Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks

Stochastic neural networks with discrete random variables are an important class of models for their expressiveness and interpretability. Since direct differentiation and backpropagation is not possible, Monte Carlo gradient estimation techniques are a popular alternative. Efficient stochastic gradient estimators, such Straight-Through and Gumbel-Softmax, work well for shallow stochastic models. Their performance, however, suffers with hierarchical, more complex models. We focus on stochastic networks with Boolean latent variables. To analyze such networks, we introduce the framework of harmonic analysis for Boolean functions to derive an analytic formulation for the bias and variance in the Straight-Through estimator. Exploiting these formulations, we propose \emph{FouST}, a low-bias and low-variance gradient estimation algorithm that is just as efficient. Extensive experiments show that FouST performs favorably compared to state-of-the-art biased estimators and is much faster than unbiased ones

Machine condition monitoring and fault diagnosis using spectral analysis techniques

There is need to continuously monitor the conditions of complex, expensive and process-critical machinery in order to detect its incipient breakdown as well as to ensure its high performance and operating safety. Depending on the application, several techniques are available for monitoring the condition of a machine. Vibration monitoring of rotating machinery is considered in this paper so as develop a selfdiagnosis tool for monitoring machines’ conditions. To achieve this a vibration fault simulation rig (VFSR) is designed and constructed so as to simulate and analyze some of the most common vibration signals encountered in rotating machinery. Vibration data are collected from the piezoelectric accelerometers placed at locations that provide rigid vibration transmission to them. Both normal and fault signals are analyzed using the singular value decomposition (SVD) algorithm so as to compute the parameters of the auto regressive moving average (ARMA) models. Machine condition monitoring is then based on the AR or ARMA spectra so as to overcome some of the limitations of the fast Fourier transform (FFT) techniques. Furthermore the estimated AR model parameters and the distribution of the singular values can be used in conjunction with the spectral peaks in making comparison between healthy and faulty conditions. Different fault conditions have been successfully simulated and analyzed using the VFSR in this paper. Results of analysis clearly indicate that this method of analysis can be further developed and used for self-diagnosis, predictive maintenance and intelligent-based monitoring

Clinical, pathological and molecular factors predicting Axillary Node involvement in primary Breast Cancer in Pakistani women

Background: Axillary lymph node involvement in primary breast cancer is one of its most important prognostic features. Thus any factors that may predict axillary lymph node involvement in this setting could be potentially helpful in treatment planning and other interventions. Objective: The objective of this study was to evaluate clinical, pathological and immuno-histochemical markers in univariate and multivariate analysis, which may be helpful predictors of axillary lymph node involvement in breast cancer. Method: A retrospective analysis of 555 cases. Of these 58% had axillary nodal positivity and 42% were negative. Conclusion: Factors of no significance included patient’s age, height, weight, age of first pregnancy, parity, marital status, menopausal status, family history of breast cancer, side of tumor. In univariate analysis the age of menarche, duration of symptoms, tumor size, site in outer quadrant, S phase and skin and nipple involvement all predicted axillary nodal involvement. The length of breast-feeding, increased intraductal component and increased PCNA were inversely proportional to nodal involvement. In multiple regression analysis however only size of the tumor, involvement of the skin and nipple and disease in the outer quadrant of breast were the factors, which assumed significanc

Machine condition monitoring and fault diagnosis using spectral analysis techniques

There is need to continuously monitor the conditions of complex, expensive and process-critical machinery in order to detect its incipient breakdown as well as to ensure its high performance and operating safety. Depending on the application, several techniques are available for monitoring the condition of a machine. Vibration monitoring of rotating machinery is considered in this paper so as develop a selfdiagnosis tool for monitoring machines’ conditions. To achieve this a vibration fault simulation rig (VFSR) is designed and constructed so as to simulate and analyze some of the most common vibration signals encountered in rotating machinery. Vibration data are collected from the piezoelectric accelerometers placed at locations that provide rigid vibration transmission to them. Both normal and fault signals are analyzed using the singular value decomposition (SVD) algorithm so as to compute the parameters of the auto regressive moving average (ARMA) models. Machine condition monitoring is then based on the AR or ARMA spectra so as to overcome some of the limitations of the fast Fourier transform (FFT) techniques. Furthermore the estimated AR model parameters and the distribution of the singular values can be used in conjunction with the spectral peaks in making comparison between healthy and faulty conditions. Different fault conditions have been successfully simulated and analyzed using the VFSR in this paper. Results of analysis clearly indicate that this method of analysis can be further developed and used for self-diagnosis, predictive maintenance and intelligent-based monitoring

Antibacterial Role of SO42-, NO3-, C2O42- and CH3CO2- Anions on Cu(II) and Zn(II) Complexes of a Thiadiazole-derived Pyrrolyl Schiff Base

