCF channels operate only asynchronously, hindering straightforward implementation of sequential business rules. Design patterns of transactional session processing are given. Surprisingly overriding the standard WCF facility results in a factor 30 performance boost for local calls.
Comparative performance of JM and FFMPEG codecs of H.264 AVC video compression standard

OF THE THESIS Comparative Performance of JM and FFMPEG Codecs of H.264 AVC Video Compression Standard by Hardik Sharma Master of Science in Electrical Engineering San Diego State University, 2012 With the rapid growth of wireless multimedia services, the demand for video transmission over wireless channels has been increasingly exponentially. This has led to the design of error resiliency schemes for obtaining better codecs. The perceptual video quality is influenced not only by the compression artifacts, but also by the channel errors. Hence, a video codec needs to accommodate contradictory requirements: coding efficiency and robustness to data loss, along with other limitations such as memory, bandwidth, real time coding and complexity. In this thesis, we have studied the comparative performance of two prominent open source H.264 AVC video codecs Joint Model (JM) and Fast Forward Motion Picture Expert Group (FFMPEG). We have studied various features of these codecs, such as macroblock tree rate control, multi-pass encoding, and psychovisual optimization model (it includes the parameters like adaptive quantization, psy-RD and psy-trellis). Observations based on the PSNR values and rate-control have been used to analyze the objective video quality for errorfree and corrupted bitstreams generated by both codecs. In addition, we have analyzed the creation of different priorities based on cumulative mean squared error (CMSE) values for dividing the I, P and B video frames into four priorities. These studies helped us in understanding which codec can be more robust against channel errors or losses and how we can improve their performance.
Quality Assessment of the Retrieval System Based on Web-Related Degree

In order to further improve the quality and efficiency of Web search, the method of retrieval system quality assessment based on Web-related degree is proposed in this paper. The specific research process is as follows. Firstly, analyzing category for query and building a set of queries, then, studying the retrieval quality differences of different search engines through assessment experiment, finally, analyzing the factors which impact Web retrieval system quality assessment through four sides separately. The study result shows that using quality assessment method for retrieval system based on Web-related degree can better improve the accuracy of the corresponding assessment indicators and distinguish ability, accuracy ratio is over 90%, which can verify the validity of this method.
Mining Frequent Item Sets Data Streams using "ÉclatAlgorithm"

Frequent pattern mining is the process of mining data in a set of items or some patterns from a largedatabase. The resulted frequent set data supports the minimum support threshold. A frequentpattern is a pattern that occurs frequently in a dataset. Association rule mining is defined as to findout association rules that satisfy the predefined minimum support and confidence from a given database. If an item set is said to be frequent, that item set supports the minimum support andconfidence. A Frequent item set should appear in all the transaction of that data base. Discoveringfrequent item sets play a very important role in mining association rules, sequence rules, web logmining and many other interesting patterns among complex data. Data stream is a real timecontinuous, ordered sequence of items. It is an uninterrupted flow of a long sequence of data. Somereal time examples of data stream data are sensor network data, telecommunication data,transactional data and scientific surveillances systems. These data produced trillions of updatesevery day. So it is very difficult to store the entire data. In that time some mining process is required.Data mining is the non-trivial process of identifying valid, original, potentially useful and ultimatelyunderstandable patterns in data. It is an extraction of the hidden predictive information from largedata base. There are lots of algorithms used to find out the frequent item set. In that Apriorialgorithm is the very first classical algorithm used to find the frequent item set. Apart from Apriori,lots of algorithms generated but they are similar to Apriori. They are based on prune and candidategeneration. It takes more memory and time to find out the frequent item set. In this paper, we havestudied about how the eclat algorithm is used in data streams to find out the frequent item sets.Eclat algorithm need not required candidate generation.
Synthesis of H/sub 2/ optimal controllers for sampled-data systems

The authors consider an H/sub 2/ optimal control problem for sampled-data systems. After defining an H/sub 2/ norm for sampled-data systems, they give a state-space solution to the optimal controller synthesis problem. They show that the H/sub 2/ optimal control problem for a sampled-data system is equivalent to a standard H/sub 2/ optimal control problem for a related discrete-time system. <<ETX>>
Error concealment using multiresolution motion estimation

