Inherent Weight Normalization in Stochastic Neural Networks

Multiplicative stochasticity such as Dropout improves the robustness and generalizability of deep neural networks. Here, we further demonstrate that always-on multiplicative stochasticity combined with simple threshold neurons are sufficient operations for deep neural networks. We call such models Neural Sampling Machines (NSM). We find that the probability of activation of the NSM exhibits a self-normalizing property that mirrors Weight Normalization, a previously studied mechanism that fulfills many of the features of Batch Normalization in an online fashion. The normalization of activities during training speeds up convergence by preventing internal covariate shift caused by changes in the input distribution. The always-on stochasticity of the NSM confers the following advantages: the network is identical in the inference and learning phases, making the NSM suitable for online learning, it can exploit stochasticity inherent to a physical substrate such as analog non-volatile memories for in-memory computing, and it is suitable for Monte Carlo sampling, while requiring almost exclusively addition and comparison operations. We demonstrate NSMs on standard classification benchmarks (MNIST and CIFAR) and event-based classification benchmarks (N-MNIST and DVS Gestures). Our results show that NSMs perform comparably or better than conventional artificial neural networks with the same architecture

Boost driven transition in the superconductivity proximitized edge of a quantum spin Hall insulator

We investigate the effects of introducing a boost (a Zeeman field parallel to the spin quantization axis) at the proximitized helical edge of a two-dimensional (2D) quantum spin Hall insulator. Our self-consistent analysis finds that a Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) superconducting phase may emerge at the edge when the boost is larger than a critical value tied to the induced pairing gap. A non-trivial consequence of retaining the 2D bulk in the model is that this boundary FFLO state supports a finite magnetization as well as finite current (flowing along the edge). This has implications for a proper treatment of the ultra-violet cutoff in analyses employing the effective one-dimensional (1D) helical edge model. Our results may be contrasted with previous studies of such 1D models, which found that the FFLO phase either does not appear for any value of the boost (in non-self-consistent calculations), or that it self-consistently appears even for infinitesimal boost, but carries no current and magnetization.Comment: 6 pages, 5 figure

Comprehensive Review of Huffman Encoding Technique for Image Compression

The image processing is used in the every field of life. It is growing field and is used by large number of users. The image processing is used in order to remove the problems present within the image. There are number of techniques which are suggested in order to improve the image. For this purpose image enhancement is commonly used. The space requirements associated with the image is also very important factor. The main aim of the various techniques of image processing is to decrease the space requirements of the image. The space requirements will be minimized by the use of compression techniques. Compression techniques are lossy and lossless in nature. This paper will conduct a comprehensive survey of the lossless compression Huffman coding in detail

Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference

Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. Probabilistic models and stochastic neural networks can explicitly handle uncertainty in data and allow adaptive learning-on-the-fly, but their implementation in a low-power substrate remains a challenge. Here, we introduce a novel hardware fabric that implements a new class of stochastic NN called Neural-Sampling-Machine that exploits stochasticity in synaptic connections for approximate Bayesian inference. Harnessing the inherent non-linearities and stochasticity occurring at the atomic level in emerging materials and devices allows us to capture the synaptic stochasticity occurring at the molecular level in biological synapses. We experimentally demonstrate in-silico hybrid stochastic synapse by pairing a ferroelectric field-effect transistor -based analog weight cell with a two-terminal stochastic selector element. Such a stochastic synapse can be integrated within the well-established crossbar array architecture for compute-in-memory. We experimentally show that the inherent stochastic switching of the selector element between the insulator and metallic state introduces a multiplicative stochastic noise within the synapses of NSM that samples the conductance states of the FeFET, both during learning and inference. We perform network-level simulations to highlight the salient automatic weight normalization feature introduced by the stochastic synapses of the NSM that paves the way for continual online learning without any offline Batch Normalization. We also showcase the Bayesian inferencing capability introduced by the stochastic synapse during inference mode, thus accounting for uncertainty in data. We report 98.25%accuracy on standard image classification task as well as estimation of data uncertainty in rotated samples

Comparative evaluation of levetiracetam and valproic acid as monotherapy on cognitive impairment in patients of epilepsy

Background: Cognitive decline with AEDs (Anti-epileptic drugs) is associated with learning and memory deficits especially in the younger age group. The data regarding the impact of levetiracetam and valproic acid as monotherapy on cognition in epileptic patients is scarce. The present study was done for evaluation of cognitive decline associated with the use of AEDs.Methods: Present study was a prospective study on 60 patients on AEDs for a period of 12 weeks. Patients were enrolled from the Department of Neurology, Swami Rama Himalayan University, Dehradun, Uttarakhand, India and divided into group A (levetiracetam) and group B (valproic acid) with 30 patients in each group. Permission from the institutional ethics committee and written informed consent was taken from all the patients. They were analyzed for cognitive impairment using MMSE and MoCA scales at baseline and 12 weeks.Results: The mean duration of disease was 2.13±1.1 years and 2.08±1.1 years and mean age of the patients was 14.67±1.9 years in group A and 16.20±1.6 years in group B. GTCS was present in 31 patients (52%) followed by partial seizures in 29 patients (48%). The mean change in the MMSE scores from baseline to 12 weeks was significant in group A 1.30±1.1 (p value <0.05) and change group B was -0.20±1.4 not statistically significant. The mean change was observed in MoCA scores from baseline to 12 weeks was significant in both groups A and B by 1.17±1.1 and -0.70±1.1 respectively (P value <0.05).Conclusions: Patients on levetiracetam showed cognitive improvement, whereas patients on valproic acid showed a decline in the MMSE and MoCA scores

Staphylococcus aureus Biofilm Infection Compromises Wound Healing by Causing Deficiencies in Granulation Tissue Collagen

Objective: The objective of this work was to causatively link biofilm properties of bacterial infection to specific pathogenic mechanisms in wound healing. Background: Staphylococcus aureus is one of the four most prevalent bacterial species identified in chronic wounds. Causatively linking wound pathology to biofilm properties of bacterial infection is challenging. Thus, isogenic mutant stains of S. aureus with varying degree of biofilm formation ability was studied in an established preclinical porcine model of wound biofilm infection. Methods: Isogenic mutant strains of S. aureus with varying degree (ΔrexB > USA300 > ΔsarA) of biofilm-forming ability were used to infect full-thickness porcine cutaneous wounds. Results: Compared with that of ΔsarA infection, wound biofilm burden was significantly higher in response to ΔrexB or USA300 infection. Biofilm infection caused degradation of cutaneous collagen, specifically collagen 1 (Col1), with ΔrexB being most pathogenic in that regard. Biofilm infection of the wound repressed wound-edge miR-143 causing upregulation of its downstream target gene matrix metalloproteinase-2. Pathogenic rise of collagenolytic matrix metalloproteinase-2 in biofilm-infected wound-edge tissue sharply decreased collagen 1/collagen 3 ratio compromising the biomechanical properties of the repaired skin. Tensile strength of the biofilm infected skin was compromised supporting the notion that healed wounds with a history of biofilm infection are likely to recur. Conclusion: This study provides maiden evidence that chronic S. aureus biofilm infection in wounds results in impaired granulation tissue collagen leading to compromised wound tissue biomechanics. Clinically, such compromise in tissue repair is likely to increase wound recidivism

Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action

Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or “golden rules,” for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice