Learning Sparse Feature Representations Using Probabilistic Quadtrees and Deep Belief Nets

Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem. In this paper, we present three labeled handwritten digit datasets, collectively called n-MNIST by adding noise to the MNIST dataset, and three labeled datasets formed by adding noise to the offline Bangla numeral database. Then we propose a novel framework for the classification of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets. On the MNIST, n-MNIST and noisy Bangla datasets, our framework shows promising results and outperforms traditional Deep Belief Networks.     

Curvature and entropy based wall boundary condition for the high order spectral volume Euler solver

A curvature and entropy based wall boundary condition is implemented in the high order spectral volume (SV) context. This method borrows ideas from the “curvature-corrected symmetry technique” developed by (Dadone A, Grossman B. Surface Boundary Conditions for Compressible Flows. AIAA J 1994; 32(2): 285–93), for a low order structured grid Euler solver. After numerically obtaining the curvature, the right state (by convention, the left state is inside the computational domain and the right state lies outside of the computational domain) face pressure values are obtained by solving a linearised system of equations. This is unlike that of the lower order finite volume and difference simulations, wherein the right state face values are trivial to obtain. The right state face density values are then obtained by enforcing entropy conservation. Accuracy studies show that simulations performed by employing the new boundary conditions deliver much more accurate results than the ones which employ traditional boundary conditions, while at the same time asymptotically reaching the desired order of accuracy. Numerical results for two-dimensional inviscid flows around the NACA0012 airfoil and over a bump with the new boundary condition showed dramatic improvements over those with the conventional approach. In all cases and orders, spurious entropy productions with the new boundary treatment are significantly reduced. In general, the numerical results are very promising and indicate that the approach has a great potential for 3D high order simulations.     

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.     

Strong fuzzy c-means in medical image data analysis

This paper presents a robust fuzzy c-means (FCM) for an automatic effective segmentation of breast and brain magnetic resonance images (MRI). This paper obtains novel objective functions for proposed robust fuzzy c-means by replacing original Euclidean distance with properties of kernel function on feature space and using Tsallis entropy. By minimizing the proposed effective objective functions, this paper gets membership partition matrices and equations for successive prototypes. In order to reduce the computational complexity and running time, center initialization algorithm is introduced for initializing the initial cluster center. The initial experimental works have done on synthetic image and benchmark dataset to investigate the effectiveness of proposed, and then the proposed method has been implemented to differentiate the different region of real breast and brain magnetic resonance images. In order to identify the validity of proposed fuzzy c-means methods, segmentation accuracy is computed by using silhouette method. The experimental results show that the proposed method is more capable in segmentation of medical images than existed methods.     

Java-MaC: A Run-Time Assurance Approach for Java Programs

We describe Java-MaC, a prototype implementation of the Monitoring and Checking (MaC) architecture for Java programs. The MaC architecture provides assurance that the target program is running correctly with respect to a formal requirements specification by monitoring and checking the execution of the target program at run-time. MaC bridges the gap between formal verification, which ensures the correctness of a design rather than an implementation, and testing, which does not provide formal guarantees about the correctness of the system.Use of formal requirement specifications in run-time monitoring and checking is the salient aspect of the MaC architecture. MaC is a lightweight formal method solution which works as a viable complement to the current heavyweight formal methods. In addition, analysis processes of the architecture including instrumentation of the target program, monitoring, and checking are performed fully automatically without human direction, which increases the accuracy of the analysis. Another important feature of the architecture is the clear separation between monitoring implementation-dependent low-level behaviors and checking high-level behaviors, which allows the reuse of a high-level requirement specification even when the target program implementation changes. Furthermore, this separation makes the architecture modular and allows the flexibility of incorporating third party tools into the architecture. The paper presents an overview of the MaC architecture and a prototype implementation Java-MaC.     

Cryo-X-ray analysis-A novel tool to better understand the physicochemical reactions at the bioglass/biological fluid interface