A condensation reaction of 2-amino-1,3,4-thiadiazole with 2-pyrrolecarboxaldehyde to form tridentate NNN donor Schiff base has been performed. The prepared Schiff base was further used for the formation of metal complexes having stoichiometry [M(L)2]Xn, where M=Cu(II) or Zn(II), L=N-(2-pyrrolylmethylene)-2-amino-1,3,4-thiadiazole, X=SO42−, NO3−, C2O42− or CH3CO2− and n=1 or 2. The new compounds described here have been characterized by their physical, spectral and analytical data, and have been screened against several bacterial strains such as Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. The antibacterial potency of the Schiff base increased upon chelation/complexation, having the same metal ion (cation) but different anions opening up a novel approach in finding new ways to fight against antibiotic resistant strains

Isatin-derived antibacterial and antifungal compounds and their transition metal complexes.

A series of isatins incorporating thiazole, thiadiazole, benzothiazole and p-toluene sulfonyl hydrazide moieties, along with their cobalt(II), copper(II), nickel(II) and zinc(II) metal complexes have been synthesized and characterized by elemental analyses, molar conductances, magnetic moments, IR, NMR and electronic spectral data. These compounds have been screened for antibacterial activity against Escherichia coli, Bacillus subtillis, Shigella flexneri, Staphylococcus aureus, Pseudomonas aeruginosa and Salmonella typhi, and for antifungal activity against Trichophyton longifusus, Candida albicans, Aspergillus flavus, Microsporum canis, Fusarium solani and Candida glaberata using the agar-well diffusion method. All the synthesized compounds have shown good affinity as antibacterial and/or antifungal agents which increased in most of the cases on complexation with the metal ions

Atrial natriuretic peptide levels in Plasma and in Cardiac tissues after chronic hypoxia in Rats

1. Atrial natriuretic peptide (ANP) levels were measured in cardiac tissues and in plasma from adult rats exposed to chronic alveolar hypoxia for periods of 2 h, 24 h and 7 days. Levels were also measured in rats that were maintained in hypoxia for 7 days and then returned to air for 24 h. 2. Plasma ANP was not altered at 2 h but was significantly increased at both 24 h and at 7 days. Plasma ANP in animals exposed to hypoxia for 7 days was normal 24 h after returning to air breathing, despite the persistence of indices of pulmonary hypertension. 3. No significant right atrial hypertrophy was observed under these conditions of chronic hypoxia. A reduction in right atrial ANP content was found at 24 h and was accompanied by a decrease in the number of electrondense granules per right atrial muscle cell. After exposure to hypoxia for 7 days, right atrial ANP and granule number was not different from control, and no alteration was found in right atrial ANP level after removal from the hypoxic environment. 4. No significant right ventricular hypertrophy was produced by exposure to hypoxia for 2 or 24 h. In the former group ventricular ANP had decreased significantly compared with control. Right ventricular hypertrophy was found in both the hypoxic groups after exposure for 7 days, when selective increases in right ventricular ANP content were found. 5. These findings are consistent with the hypothesis that ANP release occurs on exposure to chronic hypoxia and is independent of the associated cardiac hypertrophy and pulmonary vascular remodelling. The findings may have relevance to the natriuresis and reported changes in the renin-angiotensin-aldosterone axis under hypoxic conditions

An Atomistic-Based Continuum Modeling for Evaluation of Effective Elastic Properties of Single-Walled Carbon Nanotubes

The mechanical behavior of SWCNTs is characterized using an atomistic-based continuum method. At nanoscale, interatomic energy among carbon atoms and the corresponding force constants are defined. Subsequently, we used an atomistic finite element analysis to calculate the energy stored in the SWCNT model, which forms a basis for calculating effective elastic moduli. In the finite element model, the force interaction among carbon atoms in a SWCNT is modeled using load-carrying structural beams. At macroscale, the SWCNT is taken as cylindrical continuum solid with transversely isotropic mechanical properties. Equivalence of energies of both models establishes a framework to calculate effective elastic moduli of armchair and zigzag nanotubes. This is achieved by solving five boundary value problems under distinct essential-controlled boundary conditions, which generates a prescribed uniform strain field in both models. Elastic constants are extracted from the calculated elastic moduli. While results of Young’s modulus obtained in this study generally concur with the published theoretical and numerical predictions, values of Poisson’s ratio are on the high side

Relativistic and Binding Energy Corrections to Direct Photon Production In Upsilon Decay

A systematic gauge-invariant method is used to calculate the rate for an upsilon meson to decay inclusively into a prompt photon. An expansion is made in the quark relative velocity v, which is a small natural parameter for heavy quark systems. Inclusion of these O(v^2) corrections tends to increase the photon rate in the middle z range and to lower it for larger z, a feature supported by the data.Comment: 13 pages, LateX, One figure (to be published in Phys. Rev. D, Sept. 1, 1996