Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposing Multimodal Integration Model Using LSTM and Autoencoder

We propose an architecture of neural network that can learn and integrate sequential multimodal information using Long Short Term Memory. Our model consists of encoder and decoder LSTMs and multimodal autoencoder. For integrating sequential multimodal information, firstly, the encoder LSTM encodes a sequential input to a fixed range feature vector for each modality. Secondly, the multimodal autoencoder integrates the feature vectors from each modality and generate a fused feature vector which contains sequential multimodal information in a mixed form. The original feature vectors from each modality are re-generated from the fused feature vector in the multimodal autoencoder. The decoder LSTM decodes the sequential inputs from the regenerated feature vector. Our model is trained with the visual and motion sequences of humans and is tested by recall tasks. The experimental results show that our model can learn and remember the sequential multimodal inputs and decrease the ambiguity generated at the learning stage of LSTMs using integrated multimodal information. Our model can also recall the visual sequences from the only motion sequences and vice versa.
Implementation of automatic sensor and meter arrangement system

Building Energy Management System (BEMS) is IT-based monitoring and control system that provides information on the performance of some or all of the components of a building's infrastructure. BEMS can save energy and minimize the impact on the environment due to energy efficiency technologies and systems. The most important thing of Building Energy Management is the monitoring of indoor environment in a building by various sensors and the measurement of energy consumption in a building by various meters. We need to arrange sensors and meters automatically, install sensors and meters actually, and develop an automatic sensor and meter arrangement system. In this paper, we propose an automatic sensor and meter arrangement system. And we explain our four proposed automatic sensor and meter arrangement algorithms such as the optimal sensor arrangement algorithm, the sensor arrangement algorithm based on installation expenses, the sensor arrangement algorithm based on user intention, and the optimal meter arrangement. Finally we show our implementation result.
Effect of ultrafast imaging on shear wave visualization and characterization : an experimental and computational study in a pediatric ventricular model

Plane wave imaging in Shear Wave Elastography (SWE) captures shear wave propagation in real-time at ultrafast frame rates. To assess the capability of this technique in accurately visualizing the underlying shear wave mechanics, this work presents a multiphysics modeling approach providing access to the true biomechanical wave propagation behind the virtual image. This methodology was applied to a pediatric ventricular model, a setting shown to induce complex shear wave propagation due to geometry. Phantom experiments are conducted in support of the simulations. The model revealed that plane wave imaging altered the visualization of the shear wave pattern in the time (broadened front and negatively biased velocity estimates) and frequency domain (shifted and/or decreased signal frequency content). Furthermore, coherent plane wave compounding (effective frame rate of 2.3 kHz) altered the visual appearance of shear wave dispersion in both the experiment and model. This mainly affected stiffness characterization based on group speed, whereas phase velocity analysis provided a more accurate and robust stiffness estimate independent of the use of the compounding technique. This paper thus presents a versatile and flexible simulation environment to identify potential pitfalls in accurately capturing shear wave propagation in dispersive settings.
A grasping force optimization algorithm for dexterous robotic hands

The problem of grasping force optimization for a robotic system equipped with multi-fingered hands is considered in this paper. This problem is cast as a convex optimization problem, considering also joint torque constraints. A solution suitable for on-line implementation is proposed, which allows a substantial reduction of the computational load by dynamically decreasing the number of active torque constraints. Moreover, for the case of a bimanual manipulation system, a sub-optimal single-hand optimization algorithm is presented and compared with the optimal one. The effectiveness of the described methods has been tested in a simulation case study.
DOING COMPETENCIES WELL: BEST PRACTICES IN COMPETENCY MODELING

The purpose of this article is to present a set of best practices for competency modeling based on the experiences and lessons learned from the major perspectives on this topic (including applied, academic, and professional). Competency models are defined, and their key advantages are explained. Then, the many uses of competency models are described. The bulk of the article is a set of 20 best practices divided into 3 areas: analyzing competency information, organizing and presenting competency information, and using competency information. The best practices are described and explained, practice advice is provided, and then the best practices are illustrated with numerous practical examples. Finally, how competency modeling differs from and complements job analysis is explained throughout. The purpose of this article is to present a set of best practices for competency modeling based on the experiences and lessons learned from all the major perspectives on this topic including two major companies, a major consulting firm, a major university, and the Society for Industrial and Organizational Psychology (SIOP) taskforce on competency modeling. From all the different perspectives, we will delineate a set of 20 best practices and then illustrate them with practical examples from actual organizations. For the interested reader, we also link the practices to the existing literature which consists mostly of writings based on practical experience (e.g., case studies, commentaries) because little empirical
Common Risk Factors in the Us and UK Interest Rate Swap Markets: Evidence from a Non-Linear Vector Autoregression Approach

