Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion

Coupled nonnegative matrix factorization (CNMF) unmixing is proposed for the fusion of low-spatial-resolution hyperspectral and high-spatial-resolution multispectral data to produce fused data with high spatial and spectral resolutions. Both hyperspectral and multispectral data are alternately unmixed into end member and abundance matrices by the CNMF algorithm based on a linear spectral mixture model. Sensor observation models that relate the two data are built into the initialization matrix of each NMF unmixing procedure. This algorithm is physically straightforward and easy to implement owing to its simple update rules. Simulations with various image data sets demonstrate that the CNMF algorithm can produce high-quality fused data both in terms of spatial and spectral domains, which contributes to the accurate identification and classification of materials observed at a high spatial resolution.
A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator

Feature selection is a common and key problem in many classification and regression tasks. It can be viewed as a multiobjective optimisation problem, since, in the simplest case, it involves feature subset size minimisation and performance maximisation. This paper presents a multiobjective evolutionary approach for feature selection. A novel commonality-based crossover operator is introduced and placed in the multiobjective evolutionary setting. This specialised operator helps to preserve building blocks with promising performance. Selection bias reduction is achieved by resampling. We argue that this is a generic approach, which can be used in many modelling problems. It is applied to feature selection on different neural network architectures. Results from experiments with benchmarking data sets are given.
New Crossover Operator for Evolutionary Rule Discovery in XCS

XCS is a learning classifier system that combines a reinforcement learning scheme with evolutionary algorithms to evolve rule sets on-line by means of the interaction with an environment. Usually, research conducted on XCS has mainly focused on the analysis and improvement of the reinforcement learning component, overlooking the evolutionary discovery process to some extent. Recently, the first efforts towards analyzing and designing new operators for the evolutionary algorithm have been done. The selection pressure produced by different selection schemes has been studied and the rule representation of XCS has been extended to adapt evolution strategies as the discovery component of the system. This paper continues on the analysis of the evolutionary algorithms in the on-line architecture by analyzing the role of the crossover operator in the original XCS and XCS based on evolution strategies. A new recombination operator, inspired by the BLX crossover operator in realcoded genetic algorithms, is designed for XCS. The new recombination operator is experimentally compared with the traditional crossover operator of XCS on a collection of real-life classification problems. The results show the competence of the new operator, providing the best results, on average, on the tested domains.
Community-based participatory research to improve life quality and clinical outcomes of patients with breast cancer (DianaWeb in Umbria pilot study)

Introduction Breast cancer (BC) is the most frequent cancer in Europe and the International Agency for Research on Cancer (IARC) has estimated over 460 000 incident cases per year. Survival among patients with BC has increased in the past decades and EUROCARE-5 has estimated a 5-year relative survival rate of 82% for patients diagnosed in 2000–2007. There is growing evidence that lifestyle (such as a diet based on Mediterranean principles associated with moderate physical activity) may influence prognosis of BC; however, this information is not currently available to patients and is not considered in oncology protocols. Only a few epidemiological studies have investigated the role of diet in BC recurrence and metastasis. Methods and analysis DianaWeb is a community-based participatory research dedicated to patients with BC and represents a collaborative effort between participants and research institutions to determine if specified changes in lifestyle would result in improved outcomes in terms of quality of life or survival. The aim of the study is to recruit a large number of participants, to monitor their lifestyle and health status over time, to provide them tips to encourage sustainable lifestyle changes, to analyse clinical outcomes as a function of baseline risk factors and subsequent changes, and to share with patients methodologies and results. DianaWeb uses a specific interactive website (http://www.dianaweb.org/) and, with very few exceptions, all communications will be made through the web. In this paper we describe the pilot study, namely DianaWeb in Umbria. Ethics and dissemination DianaWeb does not interfere with prescribed oncological treatments; rather, it recommends that participants should follow the received prescriptions. The results will be used to plan guidelines for nutrition and physical activity for patients with BC. The pilot study was approved by the ethics committee of the University of Perugia (reference number 2015-002), and is supported by Fondazione Cassa di Risparmio di Perugia (2013.0185 021).
Real-time Body Tracking Using a Gaussian Process Latent Variable Model

