Tetranorsesquiterpenoids as Attractants of Yucca Moths to Yucca Flowers

Journal of Chemical Ecology - The obligate pollination mutualism between Yucca and yucca moths is a classical example of coevolution. Oviposition and active pollination by female yucca moths occur...     

Using Amorphous Phases in the Design of Structural Alloys

The recent discovery that amorphous alloy powders can be prepared by mechanically alloying a mixture of pure crystalline intermetallics is opening new windows to the synthesis of engineering materials. Amorphous powders synthesized by mechanical alloying may find application in the design of structural alloys, high thermal conductivity alloys, and metal-matrix composites.     

Bitwise dimensional co-clustering for analytical workloads

Analytical workloads in data warehouses often include heavy joins where queries involve multiple fact tables in addition to the typical star-patterns, dimensional grouping and selections. In this paper we propose a new processing and storage framework called bitwise dimensional co-clustering (BDCC) that avoids replication and thus keeps updates fast, yet is able to accelerate all these foreign key joins, efficiently support grouping and pushes down most dimensional selections. The core idea of BDCC is to cluster each table on a mix of dimensions, each possibly derived from attributes imported over an incoming foreign key and this way creating foreign key connected tables with partially shared clusterings. These are later used to accelerate any join between two tables that have some dimension in common and additionally permit to push down and propagate selections (reduce I/O) and accelerate aggregation and ordering operations. Besides the general framework, we describe an algorithm to derive such a physical co-clustering database automatically and describe query processing and query optimization techniques that can easily be fitted into existing relational engines. We present an experimental evaluation on the TPC-H benchmark in the Vectorwise system, showing that co-clustering can significantly enhance its already high performance and at the same time significantly reduce the memory consumption of the system.     

Job-shop scheduling in a body shop

We study a generalized job-shop problem called the body shop scheduling problem (BSSP). This problem arises from the industrial application of welding in a car body production line, where possible collisions between industrial robots have to be taken into account. BSSP corresponds to a job-shop problem where the operations of a job have to follow alternating routes on the machines, certain operations of different jobs are not allowed to be processed at the same time and after processing an operation of a certain job a machine might be unavailable for a given time for operations of other jobs. As main results we will show that for three jobs and four machines the special case where only one machine is used by more than one job is already $\mathcal NP $ -hard. This also implies that the single machine scheduling problem that asks for a makespan minimal schedule of three chains of operations with delays between the operations of a chain is $\mathcal NP $ -hard. On the positive side, we present a polynomial algorithm for the two job case and a pseudo-polynomial algorithm together with an FPTAS  for an arbitrary but constant number of jobs. Hence for a constant number of jobs we fully settle the complexity status of the problem.     

Performance models of storage contention in cloud environments

We propose simple models to predict the performance degradation of disk requests due to storage device contention in consolidated virtualized environments. Model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same server. We first propose a trace-driven approach that evaluates a queueing network with fair share scheduling using simulation. The model parameters consider Virtual Machine Monitor level disk access optimizations and rely on a calibration technique. We further present a measurement-based approach that allows a distinct characterization of read/write performance attributes. In particular, we define simple linear prediction models for I/O request mean response times, throughputs and read/write mixes, as well as a simulation model for predicting response time distributions. We found our models to be effective in predicting such quantities across a range of synthetic and emulated application workloads.     

Simultaneous nonrigid registration of multiple point sets and atlas construction

Group-wise registration of a set of shapes represented by unlabeled point-sets is a challenging problem since, usually this involves solving for point correspondence in a nonrigid motion setting. In this paper, we propose a novel and robust algorithm that is capable of simultaneously computing the mean shape represented by a probability density function from multiple unlabeled point-sets and registering them non-rigidly to this emerging mean shape. This algorithm avoids the correspondence problem by minimizing the Jensen-Shannon (JS) divergence between the point sets. We motivate the use of the JS divergence by pointing out its close relationship to hypothesis testing. We derive the analytic gradient of the cost function in order to efficiently achieve the optimal solution. JS-divergence is symmetric with no bias toward any of the given shapes to be registered and whose mean is being sought. A by product of the registration process is a probabilistic atlas defined as the convex combination of the probability densities of the input point sets being aligned. Our algorithm can be especially useful for creating atlases of various shapes present in images as well as for simultaneously (rigidly or non-rigidly) registering 3D range data sets without having to establish any correspondence. We present experimental results on real and synthetic data.     

Characterization of laser hardened steels by laser induced ultrasonic surface waves

Ultrasonic surface waves are suitable for the characterization of surface hardened materials. This is shown on laser hardened turbine blades. The martensitic microstructure within the surface layer of surface hardened steels has a lower surface wave propagation velocity than the annealed or normalized substrate material. Because the propagation velocity depends on the ratio of layer thickness to wavelengthd/, its measurement allows the determination of the hardening depth. If the surface wave frequency is high enough, the surface wave propagates mainly within the hardened layer. A correlation of the surface wave velocity to the surface hardness has been found. Because the variation of the surface velocity in hardened steels is small, a high measurement accuracy is necessary to obtain the interesting hardening parameters with sufficient certainty. Therefore, a measuring arrangement has been developed where laser pulses, guided by optical fibers to the surface hardened structure, generate simultaneously surface wave pulses at two different positions. The two ultrasonic pulses are received by a piezoelectric transducer. The surface wave velocity is obtained from the time delay between these pulses which is determined by the cross-correlation method. To evaluate simultaneously surface waves with different penetration depths from the same signal acquisition, digital filtering has been used in connection with the cross-correlation.     

Secure video communication using firefly optimization and visual cryptography

In recent years, we face an increasing interest in protecting multimedia data and copyrights due to the high exchange of information. Attackers are trying to get confidential information from various sources, which brings the importance of securing the data. Many researchers implemented techniques to hide secret information to maintain the integrity and privacy of data. In order to protect confidential data, histogram-based reversible data hiding with other cryptographic algorithms are widely used. Therefore, in the proposed work, a robust method for securing digital video is suggested. We implemented histogram bit shifting based reversible data hiding by embedding the encrypted watermark in featured video frames. Histogram bit shifting is used for hiding highly secured watermarks so that security for the watermark symbol is also being achieved. The novelty of the work is that only based on the quality threshold a few unique frames are selected, which holds the encrypted watermark symbol. The optimal value for this threshold is obtained using the Firefly Algorithm. The proposed method is capable of hiding high-capacity data in the video signal. The experimental result shows the higher capacity and video quality compared to other reversible data hiding techniques. The recovered watermark provides better identity identification against various attacks. A high value of PSNR and a low value of BER and MSE is reported from the results.