Miracle-dz/UniFAR-FTUs huggingface dataset:
Linking juvenile habitat utilization to population dynamics of California halibut

We investigated the nursery role of four coastal ecosystems for the California halibut (Paralichthys californicus) using the following metrics: (1) contribution in producing the fish that advance to older age classes, (2) connectivity of coastal systems resulting from migration of fish from juvenile to subadult habitats, and (3) effect of nursery habitat usage and availability on subadult population size, specifically evaluating the concentration hypothesis. Potential nurseries were grouped using a robust classification scheme that segregated exposed, bay, lagoon, and estuarine environments. Assignment of nursery origins for individual subadult fish via elemental fingerprinting indicated that exposed coasts, bays, lagoons, and estuaries contributed 31%, 65%, 1%, and 3% of advancing juvenile halibut during 2003, versus 49%, 33%, 16%, and 2% during 2004, respectively. These results were remarkably similar to “expected” nursery contributions derived from field surveys, suggesting that in this system juvenile distributions were a good indicator of unit‐area productivity of juvenile habitats and that densitydependent mechanisms during the juvenile phase did not regulate recruitment pulses. Elemental fingerprinting also demonstrated that individuals egressing from bays did not migrate far from their nursery origins (<10 km), resulting in reduced connectivity along the 110‐km study region over the timescale of approximately one generation. Consequently, we observed considerably higher subadult densities at sites near large bays, while populations distant from large bays appeared to be more influenced by nursery habitat limitation. Over large (~100 km) scales, the location and availability of nursery habitat alternatives had significant effects on the population dynamics of an important member of the ichthyofaunal community of southern California.
Tubular neighbors for regression and classi cationArt

The simple k nearest neighbor method is often very competitive, especially in classiication methods. When the number of predictors is large, the nearest neighbors are likely to be quite distant from the target point. Furthermore they tend to all be on one side of the target point. These are consequences of high dimensional geometry. This paper introduces a modiication of nearest neighbors that explicitly takes into account the extrapolation required in high dimensions.
EXACT FUZZY OBSERVER FOR A BAKER'S YEAST FERMENTATION PROCESS

The purpose of this work is to design an exact fuzzy observer for a bioprocess switching between two different metabolic states. A continuous baker's yeast culture is divided in two sub-models: a respiro-fermentative with ethanol production and a respirative with ethanol consumption. An exact fuzzy observer model using sector nonlinearity was built for both nonlinear models; the observer gains were designed using Linear Matrix Inequalities (LMI's). The observer premise variables depend on the state variables estimated by the fuzzy observer. Copyright © 2007 IFAC �
Visual Cryptography Based on Void-And-Cluster Halftoning Technique

In this paper, visual cryptography for halftone images is proposed, which uses threshold arrays generated by a void-and-cluster method. The proposed halftone visual cryptography (HFC) using threshold arrays shows great flexibility for a combination of share images in order to decode a secret image. Moreover, it also achieves high quality share images, high speed processing and extensibility to (k,n) the visual cryptography of three share image categories, such as (2,3) or (3,3). Simulation results show that all the above features are achieved, while a secret image is clearly decoded, and the secret image cannot be seen on each share image.
A Linear Molecule with Two Large Inverted Repeats: The Mitochondrial Genome of the Stramenopile Proteromonas lacertae

Mitochondrial evolution has given rise to a complex array of organelles, ranging from classical aerobic mitochondria to mitochondrial remnants known as hydrogenosomes and mitosomes. The latter are found in anaerobic eukaryotes, and these highly derived organelles often retain only scant evidence of their mitochondrial origins. Intermediate evolutionary stages have also been reported as facultatively or even strictly anaerobic mitochondria, and hydrogenosomes that still retain some mitochondrial features. However, the diversity among these organelles with transitional features remains rather unclear and barely studied. Here, we report the sequence, structure, and gene content of the mitochondrial DNA of the anaerobic stramenopile Proteromonas lacertae. It has a linear genome with a unique central region flanked by two identical large inverted repeats containing numerous genes and “telomeres” with short inverted repeats. Comparison with the organelle genome of the strictly anaerobic human parasite Blastocystis reveals that, despite the close similarity of the sequences, features such as the genome structure display striking differences. It remains unclear whether the virtually identical gene repertoires are the result of convergence or descent.
Discursive Aspects of Technological Innovation: The Case of the British Motor-Sport Industry

