The Complexity of Explicit Constructions

The existence of extremal combinatorial objects, such as Ramsey graphs and expanders, is often shown using the probabilistic method. It is folklore that pseudo-random generators can be used to obtain explicit constructions of these objects, if the test that the object is extremal can be implemented in polynomial time. In this paper, we pose several questions geared towards initiating a structural approach to the relationship between extremal combinatorics and computational complexity. One motivation for such an approach is to understand better why circuit lower bounds are hard. Another is to formalize connections between the two areas, so that progress in one leads automatically to progress in the other.     

Synthesis and characterization of zirconia-based catalyst for the isomerization of n-hexane

Sulfated zirconia is a very strong solid acid catalyst which can be utilized for various reactions. The present study focuses on synthesis of zirconia-based catalyst with high acidity and high surface area, particularly for isomerization reaction. Sulfated zirconia has been obtained by sulfation of zirconia prepared by hydrothermal route. The catalyst was developed by impregnating tungstophosphoric acid on sulfated zirconia by wet incipient method. The catalyst was characterized through Brunauer–Emmett–Teller (BET) surface area, temperature-programmed desorption of ammonia, temperature program reduction of hydrogen, Fourier transmission infrared spectroscopy, and thermogravimetric analysis. The results revealed that the catalyst is crystalline in nature with surface area 190–225?m² g^?1 and acidity 0.135–0.558?mmol?g^?1. Twenty-five percent conversion was obtained (as confirmed by gas chromatography) at 225°C using n-hexane as model hydrocarbon in fixed-bed microreactor.     

Scalable hybrid computation with spikes

We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity: First, like digital computation, which uses several one-bit precise logical units to collectively compute a precise answer to a computation, the hybrid scheme uses several moderate-precision analog units to collectively compute a precise answer to a computation. Second, frequent discrete signal restoration of the analog information prevents analog noise and offset from degrading the computation. And, third, a state machine enables complex computations to be created using a sequence of elementary computations. A natural choice for implementing this hybrid scheme is one based on spikes because spike-count codes are digital, while spike-time codes are analog. We illustrate how spikes afford easy ways to implement all three components of scalable hybrid computation. First, as an important example of distributed analog computation, we show how spikes can create a distributed modular representation of an analog number by implementing digital carry interactions between spiking analog neurons. Second, we show how signal restoration may be performed by recursive spike-count quantization of spike-time codes. And, third, we use spikes from an analog dynamical system to trigger state transitions in a digital dynamical system, which reconfigures the analog dynamical system using a binary control vector; such feedback interactions between analog and digital dynamical systems create a hybrid state machine (HSM). The HSM extends and expands the concept of a digital finite-state-machine to the hybrid domain. We present experimental data from a two-neuron HSM on a chip that implements error-correcting analog-to-digital conversion with the concurrent use of spike-time and spike-count codes. We also present experimental data from silicon circuits that implement HSM-based pattern recognition using spike-time synchrony. We outline how HSMs may be used to perform learning, vector quantization, spike pattern recognition and generation, and how they may be reconfigured.     

Data warehousing and data mining techniques for intrusion detection systems

This paper describes data mining and data warehousing techniques that can improve the performance and usability of Intrusion Detection Systems (IDS). Current IDS do not provide support for historical data analysis and data summarization. This paper presents techniques to model network traffic and alerts using a multi-dimensional data model and star schemas. This data model was used to perform network security analysis and detect denial of service attacks. Our data model can also be used to handle heterogeneous data sources (e.g. firewall logs, system calls, net-flow data) and enable up to two orders of magnitude faster query response times for analysts as compared to the current state of the art. We have used our techniques to implement a prototype system that is being successfully used at Army Research Labs. Our system has helped the security analyst in detecting intrusions and in historical data analysis for generating reports on trend analysis. Recommended by: Ashfaq Khokhar     

Load Balancing Parallel Explicit State Model Checking

This paper first identifies some of the key concerns about the techniques and algorithms developed for parallel model checking; specifically, the inherent problem with load balancing and large queue sizes resultant in a static partition algorithm. This paper then presents a load balancing algorithm to improve the run time performance in distributed model checking, reduce maximum queue size, and reduce the number of states expanded before error discovery. The load balancing algorithm is based on generalized dimension exchange (GDE). This paper presents an empirical analysis of the GDE based load balancing algorithm on three different supercomputing architectures—distributed memory clusters, Networks of Workstations (NOW) and shared memory machines. The analysis shows increased speedup, lower maximum queue sizes and fewer total states explored before error discovery on each of the architectures. Finally, this paper presents a study of the communication overhead incurred by using the load balancing algorithm, which although significant, does not offset performance gains.     

