Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Piecewise-Constant Smoothing: M-Smoother Revisited

A robust estimator, namely M-smoother, for piecewise-constant smoothing is revisited in this paper. Starting from its generalized formulation, we propose a numerical scheme/framework for solving it via a series of weighted-average filtering (e.g., box filtering, Gaussian filtering, bilateral filtering, and guided filtering). Because of the equivalence between M-smoother and local-histogram-based filters (such as median filter and mode filter), the proposed framework enables fast approximation of histogram filters via a number of box filtering or Gaussian filtering. In addition, high-quality piecewise-constant smoothing can be achieved via a number of bilateral filtering or guided filtering integrated in the proposed framework. Experiments on depth map denoising show the effectiveness of our framework.
Does Socio-emotional Developmental Delay Masquerade as Autism in Some Deaf Children?

It is reported that autism is more common in deaf children. It is also now well established that theory of mind delays are common in deaf children particularly where there is linguistic and communicative poverty in infancy. This paper discusses the literature in these areas and proposes that socio-emotional developmental delay is distinct from autism in deaf children with different presentation, aetiology and prognosis careful assessment should distinguish between autism and socio-emotional developmental delay in deaf children, and lead to appropriate differential interventions.
Twisted edwards-form elliptic curve cryptography for 8-bit AVR-based sensor nodes

Wireless Sensor Networks (WSNs) pose a number of unique security challenges that demand innovation in several areas including the design of cryptographic primitives and protocols. Despite recent progress, the efficient implementation of Elliptic Curve Cryptography (ECC) for WSNs is still a very active research topic and techniques to further reduce the time and energy cost of ECC are eagerly sought. This paper presents an optimized ECC implementation that we developed from scratch to comply with the severe resource constraints of 8-bit sensor nodes such as the MICAz and IRIS motes. Our ECC software uses Optimal Prime Fields (OPFs) as underlying algebraic structure and supports two different families of elliptic curves, namely Weierstraß-form and twisted Edwards-form curves. Due to the combination of efficient field arithmetic and fast group operations, we achieve an execution time of 5.8 · 106 clock cycles for a full 158-bit scalar multiplication on an 8-bit ATmega128 micro-controller, which is 2.78 times faster than the widely-used TinyECC library. Our implementation also shows that the energy cost of scalar multiplication on a MICAz (or IRIS) mote amounts to just 19~mJ when using a twisted Edwards curve over a 160-bit OPF. This result compares fairly well with the energy figures of two recently-presented hardware designs of ECC based on twisted Edwards curves.
Developing a Prognostic Micro-RNA Signature for Human Cervical Carcinoma

Cervical cancer remains the third most frequently diagnosed and fourth leading cause of cancer death in women worldwide. We sought to develop a micro-RNA signature that was prognostic for disease-free survival, which could potentially allow tailoring of treatment for cervical cancer patients. A candidate prognostic 9-micro-RNA signature set was identified in the training set of 79 frozen specimens. However, three different approaches to validate this signature in an independent cohort of 87 patients with formalin-fixed paraffin-embedded (FFPE) specimens, were unsuccessful. There are several challenges and considerations associated with developing a prognostic micro-RNA signature for cervical cancer, namely: tumour heterogeneity, lack of concordance between frozen and FFPE specimens, and platform selection for global micro-RNA expression profiling in this disease. Our observations provide an important cautionary tale for future miRNA signature studies for cervical cancer, which can also be potentially applicable to miRNA profiling studies involving other types of human malignancies.
Notice of RetractionResearch on identification of coal and waste rock based on PCA and GA-ANN

When exploring identification of coal and waste rock, 17 characteristic parameters of gray-scale histogram and gray level co-occurrence matrix (GLCM) were chosen according to their differences in gray scale and texture. Then, the principal component analysis (PCA) algorithm was used to get principal components from all the parameters chosen above. The principal components were defined as the inputs of GA-ANN to identify the experimental samples of coal and waste rock. The identification rate reaches up to 100% through simulating experiments which proves the feasibility of the characteristic parameters and principal component analysis. Finally, by comparing the simulation results with BP neural network, the identification not only demonstrates the validity in extracting the principal components from the 17 parameters, but also shows that the GA-ANN algorithm is superior to the traditional BP neural network in pattern recognition. The application of PCA combined with GA-ANN provides a new method in intelligent identification for coal and waste rock.
Accessing Data Resources in the Mouse Phenome Database for Genetic Analysis of Murine Life Span and Health Span