An error concealment scheme for MPEG video networking is presented. Cell loss occurs in the presence of network congestion and buffer overflow. This phenomenon of cell loss transforms into lost image blocks in the decoding process, which can severely degrade the viewing quality. The new method differs from the conventional concealment by its exploitation of spatial and temporal redundancies in large scale. The motion estimation is carried out by registering images within a multiresolution pyramid. The global motion is estimated in the lowest resolution level, and is then used to update and refine the local motion. The local motion is further refined iteratively at higher resolution levels. An affine transform is used to extract translation, scaling and rotation parameters. In many applications where there is significant camera motion (e.g., remote surveillance), the new method performs better than the conventional concealment.
Intelligente Algorithmen in kieferorthopädischer Diagnostik und Planung

Zusammenfassung Algorithmen und Lösungsstrategien aus dem Forschungsgebiet der Künstlichen Intelligenz (KI) spielen eine wichtige Rolle in der zunehmend digitalen Verarbeitung in der modernen Kieferorthopädie. Im Artikel wird diskutiert, inwiefern die kieferorthopädische Behandlung von aktuellen Entwicklungen profitieren kann und wo Grenzen und Gefahren gesehen werden. Anhand von 3 Beispielen zur Verwendung digitaler Modelle in der Imaging-Software OnyxCeph³™ (Software OnyxCeph3TM, Image Instruments GmbH, Chemnitz. www.onyxceph.de) wird abgeleitet, welche Erwartungen an computergestützte Diagnostik und Behandlung realistisch sind. Ziel des Artikels ist, dem Behandler Einblicke in den softwaretechnologischen Hintergrund zu geben, mit denen eine Erwartungshaltung an die KI begründet werden kann, die sowohl patientengerecht als auch zukunftsorientiert ist.
Image processing tools dedicated to quantification in 3D fluorescence microscopy

3-D optical fluorescent microscopy now becomes an efficient tool for the volume investigation of living biological samples. Developments in instrumentation have permitted to beat off the conventional Abbe limit. In any case the recorded image can be described by the convolution equation between the original object and the Point Spread Function (PSF) of the acquisition system. Due to the finite resolution of the instrument, the original object is recorded with distortions and blurring, and contaminated by noise. This induces that relevant biological information cannot be extracted directly from raw data stacks. If the goal is 3-D quantitative analysis, then to assess optimal performance of the instrument and to ensure the data acquisition reproducibility, the system characterization is mandatory. The PSF represents the properties of the image acquisition system; we have proposed the use of statistical tools and Zernike moments to describe a 3-D PSF system and to quantify the variation of the PSF. This first step toward standardization is helpful to define an acquisition protocol optimizing exploitation of the microscope depending on the studied biological sample. Before the extraction of geometrical information and/or intensities quantification, the data restoration is mandatory. Reduction of out-of-focus light is carried out computationally by deconvolution process. But other phenomena occur during acquisition, like fluorescence photo degradation named "bleaching", inducing an alteration of information needed for restoration. Therefore, we have developed a protocol to pre-process data before the application of deconvolution algorithms. A large number of deconvolution methods have been described and are now available in commercial package. One major difficulty to use this software is the introduction by the user of the "best" regularization parameters. We have pointed out that automating the choice of the regularization level; also greatly improves the reliability of the measurements although it facilitates the use. Furthermore, to increase the quality and the repeatability of quantitative measurements a pre-filtering of images improves the stability of deconvolution process. In the same way, the PSF prefiltering stabilizes the deconvolution process. We have shown that Zemike polynomials can be used to reconstruct experimental PSF, preserving system characteristics and removing the noise contained in the PSF.
Numerical Simulation in Fluid Dynamics: A Practical Introduction

Notation 1. Numerical simulation - a key technology of the future 2. The mathematical description of flows 3. The numerical treatment of the Navier-Stokes equations 4. Visualization techniques 5. Example applications 6. Free boundary value problems 7. Example applications for free boundary value problems 8. Parallelization 9. Energy transport 10. Turbulence 11. Extension to Three dimensions 12. Concluding remarks Appendix A Appendix B Bibliography Index.
Modified Raptor Code for Distributed Storage Systems

We provide a prototype system for a distributed storage model, the goal of this system is to store information in a p2p network of n nodes, and the original information could be recovered later in a computationally simple way from (1+e)k of nodes for some small e>0. For solving this problem, we employ a class of B-J codes with dimension 2 as base code and obtain a new class of LDPC code by using q-tuples to substitute the elements of , to modify and improve Raptor code. The improved Raptor Code has many advantages such as high decoding rate, flexibility in choices of the code length and rate of. As the benefits of the above characters, the storage system based on modified Raptor code has great improvement in decoding probability and parameter flexibility, and a simulation is also illustrated to prove our assumption.