Miracle-dz/UniFAR-FTUs huggingface dataset:
Regularized eigenspace-based gait recogntion system for human identification

Gait has received much interest from the biometric society in the vision field due to its utility in walker identification. In this paper, we present a regularized eigenspace-based gait recognition system for human identification. First, motion contour image (MCI) is extracted from walking sequences. In training phase, eigenfeature regularization and extraction (ERE) is applied to the gallery motion contour image to obtain regularized transformation matrix and gallery features. In test phase, regularized transformation matrix is applied to project motion contour image into the eigenspace to obtain probe features, and determine the identity based on the result of nearest neighbor classifier. Experiments are performed with the CASIA gait database A to evaluate the performance of the proposed gait recognition system. Experimental results justify the superiority of our proposed system in terms of correct classification rate.
Sightseeing Assistance via Information Inheritance with Journey Note System in Sightseeing Spots

— This research proposes a sightseeing assistance system in which tourists inherit information from previous tourists at particular spots in a sightseeing area. The proposed system provides users with a sightseeing service in order to promote new discoveries, actions, and interest through information inheritance. This has the same concept as " journey notes " , which are available at facilities such as hotels. People can browse and post information by visiting these places. In addition, the posted information has a " life span " according to each genre. The " life span " is extended depending on evaluation by tourists. As a result, valuable information remains for longer, and non-valuable information dies out. In this way, by each tourist inheriting information which they obtained in the sightseeing area, this system provides instant and novel sightseeing.
Rapid and efficient identification of the mouse leptin receptor mutation (C57BL/KsJ-db/db) by tetra-primer amplification refractory mutation system-polymerase chain reaction (ARMS-PCR) analysis

The C57BLKS/J-Leprdb mouse has a point mutation in the leptin receptor gene and is one of the most useful animal model for non-insulin dependent diabetes mellitus in human. Since the homozygote of C57BLKS/J-Leprdb mouse is infertile, detection of point mutation in the leptin receptor gene is important for efficient maintaining strains as well as mass production of homozygotes. To develop a rapid and efficient genotyping method for C57BLKS/J-Leprdb mouse, the tetra-primer amplification-refractory mutation system polymerase chain reaction (ARMS-PCR) was used. The 407 and 199 bp PCR products were amplified from normal (+/+) mice; while the 407 and 268 bp PCR products were amplified from homozygotes (db/db) mice; and the 407, 268, and 199 bp PCR products were amplified from heterozygotes (db/+) mice. This result showed that the tetra-primer ARMS-PCR analysis by us is suitable to detect point mutation of the leptin receptor gene. Taken together, our method will dramatically reduce animal use for maintenance of strains as well as production of homozygote in the C57BLKS/J-Leprdb strains.
Alcohol and criminal behaviour.

Substance abuse is a factor in some but not all incidents of crime: more than half of murders are committed when the killer is intoxicated. Males are more likely than females to be violent when consuming alcohol, and a past history of violence is predictive of future crime. The risk of violent behaviour is greater in subjects with dual diagnosis (e.g. alcohol dependence and psychiatric disorders). Offenders with an intellectual disability are overrepresented in prison and court populations, and more than 50% of them have a problem with alcohol. Since these subjects, in addition to intellectual disability and alcohol use, have psychiatric and behavioural problems, early recognition, in an effort to prevent future crimes, is an appropriate goal. Specific programmes have to address their particular needs.
Efficient Decomposition of Cosmic Microwave Background Polarization Maps into Pure E, Pure B, and Ambiguous Components.

Separation of the B component of a cosmic microwave background (CMB) polarization map from the much larger E component is an essential step in CMB polarimetry. For a map with incomplete sky coverage, this separation is necessarily hampered by the presence of ambiguous modes which could be either E or B modes. I present an efficient pixel-space algorithm for removing the ambiguous modes and separating the map into pure E and B components. The method, which works for arbitrary geometries, does not involve generating a complete basis of such modes and scales the cube of the number of pixels on the boundary of the map.
Mental illness stigma: experiences of youth with a mental disorder

Canadian society continues to stigmatize individuals with mental illness, despite the prevalence of mental illness in the population, decades of advocacy to combat mental illness stigma, and known negative sequelae of experiences of stigma by people affected by a mental illness. One negative impact of stigma is internalization of negative connotations attached to mental illness. Although there is extensive research on the stigma of mental illness, there is little information specifically about how youth with mental illness perceive the stigma of mental illness and how they respond to it. The current research sought to understand how youth who self-identify as having a mental illness experienced, perceived and internalized the stereotypes, prejudice and discrimination of the stigma of mental illness. A simple content categorization method was used to identify key themes in the transcribed interviews of eleven youth in Vancouver Canada who identified as having mental illness. Qualitative analysis identified that the most frequent perpetrators of public stigma included casual acquaintances, family, friends, school staff, mental health professionals and authority figures. Youths’ accounts linked mental illness stigma with low mental health literacy, delayed mental health treatment and a low quality of life. The findings are considered with reference to implications for prevention of stigma, including enhanced mental health literacy for mental health professionals and the public aimed at increased understanding, sensitivity and empowerment of youth with mental illness and their families.
Preconditioning for the Steady-State Navier-Stokes Equations with Low Viscosity

