Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural health monitoring from fiber-reinforced composites to steel-reinforced concrete

Accurate interpretation of sensor measurements in terms of physical changes in structures is a major challenge for the development of robust structural health monitoring systems. An active diagnostic system was proposed to detect embedded damage in fiber-reinforced composites and steel-reinforced concrete. Due to the inherent difference in material characteristics, changes in sensor measurements resulting from damage for the two material systems were found considerably different. For a given excitation, it was found that local delamination in fiber-reinforced composites reduced the measured signal strength of a nearby sensor, while rebar debond in concrete increased signal strength. Techniques based on the characteristics of these material responses to damage are being developed for adequate active sensing diagnostic systems for each material.
Nonnegative matrix factorizations as probabilistic inference in composite models

We develop an interpretation of nonnegative matrix factorization (NMF) methods based on Euclidean distance, Kullback-Leibler and Itakura-Saito divergences in a probabilistic framework. We describe how these factorizations are implicit in a well-defined statistical model of superimposed components, either Gaussian or Poisson distributed, and are equivalent to maximum likelihood estimation of either mean, variance or intensity parameters. By treating the components as hidden-variables, NMF algorithms can be derived in a typical data augmentation setting. This setting can in particular accommodate regularization constraints on the matrix factors through Bayesian priors. We describe multiplicative, Expectation-Maximization, Markov chain Monte Carlo and Variational Bayes algorithms for the NMF problem. This paper describes in a unified framework both new and known algorithms and aims at providing statistical insights to NMF.
Mercury: Hybrid Centralized and Distributed Scheduling in Large Shared Clusters

Datacenter-scale computing for analytics workloads is increasingly common. High operational costs force heterogeneous applications to share cluster resources for achieving economy of scale. Scheduling such large and diverse workloads is inherently hard, and existing approaches tackle this in two alternative ways: 1) centralized solutions offer strict, secure enforcement of scheduling invariants (e.g., fairness, capacity) for heterogeneous applications, 2) distributed solutions offer scalable, efficient scheduling for homogeneous applications. We argue that these solutions are complementary, and advocate a blended approach. Concretely, we propose Mercury, a hybrid resource management framework that supports the full spectrum of scheduling, from centralized to distributed. Mercury exposes a programmatic interface that allows applications to trade-off between scheduling overhead and execution guarantees. Our framework harnesses this flexibility by opportunistically utilizing resources to improve task throughput. Experimental results on production-derived workloads show gains of over 35% in task throughput. These benefits can be translated by appropriate application and framework policies into job throughput or job latency improvements. We have implemented and contributed Mercury as an extension of Apache Hadoop / YARN.
Improving Connection Pooling Persistence Systems

Server-sided applications often use a persistence system for storing and managing their data in a database. Such a system has become complex and includes but is not limited to a database system, drivers, connection pools, query languages, the mapping between application logic and a database data model and it involves the optimization of resources. One important resource is the connection from applications to the database system since acquiring a connection is a very expensive operation. This paper introduces monitoring facilities for the use or misuse of connections in a persistence system. In particular, it explains how the monitoring can take place in order to configure connection pooling for optimal performance. The implemented solution uses conventional programming methods as well as an aspect-oriented approach. The described facilities are integrated into the development of an enterprise-scale system implementing a communications middleware.
Electrical Vehicles in the Smart Grid: A Mean Field Game Analysis

In this article, we investigate the competitive interaction between electrical vehicles or hybrid oil-electricity vehicles in a Cournot market consisting of electricity transactions to or from an underlying electricity distribution network. We provide a mean field game formulation for this competition, and introduce the set of fundamental differential equations ruling the behavior of the vehicles at the feedback Nash equilibrium, referred here to as the mean field equilibrium. This framework allows for a consistent analysis of the evolution of the price of electricity as well as of the instantaneous electricity demand in the power grid. Simulations precisely quantify those parameters and suggest that significant reduction of the daily electricity peak demand can be achieved by appropriate electricity pricing.
Dynamic light scattering : the method and some applications

List of contributors 1. Dynamic scattering from multicomponent polymer mixtures in solution and in bulk 2. Single photon correlation techniques 3. Noise on photon correlation functions and its effects on data reduction algorithms 4. Data analysis in dynamic light scattering 5. Dynamic light scattering and linear viscoelasticity of polymers in solution and in the bulk 6. Dynamic properties of polymer solutions 7. Application of dynamic light scattering to polyelectrolytes in solution 8. Simultaneous static and dynamic light scattering: application to polymer structure analysis 9. Dynamic light scattering from dense polymer systems 10. Dynamic light scattering from polymers in solution and in bulk 11. Dynamic light scattering from polymer gels 12. Dynamic light scattering from rigid and nearly rigid rods 13. Light scattering in micellar systems 14. Critical dynamics of binary liquid mixtures and simple fluids studied using dynamic light scattering 15. Application of dynamic light scattering to biological systems 16. Diffusing-wave spectroscopy Index
Deep Reinforcement Learning to play Space Invaders