The present study deals with the short-term physicochemical reactions at the interface between bioactive glass particles [55SiO(2)-20CaO-9P(2)O(5)-12Na(2)O-4MgO. mol%] and biological fluid (Dulbecco Modified Eagle's Medium (DMEM)). The physicochemical reactions within the interface are characterized by scanning transmission electron microscopy (TEM) (STEM) associated with Energy-dispersive X-ray spectroscopy (EDXS). Microanalysis of diffusible ions such as sodium, potassium, or oxygen requires a special care. In the present investigation the cryo-technique was adopted as a suitable tool for the specimen preparation and characterization. Cryosectioning is essential for preserving the native distribution of ions so that meaningful information about the local concentrations can be obtained by elemental microanalysis. The bioglass particles immersed in biological fluid for 24 h revealed five reaction stages: (i) dealkalization of the surface by cationic exchange (Na(+), Ca(2+) with H(+) or H(3)O(+)); (ii) loss of soluble silica in the form of Si(OH)(4) to the solution resulting from the breakdown of Si--O--Si bonds (iii); repolymerization of Si(OH)(4) leading to condensation of SiO(2)); (iv) migration of Ca(2+) and PO(4) (3-) to the surface through the SiO(2)-rich layer to form CaO-P(2)O(5) film; (v) crystallization of the amorphous CaO-P(2)O(5) by incorporating OH-- or CO(3) (2-) anions with the formation of three different surface layers on the bioactive glass periphery. The thickness of each layer is approximately 300 nm and from the inner part to the periphery they consist of Si--OH, which permits the diffusion of Ca(2+) and PO(4) (3-) ions and the formation of the middle Ca--P layer, and finally the outer layer composed of Na--O, which acts as an ion exchange layer between Na(+) ions and H(+) or H(3)O(+) from the solution.     

Photophysical and photochemical investigations on triazole ring linked chalcone containing polymethacrylates

A series of polymethacrylates containing triazole ring linked chalcone were designed and synthesized. These conjugated chalcone polymers are modulate under the light depending on the pendant substituents (–N(CH₃)₂, CH₃, OCH₃, Br, H & NO₂). The steady-state absorption and emission spectroscopic techniques have been used to investigate intramolecular charge transfer (ICT) behaviour of the polymers. Absorption spectra in different organic solvents demonstrate the presence of ICT in polymer 5a. On the other hand, its excited singlet state exhibits high ICT characters as manifested by polarity of solvents. Interestingly, ICT emission maximum is strongly red shifted (53 nm). The emission intensities of fluorophore are compared with those measured after crosslinking, suggesting that the statically quenched fluorophores are entirely non-emissive. The emission decay of polymer 5a displays bi-exponentially with life time of 0.52 and 1.62 ns ascribed to presence of locally excited ICT state. Cyclic voltammogram demonstrates irreversible oxidation potential at 0.9 V indicates formation mono-radical cation.     

Tracer design for magnetic particle imaging (invited)

Magnetic particle imaging (MPI) uses safe iron oxide nanoparticle tracers to offer fundamentally new capabilities for medical imaging, in applications as vascular imaging and ultra-sensitive cancer therapeutics. MPI is perhaps the first medical imaging platform to intrinsically exploit nanoscale material properties. MPI tracers contain magnetic nanoparticles whose tunable, size-dependent magnetic properties can be optimized by selecting a particular particle size and narrow size-distribution. In this paper we present experimental MPI measurements acquired using a homemade MPI magnetometer: a zero-dimensional MPI imaging system designed to characterize tracer performance by measuring the derivative of the time-varying tracer magnetization, M'(H(t)), at a driving frequency of 25 kHz. We show that MPI performance is optimized by selecting phase-pure magnetite tracers of a particular size and narrow size distribution; in this work, tracers with 20 nm median diameter, log-normal distribution shape parameter, σ(v), equal to 0.26, and hydrodynamic diameter equal to 30 nm showed the best performance. Furthermore, these optimized MPI tracers show 4?×?greater signal intensity (measured at the third harmonic) and 20% better spatial resolution compared with commercial nanoparticles developed for MRI.     

Various strategies for reducing Nox emissions of biodiesel fuel used in conventional diesel engines: A review

Energy demand, decreasing fossil fuel reserves, and health-related issues about pollutants have led researchers to search for renewable alternative fuels to either partially or fully replace fossil fuels. Among many alternative fuels, biodiesel became one of the most popular choices due to similar properties to that of conventional diesel. Biodiesel produces slightly lower brake thermal efficiency compared to that of conventional biodiesel, but has an advantage of reduced emissions of CO₂, CO, HC, and smoke. However, biodiesel shows higher NOx emission which, when used in increased biodiesel market, may become a serious problem. Various strategies were attempted by different researcher to reduce NOx emissions. In this paper, various strategies, adapted for reducing NOx emissions of biodiesel fuel used in diesel engines for automobile applications, are reviewed and discussed. The strategies are grouped into three major groups, namely combustion treatments, exhaust after-treatments, and fuel treatments. Among various strategies discussed, fuel treatments, such as low temperature combustion, mixing fuel additives and reformulating fuel composition, reduce NOx emission without compromising other emission and performance characteristics and they seem to be promising for future biodiesel fuel.