Understanding the source of genetic variation in aging and using this variation to define the molecular mechanisms of healthy aging require deep and broad quantification of a host of physiological, morphological, and behavioral endpoints. The murine model is a powerful system in which to understand the relations across age-related phenotypes and to identify research models with variation in life span and health span. The Jackson Laboratory Nathan Shock Center of Excellence in the Basic Biology of Aging has performed broad characterization of aging in genetically diverse laboratory mice and has placed these data, along with data from several other major aging initiatives, into the interactive Mouse Phenome Database. The data may be accessed and analyzed by researchers interested in finding mouse models for specific aging processes, age-related health and disease states, and for genetic analysis of aging variation and trait covariation. We expect that by placing these data in the hands of the aging community that there will be (a) accelerated genetic analyses of aging processes, (b) discovery of genetic loci regulating life span, (c) identification of compelling correlations between life span and susceptibility for age-related disorders, and (d) discovery of concordant genomic loci influencing life span and aging phenotypes between mouse and humans.
Performance of Simulated Annealing, Tabu Search, and Evolutionary Algorithms for Multi-objective Network Partitioning

Most real optimization problems often involve multiple objectives to optimize. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of solutions, so called Pareto-optimal set. Thus, the goal of multi-objective strategies is to obtain an approximation to this set. However, the majority of this kind of problem cannot be solved exactly as they have very large and highly complex search spaces. In recent years, meta-heuristics have become important tools for solving multi-objective problems encountered in industry as well as in the theoretical field. Thus far, there exist many comparative studies about the performance of evolutionary algorithms, but are few the papers dealing with non-evolutionary strategies. The goal of this paper is to analyze the performance of both paradigms in a realistic problem. In concrete, we have adapted five multi-objective meta-heuristics, based on Simulated Annealing, Tabu Search, and Evolutionary Methods, to solve the Network Partitioning Problem.
Drosophotoxicology: An Emerging Research Area for Assessing Nanoparticles Interaction with Living Organisms

The rapid development of nanotechnology allowed the fabrication of a wide range of different nanomaterials, raising many questions about their safety and potential risks for the human health and environment. Most of the current nanotoxicology research is not standardized, hampering any comparison or reproducibility of the obtained results. Drosophotoxicology encompasses the plethora of methodological approaches addressing the use of Drosophila melanogaster as a choice organism in toxicology studies. Drosophila melanogaster model offers several important advantages, such as a relatively simple genome structure, short lifespan, low maintenance cost, readiness of experimental manipulation comparative to vertebrate models from both ethical and technical points of view, relevant gene homology with higher organisms, and ease of obtaining mutant phenotypes. The molecular pathways, as well as multiple behavioral and developmental parameters, can be evaluated using this model in lower, medium or high throughput type assays, allowing a systematic classification of the toxicity levels of different nanomaterials. The purpose of this paper is to review the current research on the applications of Drosophila melanogaster model for the in vivo assessment of nanoparticles toxicity and to reveal the huge potential of this model system to provide results that could enable a proper selection of different nanostructures for a certain biomedical application.
Uncertainty in identifying local extinctions: the distribution of missing data and its effects on biodiversity measures

Identifying local extinctions is integral to estimating species richness and geographic range changes and informing extinction risk assessments. However, the species occurrence records underpinning these estimates are frequently compromised by a lack of recorded species absences making it impossible to distinguish between local extinction and lack of survey effort—for a rigorously compiled database of European and Asian Galliformes, approximately 40% of half-degree cells contain records from before but not after 1980. We investigate the distribution of these cells, finding differences between the Palaearctic (forests, low mean human influence index (HII), outside protected areas (PAs)) and Indo-Malaya (grassland, high mean HII, outside PAs). Such cells also occur more in less peaceful countries. We show that different interpretations of these cells can lead to large over/under-estimations of species richness and extent of occurrences, potentially misleading prioritization and extinction risk assessment schemes. To avoid mistakes, local extinctions inferred from sightings records need to account for the history of survey effort in a locality.
Genetic Defects in the Growth Hormone–IGF-I Axis Causing Growth Hormone Insensitivity and Impaired Linear Growth

Human genetic defects in the growth hormone (GH)–IGF-I axis affecting the IGF system present with growth failure as their principal clinical feature. This is usually associated with GH insensitivity (GHI) presenting in childhood as severe or mild short stature. Dysmorphic features and metabolic abnormalities may also be present. The field of GHI due to mutations affecting GH action has evolved rapidly since the first description of the extreme phenotype related to homozygous GH receptor (GHR) mutations in 1966. A continuum of genetic, phenotypic, and biochemical abnormalities can be defined associated with clinically relevant defects in linear growth. The mechanisms of the GH–IGF-I axis in the regulation of normal human growth is discussed followed by descriptions of mutations in GHR, STAT5B, IGF-I, IGFALS, IGF1R, and GH1 defects causing bio-inactive GH or anti-GH antibodies. These GH–IGF-I axis defects are associated with a range of clinical, and hormonal characteristics. An up-dated approach to the clinical assessment of the patient with GHI focusing on investigation of the GH–IGF-I axis and relevant molecular studies contributing to the identification of causative genetic defects is also discussed.
Security challenges of the EPCglobal network