In this project, we explore algorithms that use reinforcement learning to play the game space invaders. The Q-Learning algorithm for reinforcement learning is modified to work on states that are extremely high dimensional(images) using a convolutional neural network and is called the Deep-Q learning algorithm. We also experiment with training on states represented by the RAM representation of the state. We also look at an extension of Q-learning known as Double Q learning, explore optimal architectures for learning .
Probabilistic compression artifacts reduction using self-similarity based noise region estimation

During compression artifact reduction process, original information as well as noise has been commonly removed, and this side effect should be importantly considered. In this paper, we propose a novel post-processing approach to alleviate the side effect of noise reduction while still reducing compression artifacts successfully. After compression artifact removal using conventional methods, we examine whether the denoised region is actually noisy or not, exploiting the relationship between noisy image and artifact reduced image. Then, the probability of a pixel to be noisy is calculated based on the noise region estimation, and a final denoised pixel is obtained by a weighted average between noisy and denoised signals with the probability. Experimental results show that the proposed method is more effective in preserving texture region while still reducing the compression noise.
Cloning and Sequence Analysis of LipL32, a Surface–Exposed Lipoprotein of Pathogenic Leptospira Spp

Background Leptospirosis is a worldwide zoonosis caused by pathogenic Leptospira species. A major challenge of this disease is the application of basic research to improve diagnostic methods and related vaccine development. Outer membrane proteins of Leptospira are potential candidates that may be useful as diagnostic or immunogenic factors in treatment and analysis of the disease. Objectives To develop an effective subunit vaccine against prevalent pathogenic Leptospira species, we sequenced and analyzed the LipL32 gene from three different Leptospira interrogans (L.interrogans) vaccinal serovars in Iran. Materials and Methods Following DNA extraction from these three serovars, the related LipL32 genes were amplified and cloned in the pTZ57R/T vector. Recombinant clones were confirmed by colony- PCR and DNA sequencing. The related sequences were subjected to homology analysis by comparing them to sequences in the Genbank database. Results The LipL32 sequences were >94% homologous among the vaccinal and other pathogenic Leptospira serovars in GenBank. This result indicates the conservation of this gene within the pathogenic Leptospires. Conclusions The cloned gene in this study may provide a potentially suitable platform for development of a variety of applications such as serological diagnostic tests or recombinant vaccines against leptospirosis.
A New Forward-Secure Threshold Signature Scheme Based on Schnorr Cryptosystem

Based on Schnorr cryptosystem, this paper proposes a new forward-secure threshold signature scheme. It ensures that both the signature's secret key and the signature are forward-secure through efficiently hiding the current secret key in the signature phase and using the time-parameter effectively in the verification phase. This scheme has the new property that it is infeasible for an attacker to forge any valid signature pertaining to the past even if he has corrupted up to more than or equal to the threshold members and has obtained the current key. It is also proven to be forward secure based on the hardness of factoring in the random oracle model.
Privacy suites: shared privacy for social networks

Creating privacy controls for social networks that are both expressive and usable is a major challenge. Lack of user understanding of privacy settings can lead to unwanted disclosure of private information and, in some cases, to material harm. We propose a new paradigm which allows users to easily choose “suites” of privacy settings which have been specified by friends or trusted experts, only modifying them if they wish. Given that most users currently stick with their default, operator-chosen settings, such a system could dramatically increase the privacy protection that most users experience with minimal time investment.
Report of the expert committee on the diagnosis and classification of diabetes mellitus.

the growth of knowledge regarding the etiology and pathogenesis of diabetes has led many individuals and groups in the diabetes community to express the need for a revision of the nomenclature, diagnostic criteria, and classification of diabetes. As a consequence, it was deemed essential to develop an appropriate, uniform terminology and a functional, working classification of diabetes that reflects the current knowledge about the disease. (1)
Fast Rebuilding B+-Trees for Index Recovery