This paper produces evidence in support of the existence of common risk factors in the US and UK interest rate swap markets. Using a multivariate smooth transition autoregression (STVAR) framework, we show that the dynamics of the US and UK swap spreads are best described by a regime-switching model. We identify the existence of two distinct regimes in US and UK swap spreads; one characterized by a "flat" term structure of US interest rates and the other characterized by an "upward" slopping US term structure. In addition, we show that there exist significant asymmetries on the impact of the common risk factors on the US and UK swap spreads. Shocks to UK oriented risk factors have a strong effect on the US swap markets during the "flat" slope regime but a very limited effect otherwise. On the other hand, US risk factors have a significant impact on the UK swap markets in both regimes. Despite their added flexibility, the STVAR models do not consistently produce superior forecasts compared to less sophisticated autoregressive (AR) and vector autoregressive (VAR) models.
Flexible Template and Model Matching Using Intensity

Intensity-based image and template matching is briefly reviewed with particular emphasis on the problems that arise when flexible templates or models are used. Use of such models and templates may often lead to a very small basin of attraction in the error landscape surrounding the desired solution and also to spurious, trivial solutions. Simple examples are studied in order to illustrate these problems which may arise from photometric transformations of the template, from geometric transforms of it or from internal parameters of the template that allow similar types of variation. It is pointed out that these problems are, from a probabilistic point of view, exacerbated by a failure to model the whole image, i.e. both the foreground object or template and the image background, which a Bayesian approach strictly requires. Some general remarks are made about the form of the error landscape to be expected in object recognition applications and suggestions made as to optimisation techniques that may prove effective in locating a correct match. These suggestions are illustrated by a preliminary example.
Micro- and nano-structural details of a spider's filter for substrate vibrations: relevance for low-frequency signal transmission

The metatarsal lyriform organ of the Central American wandering spider Cupiennius salei is its most sensitive vibration detector. It is able to sense a wide range of vibration stimuli over four orders of magnitude in frequency between at least as low as 0.1 Hz and several kilohertz. Transmission of the vibrations to the slit organ is controlled by a cuticular pad in front of it. While the mechanism of high-frequency stimulus transfer (above ca 40 Hz) is well understood and related to the viscoelastic properties of the pad's epicuticle, it is not yet clear how low-frequency stimuli (less than 40 Hz) are transmitted. Here, we study how the pad material affects the pad's mechanical properties and thus its role in the transfer of the stimulus, using a variety of experimental techniques, such as X-ray micro-computed tomography for three-dimensional imaging, X-ray scattering for structural analysis, and atomic force microscopy and scanning electron microscopy for surface imaging. The mechanical properties were investigated using scanning acoustic microscopy and nanoindentation. We show that large tarsal deflections cause large deformation in the distal highly hydrated part of the pad. Beyond this region, a sclerotized region serves as a supporting frame which resists the deformation and is displaced to push against the slits, with displacement values considerably scaled down to only a few micrometres. Unravelling the structural arrangement in such specialized structures may provide conceptual ideas for the design of new materials capable of controlling a technical sensor's specificity and selectivity, which is so typical of biological sensors.
Bone Mineral Density and Fracture Risk Assessment to Optimize Prosthesis Selection in Total Hip Replacement

The variability in patient outcome and propensity for surgical complications in total hip replacement (THR) necessitates the development of a comprehensive, quantitative methodology for prescribing the optimal type of prosthetic stem: cemented or cementless. The objective of the research presented herein was to describe a novel approach to this problem as a first step towards creating a patient-specific, presurgical application for determining the optimal prosthesis procedure. Finite element analysis (FEA) and bone mineral density (BMD) calculations were performed with ten voluntary primary THR patients to estimate the status of their operative femurs before surgery. A compilation model of the press-fitting procedure was generated to define a fracture risk index (FRI) from incurred forces on the periprosthetic femoral head. Comparing these values to patient age, sex, and gender elicited a high degree of variability between patients grouped by implant procedure, reinforcing the notion that age and gender alone are poor indicators for prescribing prosthesis type. Additionally, correlating FRI and BMD measurements indicated that at least two of the ten patients may have received nonideal implants. This investigation highlights the utility of our model as a foundation for presurgical software applications to assist orthopedic surgeons with selecting THR prostheses.
Approximating the Radii of Point Sets

