Space-Reduction Strategies for Model Checking Dynamic Software

Effective model-checking of modern object-oriented software systems requires providing support for program features such as dynamically created threads, heap-allocated objects and garbage collection. These features have often proven problematic to treat using many previous model-checking frameworks that do not provide sophisticated heap representations and optimizations.In this paper, we define a flexible framework for combined heap and thread symmetry reductions in explicit-state model checking that can be tuned to trade run-time overhead for precision. In addition, we describe various strategies for duplication-reducing state-space encodings for object-oriented heap structures. We have implemented these techniques in Bogor (our extensible software model-checking framework), and we present empirical data to support the effectiveness of these memory reductions on a collection of realistic examples and to demonstrate that they improve upon previous approaches. These techniques, formalized in a group theoretic framework, can be applied to any non-deterministic heap object diagram.     

GRAAL: A Framework for Low-Power 3D Graphics Accelerators

The GRAphics AcceLerator (GRAAL) design-exploration framework is an open system that offers a coherent development methodology for hardware/software cosimulation and codesign of embedded 3D graphics accelerators. GRAAL incorporates tools to help visually debug graphics algorithms implemented in hardware and to estimate performance in terms of throughput, power consumption, and area.     

Effective computation of immersion obstructions for unions of graph classes

In the final paper of the Graph Minors series Robertson and Seymour proved that graphs are well-quasi-ordered under the immersion ordering. A direct implication of this theorem is that each class of graphs that is closed under taking immersions can be fully characterized by forbidding a finite set of graphs (immersion obstruction set). However, as the proof of the well-quasi-ordering theorem is non-constructive, there is no generic procedure for computing such a set. Moreover, it remains an open issue to identify for which immersion-closed graph classes the computation of those sets can become effective. By adapting the tools that were introduced by Adler, Grohe and Kreutzer, for the effective computation of minor obstruction sets, we expand the horizon of computability to immersion obstruction sets. In particular, our results propagate the computability of immersion obstruction sets of immersion-closed graph classes to immersion obstruction sets of finite unions of immersion-closed graph classes.     

Phone duration modeling: overview of techniques and performance optimization via feature selection in the context of emotional speech

Accurate modeling of prosody is prerequisite for the production of synthetic speech of high quality. Phone duration, as one of the key prosodic parameters, plays an important role for the generation of emotional synthetic speech with natural sounding. In the present work we offer an overview of various phone duration modeling techniques, and consequently evaluate ten models, based on decision trees, linear regression, lazy-learning algorithms and meta-learning algorithms, which over the past decades have been successfully used in various modeling tasks. Furthermore, we study the opportunity for performance optimization by applying two feature selection techniques, the RReliefF and the Correlation-based Feature Selection, on a large set of numerical and nominal linguistic features extracted from text, such as: phonetic, phonologic and morphosyntactic ones, which have been reported successful on the phone and syllable duration modeling task. We investigate the practical usefulness of these phone duration modeling techniques on a Modern Greek emotional speech database, which consists of five categories of emotional speech: anger, fear, joy, neutral, sadness. The experimental results demonstrated that feature selection significantly improves the accuracy of phone duration prediction regardless of the type of machine learning algorithm used for phone duration modeling. Specifically, in four out of the five categories of emotional speech, feature selection contributed to the improvement of the phone duration modeling, when compared to the case without feature selection. The M5p trees based phone duration model was observed to achieve the best phone duration prediction accuracy in terms of RMSE and MAE.     

PMORSy: parallel sparse matrix ordering software for fill-in minimization†

In this paper we present PMORSy—a new parallel software package for symmetric sparse matrix ordering on shared memory systems. The NP-complete fill-in minimization problem is solved by means of multilevel nested dissection algorithm with modifications for vertex separators. Parallel processing is done in a task-based fashion with the granularity tuning. We employ threading techniques on shared memory using OpenMP 3.0 technology as opposed to the Message Passing Interface-based approach widely used for parallel sparse matrix ordering. Experimental results on symmetric matrices from the University of Florida Sparse Matrix Collection and matrices from finite-element analysis of three-dimensional strength problems show that our implementation is competitive to the ParMETIS and PT-Scotch libraries both in ordering quality and performance. The PMORSy library is publicly available from the Lobachevsky State University Supercomputing Center web-site.     

