SCOOTER: A compact and scalable dynamic labeling scheme for XML updates

Although dynamic labeling schemes for XML have been the focus of recent research activity, there are significant challenges still to be overcome. In particular, though there are labeling schemes that ensure a compact label representation when creating an XML document, when the document is subject to repeated and arbitrary deletions and insertions, the labels grow rapidly and consequently have a significant impact on query and update performance. We review the outstanding issues todate and in this paper we propose SCOOTER - a new dynamic labeling scheme for XML. The new labeling scheme can completely avoid relabeling existing labels. In particular, SCOOTER can handle frequently skewed insertions gracefully. Theoretical analysis and experimental results confirm the scalability, compact representation, efficient growth rate and performance of SCOOTER in comparison to existing dynamic labeling schemes

MFPA: Mixed-Signal Field Programmable Array for Energy-Aware Compressive Signal Processing

Compressive Sensing (CS) is a signal processing technique which reduces the number of samples taken per frame to decrease energy, storage, and data transmission overheads, as well as reducing time taken for data acquisition in time-critical applications. The tradeoff in such an approach is increased complexity of signal reconstruction. While several algorithms have been developed for CS signal reconstruction, hardware implementation of these algorithms is still an area of active research. Prior work has sought to utilize parallelism available in reconstruction algorithms to minimize hardware overheads; however, such approaches are limited by the underlying limitations in CMOS technology. Herein, the MFPA (Mixed-signal Field Programmable Array) approach is presented as a hybrid spin-CMOS reconfigurable fabric specifically designed for implementation of CS data sampling and signal reconstruction. The resulting fabric consists of 1) slice-organized analog blocks providing amplifiers, transistors, capacitors, and Magnetic Tunnel Junctions (MTJs) which are configurable to achieving square/square root operations required for calculating vector norms, 2) digital functional blocks which feature 6-input clockless lookup tables for computation of matrix inverse, and 3) an MRAM-based nonvolatile crossbar array for carrying out low-energy matrix-vector multiplication operations. The various functional blocks are connected via a global interconnect and spin-based analog-to-digital converters. Simulation results demonstrate significant energy and area benefits compared to equivalent CMOS digital implementations for each of the functional blocks used: this includes an 80% reduction in energy and 97% reduction in transistor count for the nonvolatile crossbar array, 80% standby power reduction and 25% reduced area footprint for the clockless lookup tables, and roughly 97% reduction in transistor count for a multiplier built using components from the analog blocks. Moreover, the proposed fabric yields 77% energy reduction compared to CMOS when used to implement CS reconstruction, in addition to latency improvements

Adaptive Brain Stimulation for Movement Disorders

Deep brain stimulation (DBS) has markedly changed how we treat movement disorders including Parkinson's disease (PD), dystonia, and essential tremor (ET). However, despite its demonstrable clinical benefit, DBS is often limited by side effects and partial efficacy. These limitations may be due in part to the fact that DBS interferes with both pathological and physiological neural activities. DBS could, therefore, be potentially improved were it applied selectively and only at times of enhanced pathological activity. This form of stimulation is known as closed-loop or adaptive DBS (aDBS). An aDBS approach has been shown to be superior to conventional DBS in PD in primates using cortical neuronal spike triggering and in humans employing local field potential biomarkers. Likewise, aDBS studies for essential and Parkinsonian tremor are advancing and show great promise, using both peripheral or central sensing and stimulation. aDBS has not yet been trialed in dystonia and yet exciting and promising biomarkers suggest it could be beneficial here too. In this chapter, we will review the existing literature on aDBS in movement disorders and explore potential biomarkers and stimulation algorithms for applying aDBS in PD, ET, and dystonia

Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud

Recently, database users have begun to use cloud database services to outsource their databases. The reason for this is the high computation speed and the huge storage capacity that cloud owners provide at low prices. However, despite the attractiveness of the cloud computing environment to database users, privacy issues remain a cause for concern for database owners since data access is out of their control. Encryption is the only way of assuaging users’ fears surrounding data privacy, but executing Structured Query Language (SQL) queries over encrypted data is a challenging task, especially if the data are encrypted by a randomized encryption algorithm. Many researchers have addressed the privacy issues by encrypting the data using deterministic, onion layer, or homomorphic encryption. Nevertheless, even with these systems, the encrypted data can still be subjected to attack. In this research, we first propose an indexing scheme to encode the original table’s tuples into bit vectors (BVs) prior to the encryption. The resulting index is then used to narrow the range of retrieved encrypted records from the cloud to a small set of records that are candidates for the user’s query. Based on the indexing scheme, we then design three different models to execute SQL queries over the encrypted data. The data are encrypted by a single randomized encryption algorithm, namely the Advanced Encryption Standard AES-CBC. In each proposed scheme, we use a different (secure) method for storing and maintaining the index values (BVs) (i.e., either at user’s side or at the cloud server), and we extend each system to support most of relational algebra operators, such as select, join, etc. Implementation and evaluation of the proposed systems reveals that they are practical and efficient at reducing both the computation and space overhead when compared with state-of-the-art systems like CryptDB

Querying and Updating XML Data based on Node Labeling Schemes

Ph.DDOCTOR OF PHILOSOPH

ON IMPLEMENTATION OF ROBUST AUTOTUNING OF TRANSMISSION ELECTRON MICROSCOPES

Practice shows that the current impiementations of automatic tuning of transmission elec- tron microscopes suffer from not satisfactory robustness, and this seriously limits their applicability. The paper presents a software architecture which provides a framework for the realization of a real-time automatic tuning system with improved robustness. First the transmission electron microscope tuning as general measuring/modelling process is characterized and the consequences of the improvement in robustness are identified in this context. It is concluded that both extending the models of image formation of the electron microscope into qualitative and heuristic directions, and the continuous model validation with sophisticated control are necessary for coping with these problems. Then a two-layer software architecture is presented which helps satisfying the above require- ments to a considerable extent: the lower layer contains the conventional and symbolic data/image processing components (with data/control interfaces), the upper layer - us- ing knowledge based approach extensively - realizes the higher level control based on the partial results of the processing on the lower level. (Hence, the upper level is responsible for the robustness in system-wide sense.) Main subsystems of the autotuning software are shown. A short survey of the hardware background is also given. A summary closes the paper

A Two-Level Dynamic Chrono-Scheduling Algorithm

We propose a dynamic instruction scheduler that does not need any kind of wakeup logic, as all the instructions are “programmed” on issue stage to be executed in pre-calculated cycles. The scheduler is composed of two similar levels, each one composed of simple “stations”, where the timing information is recorded. The first level is aimed to the group of instructions whose timing information cannot be calculated at issue (for example, those instructions whose latency is not predictable). The second level contains simple “stations” for the instructions whose execution and write back cycle have been already calculated. The key idea of this scheduler is to extract and record all possible information about the future execution of an instruction during its issue, so as not to look for this information again and again during wait stages at the reservation stations. Another additional advantage is that time critical parts can be identified as instruction timing information is available, so high speed and frequency logic can be used only in these parts, while the rest of the scheduler can work at lower frequencies, therefore consuming much less power. The lack of wakeup and CAM (Content Addressable Memory) means that power consumption and latencies would be presumably reduced, frequency would probably be made higher, while CPI (clock Cycles Per Instruction) would remain approximately the same.Ministerio de Educación y Ciencia TIN2006-15617- C03-03Junta de Andalucía P06-TIC-0229

Distributed databases

Mòdul 3 del llibre Database Architecture. UOC, 20122022/202

Clustering and Community Detection with Imbalanced Clusters

Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present. We show that ratio cut (RCut) or normalized cut (NCut) objectives are not tailored to imbalanced cluster sizes since they tend to emphasize cut sizes over cut values. We propose a graph partitioning problem that seeks minimum cut partitions under minimum size constraints on partitions to deal with imbalanced cluster sizes. Our approach parameterizes a family of graphs by adaptively modulating node degrees on a fixed node set, yielding a set of parameter dependent cuts reflecting varying levels of imbalance. The solution to our problem is then obtained by optimizing over these parameters. We present rigorous limit cut analysis results to justify our approach and demonstrate the superiority of our method through experiments on synthetic and real datasets for data clustering, semi-supervised learning and community detection.Comment: Extended version of arXiv:1309.2303 with new applications. Accepted to IEEE TSIP