In this paper, we present a tracking framework for capturing articulated human motions in real-time, without the need for attaching markers onto the subject's body. This is achieved by first obtaining a low dimensional representation of the training motion data, using a nonlinear dimensionality reduction technique called back-constrained GPLVM. A prior dynamics model is then learnt from this low dimensional representation by partitioning the motion sequences into elementary movements using an unsupervised EM clustering algorithm. The temporal dependencies between these elementary movements are efficiently captured by a Variable Length Markov Model. The learnt dynamics model is used to bias the propagation of candidate pose feature vectors in the low dimensional space. By combining this with an efficient volumetric reconstruction algorithm, our framework can quickly evaluate each candidate pose against image evidence captured from multiple views. We present results that show our system can accurately track complex structured activities such as ballet dancing in real-time.
Round Robin Cycle for Predictions in Wireless Sensor Networks

Use of prediction models in sensor networks proves to be efficient with respect to energy savings, as it allows sensors whose readings are predicted to remain in their idle mode, thereby consuming orders of magnitude less energy that in the active mode. In the context of continuous monitoring, where a set of sensors is typically required to regularly send their readings to a central server, an interesting approach consists in splitting the set of sensors in two subsets, such that readings of one subset are used to predict readings of the second subset. In this paper, we propose to identify several sensor subsets for predictions, that are used in turn in a round robin fashion. Identification of different sensor subsets allows to detect erroneous models or sensor failure, and to better distribute energy consumption. Efficiency of the proposed procedure is demonstrated on a set of experiments using real world sensor data.
High-Resolution Electrical Stimulation of Primate Retina for Epiretinal Implant Design

The development of retinal implants for the blind depends crucially on understanding how neurons in the retina respond to electrical stimulation. This study used multielectrode arrays to stimulate ganglion cells in the peripheral macaque retina, which is very similar to the human retina. Analysis was restricted to parasol cells, which form one of the major high-resolution visual pathways in primates. Individual cells were characterized using visual stimuli, and subsequently targeted for electrical stimulation using electrodes 9–15 μm in diameter. Results were accumulated across 16 ON and 9 OFF parasol cells. At threshold, all cells responded to biphasic electrical pulses 0.05–0.1 ms in duration by firing a single spike with latency lower than 0.35 ms. The average threshold charge density was 0.050 ± 0.005 mC/cm2, significantly below established safety limits for platinum electrodes. ON and OFF ganglion cells were stimulated with similar efficacy. Repetitive stimulation elicited spikes within a 0.1 ms time window, indicating that the high temporal precision necessary for spike-by-spike stimulation can be achieved in primate retina. Spatial analysis of observed thresholds suggests that electrical activation occurred near the axon hillock, and that dendrites contributed little. Finally, stimulation of a single parasol cell produced little or no activation of other cells in the ON and OFF parasol cell mosaics. The low-threshold, temporally precise, and spatially specific responses hold promise for the application of high-density arrays of small electrodes in epiretinal implants.
Complex Dynamics of Elementary Cellular Automata Emerging from Chaotic Rules

We show techniques of analyzing complex dynamics of cellular automata (CA) with chaotic behavior. CA are well-known computational substrates for studying emergent collective behavior, complexity, randomness and interaction between order and chaotic systems. A number of attempts have been made to classify CA functions on their space-time dynamics and to predict the behavior of any given function. Examples include mechanical computation, λ and Z-parameters, mean field theory, differential equations and number conserving features. We aim to classify CA based on their behavior when they act in a historical mode, i.e. as CA with memory. We demonstrate that cell-state transition rules enriched with memory quickly transform a chaotic system converging to a complex global behavior from almost any initial condition. Thus, just in few steps we can select chaotic rules without exhaustive computational experiments or recurring to additional parameters. We provide an analysis of well-known chaotic functions in one-dimensional CA, and decompose dynamics of the automata using majority memory exploring glider dynamics and reactions.
A Distance-Based Handover Scheme for Femtocell and Macrocell Overlaid Networks

Femtocell is considered as one of the prominent techniques to improve the cellular network coverage and the system capacity, which is applied by installing the femtocell base station, in the place, where there is low signal strength or even without any signal (e.g., home, rural area). In such a network, mobile users should make efficient handover decision when move between macrocell and femtocell. In this paper, we propose a distance-based handover scheme for femtocell and macrocell overlaid area (FMOA). In order to reduce the handover failures and avoid unnecessary handovers, we use a threshold which is calculated by the mobile user as it enters the overlaid area. Numerical simulations are presented to verify the proposed scheme. The results show that the proposed scheme is effective in handover management for both the handover failure minimization and unnecessary handover avoidance in FMOA scenario.
Phase based volume registration using cuda

