Efficient Implementation of Stochastic Inference on Heterogeneous Clusters and Spiking Neural Networks

Neuromorphic computing refers to brain inspired algorithms and architectures. This paradigm of computing can solve complex problems which were not possible with traditional computing methods. This is because such implementations learn to identify the required features and classify them based on its training, akin to how brains function. This task involves performing computation on large quantities of data. With this inspiration, a comprehensive multi-pronged approach is employed to study and efficiently implement neuromorphic inference model using heterogeneous clusters to address the problem using traditional Von Neumann architectures and by developing spiking neural networks (SNN) for native and ultra-low power implementation. In this regard, an extendable high-performance computing (HPC) framework and optimizations are proposed for heterogeneous clusters to modularize complex neuromorphic applications in a distributed manner. To achieve best possible throughput and load balancing for such modularized architectures a set of algorithms are proposed to suggest the optimal mapping of different modules as an asynchronous pipeline to the available cluster resources while considering the complex data dependencies between stages. On the other hand, SNNs are more biologically plausible and can achieve ultra-low power implementation due to its sparse spike based communication, which is possible with emerging non-Von Neumann computing platforms. As a significant progress in this direction, spiking neuron models capable of distributed online learning are proposed. A high performance SNN simulator (SpNSim) is developed for simulation of large scale mixed neuron model networks. An accompanying digital hardware neuron RTL is also proposed for efficient real time implementation of SNNs capable of online learning. Finally, a methodology for mapping probabilistic graphical model to off-the-shelf neurosynaptic processor (IBM TrueNorth) as a stochastic SNN is presented with ultra-low power consumption

Accelerating Pattern Matching in Neuromorphic Text Recognition System Using Intel Xeon Phi Coprocessor

Neuromorphic computing systems refer to the computing architecture inspired by the working mechanism of human brains. The rapidly reducing cost and increasing performance of state-of-the-art computing hardware allows large-scale implementation of machine intelligence models with neuromorphic architectures and opens the opportunity for new applications. One such computing hardware is Intel Xeon Phi coprocessor, which delivers over a TeraFLOP of computing power with 61 integrated processing cores. How to efficiently harness such computing power to achieve real time decision and cognition is one of the key design considerations. This work presents an optimized implementation of Brain-State-in-a-Box (BSB) neural network model on the Xeon Phi coprocessor for pattern matching in the context of intelligent text recognition of noisy document images. From a scalability standpoint on a High Performance Computing (HPC) platform we show that efficient workload partitioning and resource management can double the performance of this many-core architecture for neuromorphic applications

Brain-Inspired Spiking Neural Networks

Brain is a very efficient computing system. It performs very complex tasks while occupying about 2 liters of volume and consuming very little energy. The computation tasks are performed by special cells in the brain called neurons. They compute using electrical pulses and exchange information between them through chemicals called neurotransmitters. With this as inspiration, there are several compute models which exist today trying to exploit the inherent efficiencies demonstrated by nature. The compute models representing spiking neural networks (SNNs) are biologically plausible, hence are used to study and understand the workings of brain and nervous system. More importantly, they are used to solve a wide variety of problems in the field of artificial intelligence (AI). They are uniquely suited to model temporal and spatio-temporal data paradigms. This chapter explores the fundamental concepts of SNNs, few of the popular neuron models, how the information is represented, learning methodologies, and state of the art platforms for implementing and evaluating SNNs along with a discussion on their applications and broader role in the field of AI and data networks

Inhibitory effect of Labisia pumila leaf extract on angiogenesis via down-regulation of vascular endothelial growth factor

