Symbolic modular deadlock analysis

Methods in object-oriented concurrent libraries often encapsulate internal synchronization details. As a result of information hiding, clients calling the library methods may cause thread safety violations by invoking methods in an unsafe manner. This is frequently a cause of deadlocks. Given a concurrent library, we present a technique for inferring interface contracts that specify permissible concurrent method calls and patterns of aliasing among method arguments. In this work, we focus on deriving contracts that guarantee deadlock-free execution for the methods in the library. The contracts also help client developers by documenting required assumptions about the library methods. Alternatively, the contracts can be statically enforced in the client code to detect potential deadlocks in the client. Our technique combines static analysis with a symbolic encoding scheme for tracking lock dependencies, allowing us to synthesize contracts using a SMT solver. Additionally, we investigate extensions of our technique to reason about deadlocks in libraries that employ signaling primitives such as wait-notify for cooperative synchronization. Our prototype tool analyzes over a million lines of code for some widely-used Java libraries within an hour, thus demonstrating its scalability and efficiency. Furthermore, the contracts inferred by our approach have been able to pinpoint real deadlocks in clients, i.e. deadlocks that have been a part of bug-reports filed by users and developers of client code.     

A combined scheme of edge-based and node-based smoothed finite element methods for Reissner–Mindlin flat shells

In this paper, a combined scheme of edge-based smoothed finite element method (ES-FEM) and node-based smoothed finite element method (NS-FEM) for triangular Reissner–Mindlin flat shells is developed to improve the accuracy of numerical results. The present method, named edge/node-based S-FEM (ENS-FEM), uses a gradient smoothing technique over smoothing domains based on a combination of ES-FEM and NS-FEM. A discrete shear gap technique is incorporated into ENS-FEM to avoid shear-locking phenomenon in Reissner–Mindlin flat shell elements. For all practical purpose, we propose an average combination (aENS-FEM) of ES-FEM and NS-FEM for shell structural problems. We compare numerical results obtained using aENS-FEM with other existing methods in the literature to show the effectiveness of the present method.     

Theorem Proving Guided Development of Formal Assertions in a Resource-Constrained Scheduler for High-Level Synthesis

This paper presents a formal specification and a proof of correctness for the widely-used Force-Directed List Scheduling (FDLS) algorithm for resource-constrained scheduling of data flow graphs in high-level synthesis systems. The proof effort is conducted using a higher-order logic theorem prover. During the proof effort many interesting properties of the FDLS algorithm are discovered. These properties are formally stated and proved in a higher-order logic theorem proving environment. These properties constitute a detailed set of formal assertions and invariants that should hold at various steps in the FDLS algorithm. They are then inserted as programming assertions in the implementation of the FDLS algorithm in a production-strength high-level synthesis system. When turned on, the programming assertions (1) certify whether a specific run of the FDLS algorithm produced correct schedules and, (2) in the event of failure, help discover and isolate programming errors in the FDLS implementation.We present a detailed example and several experiments to demonstrate the effectiveness of these assertions in discovering and isolating errors. Based on this experience, we discuss the role of the formal theorem proving exercise in developing a useful set of assertions for embedding in the scheduler code and argue that in the absence of such a formal proof checking effort, discovering such a useful set of assertions would have been an arduous if not impossible task.     

Classification of primitive shapes using brain–computer interfaces

Brain–computer interfaces (BCIs) are recent developments in alternative technologies of user interaction. The purpose of this paper is to explore the potential of BCIs as user interfaces for CAD systems. The paper describes experiments and algorithms that use the BCI to distinguish between primitive shapes that are imagined by a user. Users wear an electroencephalogram (EEG) headset and imagine the shape of a cube, sphere, cylinder, pyramid or a cone. The EEG headset collects brain activity from 14 locations on the scalp. The data is analyzed with independent component analysis (ICA) and the Hilbert–Huang Transform (HHT). The features of interest are the marginal spectra of different frequency bands (theta, alpha, beta and gamma bands) calculated from the Hilbert spectrum of each independent component. The Mann–Whitney U-test is then applied to rank the EEG electrode channels by relevance in five pair-wise classifications. The features from the highest ranking independent components form the final feature vector which is then used to train a linear discriminant classifier. Results show that this classifier can discriminate between the five basic primitive objects with an average accuracy of about 44.6% (compared to naïve classification rate of 20%) over ten subjects (accuracy range of 36%–54%). The accuracy classification changes to 39.9% when both visual and verbal cues are used. The repeatability of the feature extraction and classification was checked by conducting the experiment on 10 different days with the same participants. This shows that the BCI holds promise in creating geometric shapes in CAD systems and could be used as a novel means of user interaction.     

Performance evaluation of a sequential minimal radial basisfunction (RBF) neural network learning algorithm

Presents a detailed performance analysis of the minimal resource allocation network (M-RAN) learning algorithm, M-RAN is a sequential learning radial basis function neural network which combines the growth criterion of the resource allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RAN. The performance of this algorithm is compared with the multilayer feedforward networks (MFNs) trained with 1) a variant of the standard backpropagation algorithm, known as RPROP and 2) the dependence identification (DI) algorithm of Moody and Antsaklis (1996) on several benchmark problems in the function approximation and pattern classification areas. For all these problems, the M-RAN algorithm is shown to realize networks with far fewer hidden neurons with better or same approximation/classification accuracy. Further, the time taken for learning (training) is also considerably shorter as M-RAN does not require repeated presentation of the training data.     

Structure and Properties of Nano-Scale Oxide-Dispersed Iron

Bulk samples of pure iron and yttria dispersed iron with and without titanium (i.e., Fe, Fe-Y₂O₃, and Fe-Y₂O₃-Ti) were prepared by hot extrusion of high-energy ball-milled powders. An examination of the microstructure using TEM revealed that the addition of titanium resulted in the reduction of the dispersoid size with a concomitant increase in the volume fraction of the dispersoids. As a result, Fe-Y₂O₃-Ti exhibited a substantial increase in hardness and tensile properties as compared to Fe and Fe-Y₂O₃. The higher hardness and strength of Fe-Y₂O₃-Ti is shown to be due to the presence of finer and higher number density of Y-Ti-O complex oxides. Dynamic strain aging in the temperature range of 423 K to 573 K (150 °C to 300 °C) was observed in all the compositions studied.     

First-Principles Investigation on Electronic Transport Properties of Tungsten Nitride Nanoribbon Based Molecular Device

The electronic transport property of tungsten nitride nanoribbon-based molecular device is investigated using density functional theory. The study on electronic transport properties are discussed in terms of device density of states and transmission spectrum of the nanoribbon. The scattering region of the nanoribbon is placed between the electrodes. For different bias voltages, the transport properties are studied. The contribution due to various orbitals leads to the peak maximum in device density of states. The transmission spectrum and transmission pathway provides insight to the transmission of charge under various bias conditions. The maximum peak in the transmission spectrum corresponds to the variation in transmission of charge with different bias voltage. The reported results will pave the way to improve the performance in the field of nanoelectronics.