Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous Models of Epidemic Spreading in Heterogeneous Dynamically Changing Random Networks

Modeling spreading processes in complex random networks plays an essential role in understanding and prediction of many real phenomena like epidemics or rumor spreading. The dynamics of such systems may be represented algorithmically by Monte-Carlo simulations on graphs or by ordinary differential equations (ODEs). Despite many results in the area of network modeling the selection of the best computational representation of the model dynamics remains a challenge. While a closed form description is often straightforward to derive, it generally cannot be solved analytically; as a consequence the network dynamics requires a numerical solution of the ODEs or a direct Monte-Carlo simulation on the networks. Moreover, Monte-Carlo simulations and ODE solutions are not equivalent since ODEs produce a deterministic solution while Monte-Carlo simulations are stochastic by nature. Despite some recent advantages in Monte-Carlo simulations, particularly in the flexibility of implementation, the computational cost of an ODE solution is much lower and supports accurate and detailed output analysis such as uncertainty or sensitivity analyses, parameter identification etc. In this paper we propose a novel approach to model spreading processes in complex random heterogeneous networks using systems of nonlinear ordinary differential equations. We successfully apply this approach to predict the dynamics of HIV-AIDS spreading in sexual networks, and compare it to historical data.
Achieving Load Balance and E ective Cachingin Clustered Web

This paper considers policies for distributing requests in clustered Web servers, wherein multiple server machines are conngured to function as a single high(er) performance Web server. We evaluate various load distribution policies with respect to both their ability to achieve good load balance (the primary goal) and also to their impact on the eeec-tiveness of per-machine caching. Trace-driven simulation is employed, with workload traces from two heavily-loaded (3-8 million requests per day) commercial Web servers. Our results show that use of current state information is necessary in achieving good load balance only when the achievable per-request bandwidth is not strongly network or client limited. Use of current state information is not found to be necessary with respect to achieving good cache behaviour. Load distribution based on a static hashed assignment of the URL space is found to yield very similar cache performance to load distribution based on current cache contents. We also nd that it is possible to achieve both good cache behaviour and good load balance, but it requires use of policies that take both objectives into consideration and that make use of information concerning current server loads.
Temporal Autoencoding Improves Generative Models of Time Series

Restricted Boltzmann Machines (RBMs) are generative models which can learn useful representations from samples of a dataset in an unsupervised fashion. They have been widely employed as an unsupervised pre-training method in machine learning. RBMs have been modified to model time series in two main ways: The Temporal RBM stacks a number of RBMs laterally and introduces temporal dependencies between the hidden layer units; The Conditional RBM, on the other hand, considers past samples of the dataset as a conditional bias and learns a representation which takes these into account. Here we propose a new training method for both the TRBM and the CRBM, which enforces the dynamic structure of temporal datasets. We do so by treating the temporal models as denoising autoencoders, considering past frames of the dataset as corrupted versions of the present frame and minimizing the reconstruction error of the present data by the model. We call this approach Temporal Autoencoding. This leads to a significant improvement in the performance of both models in a filling-in-frames task across a number of datasets. The error reduction for motion capture data is 56\% for the CRBM and 80\% for the TRBM. Taking the posterior mean prediction instead of single samples further improves the model's estimates, decreasing the error by as much as 91\% for the CRBM on motion capture data. We also trained the model to perform forecasting on a large number of datasets and have found TA pretraining to consistently improve the performance of the forecasts. Furthermore, by looking at the prediction error across time, we can see that this improvement reflects a better representation of the dynamics of the data as opposed to a bias towards reconstructing the observed data on a short time scale.
Beowulf Clusters to Support Scientific Research: A Strategic Framework

A Beowulf cluster is a network of commodity computers designed to perform parallel computations with the goal of achieving high performance. We have created two clusters to serve our college community: a testing cluster for developing parallel software and exploring new system configurations, and a stable production cluster. The cluster project has two main goals: to support interdisciplinary research applications, including Physics, Biology, Statistics, and Mathematics; and to promote undergraduate education and research projects in Computer Science. The architecture of the cluster and its surrounding student culture make the Beowulf Project flexible, practical, and sustainable.
A Treatise on Mathematical Color Inpainting of Japanese Old Statues

There are many photos of statues having lost their coloring in the databases containing Japanese old s tatues. This article is a treatise on color inpainting of these photos of old statues. In most cases, before color inpainting of historical precious statues, we must pay tremendous attention to the details of the statues and conduct in-depth researc h on them. On the contrary, in this treatise, we argue how we can contribute to the color inpainting of the photos, not just of the statues themselves, but also by using mathematical image an lysis techniques assuming no previous professional profou nd knowledge of the statue inpainting.
Multi-Modal Local Receptive Field Extreme Learning Machine for object recognition

