Stochastic re-grasp planning for vision aided capture of deforming and moving object

In the last two decades several researchers have studied the problem of grasping of a moving rigid object based on vision data. However the problem of grasping a moving and deforming object still remains unsolved. In this paper we present the development of a fast algorithm for the computation of the optimal force closure grasp points on a slowly moving and deforming object. The main focus is to find the best grasp points as the object deforms, track its position at a future instant and then transfer grasp at that location. At first the potential grasping configurations satisfying force closure are evaluated through an objective function that maximizes the grasping span while minimizing the distance between the object centroid and the intersection of the fingertip normal. A population based stochastic search strategy is adopted for computing the optimal configurations and re-localizing them as the shape undergoes translations, rotations and scaling. Experiments have been conducted to prove that the object can be tracked in real time and the optimal grasp points determined so that a three finger robot can capture it. This method works in real time so it has great potential for application in industries for grasping objects whose shapes are not clearly defined (e.g. cloth), deforming objects, or objects that are partially occluded.     

An evaluation of deadlock-handling strategies in semiconductor cluster tools

We study the deadlock handling performance of a single-blade cluster tool and present a comparison of average flowtimes under deadlock detection and resolution versus prevention. Two detection and resolution policies are tested: either resolve permanent deadlocks or resolve both permanent and transient deadlocks. A permanent deadlock requires external intervention to resolve the deadlock, whereas a transient deadlock has a positive probability that the deadlock will resolve itself over time. Prevention averts deadlock by providing sufficient in-process buffer spaces. Our experiments indicate interplay of process and robot transfer times dictate the choice of deadlock strategy. Under low robot transfer times relative to process times, providing sufficient in-process buffer to prevent deadlock or resolving both permanent and transient deadlocks can be equally effective. We conclude with some practical guidelines for operating and designing cluster tools under deadlock conditions.     

Glycerol Esters from Real Waste Cooking Oil Using a Robust Solid Acid Catalyst

Notwithstanding the food-for-fuel debate, turning waste cooking oil and fat (WCO) into a valuable product is a classic example of green chemistry. We demonstrate that sulfated zirconia and lanthanum-supported sulfated zirconia are good catalysts for the esterification of WCO free fatty acids (FFAs) with glycerol, giving high-value monoglycerides. Various catalysts were first screened using palmitic acid as a model FFA. Subsequently, six 10-kg batches of actual WCO were collected from commercial cookeries over a period of 28 weeks, pre-treated in a purpose-built decanter and esterified with industrial grade glycerol using heterogeneous catalysis. Good yield and turnovers were obtained. The pre-treatment stage and the catalytic esterification experiments are described and discussed.     

Algorithmic Algebraic Model Checking III: Approximate Methods

We present computationally efficient techniques for approximate model-checking using bisimulation-partitioning, polyhedra, grids and time discretization for semi-algebraic hybrid systems, and demonstrate how they relate to and extend other existing techniques.     

Role of collective ownership and coding standards in coordinating expertise in software project teams

Software development is a complex undertaking that continues to present software project teams with numerous challenges. Software project teams are adopting extreme programming (XP) practices in order to overcome the challenges of software development in an increasingly dynamic environment. The ability to coordinate software developers' efforts is critical in such conditions. Expertise coordination has been identified as an important emergent process through which software project teams manage non-routine challenges in software development. However, the extent to which XP enables software project teams to coordinate expertise is unknown. Drawing on the agile development and expertise coordination literatures, we examine the role of collective ownership and coding standards as processes and practices that govern coordination in software project teams. We examine the relationship between collective ownership, coding standards, expertise coordination, and software project technical quality in a field study of 56 software project teams comprising 509 software developers. We found that collective ownership and coding standards play a role in improving software project technical quality. We also found that collective ownership and coding standards moderated the relationship between expertise coordination and software project technical quality, with collective ownership attenuating the relationship and coding standards strengthening the relationship. Theoretical and practical implications of the findings are discussed.     

Event extraction using behaviors of sentiment signals and burst structure in social media

Significant world events often cause the behavioral convergence of the expression of shared sentiment. This paper examines the use of the blogosphere as a framework to study user psychological behaviors, using their sentiment responses as a form of ‘sensor’ to infer real-world events of importance automatically. We formulate a novel temporal sentiment index function using quantitative measure of the valence value of bearing words in blog posts in which the set of affective bearing words is inspired from psychological research in emotion structure. The annual local minimum and maximum of the proposed sentiment signal function are utilized to extract significant events of the year and corresponding blog posts are further analyzed using topic modeling tools to understand their content. The paper then examines the correlation of topics discovered in relation to world news events reported by the mainstream news service provider, Cable News Network, and by using the Google search engine. Next, aiming at understanding sentiment at a finer granularity over time, we propose a stochastic burst detection model, extended from the work of Kleinberg, to work incrementally with stream data. The proposed model is then used to extract sentimental bursts occurring within a specific mood label (for example, a burst of observing ‘shocked’). The blog posts at those time indices are analyzed to extract topics, and these are compared to real-world news events. Our comprehensive set of experiments conducted on a large-scale set of 12 million posts from Livejournal shows that the proposed sentiment index function coincides well with significant world events while bursts in sentiment allow us to locate finer-grain external world events.     

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images (‘No Reference’ metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.     

The Quantum Complexity of Set Membership

   Abstract. We study the quantum complexity of the static set membership problem: given a subset S (|S| ≤ n ) of a universe of size m ( >> n ), store it as a table, T: {0,1} ^r --> {0,1} , of bits so that queries of the form ``Is x in S ?' can be answered. The goal is to use a small table and yet answer queries using few bit probes. This problem was considered recently by Buhrman et al. [BMRV], who showed lower and upper bounds for this problem in the classical deterministic and randomized models. In this paper we formulate this problem in the ``quantum bit probe model'. We assume that access to the table T is provided by means of a black box (oracle) unitary transform O _T that takes the basis state | y,b > to the basis state | y,b