Introduction The "Internet of Things," once reality, will have to rely on a global IT infrastructure that provides information about all those "things" in a secure and reliable manner. The EPCglobal Network is a proposal for a widely distributed information system to offer such services. But it may introduce more challenges concerning security, privacy, and political control than was initially anticipated. If the vision of many RFID proponents becomes true, more and more common objects will soon acquire a cyber presence. Objects will be equipped with RFID tags containing identification data and possibly some additional information about the object in question (data on tag). To keep tag costs low, one may often just store an identifier and use it as a key to access databases containing the actual object information (data on network). This second approach is typical for "EPC tags"—RFID tags that aim to replace the conventional barcode system. They use an Electronic Product Code (EPC, see Figure 1), which is globally unique, as a key to retrieve information from the EPCglobal Network, envisioned as a large distributed system of databases. The EPC standard represents a numbering framework that is independent of specific hardware features, such as tag generation or specific radio frequency. The databases compromising the EPCglobal Network are to be run by manufacturers, logistic providers, retailers, or third parties, and can be accessed via special web services called EPC Information Services (EPCIS). The network architecture is designed and administered by the standardization consortium EPCglobal, which is a joint venture of GS1 U.S. (formerly Uniform Code Council) and GS1 (formerly EAN International). By improving the information flow, as objects pass from suppliers to manufacturers, distributors, retail stores, and customers, the EPCglobal Network aims to facilitate cooperation within supply chains and thus to make them more efficient. Once established, it could also be used to support a wide range of applications in the area of ubiquitous computing. An often-cited example is the "smart home," in which "intelligent" cupboards and fridges could be realized using RFID technology. By scanning the RFID tags on objects and using the EPCglobal Network for information retrieval, such devices can identify their current content and offer new services like food counseling or automated replenishing of goods. As a result of this broadened use of the EPCglobal Network, its security context would change from closed supply chains to the rather open environments of ubiquitous computing–just like the security context of the Internet was changed by moving from relatively closed groups of fellow researchers to the global environment it represents today. In this article, we first describe the EPCglobal Network architecture, as currently specified. We then discuss its security and privacy risks, as well as possible countermeasures. We conclude with suggestions on how to improve existing design proposals, once appropriate security and privacy requirements have been established.
Comparison of Relative Waist Circumference between Asian Indian and US Adults

Background. Relative to Europeans, Asian Indians have higher rates of type 2 diabetes and cardiovascular disease. Whether differences in body composition may underlie these population differences remains unclear. Methods. We compared directly measured anthropometric data from the Chennai Urban Rural Epidemiology Study (CURES) survey of southern Indians (I) with those from three US ethnic groups (C: Caucasians, A: African Americans, and M: Mexican Americans) from NHANES III (Third National Health and Nutrition Examination Survey). A total of 15,733 subjects from CURES and 5,975 from NHANES III met inclusion criteria (age 20–39, no known diabetes). Results. Asian Indian men and women had substantially lower body mass index, waist circumference, hip circumference, waist-to-hip ratio, and body surface area relative to US groups (P values <0.0001). In contrast, the mean (±se) waist-weight ratio was significantly higher (P < 0.001) in I (men 1.35 ± 0.002 and women 1.45 ± 0.002) than in all the US groups (1.09, 1.21, and 1.14 in A, M, and C men; 1.23, 1.33, and 1.26 in A, M, and C women (se ranged from 0.005 to 0.006)). Conclusions. Compared to the US, the waist-weight ratio is significantly higher in men and women from Chennai, India. These results support the hypothesis that Southeast Asian Indians are particularly predisposed toward central adiposity.
Fashion 10000: an enriched social image dataset for fashion and clothing

In this work, we present a new social image dataset related to the fashion and clothing domain. The dataset contains more than 32000 images, their context and social metadata. Furthermore the dataset is enriched with several types of annotations collected from the Amazon Mechanical Turk (AMT) crowdsourcing platform, which can serve as ground truth for various content analysis algorithms. This dataset has been successfully used at the Crowdsourcing task of the 2013 MediaEval Multimedia Benchmarking initiative. The dataset contributes to several research areas such as Crowdsourcing, multimedia content and context analysis as well as hybrid human/automatic approaches. In this paper, the dataset is described in detail and the dataset collection strategy, statistics, applications of dataset and its contribution to MediaEval 2013 is discussed.
ADAPTIVE CONTROL OF A MOBILE ROBOT FOR THE FRAIL VISUALLY IMPAIRED