Learning rich representations efficiently plays an important role in multi-modal recognition task, which is crucial to achieve high generalization performance. To address this problem, in this paper, we propose an effective Multi-Modal Local Receptive Field Extreme Learning Machine (MM-ELM-LRF) structure, while maintaining ELM's advantages of training efficiency. In this structure, ELM-LRF is firstly conducted for feature extraction for each modality separately. And then, the shared layer is developed by combining these features from each modality. Finally, the Extreme Learning Machine (ELM) is used as supervised feature classifier for the final decision. Experimental validation on Washington RGB-D Object Dataset illustrates that the proposed multiple modality fusion method achieves better recognition performance.
Comparative Evaluation of Lifetime Enhancement with Fault Avoidance on Dynamically Reconfigurable Devices

Fault tolerant methods using dynamically reconfigurable devices have been studied to overcome wear-out failures. However, quantitative comparisons have not been sufficiently assessed on device lifetime enhancement with these methods, whereas they have mainly been evaluated individually from various viewpoints such as additional hardware overheads, performance, and downtime for fault recovery. This paper presents quantitative lifetime evaluations performed by simulating the fault-avoidance procedures of five representative methods under the same conditions in wear-out scenarios, applications, and device architecture. The simulation results indicated that improvements of up to 70% mean-time-tofailure (MTTF) in comparison with ideal fault avoidance could be achieved by using methods of fault avoidance with ‘row direction shift’ and ‘dynamic partial reconfiguration’. ‘Column shift’, on the other hand, attained a high degree of stability with moderate improvements in MTTF. The experimental results also revealed that spare basic elements (BEs) should be prevented from aging so that improvements in MTTF would not be adversely affected. Moreover, we found that the selection of initial mappings guided by wire utilization could increase the lifetimes of partial reconfiguration based fault avoidance. key words: fault avoidance, lifetime enhancement, mean-time-to-failure (MTTF), partial reconfiguration
Social media monitoring and understanding: an integrated mixed methods approach for the analysis of social media

It is increasingly apparent that social media is a unique and important resource to explore spontaneous relations and discourses among individuals. Data analysis in social media, however, is difficult because approaches have not been streamlined. In this paper, we move from the discussion of present perspectives in the analysis of social media to then discuss the methodological strategies available and their limits. We finally discuss the value of choosing a mixed methods approach to the analysis of social media, exemplifying its heuristic contribution by a real research case.
Tannins of Constant Structure in Medicinal and Food Plants—Hydrolyzable Tannins and Polyphenols Related to Tannins

In addition to the commonly used classification as hydrolyzable tannins and condensed tannins, tannins can also be categorized into two other types: polyphenols of constant chemical structure (Type A) and polyphenols of variable composition (Type B). Both types of tannins and related polyphenols account for a large part of plant polyphenols, but accurate structure-activity correlations on a molecular basis can be determined mainly for type A compounds, among which are hydrolysable tannins such as the ellagitannins and their oxidized congeners, some gallotannins, epigallocatechin gallate, caffetannins, etc. Among the activities determined on a molecular basis are the chemical, biological and pharmacological actions such as superoxide anion scavenging, apoptosis, antitumor, anti-EVB, anti-MRSA and anti-plasmin inhibitory activities, etc., in addition to their fundamental activities, i.e., binding to proteins, large molecular compounds and metallic ions, and antioxidant activities. Some structure-specific activities were found for the condensation of dehydroellagitannins with co-existing compounds under mild conditions, and the host-mediated antitumor actions of ellagitannin oligomers. Structures and activities of metabolites of geraniin, a dehydroellagitannin, were revealed. Some stilbenoids and phlorotannins of firm structures have been known to have many activities similar to those of the type A tannins.
Nonparametric Denoising Methods Based on Contourlet Transform with Sharp Frequency Localization: Application to Low Exposure Time Electron Microscopy Images

