Miracle-dz/UniFAR-FTUs huggingface dataset:
Miniaturized unconstrained valves with pressure control for driving a robot finger

Pneumatic actuators in robot hands and walking robots are being re-evaluated. Human sized robots are especially able to make use of the characteristics of McKibben actuators. However, there is a need for compact control valves to drive the actuators. Miniaturized unconstrained valves are novel types of pneumatic control valves that, together with valves and their driver circuits (PZT drivers), may drive actuators. We have developed small valves, PZT drivers and McKibben actuators applicable to pressure control, for human sized fingers. These robot fingers had one degree of freedom and could be stretched by the actuator. Experiments were performed to validate the developed system.
Simulation based verification with range based signal representations for mixed-signal systems

Verification of either analog or digital systems is known to be difficult. Verifying modern mixed-signal systems increases the complexity further. This paper presents a range based approach to verify mixed-signal systems including parameter variation by a single simulation run. Analog systems are simulated by a SPICE-like simulator which uses a special range arithmetic, called affine arithmetic. The simulation of digital systems is extended to include the earliest and latest possible arrival times for every signal. Special converters transform the signals between each domain. The speed-up of this framework compared to traditional Monte-Carlo simulations was up to 500.
In-Network Computation of the Transition Matrix for Distributed Subspace Projection

In this paper, we develop a novel strategy to compute the transition matrix for the projection problem in a distributed fashion through gossiping in Wireless Sensor Networks. So far, the transition matrix had to be computed off-line by a third party and then provided to the network. The Subspace Projection Problem is useful in various application scenarios (e.g. spectral spatial maps in cognitive radios) and consists of projecting the observed sampled spatial field into a subspace of interest with lower dimension. Although the actual exact computation of the optimal transition matrix is not feasible in a distributed way, we develop an algorithm that is based on well known results from linear algebra and a distributed genetic algorithm in order to compute an approximation of the optimal matrix to a desired precision.
Fuzzy Comprehensive Evaluation on the Sustainable Development of Equipment Manufacturing Industry Based on Improved Algorithm

The core of fuzzy comprehensive evaluation is membership degree transformation. But the existing transformation methods should be questioned, because redundant data in index membership degree is also used to compute object membership degree, which is not useful for object classification. The paper eliminated the redundant data in index membership for object classification by defining distinguishable weight, extracted valid values to compute object membership, and applied the new algorithm in fuzzy comprehensive evaluation on the sustainable development of equipment manufacturing industry.
Deepwater Drilling: Law, Policy, and Economics of Firm Organization and Safety

Although the causes of the Deepwater Horizon spill are not yet conclusively identified, significant attention has focused on the safety-related policies and practices—often referred to as the safety culture—of BP and other firms involved in drilling the well. This paper defines and characterizes the economic and policy forces that affect safety culture and identifies reasons why those forces may or may not be adequate or effective from the public’s perspective. Two potential justifications for policy intervention are that: a) not all of the social costs of a spill may be internalized by a firm; and b) there may be principal-agency problems within the firm, which could be reduced by external monitoring. The paper discusses five policies that could increase safety culture and monitoring: liability, financial responsibility (a requirement that a firm’s assets exceed a threshold), government oversight, mandatory private insurance, and risk-based drilling fees. We find that although each policy has a positive effect on safety culture, there are important differences and interactions that must be considered. In particular, the latter three provide external monitoring. Furthermore, raising liability caps without mandating insurance or raising financial responsibility requirements could have a small effect on the safety culture of small firms that would declare bankruptcy in the event of a large spill. The paper concludes with policy recommendations for promoting stronger safety culture in offshore drilling; our preferred approach would be to set a liability cap for each well equal to the worst-case social costs of a spill, and to require insurance up to the cap.
A DIGITAL VLSI LOW POWER INTEGRATED CIRCUIT ARCHITECTURE FOR DELAY ESTIMATION

The design of a low power digital VLSI CMOS integrated circuit for the measurement of signals in the range [10, 300] Hz is presented. The architecture performs a delay calculation in order to determine the bearing angle of a sound source. Restrictions regarding power dissipation are to be improved against a previous implementation, while keeping computing accuracy. A Verilog RTL preliminary implementation is tested on a Xilinx® FPGA in order to determine performance of the calculation algorithm and tuning-up the digital structure.
Energy Auto-Tuning using the Polyhedral Approach

