Miracle-dz/UniFAR-FTUs huggingface dataset:
Human motion reconstruction from sparse 3D motion sensors using kernel CCA‐based regression

This paper presents a real‐time performance animation system that reproduces full‐body character animation based on sparse three‐dimensional (3D) motion sensors on a performer. Producing faithful character animation from this setting is a mathematically ill‐posed problem, because input data from the sensors are not sufficient to determine the full degrees of freedom of a character. Given the input data from 3D motion sensors, we select similar poses from a motion database and build an online local model that transforms the low‐dimensional input signal into a high‐dimensional character pose. A regression method based on kernel canonical correlation analysis (CCA) is employed, because it effectively handles a wide variety of motions. Examples show that various human motions are naturally reproduced by the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.
An Estimation of Fuel Demand Elasticities for Spain An Aggregated Panel Approach Accounting for Diesel Share

In this paper, an econometric analysis of transport fuel demand elasticities is presented for Spain, for the period 2000 to 2007, giving emphasis to the diesel share of the road transport fleet. Estimation results show a significant impact of the share of diesel-powered vehicles, when considering the specific gasoline and diesel demand, but the same does not hold when considering the total fuel demand, where the impact is much lower. In addition, a simple tax reform simulation is carried out, showing that greater savings in CO 2 emissions are achievable only in the long run. © 2015 LSE and the University of Bath
Using the 5W+1H Model in Reporting Systematic Literature Review: A Case Study on Software Testing for Cloud Computing

This paper documents a case study of using the 5W+1H model for reporting systematic literature review on software testing for cloud computing. To our knowledge, this is the first systematic literature review that applies the 5W+1H model, which is widely used in the journalism domain, to report the full picture of the research area in both software engineering and services computing. Existing guidelines on systematic literature review heavily rely on the researcher to pose the right research questions, and the review results are tightly focused on these research questions. For researchers new to a field, defining the right research questions that are effective in revealing the critical issues in the field can be challenging. Our case study demonstrates that the 5W+1H model provides an easy aid for the researcher to get over such initial challenges. As the researcher becomes more familiar with the field, he/she may then refine the research questions by adding more topic-specific contexts. In this way, the 5W+1H model serves to provide an exploratory framework to shape a systematic literature review. Applying to software testing for cloud computing, we are able to synthesize a comprehensive picture of recent researches on the field, including publication pattern, article citation immediacy, research topic diversity, research ideas for addressing testing challenges at different cloud service architectural layers. Based on the case study, we summarize the lessons learned on using the 5W+1H model in reporting systematic literature review.
Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex

It has been empirically established that the cerebral cortical areas defined by Brodmann one hundred years ago solely on the basis of cellular organization are closely correlated to their function, such as sensation, association, and motion. Cytoarchitectonically distinct cortical areas have different densities and types of neurons. Thus, signaling patterns may also vary among cytoarchitectonically unique cortical areas. To examine how neuronal signaling patterns are related to innate cortical functions, we detected intrinsic features of cortical firing by devising a metric that efficiently isolates non-Poisson irregular characteristics, independent of spike rate fluctuations that are caused extrinsically by ever-changing behavioral conditions. Using the new metric, we analyzed spike trains from over 1,000 neurons in 15 cortical areas sampled by eight independent neurophysiological laboratories. Analysis of firing-pattern dissimilarities across cortical areas revealed a gradient of firing regularity that corresponded closely to the functional category of the cortical area; neuronal spiking patterns are regular in motor areas, random in the visual areas, and bursty in the prefrontal area. Thus, signaling patterns may play an important role in function-specific cerebral cortical computation.
Generation of energy efficient trajectories for NIMS3D, a three-dimensional cabled robot

In this paper we describe an algorithm to generate energy efficient trajectories for NIMS3D, a three-dimensional cabled robotic platform. Optimized parabolic paths are used to exploit the relatively low I2R loss associated with operation in lower regions of the workspace. Trajectory optimization is sufficiently fast to enable real time operation. Experimental results on a physical system for a three cable deployment show substantial reductions in energy consumption as compared to linear trajectories.
Analysis of Multiconductor Coupled-Line Marchand Baluns for Miniature MMIC Design

The analysis and systematic design procedure for multiconductor coupled-line Marchand baluns are presented in this paper. A simple two-conductor coupled-line model is used to analyze the Marchand balun and simplify the analysis significantly. Two monolithic balanced frequency doublers with miniature Marchand baluns are implemented to verify the design procedure. Both the chips achieve the smallest chip sizes at their operating frequencies with comparable performance.
CHARACTERIZING YEAR 11 STUDENTS’ EVALUATION OF A STATISTICAL PROCESS

