Micro Adaptivity in Vectorwise

Performance of query processing functions in a DBMS can be affected by many factors, including the hardware platform, data distributions, predicate parameters, compilation method, algorithmic variations and the interactions between these. Given that there are often different function implementations possible, there is a latent performance diversity which represents both a threat to performance robustness if ignored (as is usual now) and an opportunity to increase the performance if one would be able to use the best performing implementation in each situation. Micro Adaptivity, proposed here, is a framework that keeps many alternative function implementations ("flavors") in a system. It uses a learning algorithm to choose the most promising flavor potentially at each function call, guided by the actual costs observed so far. We argue that Micro Adaptivity both increases performance robustness, and saves development time spent in finding and tuning heuristics and cost model thresholds in query optimization. In this paper, we (i) characterize a number of factors that cause performance diversity between primitive flavors, (ii) describe an e-greedy learning algorithm that casts the flavor selection into a multi-armed bandit problem, and (iii) describe the software framework for Micro Adaptivity that we implemented in the Vectorwise system. We provide micro-benchmarks, and an overall evaluation on TPC-H, showing consistent improvements

Recognising facial expressions in video sequences

We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base

Perturbation of adhesion molecule-mediated chondrocyte-matrix interactions by 4-hydroxynonenal binding: implication in osteoarthritis pathogenesis

ABSTRACT: INTRODUCTION: Objectives were to investigate whether interactions between human osteoarthritic chondrocytes and 4-hydroxynonenal (HNE)-modified type II collagen (Col II) affect cell phenotype and functions and to determine the protective role of carnosine (CAR) treatment in preventing these effects. METHODS: Human Col II was treated with HNE at different molar ratios (MR) (1:20 to 1:200; Col II:HNE). Articular chondrocytes were seeded in HNE/Col II adduct-coated plates and incubated for 48 hours. Cell morphology was studied by phase-contrast and confocal microscopy. Adhesion molecules such as intercellular adhesion molecule-1 (ICAM-1) and alpha1beta1 integrin at protein and mRNA levels were quantified by Western blotting, flow cytometry and real-time reverse transcription-polymerase chain reaction. Cell death, caspases activity, prostaglandin E2 (PGE2), metalloproteinase-13 (MMP-13), mitogen-activated protein kinases (MAPKs) and nuclear factor-kappa B (NF-kappaB) were assessed by commercial kits. Col II, cyclooxygenase-2 (COX-2), MAPK, NF-kappaB-p65 levels were analyzed by Western blotting. The formation of alpha1beta1 integrin-focal adhesion kinase (FAK) complex was revealed by immunoprecipitation. RESULTS: Col II modification by HNE at MR approximately 1:20, strongly induced ICAM-1, alpha1beta1 integrin and MMP-13 expression as well as extracellular signal-regulated kinases 1 and 2 (ERK1/2) and NF-kappaB-p65 phosphorylation without impacting cell adhesion and viability or Col II expression. However, Col II modification with HNE at MR approximately 1:200, altered chondrocyte adhesion by evoking cell death and caspase-3 activity. It inhibited alpha1beta1 integrin and Col II expression as well as ERK1/2 and NF-kappaB-p65 phosphorylation, but, in contrast, markedly elicited PGE2 release, COX-2 expression and p38 MAPK phosphorylation. Immunoprecipitation assay revealed the involvement of FAK in cell-matrix interactions through the formation of alpha1beta1 integrin-FAK complex. Moreover, the modification of Col II by HNE at a 1:20 or approximately 1:200 MR affects parameters of the cell shape. All these effects were prevented by CAR, an HNE-trapping drug. CONCLUSIONS: Our novel findings indicate that HNE-binding to Col II results in multiple abnormalities of chondrocyte phenotype and function, suggesting its contribution in osteoarthritis development. CAR was shown to be an efficient HNE-snaring agent capable of counteracting these outcomes

β1A Integrin Is a Master Regulator of Invadosome Organization and Function

Use of patterned surfaces, reverse genetics, and time-controlled photoinactivation showed that β1 but not β3 integrins are required for invadosome formation, self-assembly, and stabilization into a ring structure. The activation state of β1 as well as its phosphorylation by protein kinase C on Ser785 control these process and link to the degradative function

Massive Parallel Readout Circuits for In-Vivo Signal Acquisition

Multi-electrode neural recordings are becoming standard practice in basic neuroscience research, and knowledge gained from these studies is beginning to enable clinical and neuroprosthetic applications. However, there is clear need from neuroscientists towards higher number of recording sites per given area, low-power dissipation, and wireless data connectivity to facilitate better and more accurate measurement of neural signals. In parallel, the requirements of signal quality and data rate are continuing to be the key specifications for the integrity of the neural signals. Unfortunately, the signal quality, miniature size, and low-power dissipation are contradicting requirements for the realization of the neural probes, especially in terms of hardware design. As the noise of analog circuits increases with reducing area, new solutions are required for the implementation of miniature size readout arrays. On the other hand, the increasing number of electrodes leads to larger amount of data, which in turn increases both the signal processing power and the data transmission power. The purpose of this PhD topic is to explore different design methodologies and architecture to achieve miniature and densely packaged neural recording arrays that will meet the signal quality constrains of neuroscientists and power dissipation constrains of the available power resources. This will require innovation in the field of integrated circuit design and the investigation of new techniques to reduce the size and power dissipation of the readout circuits. In order to achieve this target, the candidate is expected to investigate in detail the state-of-the-art, explore new and innovative system and building block architectures, and come up with new concepts and architectures that will enable to increase the number of recording sites for neural monitoring systems. He will also be looking at system design challenges to handle the increasing amount of data and investigate lowpower methodologies for wireless data transmission.nrpages: 211status: publishe

Micro Adaptivity In Vectorwise

htmlabstractPerformance of query processing functions in a DBMS can be affected by many factors, including the hardware platform, data distributions, predicate parameters, compilation method, algorithmic variations and the interactions between these. Given that there are often different function implementations possible, there is a latent performance diversity which represents both a threat to performance robustness if ignored (as is usual now) and an opportunity to increase the performance if one would be able to use the best performing implementation in each situation. Micro Adaptivity, proposed here, is a framework that keeps many alternative function implementations ("flavors") in a system. It uses a learning algorithm to choose the most promising flavor potentially at each function call, guided by the actual costs observed so far. We argue that Micro Adaptivity both increases performance robustness, and saves development time spent in finding and tuning heuristics and cost model thresholds in query optimization. In this paper, we (i) characterize a number of factors that cause performance diversity between primitive flavors, (ii) describe an e-greedy learning algorithm that casts the flavor selection into a multi-armed bandit problem, and (iii) describe the software framework for Micro Adaptivity that we implemented in the Vectorwise system. We provide micro-benchmarks, and an overall evaluation on TPC-H, showing consistent improvements