As the HPC community moves into the exascale computing era, application energy has become a big concern. Tuning for energy will be essential in the eort to overcome the limited power envelope. How is tuning for lower energy related to tuning for faster execution? Understanding that relationship can guide both performance and energy tuning for exascale. In this paper, a strong correlation is presented between the two that allows tuning for execution to be used as a proxy for energy tuning. We also show that polyhedral compilers can eectively tune a realistic application for both time and energy. For a large number of variants of the Polybench programs and LULESH energy consumption is strongly correlated with total execution time. Optimizations can increase the power and energy required between variants, but the variant with minimum execution time also has the lowest energy usage. The polyhedral framework was also used to optimize a 2D cardiac wave propagation simulation application. Various loop optimizations including fusion, tiling, vectorization, and auto-parallelization, achieved a 20% speedup over the baseline OpenMP implementation, with an equivalent reduction in energy on an Intel Sandy Bridge system. On an Intel Xeon Phi system, improvements as high as 21% in execution time and 19% reduction in energy are obtained.
COLO notes

x ∈ Σ∗ is a sequence from the alphabet Σ, xi is the ith symbol, where p(xi) is a distribution over Σ independent of i. (Actually, a full model of the empirical distribution would also have to include a model of the length distribution P (n). But let’s neglect that. ) On his third level, he distinguished different p(xi) for different parts of the sequence. Distinguishing different p(xi) for every i leads to PBIL:
Automated story-based commentary for sports

Automated sports commentary is a form of automated narrative and human-computer interaction. Sports commentary exists to keep the viewer informed and entertained. One way to entertain the viewer is by telling brief stories relevant to the game in progress. We introduce a system called the Sports Commentary Recommendation System (SCoReS) that can automatically suggest stories for commentators to tell during games. Through several user studies, we compared commentary using SCoReS to three other types of commentary and showed that SCoReS adds significantly to the broadcast across several enjoyment metrics. We also collected interview data from professional sports commentators who positively evaluated a demonstration of the system. We conclude that SCoReS can be a useful broadcast tool, effective at selecting stories that add to the enjoyment and watchability of sports. SCoReS is a step toward automating sports commentary, and thus automating narrative.
Leveraging sources of collective wisdom on the web for discovering technology synergies

One of the central tasks of R&D strategy and portfolio management at large technology companies and research institutions refers to the identification of technological synergies throughout the organization. These efforts are geared towards saving resources by consolidating scattered expertise, sharing best practices, and reusing available technologies across multiple product lines. In the past, this task has been done in a manual evaluation process by technical domain experts. While feasible, the major drawback of this approach is the enormous effort in terms of availability and time: For a structured and complete analysis every combination of any two technologies has to be rated explicitly. We present a novel approach that recommends technological synergies in an automated fashion, making use of abundant collective wisdom from the Web, both in pure textual form as well as classification ontologies. Our method has been deployed for practical support of the synergy evaluation process within our company. We have also conducted empirical evaluations based on randomly selected technology pairs so as to benchmark the accuracy of our approach, as compared to a group of general computer science technologists as well as a control group of domain experts.
Integrated features of haar-like wavelet filters

In this paper a novel feature descriptor by integration of cooccurrence of Haar like wavelet filter (CHLWF) with color histogram (CH) is proposed for content based image retrieval (CBIR). The proposed feature is capable of extracting image properties from different visual perspectives in order to give image representation almost similar to human interpretation. It helps in the improvement of retrieval results in terms of various performance measures. An effort is made to reduce the sensitivity to noise and illumination changes by working on average intensities of the regions. CHLWF is extracted from images and employs only maximal edge responses in feature computation hence, only prominent directional information which can substantially represent an image is used. It provides textural information efficiently with reduced computational complexity. CHLWF also eliminates the step involved in the decision making of thresholds by automatic quantization of coefficients. All these properties assist to show the efficient image representation through CHLWF. Further with the integration of CH feature color information is also incorporated in the final feature and results are improved with respect to related state of art techniques. Since, the main aim of introducing this feature is to improve effectiveness of retrieval system hence; it is affirmed by evaluating the retrieval performance on Corel 1000 benchmark image database.
A review of tonereproductiontechniques

The ultimate aim of realistic graphics is the creation of images that provoke the same responses that a viewer would have to a real scene. While research into ways of rendering images provides us with better and faster methods, we do not necessarily see their full effect due to limitations of the display hardware. The low dynamic range of a standard computer monitor requires some form of mapping to produce images that are perceptually accurate. Tone reproduction operators attempt to replicate the effect of real-world luminance intensities. This paper reviews the work to date on tone reproduction techniques. It includes an investigation into the need for accurate tone reproduction and a discussion of techniques to date. The future of tone reproduction is considered, together with the implications of advances in display hardware.
Personal travel management: the adoption and consideration of travel‐related strategies