We present a method for fast phase based registration of volume data for medical applications. As the number of different modalities within medical imaging increases, it becomes more and more important with registration that works for a mixture of modalities. For these applications the phase based registration approach has proven to be superior. Today there seem to be two kinds of groups that work with medical image registration, one that works with refining of the registration algorithms and one that works with implementation of more simple algorithms on graphic cards for speeding up the algorithms. We put the work from these groups together and get the best from both worlds. We achieve a speedup of 10–30 compared to our CPU implementation, which makes fast phase based registration possible for large medical volumes.
Proteomics in Alzheimer's disease: insights into potential mechanisms of neurodegeneration

Proteomics involves the identification of unknown proteins following their separation, often using two‐dimensional electrophoresis, digestion of particular proteins of interest by trypsin, determination of the molecular weight of the resulting peptides, and database searching to make the identification of the proteins. Application of proteomics to Alzheimer's disease (AD), the major dementing disorder of the elderly, has just begun. Differences in protein expression and post‐translational modification (mostly oxidative modification) of proteins from AD brain and peripheral tissue, as well as in brain from rodent models of AD, have yielded insights into potential molecular mechanisms of neurodegeneration in this dementing disorder. This review surveys the proteomics studies relevant to AD, from which new understandings of the pathology, biochemistry, and physiology of AD are beginning to emerge.
RFID cardinality estimation with blocker tags

The widely used RFID tags impose serious privacy concerns as a tag responds to queries from readers no matter they are authorized or not. The common solution is to use a commercially available blocker tag which behaves as if a set of tags with known blocking IDs are present. The use of blocker tags makes RFID estimation much more challenging as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose REB, the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. The basic idea of REB is to conduct statistically inference from the two sets of responses and estimate the number of genuine tags. We conduct extensive simulations to evaluate the performance of REB, in terms of time-efficiency and estimation reliability. The experimental results reveal that our REB scheme runs tens of times faster than the fastest identification protocol with the same accuracy requirement.
Social class, habitus and reflexivity : an analysis of trainee teachers' understandings

This thesis examines the relationship between trainee teachers’ social class backgrounds and their early professional identity development in placement schools. Reasons why they seek to train in specific schools and how trainees’ social class backgrounds affect their choice of placement schools is explored. The concepts of dispositional understanding and habitus are used to develop an understanding of the social class values trainee teachers bring to an initial teacher training course and consequently, how these concepts are made manifest during training placements. My epistemological position as a qualitative researcher defines the framework for how I gather and interpret my data. Using interviews that explore social backgrounds and details of placement experiences provides data that is rich in personal detail, as well as giving insight into how trainees perceive their training placements and early career professional identity development. The findings indicate that trainees research their school-based placements in order to ensure that they have an increased chance of successfully completing their training. This leads to trainees preferring placements in what they perceive to be successful schools. Making such choices reduces the potential for failure through coming into contact with school students who may, through the trainees’ perceptions of such students, disrupt trainees’ progress. In doing so, they seek to detach themselves from students whom they perceive may damage their chances of successfully completing placements and ultimately, their entry into teaching. Analysis of trainees’ recall of taught elements of their training reveals that they privilege information relating to ethnicity, race, gender or religion over students’ socio-economic status. Finally, analysis of policy shows that with future changes to initial teacher training there are implications for courses due to elimination and recruitment to schools in areas of social deprivation.
NUMERICAL INTEGRATION OF THE PRIMITIVE EQUATIONS ON THE HEMISPHERE

Abstract A 48-hr. forecast for the entire Northern Hemisphere of a barotropic hydrostatic atmosphere is made with the “primitive equations. ” Overlapping Mercator and stereographic grids are used, together with the finite-difference scheme proposed by Eliassen. Initial data corresponded to a Haurwitz-type pattern of wave number 4. The initial wind field was nondivergent and the initial geopotential field satisfied the balance equation. The computations seem to be stable and well behaved, except for two small temporary irregularities. The amplitude of the gravity-inertia waves present in the forecast geopotential field is about 1/30 that of the large-scale field. It can be shown that this is due to the neglect, in the initial data, of the quasi-geostrophically conditioned divergence field. The computational technique itself therefore does not give any unreal prominence to the “meteorological noise. ” The computational characteristics and stability criterion of the Eliassen finite-difference system are invest.
A Distributed Multichannel MAC Protocol for Multihop Cognitive Radio Networks