Purpose: To investigate the anti-angiogenic activity of a methanol leaf extract of Labisia pumila (ME), and its bioactive water fraction (WF), using in vitro models.Methods: The antioxidant activity and total phenolic contents of ME and WF were assessed using DPPH and Folin–Ciocalteu reagents. Antiproliferative effects of extracts towards human umbilical vein endothelial cells (HUVECs) were evaluated using MTT assay. Isolated rat aortic ring and matrigel tube formation assays were performed to assess the antiangiogenic potential of Me and its WF. Levels of VEGF protein in the cell lysates were measured using ELISA kit.Results: Among all the extracts prepared, ME and its WF showed higher total phenolic contents and exhibited moderate antioxidant effects. Significant (p < 0.001) suppression of microvessels outgrowth with half-maximal concentration (IC50) values of 20 and 26 μg/mL for ME and WF, was observed in rat aortic ring assay. ME and its WF halted proliferation and tube formation capacity of HUVECs in in vitro assays. Marked reduction in VEGF levels was observed in lysates of HUVECs treated with ME and its WF.Conclusion: Labisia pumila leaf extract and its water fraction halted angiogenesis by blocking VEGF secretion leading to inhibition of endothelial cells proliferation and differentiation which is suggested to be due to its phenolic antioxidant contents.Keywords: Labisia pumila, Anti-angiogenesis, Antioxidant, Tube formation, Rat aort

Ethyl-p-methoxycinnamate isolated from kaempferia galanga inhibits inflammation by suppressing interleukin-1, tumor necrosis factor-α, and angiogenesis by blocking endothelial functions

OBJECTIVE: The present study aimed to investigate the mechanisms underlying the anti-inflammatory and anti-angiogenic effects of ethyl-p-methoxycinnamate isolated from Kaempferia galanga. METHODS: The anti-inflammatory effects of ethyl-p-methoxycinnamate were assessed using the cotton pellet granuloma assay in rats, whereby the levels of interleukin-1 and tumor necrosis factor-α were measured in the animals' blood. In addition, the levels of interleukin, tumor necrosis factor, and nitric oxide were measured in vitro using the human macrophage cell line (U937). The analgesic effects of ethyl-p-methoxycinnamate were assessed by the tail flick assay in rats. The anti-angiogenic effects were evaluated first by the rat aortic ring assay and, subsequently, by assessing the inhibitory effects of ethyl-p-methoxycinnamate on vascular endothelial growth factor, proliferation, migration, and tube formation in human umbilical vein endothelial cells. RESULTS: Ethyl-p-methoxycinnamate strongly inhibited granuloma tissue formation in rats. It prolonged the tail flick time in rats by more than two-fold compared with the control animals. The inhibition of interleukin and tumor necrosis factor by ethyl-p-methoxycinnamate was significant in both in vivo and in vitro models; however, only a moderate inhibition of nitric oxide was observed in macrophages. Furthermore, ethyl-p-methoxycinnamate considerably inhibited microvessel sprouting from the rat aorta. These mechanistic studies showed that ethyl-p-methoxycinnamate strongly inhibited the differentiation and migration of endothelial cells, which was further confirmed by the reduced level of vascular endothelial growth factor. CONCLUSION: Ethyl-p-methoxycinnamate exhibits significant anti-inflammatory potential by inhibiting pro-inflammatory cytokines and angiogenesis, thus inhibiting the main functions of endothelial cells. Thus, ethyl-p-methoxycinnamate could be a promising therapeutic agent for the treatment of inflammatory and angiogenesis-related diseases

Towards social network based ontology evolution wiki for an ontology evolution

There is a lack of well-maintained ontologies thus ontology evolution now becomes an important filed of ontology research. The evolution may reflect new categories of systems being evaluated on broader and different understandings of certain concepts and relations. Alternatively ontologies evolve because the conceptualization improves. For ontology evolution, we focus in this paper a social network based approach in which the user community has direct control over the evolution of the ontologies. Ontologies can be enriched, learnt, and obtained from social network users using various empirical techniques. In this paper, we ground the social network based approach on the philosophy of wikis so called ontology Evolution Wiki

STROKE OUTCOMES IN NON-DIABETIC, DENOVO DIABETIC AND DIABETIC INDIAN PATIENTS MEASURED BY MODIFIED RANKIN SCALE: AN OBSERVATIONAL STUDY