Effects of human immunodeficiency virus sera and macrophage supernatants on mesangial cell proliferation and matrix synthesis

Patients with human immunodeficiency virus (HIV) infection are prone to the development of focal segmental glomerulosclerosis, a lesion in which increased mesangial cell proliferation and matrix synthesis may play a role. We undertook the present study to determine whether HIV sera may affect mesangial cell proliferation and matrix synthesis either directly or indirectly via effects on macrophage supernatants. Pooled HIV sera was found to significantly enhance (P < 0.01) mesangial cell proliferation in a concentration-related manner. Mesangial cell proliferation was significantly suppressed by two medications commonly utilized in HIV-infected patients, azidothymidine and trimethoprim/sulfamethoxazole, and was not significantly altered by lipopolysaccharide, suggesting that these medications as well as recurrent infection are unlikely to account for the proliferative effect of HIV sera. Supernatants from HIV sera-treated macrophages were found to significantly enhance (P < 0.01) mesangial cell incorporation of [3H]proline, a marker for synthesis of the matrix component collagen, compared to supernatants from control sera-treated macrophages. These results suggest that HIV sera may directly enhance mesangial cell proliferation and may indirectly increase mesangial cell matrix synthesis by altering macrophage secretory products. These effects may play a role in the development of glomerulosclerosis in patients with HIV infection.     

Native and oxidized low density lipoproteins modulate mesangial cell apoptosis

Hyperlipidemia has been demonstrated to contribute to hypercellularity of the mesangium in experimental animal models of glomerulosclerosis. We studied whether it also has the potential to convert a hypercellular mesangium into a hypocellular one by inducing mesangial cell (MC) apoptosis. Low density lipoprotein (LDL) enhanced (P < 0.001) mouse mesangial cell (MMC) proliferation at lower concentrations (control, 10.3 +/- 0.3 vs. LDL 100 micrograms/ml, 24.2 +/- 0.3 x 10(4) cells/ml) but augmented (P < 0.001) apoptosis at higher concentrations (control, 5.6 +/- 0.5% vs. LDL, 500 micrograms/ml 26.2 +/- 3.4% apoptotic cells/field). Oxidized (OX) LDL enhanced MMC apoptosis in concentrations of 50 to 200 micrograms/dl. There was a direct relationship between MMC apoptosis and oxidation of LDL as judged by measuring thiobarbituric acid reactive species (TBARS). Since superoxide dismutase (SOD) attenuated (P < 0.001) LDL-induced MMC apoptosis, it seems to be mediated through the generation of free radicals by mesangial cells (control, 4.3 +/- 1.5%; LDL, 200 micrograms/ml, 19.4 +/- 0.5%; LDL + SOD, 8.1 +/- 1.3% apoptotic cells/field). LDL also induced a similar effect on human mesangial cells. These studies were further confirmed by DNA fragment assays and ELISA for programmed cell death. LDL treated cells also showed enhanced mRNA expression for RSG-2, a marker for active cell death. These in vitro results provide a basis for the speculation that LDL has the potential to cause an initial hypercellular and subsequent hypocellular mesangium in the course of the development of glomerulosclerosis.     

Computation methods for hydrodynamic problems (Reynold's equation)

This paper deals with the application of numerical methods to finding approximate solutions for a two-dimensional Reynolds' equation.Pressure height parameters ($\text{P}{}^1$) are evaluated at a series of discrete points on a rectangular mesh in the circumferential direction. Using these parameters the Reynold's equation is transformed into its equivalent finite difference form.In this form the equation gives the value of the pressure height parameter $\text{P}{}^1$ in terms of the values at neighbouring points and the corresponding influence coefficients. The finite difference equation is solved using an interactive method.Jacobi, Gauss-Seidel and successive over-relaxation methods are discussed. It is observed that by using an optimum over-relaxation factor, much computer time can be saved. Finally a convergence criterion is discussed which decides the iteration process.