We consider the problem of computing the outer-radii of point sets. In this problem, we are given integers $n, d$, and $k$, where $k \le d$, and a set $P$ of $n$ points in $\Re^d$. The goal is to compute the outer $k$-radius of $P$, denoted by ${\cal R}_k(P)$, which is the minimum over all $(d-k)$-dimensional flats $F$ of $\max_{p \in P} d(p,F)$, where $d(p,F)$ is the Euclidean distance between the point $p$ and flat $F$. Computing the radii of point sets is a fundamental problem in computational convexity with many significant applications. The problem admits a polynomial time algorithm when the dimension $d$ is constant [U. Faigle, W. Kern, and M. Streng, Math. Program. , 73 (1996), pp. 1-5]. Here we are interested in the general case in which the dimension $d$ is not fixed and can be as large as $n$, where the problem becomes NP-hard even for $k=1$. It is known that $R_k(P)$ can be approximated in polynomial time by a factor of $(1 + \varepsilon)$ for any $\varepsilon > 0$ when $d - k$ is a fixed constant [M. Babdoiu, S. Har-Peled, and P. Indyk, in Proceedings of the ACM Symposium on the Theory of Computing, 2002; S. Har-Peled and K. Varadarajan, in Proceedings of the ACM Symposium on Computing Geometry, 2002]. A polynomial time algorithm that guarantees a factor of $O(\sqrt{\log n})$ approximation for $R_1(P)$, the width of the point set $P$, is implied by the results of Nemirovski, Roos, and Terlaky [Math. Program. , 86 (1999), pp. 463-473] and Nesterov [Handbook of Semidefinite Programming Theory, Algorithms, Kluwer Academic Publishers, Norwell, MA, 2000]. In this paper, we show that $R_k(P)$ can be approximated by a ratio of $O(\sqrt{\log n})$ for any $1 \leq k \leq d$, thus matching the previously best known ratio for approximating the special case $R_1 (P)$, the width of point set $P$. Our algorithm is based on semidefinite programming relaxation with a new mixed deterministic and randomized rounding procedure. We also prove an inapproximability result that gives evidence that our approximation algorithm is doing well for a large range of $k$. We show that there exists a constant $\delta > 0$ such that the following holds for any $0 < \eps < 1$: there is no polynomial time algorithm that approximates $R_k(P)$ within $(\log n)^{\delta}$ for all $k$ such that $k \leq d - d^{\varepsilon}$ unless NP $\subseteq$ DTIME $[2^{(\log m)^{O(1)}}]$. Our inapproximability result for $R_k(P)$ extends a previously known hardness result of Brieden [Discrete Comput. Geom. , 28 (2002), pp. 201-209] and is proved by modifying Brieden’s construction using basic ideas from probabilistically checkable proofs (PCP) theory.
EnsembleGraph: Interactive visual analysis of spatiotemporal behaviors in ensemble simulation data

This paper presents a novel visual analysis tool, EnsembleGraph, which aims at helping scientists understand spatiotemporal similarities across runs in time-varying ensemble simulation data. We abstract the input data into a graph, where each node represents a region with similar behaviors across runs and nodes in adjacent time frames are linked if their regions overlap spatially. The visualization of this graph, combined with multiple-linked views showing details, enables users to explore, select, and compare the extracted regions that have similar behaviors. The driving application of this paper is the study of regional emission influences over tropospheric ozone, based on the ensemble simulations conducted with different anthropogenic emission absences using MOZART-4. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations.
Three-dimensional structure of the radial spokes reveals heterogeneity and interactions with dyneins in Chlamydomonas flagella

Cryo–electron tomography of Chlamydomonas flagella reveals previously uncharacterized features of the radial spokes, including structural heterogeneity and direct interactions with dyneins and between the spoke heads. A “radial spoke 3 stand-in” occupies what would be the site of a third spoke in organisms with spoke triplets.
Quantity and Quality of Dill Essential Oil as Influenced by Organic Fertilizers

Increasing attention is being paid to use of organic fertilizers such as manure and vermicompost which can increased yield and protect the environment. Replacing chemical fertilizers with manures has the benefit of low production cost and imparts beneficial effects on soil. Then an experiment was conducted during 2013 to measure effects of organic fertilizers on essential oil of dill (Anethum graveolens L.). Use of organic fertilizers beneficially affected seed yield, percent of essential oil, and essential oil yield. Essential oil percent was highest due to treatment with compost tea applied to the soil compared to foliar application. The combination of 20 t∙ha-1 of manure and 7 t∙ha-1of vermicompost, without compost tea,producedthe maximum essential oil yield (23.85 kg∙ha-1). The GC-MS analysis of dill essential oil indicated 94% of essential oil compounds were made up of: carvone, α-phellandrene, p-cymene, dillapiole and trans-dihydrocarvone. The maximum value of carvone (73.58%) was obtained by application of 20 t∙ha-1 of manure and 15 t∙ha-1of vermicompost and compost tea in form of soil application.
Towards a reputation-based model of social web search

While web search tasks are often inherently collaborative in nature, many search engines do not explicitly support collaboration during search. In this paper, we describe HeyStaks (www.heystaks.com), a system that provides a novel approach to collaborative web search. Designed to work with mainstream search engines such as Google, HeyStaks supports searchers by harnessing the experiences of others as the basis for result recommendations. Moreover, a key contribution of our work is to propose a reputation system for HeyStaks to model the value of individual searchers from a result recommendation perspective. In particular, we propose an algorithm to calculate reputation directly from user search activity and we provide encouraging results for our approach based on a preliminary analysis of user activity and reputation scores across a sample of HeyStaks users.
Assessing the effectiveness of synthetic and biologic disease-modifying antirheumatic drugs in psoriatic arthritis – a systematic review

