Stochastic IMT (insulator-metal-transition) neurons: An interplay of thermal and threshold noise at bifurcation

Artificial neural networks can harness stochasticity in multiple ways to enable a vast class of computationally powerful models. Electronic implementation of such stochastic networks is currently limited to addition of algorithmic noise to digital machines which is inherently inefficient; albeit recent efforts to harness physical noise in devices for stochasticity have shown promise. To succeed in fabricating electronic neuromorphic networks we need experimental evidence of devices with measurable and controllable stochasticity which is complemented with the development of reliable statistical models of such observed stochasticity. Current research literature has sparse evidence of the former and a complete lack of the latter. This motivates the current article where we demonstrate a stochastic neuron using an insulator-metal-transition (IMT) device, based on electrically induced phase-transition, in series with a tunable resistance. We show that an IMT neuron has dynamics similar to a piecewise linear FitzHugh-Nagumo (FHN) neuron and incorporates all characteristics of a spiking neuron in the device phenomena. We experimentally demonstrate spontaneous stochastic spiking along with electrically controllable firing probabilities using Vanadium Dioxide (VO$_2$) based IMT neurons which show a sigmoid-like transfer function. The stochastic spiking is explained by two noise sources - thermal noise and threshold fluctuations, which act as precursors of bifurcation. As such, the IMT neuron is modeled as an Ornstein-Uhlenbeck (OU) process with a fluctuating boundary resulting in transfer curves that closely match experiments. As one of the first comprehensive studies of a stochastic neuron hardware and its statistical properties, this article would enable efficient implementation of a large class of neuro-mimetic networks and algorithms.Comment: Added sectioning, Figure 6, Table 1, and Section II.E Updated abstract, discussion and corrected typo

Green Accounting: what? Why? Where we are now and where we are heading - A Closer Look

Awareness of environmental limits has led to a proliferation of accounting methodologies designed to measure the impact of human activity on the earth's ecological systems and resources. Such methodologies can be collectively described as green accounting, and categorised in three different ways; first, by whose actions are being accounted for; second, by the time period being considered; third, by how environment impacts are measured. Current practice tends to focus on parallel reporting with financial accounting still having greater importance. Green accounting remains largely voluntary and unaudited. The key challenges for green accounting can be summarised as first to determining the scale of change in human activity required to prevent environmental degradation and incorporating some reference to these limits within its metrics, and second to be effective in prompting the necessary behavioral change within the necessary timescale

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

HI tomographic imaging of the Cosmic Dawn and Epoch of Reionization with SKA

We provide an overview of 21cm tomography of the Cosmic Dawn and Epoch of Reionization as possible with SKA-Low. We show why tomography is essential for studying CD/EoR and present the scales which can be imaged at different frequencies for the different phases of SKA- Low. Next we discuss the different ways in which tomographic data can be analyzed. We end with an overview of science questions which can only be answered by tomography, ranging from the characterization of individual objects to understanding the global processes shaping the Universe during the CD/EoRComment: 14 pages, 3 figures. Accepted for publication in the SKA Science Book 'Advancing Astrophysics with the Square Kilometre Array', to appear in 2015. PoS(AASKA14)01

PERIOPERATIVE EFFECTS OF INTRATHECAL CLONIDINE AND FENTANYL WITH HYPERBARIC BUPIVACAINE IN SPINAL ANESTHESIA FOR VAGINAL HYSTERECTOMY

Objectives: Intrathecal fentanyl and clonidine are effective analgesics with different mechanisms of action. This study compares 25 μg of both thesedrugs given intrathecally regarding onset, quality, and duration of hyperbaric bupivacaine-induced spinal block and side effects.Methods: A total of 90 patients of ASA I and II were randomly allocated into three equal groups. Group A received 0.5 ml of 0.9% normal saline(placebo), Group B and Group C received 25 μg fentanyl and clonidine intrathecally added to 2.5 ml of 0.5% hyperbaric bupivacaine, respectively. Theonset and regression time of sensory and motor blocks were recorded along with hemodynamic change, side effects, pain intensity (in terms of visualanalog score (VAS), and time to first rescue analgesic.Results: Intrathecal clonidine (25 μg) significantly prolongs sensory and motor blocks, with prolonged duration of analgesia in comparison withintrathecal fentanyl (25 μg) (325±15 minutes vs. 240±7.6 minutes). VAS score was similar, but sedation was more in clonidine group.Conclusion: We conclude that low-dose intrathecal clonidine is an effective adjuvant to bupivacaine for spinal anesthesia and provides betterpostoperative analgesia in comparison with intrathecal fentanyl.Keywords: Clonidine, Fentanyl, Bupivacaine, Regional, Spinal, Postoperative pain

A organização de conceitos para recuperação da informação

Os princípios requeridos para uma Classificação geral e métodos a serem aplicados em indexação pré-coordenada foram examinados e baseados na evidência psicológica da natureza do conhecimento.Um esquema compatível a ambos foi delineado. Para tanto sentiu-se a necessidade de uma divisão primária em tipos de conceitos básicos, a subdivisão destes em várias colunas paralelas, o arranjo de termos ou conceitos em cada coluna, em diferentes níveis de complexidade, a representação de classes genéricas em qualquer nível, e uma apresentação em separado de tipos mais complexos de conceitos ou termos que são heterogêneos em relação aos tipos de conceitos básicos. Estas diferentes linhas de desenvolvimento de conceitos podem ser representadas por diferentes direções em um diagrama multidimensional. As diferentes dimensões apresentam as relações fundamentais entre conceitos. Outras relações menos fundamentais, portanto não incorporadas, podem ser introduzidas em forma de símbolos entre conceitos expressando relações explícitas. Esse esquema que pode ser melhor denominado "organização de conceitos", ao invés de classificação, é particularmente valioso em recuperação da informação.   http://revista.ibict.br/ciinf/article/view/8