Objective: Hyperglycemia is a known risk factor which adversely impacts the outcomes in stroke patients compared to patients with normal blood glucose levels. Patients suffering from an acute stroke who are previously nonhyperglycemic may show elevated blood glucose levels. The present study was designed to measure the outcomes in denovo diabetic and diabetic stroke patients compared to nondiabetics.Methods: A prospective observational study over a period of 6 mo, in which 103 patients were divided into three cohorts based on their blood glucose levels (nondiabetic, denovo diabetic and diabetics). The modified Rankin scale (mRS) score was calculated at in-hospital admission and discharge in these three cohorts. The initial and final scores were correlated and mean differences with respect to outcomes between all the three cohorts was calculated.Results: The mean mRS at the time of hospital admission in diabetics and nondiabetics was 3.6±0.81 and 3.3±0.78 which decreased to 2.8±0.95 and 2.9±0.83 respectively at the time of discharge. The mean mRS score in denovo diabetic stroke patients during in-hospital admission was 4±0.81 which was calculated as 3.7±0.85 at the time of discharge. The mean difference in mRS score in diabetics vs non-diabetics was found to be 0.73±0.8 (p =<0.001). The mean difference in mRS score of denovo diabetics vs non-diabetics and denovo diabetics vs diabetics was 0.30±0.63 and 0.38±0.61 respectively (p = 0.1).Conclusion: Results of these observational study in Indian patients, highlights the need for controlling hyperglycemia in stroke patients to improve outcomes and to prevent mortality arising out of acute stroke attacks

In vitro antimetastatic activity of Agarwood (Aquilaria crassna) essential oils against pancreatic cancer cells

Background: Pancreatic cancer is one of the most lethal malignant tumors which remains a rampant killer across the globe. Lack of early diagnosis and toxic drugs have failed to improve the survival rate of pancreatic cancer patients, thus new agents that are safe, available and effective are urgently needed. Objective: The study aimed to investigate the efficacy of Agarwood essential oils in the inhibition of metastasis and induction of apoptosis in the pancreatic cell line (MIA PaCa-2). Methods: Essential oils of Aquilaria crassna were obtained by hydrodistillation. Chemical characterization was analyzed using FTIR and GCMS. The effects of essential oils against three steps of metastases have been investigated, including cell proliferation, migration and clonogenicity. Hoechst and rhodamine assays confirmed the mechanism of pancreatic cancer cell death. Results: The results showed that essential oils exhibited potent cytotoxic activity against MIA PaCa-2 cells with an IC50 (11 ± 2.18 μg/ml). Cell migration was effectively inhibited at (10 μg/ml). Moreover, at a sub-toxic dose (5 μg/mL), essential oils obstructed the colony formation properties of MIA PaCa-2 significantly. The mechanism of cell death was determined due to the induction of nuclear condensation and disruption of mitochondrial membrane potential in the cells. Interestingly, several active components were existed in the chemical profile of the essential oils extract such as β-Caryophyllene, 1-Phenanthrenecarboxylic acid, azulene, naphthalene and Cyclodecene. Conclusion: The present study elucidated for the first time the anti-pancreatic cancer properties of A. crassna essential oils, It can be concluded that the anticancer effects of the extract could be due to the synergistic effect of the biologically active phytoconstituents present in the essential oils

Eupalitin from Asparagus falcatus (Linn.) has anticancer activity and induces activation of caspases 3/7 in human colorectal tumor cells

3,5,4′-Trihydroxy-6,7-dimethoxy-flavone (Eupalitin) has been isolated from the leaves of Asparagus falcatus (Linn.). Anti-proliferation and apoptosis studies were conducted on eupalitin. Results showed that eupalitin exhibited significant cytotoxicity against human colorectal tumor cells. It is found that eupalitin induces the activation of caspases 3/7, a hallmark of apoptosis. The study suggests that the anti-proliferative property of eupalitin towards the human colorectal tumor cells may be probably due to its capability to induce apoptosis in cells