We introduce a preconditioner for the linearized Navier--Stokes equations that is effective when either the discretization mesh size or the viscosity approaches zero. For constant coefficient problems with periodic boundary conditions, we show that the preconditioning yields a system with a single eigenvalue equal to 1, so that performance is independent of both viscosity and mesh size. For other boundary conditions, we demonstrate empirically that convergence depends only mildly on these parameters and we give a partial analysis of this phenomenon. We also show that some expensive subsidiary computations required by the new method can be replaced by inexpensive approximate versions of these tasks based on iteration, with virtually no degradation of performance.
A note from the editors : changes and stability – looking back to 2012 and forward to 2013

Editorials A note from the editors: changes and stability – looking At the end of every year, editors reflect on their activities , count their successes and check if they met the goals set for their journals. When we think about 2012, one of our achievements stands out – our first impact factor (for 2011). With the allocated 6.15, we shot far beyond our expectations and were placed among the top-10 leading journals in our field [1]. Interestingly, most of the competing journals have profit-oriented business models, different from Eurosurveillance, which has a non-commercial publisher, the European Centre for Disease Prevention and Control (ECDC). The attention gained through the impact factor resulted in a steep increase in the number of articles submitted to the journal. This increase concerned mainly regular articles (462 compared with 247 in 2011). While the vast majority of submissions were from Europe, considerably more articles came also from Asia and Africa. We are thankful that authors consider Eurosurveillance a good channel to disseminate their research findings. Of course, the high number of submissions has impacted on our daily routines and on those who collaborate with us. In 2012, more experts than in previous years were contacted and agreed to support us with their scientific judgment about the merits of papers submitted. Some 500 experts (2011: some 380, 2010: some 330) from across the world dedicated time, often on short notice and with tight deadlines, to review for us. We are grateful for their assistance in the evaluation of submissions. In this issue, we publish a list with the names of our peer reviewers in 2012 [2]. We continue to receive informal guidance from our board members, both associate editors and editorial advisors, numerous colleagues at ECDC and other scientists. Even though they remain unnamed here, we highly appreciate their willingness to help and inspire us. While we had more contacts with our reviewers and authors in 2012, we worked hard to keep up with timely publication and short turnover times. We published 100 rapid communications, 86 regular papers, 14 editorials , and 42 letters and other content. The rejection rate 43% for rapid communications and 76% for regular papers. However, this should not discourage those who have important data and research from submitting them to us because one of our main objectives remains to contribute to a balanced scientific evidence-base in the fields of epidemiology, surveillance, …
Channel Sensing Algorithm Based on Neural Networks for Cognitive Wireless Mesh Networks

Spectrum is always a kind of rare resource in wireless communication especially in wireless mesh networks (WMNs) which own the wireless connectivity backbone. But in most cases we seldom take full advantage of the spectrum. This problem can be alleviated by the cognitive radio paradigm that aims at devising spectrum sensing and management techniques, thereby allowing radios to intelligently locate and use frequencies other than those in the ISM band. In cognitive radio, channel sensing should be able to detect the signals from other systems which are using the same frequency band in order to avoid the mutual interference, which is so important that determine the cognitive radio success. In this paper, we propose a channel sensing algorithm based on ART-2 (adaptive resonance theory) neural networks which well satisfy the WMNs structure. Simulation results show that the proposed algorithm can give the more correct sensing results and remarkably improves the cognitive WMNs performance.
The Effects of a Computer-Aided Listening Comprehension Intervention on the Generalized Communication of Students With Autism Spectrum Disorder and Intellectual Disability

In this study, a computer-aided listening comprehension intervention package supported both listening comprehension and communication goals for three students with autism spectrum disorder (ASD) and intellectual disability (ID). The package consisted of systematic instruction (i.e., system of least prompts [SLP] procedure) to teach listening comprehension, an iPad-supported electronic communication book, and a peer support arrangement. The students with ASD and ID who participated in the study increased both listening comprehension and communication skills, while showing an increase in generalizing communication turns to interactions with their peers without disabilities. The researchers found a functional relation between the SLP procedure and both dependent variables. All three participants experienced concurrent growth between the dependent variables, implying a connection between text-based listening comprehension and communication outcomes. Further implications for academic instruction for students with ASD who use augmentative and alternative communication as well as for future inquiry concerning cross-modal generalization to social communication discourse are discussed.
Models of Primary Care and Appraisal Frameworks