Traveler behavior plays a role in the effectiveness of travel demand management (TDM) policies. Personal travel management is explored in this paper by analyzing individuals' adoption and consideration of 17 travel‐related alternatives in relation to socio‐demographic, mobility, travel‐related attitude, personality and lifestyle preference variables. The sample comprises 1282 commuters living in urban and suburban neighborhoods of the San Francisco Bay Area. Among the findings: females were more likely to have adopted/considered the more ‘costly’ strategies; those with higher mobility were more likely to have adopted/considered travel‐maintaining as well as travel‐reducing strategies; and those who like travel and want to do more are less likely to consider travel‐reducing strategies. These findings, when combined with those of earlier work on this subject, present a compelling argument for the need to further understand traveler behavior – particularly in response to congestion and TDM policies.
Optimizing sparse matrix-vector multiplication on CUDA

in recent years, GPUs have attracted the attention of many application developers as powerful massively parallel system. CUDA as a general purpose parallel computing architecture make GPUs an appealing choice to solve many complex computational problems in a more efficient way. In this paper, we discuss implementing optimizing spare matrix-vector multiplication on NVIDIA GPUs using CUDA programming model. We outline three optimizations include: (1) optimized CSR storage format, (2) optimized threads mapping, and (3) avoiding divergence judgment. We experimentally evaluate our optimizations on GeForce 9600 GTX, connect to Windows xp 64-bit system. In comparison with NVIDIA's SpMV library and NVIDIA's CUDDPA library, the results show that optimizing sparse matrix-vector multiplication on CUDA achieves better performance than other SpMV implementations.
Using the Stochastic Gradient Method to FitPolychotomous Regression

Kooperberg, Bose, and Stone (1997) introduced polyclass, a methodology that uses adap-tively selected linear splines and their tensor products to model conditional class probabilities. The authors attempted to develop a methodology that would work well on small and moderate size problems and would scale up to large problems. However, the version of polyclass that was developed for large problems was impractical in that it required two months of cpu time to apply it to a large data set. A modiication to this methodology involving the use of the stochastic gradient (on-line) method in tting polyclass models to given sets of basis functions is developed here that makes the methodology applicable to large data sets. In particular, it is successfully applied to a phoneme recognition problem involving 45 phonemes, 81 features, 150,000 cases in the training sample, 1000 basis functions, and 44,000 unknown parameters. Comparisons with neural networks are made both on the original problem and on a three-vowel subproblem.
On the relation between dependence and diversity in multiple classifier systems

In this paper we investigate the issues of independence and diversity among individual classifiers participating in a multiple classifier fusion scheme. First we present a formal definition of statistically independent classifiers. Then we focus on testing the independence between two classifiers. Dependence of two classifiers leads to the conclusion that every ensemble of classifiers in which they participate is not an independent scheme. Previous studies have argued that independence of the classifiers infuses diversity in the multi-classifier system, which is directly related to improved performance. Consequently, we introduce a measure for the degree of diversity as expressed by the agreement among the classifiers' outputs in such an ensemble. A number of examples drawn from diverse domains in pattern recognition are also given to illustrate the relation between classifier dependence and diversity estimation. Our results suggest the measurement of the classifiers' decisions agreement as an informative measure of the strength of association among dependent classifiers.
Impartial Spectrum Decision under Interference Temperature Model in Cognitive Wireless Mesh Networks

Secondary users, in most present spectrum decision schemes, usually endeavored to avoid coexisting with primary users, at the expense of some spectrum resources and handoff latency. Interference temperature model which allowed simultaneous communication between secondary users and primary users solved this problem. A new metric called outage probability was proposed for the spectrum handoff under interference temperature limits in cognitive wireless mesh networks. Furthermore, the policy was applied to the probability-based spectrum decision strategy. Simulation results show that the proposed decision scheme reduces the outage probability and overall transmission time of secondary users, simultaneously guarantees the fairness in the channel utilization, and further improves the network performance.
Cosmic shear analysis with CFHTLS deep data