This paper describes the development and evaluation of a novel robot mobility aid for frail Visually Impaired People (VIPs). Frailty makes the use of conventional mobility aids for the blind difficult or impossible and consequently VIPs are heavily dependent on carers for their personal mobility. In the context of a rapidly increasing proportion of elderly in the population this level of support may not always be available in the future. The aim of this research is to develop a robot that will increase the independence of frail VIPs. This paper will describe the walking aid and its overall control system. The controller adapts its operating mode to satisfy the constraints imposed by both the environment and the user using a probabilistic reasoning system. The reasoning system and the software architecture of the robot will be described in detail as will the evaluation of the robot in a residential home for visually impaired men. BACKGROUND Dual disability can severely limit the range of mobility aids a person may use. This is particularly true of the frail VIPs. 75% of VIPs are aged 65+ and frailty is also common among this age group. An estimate of the number of people can be achieved by analysing the survey data produced by Ficke [1]. His study of nursing home residents in the USA showed that of the 1.5 million residents, 22% were visually impaired and 70% had mobility impairments. His survey did not directly measure the incidence of dual disability however Rubin and Salive [2] have noted the correlation between visual impairment and frailty. Mobile robot technology has been applied in assistive technology to develop smart wheelchairs [6] [7] [8]. The mobility aid described in this paper, the Personal Adaptive Mobility AID (PAM-AID), aims to improve the independent mobility by assisting a frail VIP to take moderate exercise
Analysis of shapes to measure surfaces: An approach for detection of deformations

This paper presents a method to analyse 3D planar surfaces and to measure variations on it. The method is oriented to the detection of deformations on the elastic object surfaces formed by flat faces. These deformations are usually caused when two bodies, a solid and another elastic object, come in contact and there are contact pressures among their faces. Our method describes a strategy to model the shape of deformation using a mathematical approach based on two concepts: Histogram and Map of curvature. In particular, we describe the algorithm for deformations in order to use it in visual control and inspection tasks for manipulation processes with robot hands. Several experiments and their results are shown to evaluate the validity and robustness of the method to detect and measure deformations in grasping tasks. To do it, some virtual scenarios were created to simulate contacts with fingers of a hand robot.
Low voltage ride-through capabilities of wind plant combining different turbine technologies

In this paper, the low voltage ride-through (LVRT) capabilities of an hybrid wind plant is studied. The hybrid wind plant considered combines fixed speed wind turbines (FSWTs) based on classical induction generator and full power converter variable speed wind turbines (VSWTs) with permanent magnet generator. However, it must be able to respect grid code requirements. A specific control of the variable speed wind turbine power converters is proposed and described in this paper to support FSWTs during voltage dips: the gride side converter is used as a STATCOM during voltage drop to provide the maximum of reactive power. Simulation results show the ability of the wind plant to ride-through a voltage dip with this control.
Improving Bilayer Product Quantization for Billion-Scale Approximate Nearest Neighbors in High Dimensions

The top-performing systems for billion-scale high-dimensional approximate nearest neighbor (ANN) search are all based on two-layer architectures that include an indexing structure and a compressed datapoints layer. An indexing structure is crucial as it allows to avoid exhaustive search, while the lossy data compression is needed to fit the dataset into RAM. Several of the most successful systems use product quantization (PQ) for both the indexing and the dataset compression layers. These systems are however limited in the way they exploit the interaction of product quantization processes that happen at different stages of these systems. Here we introduce and evaluate two approximate nearest neighbor search systems that both exploit the synergy of product quantization processes in a more efficient way. The first system, called Fast Bilayer Product Quantization (FBPQ), speeds up the runtime of the baseline system (Multi-D-ADC) by several times, while achieving the same accuracy. The second system, Hierarchical Bilayer Product Quantization (HBPQ) provides a significantly better recall for the same runtime at a cost of small memory footprint increase. For the BIGANN dataset of billion SIFT descriptors, the 10% increase in Recall@1 and the 17% increase in Recall@10 is observed.
Neural Network Classifiers for Optical Chinese Character Recognition

We describe a new, publicly accessible Chinese character recognition system based on a nearest neighbor classifier that utilizes a number of sophisticated techniques to improve its performance. To increase throughput, a 400dimensional feature space is compressed through multiple discriminant analysis techniques to 100 dimensions. Recognition accuracy is improved by scaling these dimensions to achieve uniform variance. Two neural network classifiers are compared using the new feature space, Kohonen’s Learning Vector Quantization and Geva and Sitte’s Decision Surface Mapping. Experiments with a 37,000 character ground truthed dataset show performance comparable to other systems in the literature. We are now employing noise and distortion models to quantify the robustness of the recognizer on realistic page images.