Rebuilding an index is an essential step for database recovery. Fast recovery of the index is a necessary condition for fast database recovery. The B+-Tree is the most popular index structure in database systems. In this paper, we present a fast B+-Tree rebuilding algorithm called Max-PL. The main idea of Max-PL is that at first it constructs a B+-Tree index structure using the pre-stored max keys of the leaf nodes, and then inserts the keys and data pointers into the index. The algorithm employs a pipelining mechanism for reading the data records and inserting the keys into the index. It also exploits parallelisms in several phases to boost the overall performance. We analyze the time complexity and space requirement of the algorithm, and perform the experimental study to compare its performance to other B+-Trees rebuilding algorithms; Sequential Insertion and Batch-Construction. The results show that our algorithm runs on average at least 670% faster than Sequential Insertion and 200% faster than Batch-Construction.
Counting polynomials for linear codes, hyperplane arrangements, and matroids

Thomas-decomposition of a polynomial systems and the resulting counting polynomials are applied to the theory of linear codes, hyperplane arrangements, and vector matroids to reinterpret known polynomials such as characteristic polynomials and weight enu- merator, to introduce a new polynomial counting the matrices defin- ing the same matroid, and to introduce the concept of essential flats revealing a structure which allows to rewrite the rank generating poly- nomial as a sum of products of univariate polynomials. Our concepts make no essential distinction between finite and infinite fields.
Resolving ethical dilemmas: Exploring the role of moral principles

Abstract : We sought to answer two questions via this exploratory study. First, we investigated whether or not individual differences in moral principle selection/preference existed in response to six moral dilemmas. Second, we sought to find out whether they were related to the demographic variables of sex and age. As part of a larger study on moral decision making, 64 participants read and reflected upon six ethical dilemmas, rated the extent to which 5 moral principles influenced their decisions regarding these dilemmas, and selected their preferred courses of action in response to each dilemma. Results of multilevel analyses suggest that there was significant within- and between-subjects variability in the extent to which the participants relied on various moral principles to resolve the dilemmas. That is, clear individual differences existed in the moral principles that the participants reported had guided their moral responses to the dilemmas. We also found that different moral principles were invoked to differing extents depending on the domain of the decision (non-moral, military moral and non-military moral). Of the individual difference variables assessed here, results revealed that age was a significant predictor of moral principle preference, with older adults being more likely to use virtue- and care-based principles than did younger adults. Sex was not significantly associated with moral principle selection. We discuss the potential implications of individual differences in the use of moral principles and offer directions for future research in this area.
Energy-Efficient Cooperative Spectrum Sensing: A Survey

The article analyzes the problem of energy efficient techniques in cooperative spectrum sensing (CSS). Although it was proven that single-device sensing is not sufficient for reliable sensing, cooperative spectrum sensing was proposed, burdened, however, with great overhead. Thus, work on the topic of energy efficient cooperative schemes gained more interest, which resulted in a number of energy efficient cooperative algorithm proposals. In this work, we try to classify the possible directions in energy efficient CSS and present a limited set of works introducing new ideas to an energy efficient CSS algorithm.
A Functional Verification based Fault Injection Environment

Fault injection is needed for different purposes such as analyzing the reaction of a system in a faulty environment or validating fault-detection and/or fault-correction techniques. In this paper we propose a simulation-based fault injection tool able to work at different abstraction levels and with user-defined fault models. By exploiting the facilities provided by a functional verification environment it allows to speed up the entire fault injection process: from the creation of the workload to the analysis of the results of injection campaigns. Moreover, the adoption of techniques to optimize the fault list significantly reduces the simulation time. Being the tool targeted to the validation of dependable systems, it includes a way to extract information from the Failure Mode and Effect Analysis and to correlate fault injection results with estimates.
Context-based occlusion detection for robust visual tracking

Occlusion is one of the most challenging factors in visual tracking. In this paper, we propose a novel context-based occlusion detection algorithm for robust visual tracking. The basic idea of our algorithm is that occlusion indicates that some background points in previous frame move into the target region in current frame. Our algorithm investigates background patches with background trackers. The occlusion is examined by the a occlusion detector. The template updating strategy is that if occlusion is detected, the target template stops updating. Comprehensive experiments in CVPR2013 Online Objecting Tracking Benchmark (OOTB) show that our tracker achieves comparable performance with other state-of-art trackers.
Toxin-Based Targeted Therapy for Malignant Brain Tumors