Abstract The Models of Child Health Appraised (MOCHA) project identified the different models of primary care that exist for children, examined the particular attributes that might be different from those directed at adults and considered how these models might be appraised. The project took the multiple and interrelated dimensions of primary care and simplified them into a conceptual framework for appraisal. A general description of the models in existence in all 30 countries of the EU and EEA countries, focusing on lead practitioner, financial and regulatory and service provision classifications, was created. We then used the WHO ‘building blocks’ for high-performing health systems as a starting point for identifying a good system for children. The building blocks encompass safe and good quality services from an educated and empowered workforce, providing good data systems, access to all necessary medical products, prevention and treatments, and a service that is adequately financed and well led. An extensive search of the literature failed to identify a suitable appraisal framework for MOCHA, because none of the frameworks focused on child primary care in its own right. This led the research team to devise an alternative conceptualisation, at the heart of which is the core theme of child centricity and ecology, and the need to focus on delivery to the child through the life course. The MOCHA model also focuses on the primary care team and the societal and environmental context of the primary care system.
LOT-ECC: Localized and tiered reliability mechanisms for commodity memory systems

Memory system reliability is a serious and growing concern in modern servers. Existing chipkill-level memory protection mechanisms suffer from several draw-backs. They activate a large number of chips on every memory access - this increases energy consumption, and reduces performance due to the reduction in rank-level parallelism. Additionally, they increase access granularity, resulting in wasted bandwidth in the absence of sufficient access locality. They also restrict systems to use narrow-I/O ×4 devices, which are known to be less energy-efficient than the wider ×8 DRAM devices. In this paper, we present LOT-ECC, a localized and multi-tiered protection scheme that attempts to solve these problems. We separate error detection and error correction functionality, and employ simple checksum and parity codes effectively to provide strong fault-tolerance, while simultaneously simplifying implementation. Data and codes are localized to the same DRAM row to improve access efficiency. We use system firmware to store correction codes in DRAM data memory and modify the memory controller to handle data mapping. We thus build an effective fault-tolerance mechanism that provides strong reliability guarantees, activates as few chips as possible (reducing power consumption by up to 44.8% and reducing latency by up to 46.9%), and reduces circuit complexity, all while working with commodity DRAMs and operating systems. Finally, we propose the novel concept of a heterogeneous DIMM that enables the extension of LOT-ECC to ×16 and wider DRAM parts.
An Information Retrieval Model for the Semantic Web

Exploring the metadata associated with documents in the Semantic Web is a way to increase the precision of information retrieval systems. Systems have been established so far failed to overcome fully the limitations of search based on keywords. Such systems are built from variations of classic models that represent information by keywords and work upon statistical correlations. This work proposes an information retrieval model to find information items with similar semantic content that a given user’s query. The information items internal representation is based on user interest groups, called "semantic cases". The model also defines a similarity measure for ordering the results based on semantic distance between semantic cases items.
Distributed Aggregation in the Presence of Uncertainty: A Statistical Physics Approach

We present a statistical physics inspired approach to modeling, analysis, and design of distributed aggregation control policies for teams of homogeneous and heterogeneous robots. We assume high-level agent behavior can be described as a sequential composition of lower-level behavioral primitives. Aggregation or division of the collective into distinct clusters is achieved by developing a macroscopic description of the ensemble dynamics. The advantages of this approach are twofold: 1) the derivation of a low dimensional but highly predictive description of the collective dynamics and 2) a framework where interaction uncertainties between the low-level components can be explicitly modeled and control. Additionally, classical dynamical systems theory and control theoretic techniques can be used to analyze and shape the collective dynamics of the system. We consider the aggregation problem for homogeneous agents into clusters located at distinct regions in the workspace and discuss the extension to heterogeneous teams of autonomous agents. We show how a macroscopic model of the aggregation dynamics can be derived from agent-level behaviors and discuss the synthesis of distributed coordination strategies in the presence of uncertainty.
Adhesion molecules and differentiation syndrome: phenotypic and functional analysis of the effect of ATRA, As2O3, phenylbutyrate, and G-CSF in acute promyelocytic leukemia