A discursive approach to technological innovation recognises that scientific and technical innovations are the products of groups of people. The subject of this paper is how this insight from the sociology of scientific knowledge can make a contribution to debates in economic geography. Principally drawing on the work of social constructionists, this approach is used to provide insights into the reasons for both the creation and the maintenance of the geographical agglomeration of small firms constituted by the British motor-sport industry.
Analysis and research on microblogging network model based on crawler data

Microblogs are very popular large-scale communication social networks nowadays in China. In order to study the characteristics of Sina Microblogs, we collect a large amount of users' data based on the Sina Microblogs and place them in the database. Using these data, we visualize the Microblogs network model and create a complex network model. We study Sina Microblogs' network properties through the data of the complex network model, such as nodes, links, degrees clustering coefficient and average distance. Then we analyse the similarity and difference between Sina and Twitter. The visualization of complex network helps users easily identify the central node and the edge area, so that information would be spread more effective and quickly in the shortest possible time through the specified node. So we discuss this method to deal with emergences especially natural disasters.
A risk minimization framework for domain adaptation

Supervised learning algorithms usually require high quality labeled training set of large volume. It is often expensive to obtain such labeled examples in every domain of an application. Domain adaptation aims to help in such cases by utilizing data available in related domains. However transferring knowledge from one domain to another is often non trivial due to different data distributions among the domains. Moreover, it is usually very hard to measure and formulate these distribution differences. Hence we introduce a new concept of label-relation function to transfer knowledge among different domains without explicitly formulating the data distribution differences. A novel learning framework, Domain Transfer Risk Minimization (DTRM), is proposed based on this concept. DTRM simultaneously minimizes the empirical risk for the target and the regularized empirical risk for source domain. Under this framework, we further derive a generic algorithm called Domain Adaptation by Label Relation (DALR) that is applicable to various applications in both classification and regression settings. DALR iteratively updates the target hypothesis function and outputs for the source domain until it converges. We provide an in-depth theoretical analysis of DTRM and establish fundamental error bounds. We also experimentally evaluate DALR on the task of ranking search results using real-world data. Our experimental results show that the proposed algorithm effectively and robustly utilizes data from source domains under various conditions: different sizes for source domain data; different noise levels for source domain data, and different difficulty levels for target domain data.
A Support Vector Based Fuzzy Neural Network Approach for Mass Classification in Mammography

In this paper, a new approach for mass classification in digital mammograms based on contourlet texture features and support-vector-based fuzzy neural network (SVFNN) classifier is presented. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional data spaces, the efficient human-like reasoning of fuzzy in handling uncertainty information, and learning property of neural networks. Each mammogram is segmented to regions of interest and features are extracted in frequency domain by contourlet coefficients. One of the main contribution of this research is taking benefit from the superiority of the contourlet compared to the multi-scale techniques and use SVFNN for mass classification. MIAS1 data set is used to evaluate the proposed method. Experimental results demonstrate that the method presented is a promising method for mass classification in mammography.
Symbiodinium Transcriptomes: Genome Insights into the Dinoflagellate Symbionts of Reef-Building Corals

Dinoflagellates are unicellular algae that are ubiquitously abundant in aquatic environments. Species of the genus Symbiodinium form symbiotic relationships with reef-building corals and other marine invertebrates. Despite their ecologic importance, little is known about the genetics of dinoflagellates in general and Symbiodinium in particular. Here, we used 454 sequencing to generate transcriptome data from two Symbiodinium species from different clades (clade A and clade B). With more than 56,000 assembled sequences per species, these data represent the largest transcriptomic resource for dinoflagellates to date. Our results corroborate previous observations that dinoflagellates possess the complete nucleosome machinery. We found a complete set of core histones as well as several H3 variants and H2A.Z in one species. Furthermore, transcriptome analysis points toward a low number of transcription factors in Symbiodinium spp. that also differ in the distribution of DNA-binding domains relative to other eukaryotes. In particular the cold shock domain was predominant among transcription factors. Additionally, we found a high number of antioxidative genes in comparison to non-symbiotic but evolutionary related organisms. These findings might be of relevance in the context of the role that Symbiodinium spp. play as coral symbionts. Our data represent the most comprehensive dinoflagellate EST data set to date. This study provides a comprehensive resource to further analyze the genetic makeup, metabolic capacities, and gene repertoire of Symbiodinium and dinoflagellates. Overall, our findings indicate that Symbiodinium possesses some unique characteristics, in particular the transcriptional regulation in Symbiodinium may differ from the currently known mechanisms of eukaryotic gene regulation.
Prospects and Pitfalls of Pregnancy-Associated Malaria Vaccination Based on the Natural Immune Response to Plasmodium falciparum VAR2CSA-Expressing Parasites