Evaluating the statistical process is considered a higher order skill and has received little emphasis in instruction. This study analyses thirty 15-year-old students’ responses to two statistics assessment tasks, which required evaluation of a statistical investigation. The SOLO taxonomy is used as a framework to develop a hierarchy of responses. Focusing on the quality of response allowed insight into and suggestions for how instruction might be improved. The implications for teaching, assessment, and the curriculum are discussed.
The TRPV1 receptor is associated with preferential stress in large dorsal root ganglion neurons in early diabetic sensory neuropathy

Chronic diabetic neuropathy is associated with peripheral demyelination and degeneration of nerve fibers. The mechanism(s) underlying neuronal injury in diabetic sensory neuropathy remain poorly understood. Recently, we reported increased expression and function of transient receptor potential vanilloid 1 (TRPV1) in large dorsal root ganglion (DRG) neurons in diabetic sensory neuropathy. In this study, we examined the effects of TRPV1 activation on cell injury pathways in this subpopulation of neurons in the streptozotocin‐induced diabetic rat model. Large DRG neurons from diabetic (6–8 weeks) rats displayed increased oxidative stress and activation of cell injury markers compared with healthy controls. Capsaicin (CAP) treatment induced decreased labeling of MitoTracker Red and increased cytosolic cytochrome c and activation of caspase 3 in large neurons isolated from diabetic rats. CAP treatment also induced oxidative stress in large diabetic DRG neurons, which was blocked by pre‐treatment with caspase or calpain inhibitor. In addition, both μ‐calpain expression and calpain activity were significantly increased in DRG neurons from diabetic rats after CAP treatment. Treatment with capsazepine, a competitive TRPV1 antagonist, markedly reduced these abnormalities in vitro and prevented activation of cell injury in large DRG neurons in diabetic rats in vivo. These results suggest that activation of the TRPV1 receptor activates pathways associated with caspase‐dependent and calpain‐dependent stress in large DRG neurons in STZ‐diabetic rats. Activation of the TRPV1 receptor may contribute to preferential neuronal stress in large DRG neurons relatively early in diabetic sensory neuropathy.
Improving FFT Frequency Measurement Resolution by Parabolic and Gaussian Spectrum Interpolation

Discrete spectra can be used to measure frequencies of sinusoidal signal components. Such a measurement consists of digitizing a compound signal, performing windowing of the signal samples and computing their discrete magnitude spectrum, usually by means of the Fast Fourier Transform algorithm. Frequencies of individual components can be evaluated from their locations in the discrete spectrum with a resolution depending on the number of samples. However, the frequency of a sinusoidal component can be determined with improved resolution by fitting an interpolating parabola through the three largest consecutive spectrum bins corresponding to the component. The abscissa of its maximum constitutes a better frequency approximation. Such a method has been used for tune measurement systems in circular accelerators. This paper describes the efficiency of the method, depending on the windowing function applied to the signal samples. A typical interpolation gain is one order of magnitude. Better results are obtained with Gaussian interpolation, offering frequency resolution improvement by more than two orders of magnitude when used with windows having fast sidelobe decay. An improvement beyond three orders of magnitude is possible with steep Gaussian windows. These results are confirmed by laboratory measurements. Both methods assume the measured frequency to be constant during acquisition and the spectral peak corresponding to the measured component to constitute a local maximum in a given band of the input signal discrete spectrum.
Relationship of family employment status to husband and wife marital satisfaction and sex role preferences

The purpose of the study was to investigate the sex role preferences and marital satisfaction of husbands and wives in dual career, dual earner, and single earner family types. The study was descriptive and utilized the survey approach in obtaining responses from both husbands and wives in the three differing occupational types. The total sample consisted of 196 respondents. Sex role preferences were measured by an instrument developed by Scanzoni (1970) and marital satisfaction scores were measured by the Dyadic Adjustment Scale (Spanier, 1976). Occupational types were delineated by both the husbands’ and wives’ current work, status. All families surveyed had at least one preschoolaged child and were in the early parenthood stages of the family life cycle. When measuring both marital satisfaction and sex role preference scores, occupational type was found to be a significant factor.
A Passage Retrieval System using Query Expansion and Emphasis

We developed a patent retrieval system with the corresponding very large number of patents from NTCIR6 Patent Retrieval Task. And we developed a method of refining and emphasizing query. Our retrieval system consisting of four PCs could make indices of all claims in specifications for ten years. Then we confirmed that the query emphasis was better mean average precision than merely query expansion. And we had tried to reduce the number of results with the belief assessment.
Stable reagents for determination of serum triglycerides by a colorimetric Hantzsch condensation method.