Image denoising is a very important step in cryo-transmission electron microscopy (cryo-TEM) and the energy filtering TEM images before the 3D tomography reconstruction, as it addresses the problem of high noise in these images, that leads to a loss of the contained information. High noise levels contribute in particular to difficulties in the alignment required for 3D tomography reconstruction. This paper investigates the denoising of TEM images that are acquired with a very low exposure time, with the primary objectives of enhancing the quality of these low-exposure time TEM images and improving the alignment process. We propose denoising structures to combine multiple noisy copies of the TEM images. The structures are based on Bayesian estimation in the transform domains instead of the spatial domain to build a novel feature preserving image denoising structures; namely: wavelet domain, the contourlet transform domain and the contourlet transform with sharp frequency localization. Numerical image denoising experiments demonstrate the performance of the Bayesian approach in the contourlet transform domain in terms of improving the signal to noise ratio (SNR) and recovering fine details that may be hidden in the data. The SNR and the visual quality of the denoised images are considerably enhanced using these denoising structures that combine multiple noisy copies. The proposed methods also enable a reduction in the exposure time.
How to Work with Android within a (FB-)Aodv Network

Android is a complete and innovative Open Source platform for mobile devices. FB-aodv is an extension of the existing AODV protocol. This work propose creation of a Mobile Ad-hoc Network with Android smart phones. We used FB-aodv to manage the networks and some bash script to share informations within the networks. MANET was tested by simple applications to view local neighbors, to send sms and voice over the networks.
Children's Heart Rate and Vicariously Aroused Affect in Response to Others' Differing Emotional Experiences

This study examined vicariously induced Heart Rate (HR) patterns in response to others' sadness, fear, anger and happiness, in children (N = 44, 22 girls and 22 boys, aged 7 to 10), when confronted with a brief emotion evocative film consisting of a series of evocative episodes each of which was of at least moderate intensity. HR was consistently higher relative to baseline in response to others' fear (p < .001), sadness (p < .011), anger (p < .014) and the positive emo- tion of happiness/surprise (p < .002). These findings suggest that HR can reliably be used as a marker of vicariously aroused affect in response to a range of different emotions in children, given that stimulus intensity is at least moderate.
A Networked Predictive Control Algorithm Based on T-S Fuzzy Model

Network time-delay is usually uncertain or random, packet loss and timing disorder also can be summed up in a certain degree of time-delay. In this paper, we focused on networked control systems (NCS) with bounded uncertain time delays. Utilizing extended TakagiSugeno (T-S) fuzzy models, the characteristics of such a system is better expressed. Based on these fuzzy models, a fuzzy generalized predictive control algorithm based on Markov uncertain delay and subspace Toeplitz equations is proposed. By introducing subspace Toeplitz prediction equation, the overall optimal control input is obtained by fuzzy "blending" of all subspaces control law. Finally, a simulation example was given.
Strategies for Environmental Sound Measurement, Modelling, and Evaluation

This thesis is a portfolio of research into three aspects of environmental sound: its measurement, modelling, and evaluation. In each of these areas, this body of work aims to make use of soundscape methodologies in order to develop an understanding of different aspects of our relationship with our sonic environments. This approach is representative of the nature of soundscape research, which makes use of elements of many other research areas, including acoustics, psychology, sociology, and musicology. The majority of prior acoustic measurement research has considered indoor recording, often of music, and measurement of acoustic parameters of indoor spaces such as concert halls and other performance spaces. One strand of this research has investigated how best to apply such techniques to the recording of environmental sound, and to the measurement of the acoustic impulse responses of outdoor spaces. Similarly, the majority of prior work in the field of acoustic modelling has also focussed mainly on indoor spaces. Presented here is the Waveguide Web, a novel method for the acoustic modelling of sparsely reflecting outdoor spaces. In the field of sound evaluation of sound, recent years have seen the development of soundscape techniques for the subjective rating of environmental sound, allowing for a better understanding of our relationship with our sonic surroundings. Research presented in this thesis has focussed on how best to improve these approaches in a suitably robust and intuitive manner, including the integration of visual stimuli in order to investigate the multi-modal perception of our surroundings. The aim of this thesis in making contributions to these three fields of environmental sound research is, in part, to highlight the importance of developing a comprehensive understanding of our sonic environments. Such an understanding could ultimately lead to the alleviation of noise problems, encourage greater engagement with environmental sound in the wider population, and allow for the design of more positive, restorative, soundscapes.
On the Capacity of the Discrete-Time Poisson Channel

The large-inputs asymptotic capacity of a peak-power and average-power limited discrete-time Poisson channel is derived using a new firm (nonasymptotic) lower bound and an asymptotic upper bound. The upper bound is based on the dual expression for channel capacity and the notion of capacity-achieving input distributions that escape to infinity. The lower bound is based on a lower bound on the entropy of a conditionally Poisson random variable in terms of the differential entropy of its conditional mean.
A tool for automated evaluation of algorithms