Pregnancy-associated malaria, a manifestation of severe malaria, is the cause of up to 200,000 infant deaths a year, through the effects of placental insufficiency leading to growth restriction and preterm delivery. Development of a vaccine is one strategy for control. Plasmodium falciparum-infected red blood cells accumulate in the placenta through specific binding of pregnancy-associated parasite variants that express the VAR2CSA antigen to chondroitin sulphate A on the surface of syncytiotrophoblast cells. Parasite accumulation, accompanied by an inflammatory infiltrate, disrupts the cytokine balance of pregnancy with the potential to cause placental damage and compromise foetal growth. Multigravid women develop immunity towards VAR2CSA-expressing parasites in a gravidity-dependent manner which prevents unfavourable pregnancy outcomes. Although current vaccine design, targeting VAR2CSA antigens, has succeeded in inducing antibodies artificially, this candidate may not provide protection during the first trimester and may only protect those women living in areas endemic for malaria. It is concluded that while insufficient information about placental-parasite interactions is presently available to produce an effective vaccine, incremental progress is being made towards achieving this goal.
Prototype of a virtual environment develop platform based on DirectX

Constructing a robust and natural virtual environment is very necessary and important for the research of virtual reality. This article propose a generic virtual environment platform-a prototype system for visual simulation oriented virtual environment platform based on DirectX SDK, it supports features like model import, scene edit and so on. The prototype system is descried in mainly two aspects: architecture and implementation, referring to some key techniques: such as import of mesh model, collision detection based on oriented bounding box (OBB), self defining script.
Access Pattern Confidentiality-Preserving Relational Databases: Deployment Concept and Efficiency Evaluation

In this paper, we address the question whether access pattern confidentiality-preserving databases with an underlaying B-tree index structure are feasible in practice by proposing integrative deployment concepts that support important database query functionalities based on ORAM and shuffled B-trees. Furthermore, we provide a rigorous efficiency evaluation to determine the cost of the proposed concepts with regard to storage, network, and query latency as well as to investigate the influence of scenario factors like database size, number of records, and network bandwidth. In particular, we show that ORAM-based concepts only cause an overhead of factor 5.9 for evaluating equality conditions on a database with up to 10 million records.
SURFACE ELECTROMYOGRAPHY : DETECTION AND RECORDING

When detecting and recording the EMG signal, there are two main issues of concern that influence the fidelity of the signal. The first is the signal to noise ratio. That is, the ratio of the energy in the EMG signal to the energy in the noise signal. In general, noise is defined as electrical signals that are not part of the wanted EMG signal. The other is the distortion of the signal, meaning that the relative contribution of any frequency component in the EMG signal should not be altered. It is well established that the amplitude of the EMG signal is stochastic (random) in nature and can be reasonably represented by a Gausian distribution function. The amplitude of the signal can range from 0 to 10 mV (peak-to-peak) or 0 to 1.5 mV (rms). The usable energy of the signal is limited to the 0 to 500 Hz frequency range, with the dominant energy being in the 50-150 Hz range. Usable signals are those with energy above the electrical noise level. An example of the frequency spectrum of the EMG signal is presented in Figure 1. Figure 1: Frequency spectrum of the EMG signal detected from the Tibialis Anterior muscle during a constant force isometric contraction at 50% of voluntary maximum.
Evolving a Research Vision into a Practical Tool

Having developed gIBIS and QOC as Argumentation-based Design Rationale (DR) approaches in the late 1980s/mid-1990s, we report on the subsequent evolution of this DR paradigm. Our primary claim is that with robust hypermedia software being used by professionals in diverse contexts, representations of this sort are demonstrably both practical and useful to capture. The Compendium approach has maintained a strong emphasis on keeping the representational scheme as simple as possible to enable real time and asynchronous Conversational Modelling, an approach which extends Conklin’s Dialogue Modelling with IBIS-templates specialised for systematic modelling. We have evolved an approach to tackling the DR capture bottleneck through a combination of human facilitative skill, hypertext structure, modelling methods, and an open technical architecture to assist interoperability. We present examples to illustrate the applications this work has found, in particular, we report on new work which integrates argumentation-based DR with design meeting video records, to create DR in the form of video annotated with IBIS-based semantics, accessible through the widely available Access Grid collaboration environment. 2 BUCKINGHAM SHUM, ET AL.
A Hybrid Model for Solving TSP Based on Artificial Immune and Ant Colony