Background and Objectives Differentiation Syndrome (DS) is a treatment complication which can occur in patients treated with acute promyelocytic leukemia (APL) with all transretinoic acid (ATRA) or As2O3, and is characterized by enhanced leukocyte transmigration. As2O3, Phenylbutyrate (PB) and G-CSF are known to potentiate ATRA effects. Our aim was to analyze the changes in expression and function of adhesion molecules induced by ATRA, As2O3, G-CSF and PB, and their association. Design and Methods APL blasts and NB4 cells were treated with ATRA, As2O3, PB, G-CSF or their association and the expression of adhesion molecules was determined by flow cytometry. Cell adhesion was evaluated in vitro using Matrigel and for the in vivo analysis, Balb-c mice were injected with NB4 cells pre-treated with ATRA, As2O3, ATRA+G-CSF or ATRA+As2O3. In addition, CD54 and CD18 knock-out mice were injected with NB4 cells and concomitantly treated with ATRA. In both models, the MPO activity in the lungs was determined 6 hours after the injection of the cells. Results In NB4 and APL blasts, ATRA and As2O3 increased CD54 expression, but no synergism was detected. CD11b and CD18 were also up-regulated by ATRA in primary cells. PB and G-CSF had no effect, but the latter potentiated ATRA-induced CD18 up-regulation. These changes were accompanied by increased adhesion to Matrigel and to lung microvasculature, and reversed by anti-CD54, anti-CD18 antibodies. In CD54 and CD18 knock-out mice the ATRA effect was canceled. Interpretation and Conclusions The use of As2O3, PB and G-CSF in association with ATRA should not aggravate DS in APL.
A Two-surface Discrete Sliding Mode Control based on Approach Angle Reaching Law

Sliding mode controllers are known to be robust to parameter changes, disturbances and uncertainties. In this paper a novel discrete time sliding mode controller with two surfaces is proposed to improve the speed of response. Here, approach angle reaching law is used to take the trajectory to the surfaces. The advantage is that the system trajectory is bought to the first surface with maximum velocity thereby shortening the reaching phase. The scheme assures robustness, asymptotic stability and fast transients with minimum chattering.
Interculturality in Online Learning: Instructor and Student Accommodations

As more distance education courses broaden their reach across borders, the chances of online classes being multicultural are high. This means that more often students may find themselves in courses designed for and by a host culture that differs from their own regarding its approach to teaching and learning. Compounding the difficulties inherent in a novel cultural environment and its unfamiliar forms of academic discourse are challenges represented by the medium itself. This year-long case study examines the experiences and reflections of a Chinese graduate student and her U.S. instructor; specifically the accommodations both made to mediate differences and mitigate these challenges. Analyses suggest that when both parties take differences into account and exercise thoughtful accommodations relative to both the challenges of the online medium and language socialization, positive learning experiences can result.
Interest rate rules in a macroeconometric model of a small open economy

We evaluate the performance of Taylor type interest rate rules in a small scale macroeconometric model of the Norwegian economy based on coun- terfactual simulations over the period from 1995q1 to 2000q4. We employ inter alia "real time" interest rate rules based on variables less prone to measurement errors, and "open economy" rules allowing for interest rate re- sponses to exchange rate misalignments. Both backward-looking rules and forward-looking rules are investigated. The performance of the employed rules is evaluated by standard efficiency measures and by deriving the mean deviations from targets, which may be of interest for policy makers, es- pecially over short time horizons. A statistical measure which takes into account both the variability and the bias is the root mean squared target error (RMSTE), an analogue to the well known root mean squared forecast error. Finally we conduct simulation experiments where we vary the weights in the interest rate rules as well as the weights of the variables in the pol- icymaker's loss function. The results are summarized in response surfaces estimated over the range of weights considered in the simulations.
Feature Extraction Using Memristor Networks

Crossbar arrays of memristive elements are investigated for the implementation of dictionary learning and sparse coding of natural images. A winner-take-all training algorithm, in conjunction with Oja's rule, is used to learn an overcomplete dictionary of feature primitives that resemble Gabor filters. The dictionary is then used in the locally competitive algorithm to form a sparse representation of input images. The impacts of device nonlinearity and parameter variations are evaluated and a compensating procedure is proposed to ensure the robustness of the sparsification. It is shown that, with proper compensation, the memristor crossbar architecture can effectively perform sparse coding with distortion comparable with ideal software implementations at high sparsity, even in the presence of large device-to-device variations in the excess of 100%.
Data prefetching in a cache hierarchy with high bandwidth and capacity

In this paper we evaluate four hardware data prefetchers in the context of a high-performance three-level on chip cache hierarchy with high bandwidth and capacity. We consider two classic prefetchers (Sequential Tagged and Stride) and two correlating prefetchers: PC/DC, a recent method with a superior score and low-sized tables, and P-DFCM, a new method. Like PC/DC, P-DFCM focuses on local delta sequences, but it is based on the DFCM value predictor. We explore different prefetch degrees and distances. Running SPEC2000, Olden and IAbench applications, results show that this kind of cache hierarchy turns prefetching aggressiveness into success for the four prefetchers. Sequential Tagged is the best, and deserves further attention to cut it losses in some applications. PC/DC results are matched or even improved by P-DFCM, using far fewer accesses to tables while keeping sizes low.