We present the first cosmic shear measurements obtained from the T0001 release of the Canada-France-Hawaii Telescope Legacy Survey. The data set covers three uncorrelated patches (D1, D3 and D4) of one square degree each, observed in u*, g', r', i' and z' bands, to a depth of i' = 25.5. The deep, multi-colour observations in these fields allow for several data-quality controls. The lensing signal is detected in both r' and i' bands and shows similar amplitude and slope in both filters. B-modes are found to be statistically zero at all scales. Using multi-colour information, we derived a photometric redshift for each galaxy and use this to separate the background source sample into low-z and high-z subsamples. A stronger shear signal is detected from the high-z subsample than from the low-z subsample, as expected from weak lensing tomography. While further work is needed to model the effects of errors in the photometric redshifts, this result suggests that it will be possible to obtain constraints on the growth of dark matter fluctuations with lensing wide field surveys. The combined Deep and Wide surveys give σ 8 = 0.89 ± 0.06 assuming the Peacock & Dodds non-linear scheme (P&D), and σ 8 = 0.86 ± 0.05 for the halo model and Ω m = 0.3. We assumed a Cold Dark Matter model with flat geometry and have marginalized over the systematics, the Hubble constant and redshift uncertainties. Using data from the Deep survey, the 1σ upper bound for ω 0 , the constant equation of state parameter is ω 0 < -0.8.
Tools to visualise the structure of multi-agent conservations at various levels of analysis

Multi-Agent Based Simulation (MABS) is a suitable way to simulate the dynamics of natural or social systems. However, observing and analysing large-scale simulations involving numerous, complex and heterogeneous agents is still a challenge. Understanding simulation results requires the user to perform the following visual information-seeking tasks: (i) overview, (ii) zoomand-filter, (iii) details-on-demand. This paper concentrates on the analysis of conversations among agents in MABS at various scales, from global overview to local details.
Input-to-state stabilizing sub-optimal nonlinear MPC algorithms with an application to DC-DC converters

This paper focuses on the synthesis of computationally friendly sub-optimal nonlinear Model Predictive Control (MPC) algorithms with guaranteed robust stability. The input-to-state stability framework is employed to analyze the robustness of the resulting MPC closed-loop systems. Two new sub-optimal nonlinear MPC schemes are proposed, based on a contraction argument and an artificial Lyapunov function, respectively. The developed theory is illustrated by applying it to control a Buck-Boost DC-DC converter. Copyright c ∞2006 IFAC
3D level set image segmentation refined by intelligent agent swarm

The level set method of surface representation and deformation has found many applications in image processing, especially with regard to segmentation. Naive numerical solutions have long since given way to much more efficient narrow band methods, where updates to the scalar field are performed only within a number of layers either side of the surface. This paper presents our implementation of automated segmentation via the sparse field narrow band approach. We use k-means clustering of regularly sampled points to provide initialisation of seed locations and mean voxel values, removing the need for manual seed and parameter selection. We demonstrate this method by segmenting grey matter and white matter from a simulated T1 MRI scan. Anisotropy can easily cause this and other segmentation methods to misclassify voxels, however. Often it is clear in a 3D view of the resulting surface which regions require refinement. With available extensions to the sparse field and rendering algorithms we can now efficiently perform purely localised modifications and re-rendering of the surface. These extensions, plus properties inherent in the level set method, allow us to easily situate swarms of agents on the surface, intelligently inhabiting and modifying the zero-level set. By a simple weighting of movement direction, we can direct the swarm to any user-specified point on the surface in order to affect repairs and refinement.
Weakly Nonlinear Geometric Optics for Hyperbolic Systems of Conservation Laws

We present a new approach to analyze the validation of weakly nonlinear geometric optics for entropy solutions of nonlinear hyperbolic systems of conservation laws whose eigenvalues are allowed to have constant multiplicity and corresponding characteristic fields to be linearly degenerate. The approach is based on our careful construction of more accurate auxiliary approximation to weakly nonlinear geometric optics, the properties of wave front-tracking approximate solutions, the behavior of solutions to the approximate asymptotic equations, and the standard semigroup estimates. To illustrate this approach more clearly, we focus first on the Cauchy problem for the hyperbolic systems with compact support initial data of small bounded variation and establish that the L 1-estimate between the entropy solution and the geometric optics expansion function is bounded by O(ϵ2), independent of the time variable. This implies that the simpler geometric optics expansion functions can be employed to study the behavior of general entropy solutions to hyperbolic systems of conservation laws. Finally, we extend the results to the case with non-compact support initial data of bounded variation.