Artificial immune algorithm has rapid and random overall search ability, but cannot utilize system feedback information sufficiently, which results in redundancy and iteration as well as low solving efficiency. Ant colony algorithm has distributed parallel overall search ability, and can be converged on optimal path by the accumulation and update of information pheromone, but there is a lack of early stage pheromone, and the solving speed is low. This thesis put forth a hybrid algorithm based on artificial immune algorithm and ant colony algorithm, which applies artificial immune algorithm to generate pheromone distribution, and ant colony algorithm for optimal solving. When this algorithm is applied to make computer simulation to solve TSP, it turned out that this algorithm is an optimal method with preferable converging speed and search ability. Keywords— artificial immune; ant colony algorithm; TSP
An Improved Ant Colony Optimization Approach for Optimization of Process Planning

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPCs). A pheromone updating strategy proposed in this paper is incorporated in the standard ACO, which includes Global Update Rule and Local Update Rule. A simple method by controlling the repeated number of the same process plans is designed to avoid the local convergence. A case has been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach.
An efficient low-cost antialiasing method based on adaptive postfiltering

Aliasing in computer-synthesized images not only limits the realism of the images, but also affects the user's concentration. Many antialiasing methods have been proposed to solve this problem, but almost all of them are computation intensive, and some of them are also memory intensive. While these may not be limitations for high-end applications such as medical visualization and architectural design, this kind of antialiasing methods may still be far too costly for the low-cost applications. In this paper, we propose an antialiasing method that operates in the image domain. It is based on fitting curves to the discontinuity edges extracted from the aliased images to reshade those edge pixels. (Note that a curve may be considered as a general form of lines.) To improve the performance and the simplicity of the method, we propose to preprocess all possible edge patterns and fit curves in advance. During runtime, we only need to construct an index to obtain the filtering information from a lookup table. The new method is extremely simple and efficient. It provides a very good compromise between hardware cost and output image quality. In addition, because the new method has a very low computational cost, and hence low power consumption for hardware implementation, it is particularly suitable for low-cost mobile applications such as computer game consoles and palm computers, where low implementation cost and low power consumption are important design factors.
Gerstner Laboratory for Intelligent Desicion Making and Control Czech Technical University in Prague Series of Research Reports Report No : GL 147 / 02 Diagnosis of Fault Tolerant Multi-modular Systems on the Basis of Model of Interacting

Checking and diagnosing of fault-tolerant multi-modular systems performed on the basis of model of interacting. The main problems of implementing the model of interacting are considered. New organization of self-control and self-diagnosis based on the wandering diagnostic nucleus is suggested. Detailed consideration is given to the strategies of system self-control and self-diagnosis based on the wandering diagnostic nucleus.
On the Sharpness of Some Results Relating Cuts and Crossing Numbers

It is already known that for very small edge cuts in graphs, the crossing number of the graph is at least the sum of the crossing number of (slightly augmented) components resulting from the cut. Under stronger connectivity condition in each cut component that was formalized as a graph operation called zip product, a similar result was obtained for edge cuts of any size, and a natural question was asked, whether this stronger condition is necessary. In this paper, we prove that the relaxed condition is not sufficient when the size of the cut is at least four, and we prove that the gap can grow quadratically with the cut size.
Data Separation by Sparse Representations

Recently, sparsity has become a key concept in various areas of applied mathematics, computer science, and electrical engineering. One application of this novel methodology is the separation of data, which is composed of two (or more) morphologically distinct constituents. The key idea is to carefully select representation systems each providing sparse approximations of one of the components. Then the sparsest coefficient vector representing the data within the composed - and therefore highly redundant - representation system is computed by $\ell_1$ minimization or thresholding. This automatically enforces separation. This paper shall serve as an introduction to and a survey about this exciting area of research as well as a reference for the state-of-the-art of this research field. It will appear as a chapter in a book on "Compressed Sensing: Theory and Applications" edited by Yonina Eldar and Gitta Kutyniok.