We describe a modified method for determining triglycerides in serum, in which alumina is used to adsorb interfering substances, and the glycerol content of triglycerides is determined colorimetrically. In contrast to all previous Hantzsch condensation procedures in which periodate and acetylacetone are used, the working periodate and the working acetylacetone reagents are stable for at least six months because they are differently formulated. The principle that governs this increased stability applies to all Hantzsch condensation methods for triglycerides.
Molecular and Biochemical Characterization of Two Xylanase-Encoding Genes from Cellulomonas pachnodae

ABSTRACT Two xylanase-encoding genes, named xyn11A andxyn10B, were isolated from a genomic library ofCellulomonas pachnodae by expression in Escherichia coli. The deduced polypeptide, Xyn11A, consists of 335 amino acids with a calculated molecular mass of 34,383 Da. Different domains could be identified in the Xyn11A protein on the basis of homology searches. Xyn11A contains a catalytic domain belonging to family 11 glycosyl hydrolases and a C-terminal xylan binding domain, which are separated from the catalytic domain by a typical linker sequence. Binding studies with native Xyn11A and a truncated derivative of Xyn11A, lacking the putative binding domain, confirmed the function of the two domains. The second xylanase, designated Xyn10B, consists of 1,183 amino acids with a calculated molecular mass of 124,136 Da. Xyn10B also appears to be a modular protein, but typical linker sequences that separate the different domains were not identified. It comprises a N-terminal signal peptide followed by a stretch of amino acids that shows homology to thermostabilizing domains. Downstream of the latter domain, a catalytic domain specific for family 10 glycosyl hydrolases was identified. A truncated derivative of Xyn10B bound tightly to Avicel, which was in accordance with the identified cellulose binding domain at the C terminus of Xyn10B on the basis of homology. C. pachnodae, a (hemi)cellulolytic bacterium that was isolated from the hindgut of herbivorous Pachnoda marginata larvae, secretes at least two xylanases in the culture fluid. Although both Xyn11A and Xyn10B had the highest homology to xylanases from Cellulomonas fimi, distinct differences in the molecular organizations of the xylanases from the twoCellulomonas species were identified.
From one tree to a forest: a unified solution for structured web data extraction

Structured data, in the form of entities and associated attributes, has been a rich web resource for search engines and knowledge databases. To efficiently extract structured data from enormous websites in various verticals (e.g., books, restaurants), much research effort has been attracted, but most existing approaches either require considerable human effort or rely on strong features that lack of flexibility. We consider an ambitious scenario -- can we build a system that (1) is general enough to handle any vertical without re-implementation and (2) requires only one labeled example site from each vertical for training to automatically deal with other sites in the same vertical? In this paper, we propose a unified solution to demonstrate the feasibility of this scenario. Specifically, we design a set of weak but general features to characterize vertical knowledge (including attribute-specific semantics and inter-attribute layout relationships). Such features can be adopted in various verticals without redesign; meanwhile, they are weak enough to avoid overfitting of the learnt knowledge to seed sites. Given a new unseen site, the learnt knowledge is first applied to identify page-level candidate attribute values, while inevitably involve false positives. To remove noise, site-level information of the new site is then exploited to boost up the true values. The site-level information is derived in an unsupervised manner, without harm to the applicability of the solution. Promising experimental performance on 80 websites in 8 distinct verticals demonstrated the feasibility and flexibility of the proposed solution.
A simple and robust scoring technique for binary classification

A new simple scoring technique is developed in a binary supervised classification context when only a few observations areavailable. It consists in two steps: in the first one partial scores are obtained, one for each predictor, either categorical orcontinuous. Each partial score is a discrete variable with 7 values ranging from -3 to 3, based upon an empirical comparison ofthe distributions for each class. In a second step the partial scores are added and standardised into a global score, which allowsa decision rule.This simple technique is successfully compared with classical supervised techniques for a classical benchmark and has beenproved to be especially well fitted in an industrial problem.
Fuzzy Q-Learning with an adaptive representation