A cognitive radio (CR) network should be able to sense its environment and adapt communication to utilize the unused licensed spectrum without interfering with licensed users. In this paper, we look at CR-enabled networks with distributed control. As CR nodes need to hop from channel to channel to make the most use of the spectrum opportunities, we believe distributed multichannel medium access control (MAC) protocols to be key enablers for these networks. In addition to the spectrum scarcity, energy is rapidly becoming one of the major bottlenecks of wireless operations and has to be considered as a key design criterion. We present here an energy-efficient distributed multichannel MAC protocol for CR networks (MMAC-CR). Simulation results show that the proposed protocol significantly improves performance by borrowing the licensed spectrum and protects primary users (PUs) from interference, even in hidden terminal situations. Sensing costs are evaluated and shown to contribute only 5% to the total energy cost.
Themes and Variations: Regulation of RpoN-Dependent Flagellar Genes across Diverse Bacterial Species

Flagellar biogenesis in bacteria is a complex process in which the transcription of dozens of structural and regulatory genes is coordinated with the assembly of the flagellum. Although the overall process of flagellar biogenesis is conserved among bacteria, the mechanisms used to regulate flagellar gene expression vary greatly among different bacterial species. Many bacteria use the alternative sigma factor σ 54 (also known as RpoN) to transcribe specific sets of flagellar genes. These bacteria include members of the Epsilonproteobacteria (e.g., Helicobacter pylori and Campylobacter jejuni), Gammaproteobacteria (e.g., Vibrio and Pseudomonas species), and Alphaproteobacteria (e.g., Caulobacter crescentus). This review characterizes the flagellar transcriptional hierarchies in these bacteria and examines what is known about how flagellar gene regulation is linked with other processes including growth phase, quorum sensing, and host colonization.
Using AI Approach to Solve an Integrated Three-Echelon Supply Chain Model with Strategic Alliances

This paper develops a mathematical inventory model of deteriorating item taking into account a three-echelon supply chain vertical integration through strategic alliances. The objective of this model is to minimize the joint total relevant cost and to devise a compensation policy. Due to the complexity of the non-linear problems, it is not possible to find the global optimum analytically. Following the physical phenomenon of annealing, a soft computing method, Simulated Annealing (SA), has been developed to find the global optimum for the complex cost function through stochastic search process. A numerical example, sensitivity analysis, and the effects of the compensation policy on the optimal results are presented to validate the results of the proposed integrated model. The proposed mathematical model shows how an integrated approach to decision making can achieve a global optimum.
Patterned Search Planning and Testing for the Robotic Antarctic Meteorite Search

The goal of the Robotic Antarctic Meteorite Search is to enable discovery of meteorites in Antarctica by a mobile robot. The extreme environment makes it one of the best places to find meteorites, but one of the worst places for humans to work. The meteorite-finding robot will traverse an ice field in a pattern designed to cover the area completely, stopping to investigate potential meteorites with an array of sensors. High level autonomy is needed for this project in many areas: scientific sensing, scientific analysis, navigational sensing, navigational planning, and mission planning. Navigational planning for this project primarily involves generating coverage patterns for traversing the ice fields as completely as possible. Multiple types of patterns are considered, and some of their benefits and problems are discussed. Several of the coverage patterns have been tested in a gravel slag heap in Pittsburgh and on the ice in Antarctica, and the results of these tests are described. The affect of different environments and robot locomotion configurations on control issues, such as the implementation of the pure pursuit algorithm, and maintaining and regaining a path, are discussed.
Dynamics of Asymmetric Information and Capital Structure

I propose a model of financing decisions in an environment where asymmetric information changes through time. My goal is to determine the optimal sequence of securities as a function of the amount and dynamics of the asymmetric information advantage that insiders of the firm have with respect to outside investors. I identify a novel cost of debt that arises in this dynamic setting. Whereas in a one period model, debt is always preferred to equity because it is less sensitive to the private information managers have, with multiple overlapping investment projects, debt issues today make future security issues more sensitive to the degree of asymmetric information in the issuance period. I use the dispersion of analyst forecasts for different horizons to proxy for current and future asymmetric information and examine the financing of a large panel of US firms. I find that future adverse selection costs affect negatively the debt component of new external financing and positively the cash reserves of the firm. This evidence is consistent with the prediction of my model that companies try to minimize adverse selection costs intertemporally. 1 NYU Stern School of Business, email: nhalov@stern.nyu. edu. I am grateful to Kose John for invaluable guidance and discussions. I also want to thank Heitor Almeida, Yakov Amihud, Menachem Brenner, Florian Heider, Eli Ofek and Daniel Wolfenzon for helpful comments and suggestion. Usual disclaimer applies.