In vitro and in vivo brain ablation created by high-intensity focused ultrasound and monitored by MRI

In this paper, magnetic resonance imaging (MRI) is investigated for monitoring small and large lesions created by high-intensity focused ultrasound (HIFU) in freshly excised lamb brain and in rabbit brain in vivo. A single-element spherically focused transducer of 5 cm diameter, focusing at 10 cm and operating at 1 MHz was used. A prototype MRI-compatible positioning device that is used to navigate the transducer is described. The effects of HIFU were investigated using T1-W and T2-W fast spin echo (FSE) and fluid-attenuated inversion recovery (FLAIR). T2-W FSE and FLAIR show better anatomical details within the brain than T1-W FSE, but with T1-W FSE, the contrast between lesion and brain is higher for both thermal and bubbly lesions. The best contrast between lesion and brain with T1-W FSE is obtained with TR above 500 ms, whereas with T2-W FSE, the best contrast is observed between 40 and 60 ms. The maximum contrast to noise ratio (CNR) measured with T1-W FSE was approximately 20. With T2-W FSE, the corresponding CNR was approximately 12. With this system, we were able to create large lesions (by producing overlapping lesions), and it was possible to monitor these lesions with MRI with excellent contrast. The length of the lesions in vivo brain was much higher than the length in vitro, indicating that the penetration in the in vitro brain is limited, possibly by reflection due to trapped bubbles in the blood vessels. This paper demonstrates that HIFU has the potential to treat brain tumors in humans. This could be done either using a single-element transducer with a frequency around 1 MHZ or using a multi-element transducer.     

Highly conductive multiwall carbon nanotube and epoxy composites produced by three-roll milling

Length and diameter distributions as well as the conductivity of bucky papers made of different multiwalled carbon nanotubes (MWCNTs) have been measured. While the average diameter is in good agreement with the manufacturer specification, the average length is significantly shorter than that given by the manufacturer. Highly conductive and homogeneous dispersions of up to 8 wt% MWCNTs in an epoxy resin are obtained by three-roll milling. The influence of shear intensity and number of passes on the nanotube dispersion and composite conductivity is investigated. The aspect ratio of the MWCNTs is one of the most important parameters that determine the composite conductivity and the percolation behavior. We have found that the composite conductivity increases almost 10 times as the aspect ratio increases 5.5 times. The percolation parameters in conjunction with the optical microscopy revealed a kinetic rather than statistic percolation. We have observed that three-roll milling induces extended alignment of MWCNTs with high aspect ratio at moderate loading (2-4 wt%).     

Explicit model predictive control through robust optimization

A strategy that calculates an explicit state feedback policy to regulate constrained uncertain discrete-time uncertain linear systems is presented. We consider uncertain processes, affected by box-bounded multiplicative uncertainty as well as bounded additive uncertainty with linear state and inputs constraints. The proposed method includes (i) the calculation of a terminal set constraint and (ii) the robust reformulation of state constraints in the prediction horizon. These features allow the derivation of the desired policy by solving a single multiparametric quadratic programming problem that guarantees feasible operation in the presence of uncertainty. Additionally, we employ variable and constraint elimination approaches to enhance the computational performance of the strategy. We demonstrate the steps and benefits of these developments with a numerical example and a chemical engineering case study.     

Non-van der Waals 2D Materials for Electrochemical Energy Storage

The development of advanced electrode materials for the next generation of electrochemical energy storage (EES) solutions has attracted profound research attention as a key enabling technology toward decarbonization and electrification of transportation. Since the discovery of graphene's remarkable properties, 2D nanomaterials, derivatives, and heterostructures thereof, have emerged as some of the most promising electrode components in batteries and supercapacitors owing to their unique and tunable physical, chemical, and electronic properties, commonly not observed in their 3D counterparts. This review particularly focuses on recent advances in EES technologies related to 2D crystals originating from non-layered 3D solids (non-van der Waals; nvdW) and their hallmark features pertaining to this field of application. Emphasis is given to the methods and challenges in top-down and bottom-up strategies toward nvdW 2D sheets and their influence on the materials’ features, such as charge transport properties, functionalization, or adsorption dynamics. The exciting advances in nvdW 2D-based electrode materials of different compositions and mechanisms of operation in EES are discussed. Finally, the opportunities and challenges of nvdW 2D systems are highlighted not only in electrochemical energy storage but also in other applications, including spintronics, magnetism, and catalysis.