Reinforcement learning (RL) is learning how to map states to actions so as to maximise a numeric reward signal. Fuzzy Q-learning (FQL) extends the RL technique Q-learning to large or continuous problems and has been applied to a wide range of applications from data mining to robot control. Typically, FQL uses a uniform or pre-defined internal representation provided by the human designer. A uniform representation usually provides poor generalisation for control applications, and a pre-defined representation requires the designer to have an in-depth knowledge of the desired control policy. In this paper, the approach taken is to reduce the reliance on a human designer by adapting the internal representation, to improve the generalisation over the control policy, during the learning process. A hierarchical fuzzy rule based system (HFRBS) is used to improve the generalisation of the control policy through iterative refinement of an initial coarse representation on a classical RL problem called the mountain car problem. The process of adapting the representation is shown to significantly reduce the time taken to learn a suitable control policy.
Odenplan: a media façade design process

In this paper we present an example of how to work with the challenges inherent in media façade design processes. We base the paper on our experiences from the creation of a series of design proposals for a media façade on the Odenplan subway station in Stockholm, Sweden. We approach the question of how to design for media façades by discussing how we have structured our design process to address specific sets of challenges outlined in previous literature in the field of media architecture. In our view, such research is valuable in that it helps establish common ground for researchers and practitioners in a developing field by building a repertoire of approaches, as well as highlight important issues that need to be addressed in this emergent field.
Rethinking the Knowledge-Based Organization

Many companies have embraced the notion that to operate effectively in today's economy, it is necessary to become a knowledge-based organization. But few truly understand what that means or how to carry out the changes required to bring it about. Perhaps the most common misunderstanding is the view that the more a company's products or services have knowledge at their core, the more the organization is, by definition, knowledge based. But products and services are only what are visible or tangible to customers ? they're the tip of the iceberg. Like the iceberg, most of what enables a company to produce anything lies below the surface, hidden within the so-called invisible assets of the organization ? its knowledge about what it does, how it does it and why. In the course of working with more than 30 companies over the past eight years, the author found that a knowledge-based organization is made up of four elements. Each one forms a basis for evaluating the degree to which knowledge is an integral part of the organization and the way it competes. Executives who understand how the four elements interact will be able to start changing their companies to take advantage of the intellectual assets hidden below the surface.
Automation and integration of components for generalized semantic markup of electronic medical texts

Our group has built an information retrieval system based on a complex semantic markup of medical textbooks. We describe the construction of a set of web-based knowledge-acquisition tools that expedites the collection and maintenance of the concepts required for text markup and the search interface required for information retrieval from the marked text. In the text markup system, domain experts (DEs) identify sections of text that contain one or more elements from a finite set of concepts. End users can then query the text using a predefined set of questions, each of which identifies a subset of complementary concepts. The search process matches that subset of concepts to relevant points in the text. The current process requires that the DE invest significant time to generate the required concepts and questions. We propose a new system--called ACQUIRE (Acquisition of Concepts and Queries in an Integrated Retrieval Environment)--that assists a DE in two essential tasks in the text-markup process. First, it helps her to develop, edit, and maintain the concept model: the set of concepts with which she marks the text. Second, ACQUIRE helps her to develop a query model: the set of specific questions that end users can later use to search the marked text. The DE incorporates concepts from the concept model when she creates the questions in the query model. The major benefit of the ACQUIRE system is a reduction in the time and effort required for the text-markup process. We compared the process of concept- and query-model creation using ACQUIRE to the process used in previous work by rebuilding two existing models that we previously constructed manually. We observed a significant decrease in the time required to build and maintain the concept and query models.
New algorithms for decoding in the rank metric and an attack on the LRPC cryptosystem

We consider the decoding problem or the problem of finding low weight codewords for rank metric codes. We show how additional information about the codeword we want to find under the form of certain linear combinations of the entries of the codeword leads to algorithms with a better complexity. This is then used together with a folding technique for attacking a McEliece scheme based on LRPC codes. It leads to a feasible attack on one of the parameters suggested in [11].
Sequential UBM adaptation for speaker verification

GMM-UBM-based speaker verification heavily relies on a well trained UBM. In practice, it is not often easy to obtain an UBM that fully matches acoustic channels in operation. To solve this problem, we propose a novel sequential MAP adaptation approach: by being sequentially updated with data from new enrollments, the UBM learns and converges to the working channel. Our experiments are conducted on a time-varying speech database, with two channel-mismatched UBMs as the initial model. The results confirm that the sequential UBM adaptation provides significant performance improvement, leading to a relative EER reduction of 6.3% and 14.8% for the two mismatched UBMs, respectively.