Testing algorithms and systems involves trying different sets of parameter values on different domains or data sets. Even for a moderate number of parameters and domains the number of possible experiments can get very large due to the combinatorial explosion. Evaluating the outcome of these experiments requires comparing the results, which is often done by writing a script or inspecting the result files manually. For a new algorithm or version, the work has to be done over again. With hundreds, thousands, or even more possible experiments, both the preparation and the evaluation can become complex and tedious. In this demonstrator we present a software tool, called ET, for evaluating the parameters of an algorithm or system, either automatically or controlled by the user. It allows to launch large numbers of experiments in just a few clicks, visually explore the results and analyze the performance of the algorithm.
Diagnosis of Broken Bar Fault in Induction Machines Using Discrete Wavelet Transform without Slip Estimation

The aim of this paper is to present a wavelet-based method for broken bar fault detection in induction machines. The frequency-domain methods which are commonly used need speed information or accurate slip estimation for frequency components localization in any spectrum. Nevertheless, the fault frequency bandwidth can be well defined for any induction machine due to numerous previous investigations. The proposed approach consists in the energy evaluation of this known bandwidth with time-domain analysis using the discrete wavelet transform (DWT). Then, it has been applied to the stator current space vector magnitude and the instantaneous magnitude of the stator current signal for different broken bar fault severities and load levels.
A matrix analysis of different centrality measures

Node centrality measures including degree, eigenvector, Katz and subgraph centralities are analyzed for both undirected and directed networks. We show how parameter-dependent measures, such as Katz and subgraph centrality, can be "tuned" to interpolate between degree and eigenvector centrality, which appear as limiting cases of the other measures. We interpret our finding in terms of the local and global influence of a given node in the graph as measured by graph walks of different length through that node. Our analysis gives some guidance for the choice of parameters in Katz and subgraph centrality, and provides an explanation for the observed correlations between different centrality measures and for the stability exhibited by the ranking vectors for certain parameter ranges. The important role played by the spectral gap of the adjacency matrix is also highlighted.
Eye Tracking as a Tool for Machine Translation Error Analysis

We present a preliminary study where we use eye tracking as a complement to machine translation (MT) error analysis, the task of identifying and classifying MT errors. We performed a user study where subjects read short texts translated by three MT systems and one human translation, while we gathered eye tracking data. The subjects were also asked comprehension questions about the text, and were asked to estimate the text quality. We found that there are a longer gaze time and a higher number of fixations on MT errors, than on correct parts. There are also differences in the gaze time of different error types, with word order errors having the longest gaze time. We also found correlations between eye tracking data and human estimates of text quality. Overall our study shows that eye tracking can give complementary information to error analysis, such as aiding in ranking error types for seriousness.
What affects the mental health of people seeking asylum in the UK? A narrative analysis of migration stories

ABSTRACT This paper aims to understand how people seeking asylum make sense of their migration experience and the factors impacting the mental health of people seeking asylum in the UK how this influences mental health and well-being. We analyse three extended unstructured interviews with people who have sought asylum in the UK that were generated through interviews with a theatre company. This is augmented by two interviews with theatre company staff about the interview process. Transcripts are analysed using simultaneous thematic and narrative analysis. Seven themes are identified; the overarching theme is one of dehumanisation due to the asylum process and public discourse. Narrative analysis demonstrates how experiences of torture and lack of control in countries of origin continued in UK. Moreover, the asylum process inhibited people reconciling their pre-migration trauma and stopped interviewees planning for their future, leading to a loss of identity. Interviewers worked with interviewees to create a narrative to prompt the audience into action on migrants’ rights. Results suggest the importance of creating a secure, trusting space where people can purposively construct their narratives of migration to reframe traumatic experiences.
Learning motion and impedance behaviors from human demonstrations

Human-robot skill transfer has been deeply investigated from a kinematic point of view, generating various approaches to increase the robot knowledge in a simple and compact way. Nevertheless, social robotics applications require a close and active interaction with humans in a safe and natural manner. Torque controlled robots, with their variable impedance capabilities, seem a viable option toward a safe and profitable human-robot interaction. In this paper, an approach is proposed to simultaneously learn motion and impedance behaviors from tasks demonstrations. Kinematic aspects of the task are represented in a statistical way, while the variability along the demonstrations is used to define a variable impedance behavior. The effectiveness of our approach is validated with simulations on real and synthetic data.