Background Psoriatic arthritis is an inflammatory arthritis the primary manifestations of which are locomotor and skin disease. Although a number of guidelines have been published citing strategies for reducing disease progression, the evidence base for disease-modifying agents is unclear. This forms the focus of this systematic review. Methods The systematic review was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 checklist. We selected randomized controlled trials (RCTs) that looked at the impact of interventions with disease-modifying agents, either synthetic drugs or biologics on musculoskeletal outcomes, notably American College of Rheumatology 20 percent responders. Results were analyzed using Review Manager 5.1. 6 (Cochrane Collaboration, Oxford, UK). Whilst our primary focus was on published trials, we also looked at new trials presented in abstract form in 2013–2014 that were not yet published to avoid omitting important and up-to-date information on developing treatments. Results Our in-depth analysis included 28 trials overall enrolling 5,177 patients published between the 1980s and now as well as limited analysis of some studies in abstract form as described earlier. The most frequently available locomotor outcome measure was the American College of Rheumatology 20 percent responders. The risk ratio for achieving an American College of Rheumatology 20 percent responders response was positive in favor of treatment (risk ratio 2.30; 95% confidence interval 1.78–2.96); however, there was evidence of considerable heterogeneity between trials. Overall randomized controlled trials of established synthetic disease-modifying agents were largely negative (methotrexate, ciclosporin and sulfasalazine) though leflunomide showed a small positive effect. A new synthetic agent, apremilast, did show a positive benefit. For biologics, TNF inhibitors already licensed for use were effective and similar benefits were seen with newer agents including ustekinumab, secukinumab, brodalumab, and abatacept, although the latter did not impact on skin problems. Important limitations of the systematic review included, first, the fact that for many agents there were little data and, second, much of the recent data for newer biologics were only available in abstract form. Conclusion Conventional disease-modifying agents, with the possible exception of leflunomide, do not show clear evidence of disease-modifying effects in psoriatic arthritis, though a newer synthetic disease-modifying agents, apremilast, appears more effective. Biologic agents appear more beneficial, although more evidence is required for newer agents. This review suggests that it may be necessary to review existing national and international management guidelines for psoriatic arthritis.
An Improvement in Partial Order Reduction Using Behavioral Analysis

Efficacy of partial order reduction in reducing state space relies on adequate extraction of the independence relation among possible behaviors. However, traditional approaches by statically analyzing system model structures are often not able to reveal enough independence for reduction. To address such a problem, this paper presents a behavioral analysis approach that uses a compositional reachability analysis method to generate the over-approximate local state spaces for all modules in a system where a much more precise independence relation can be extracted for partial order reduction. Compared to the static analysis approaches, significantly higher reduction on complexity can be seen in a number of non-trivial examples, and as a consequence, dramatically less time and memory are required to finish these examples.
Discrete all-pole modeling

A method for parametric modeling and spectral envelopes when only a discrete set of spectral points is given is introduced. This method, called discrete all-pole (DAP) modeling, uses a discrete version of the Itakura-Saito distortion measure as its error criterion. One result is an autocorrelation matching condition that overcomes the limitations of linear prediction and produces better fitting spectral envelopes for spectra that are representable by a relatively small discrete set of values, such as in voiced speech. An iterative algorithm for DAP modeling that is shown to converge to a unique global minimum is presented. Results of applying DAP modeling to real and synthetic speech are also presented. DAP modeling is extended to allow frequency-dependent weighting of the error measure, so that spectral accuracy can be enhanced in certain frequency regions. >
Discovering Data Set Nature through Algorithmic Clustering Based on String Compression

Text data sets can be represented using models that do not preserve text structure, or using models that preserve text structure. Our hypothesis is that depending on the data set nature, there can be advantages using a model that preserves text structure over one that does not, and vice versa. The key is to determine the best way of representing a particular data set, based on the data set itself. In this work, we proposde B“orjae to investigate this problem by combining text distortion and algorithmic clustering based on string compression. Specifically, a distortion technique previously developed by the authors is applied to destroy text structure progressively. Following this, a clustering algorithm based on string compression is used to analyze the effects of the distortion on the information contained in the texts. Several experiments are carried out on text data sets and artificially-generated data sets. The results show that in strongly structural data sets the clustering results worsen as text structure is progressively destroyed. Besides, they show that using a compressor which enables the choice of the size of the left-context symbols helps to determine the nature of the data sets. Finally, the results are contrasted with a method based on multidimensional projections and analogous conclusions are obtained.