Despite advances in conventional treatment modalities for malignant brain tumors—surgery, radiotherapy, and chemotherapy—the prognosis for patients with high-grade astrocytic tumor remains dismal. The highly heterogeneous and diffuse nature of astrocytic tumors calls for the development of novel therapies. Advances in genomic and proteomic research indicate that treatment of brain tumor patients can be increasingly personalized according to the characteristics of the targeted tumor and its environment. Consequently, during the last two decades, a novel class of investigative drug candidates for the treatment of central nervous system neoplasia has emerged: recombinant fusion protein conjugates armed with cytotoxic agents targeting tumor-specific antigens. The clinical applicability of the tumor-antigen-directed cytotoxic proteins as a safe and viable therapy for brain tumors is being investigated. Thus far, results from ongoing clinical trials are encouraging, as disease stabilization and patient survival prolongation have been observed in at least 109 cases. This paper summarizes the major findings pertaining to treatment with the different antiglioma cytotoxins at the preclinical and clinical stages.
Variational frameworks for DT-MRI estimation, regularization and visualization

We address three crucial issues encountered in DT-MRI (diffusion tensor magnetic resonance imaging): diffusion tensor estimation, regularization and fiber bundle visualization. We first review related algorithms existing in the literature and propose then alternative variational formalisms that lead to new and improved schemes, thanks to the preservation of important tensor constraints (positivity, symmetry). We illustrate how our complete DT-MRI processing pipeline can be successfully used to construct and draw fiber bundles in the white matter of the brain, from a set of noisy raw MRl images.
Non-Covalent Functionalization of Graphene and Multiwalled Carbon Nanotubes Composites for Transparent Conductive Films

In this paper, dry ice is converted into few-layer graphene, which can be dispersed stably in N, N-Dimethylformamide (DMF) by adding pyrene-1-boronic as a stabilizer that non-covalently functionalizes the surface of graphene to obtain homogeneous colloidal suspensions. Moreover, we make use of vacuum filtration transferring for fabricating transparent conducting graphene films by incorporating multiwalled carbon nanotubes (MWNTs). The increased conductivity is ascribed to the formation of a more efficient network. Here a transmittance of 81% at 550 nm and a sheet resistance as low as 38.17 KΩ/sq are obtained.
Large Scale KNN-Graph Approximation

Efficiently constructing the K-Nearest Neighbor Graph (K-NNG) of large and high dimensional datasets is crucial for many applications with feature-rich objects, such as images or other multimedia content. In this paper we investigate the use of high dimensional hashing methods for efficiently approximating the K-NNG in distributed environments. We first discuss the importance of balancing issues on the performance of such approaches and show why the baseline approach using Locality Sensitive Hashing (LSH) does not perform well. Our new KNN-join method is based on RMMH, a recently introduced hash function family based on randomly trained classifiers. We show that the resulting hash tables are much more balanced and that the number of resulting collisions can be greatly reduced without degrading quality. We further improve the load balancing of our distributed approach by designing a parallelized local join algorithm. We show that our method outperforms state-of-the-art in centralized settings and that it is efficiently scalable given its inherently distributed design. We finally present a distributed implementation of our method using a MapReduce framework and evaluate its performance on a large dataset.
A Novel Feature Extraction Method for Epileptic EEG Based on Degree Distribution of Complex Network

Automatic seizure detection is significant in relieving the heavy workload of inspecting prolonged electroencephalograph (EEG). Feature extraction method for automatic epileptic seizure detection has important research significance because the extracted feature seriously affects the detection algorithm performance. Recently complex network theory shows its advantages to analyze the nonlinear and nonstationary signals. In this paper, we propose a novel feature extraction method for epileptic EEG based on a statistical property of complex network. The EEG signal is first converted to complex network and the degree of every node in the network is computed. By analyzing the degree distribution, the weighted mean value of degree distribution is extracted as classification feature. A public dataset was utilized for evaluating the classifying performance of the extracted feature. Experimental results show that the extracted feature achieves not only higher classification accuracy up to 96.50% but also a very fast computation speed, which indicate the extracted feature can clearly distinguish the ictal EEG from interictal EEG and has great potentiality of realtime epileptic seizures detection. Key-Words: Feature Extraction Method, Epileptic Seizure Detection, Electroencephalograph (EEG), Degree Distribution, Complex Network, Nonlinear Time Series Analysis
Formability created by the bauschinger effect

This paper describes tests to determine if the Bauschinger effect can act as an additional source of formability, this may possibly explain the enhanced formability observed in incremental sheet forming Small cylindrical products have been made by deep drawing and wall ironing. The upper edges have been necked to various levels of reduction. Tensile test specimens have been cut from the necked upper edges and tested, causing an almost perfect stress/strain reversal. An increase of strain could be observed indeed. The results are discussed using the Considere condition as a guideline.