Reliable and Energy Efficient MLC STT-RAM Buffer for CNN Accelerators

We propose a lightweight scheme where the formation of a data block is changed in such a way that it can tolerate soft errors significantly better than the baseline. The key insight behind our work is that CNN weights are normalized between -1 and 1 after each convolutional layer, and this leaves one bit unused in half-precision floating-point representation. By taking advantage of the unused bit, we create a backup for the most significant bit to protect it against the soft errors. Also, considering the fact that in MLC STT-RAMs the cost of memory operations (read and write), and reliability of a cell are content-dependent (some patterns take larger current and longer time, while they are more susceptible to soft error), we rearrange the data block to minimize the number of costly bit patterns. Combining these two techniques provides the same level of accuracy compared to an error-free baseline while improving the read and write energy by 9% and 6%, respectively

Optimal Operation of Micro-grids Considering the Uncertainties of Demand and Renewable Energy Resources Generation

Nowadays, due to technical and economic reasons, the distributed generation (DG) units are widely connected to the low and medium voltage network and created a new structure called micro-grid. Renewable energies (especially wind and solar) based DGs are one of the most important generations units among DG units. Because of stochastic behavior of these resources, the optimum and safe management and operation of micro-grids has become one of the research priorities for researchers. So, in this study, the optimal operation of a typical micro-grid is investigated in order to maximize the penetration of renewable energy sources with the lowest operation cost with respect to the limitations for the load supply and the distributed generation resources. The understudy micro-grid consists of diesel generator, battery, wind turbines and photovoltaic panels. The objective function comprises of fuel cost, start-up cost, spinning reserve cost, power purchasing cost from the upstream grid and the sales revenue of the power to the upstream grid. In this paper, the uncertainties of demand, wind speed and solar radiation are considered and the optimization will be made by using the GAMS software and mixed integer planning method (MIP).Article History: Received May 21, 2016; Received in revised form July 11, 2016; Accepted October 15, 2016; Available onlineHow to Cite This Article: Jasemi, M., Adabi, F., Mozafari, B., and Salahi, S. (2016) Optimal Operation of Micro-grids Considering the Uncertainties of Demand and Renewable Energy Resources Generation, Int. Journal of Renewable Energy Development, 5(3),233-248.http://dx.doi.org/10.14710/ijred.5.3.233-24

A new methodology for deriving the efficient frontier of stocks portfolios: An advanced risk-return model

In this paper after a general literature review on the concept of Efficient Frontier (EF), an important inadequacy of the Variance based models for deriving EFs and the high necessity for applying another risk measure is exemplified. In this regard for this study the risk measure of Lower Partial Moment of the first order is decided to replace Variance. Because of the particular shape of the proposed risk measure, one part of the paper is devoted to development of a mechanism for deriving EF on the basis of new model. After that superiority of the new model to old one is shown and then the shape of new EFs under different situations is investigated. At last it is concluded that application of LPM of the first order in financial models in the phase of deriving EF is completely wise and justifiable

The pseudo‐natural product rhonin targets RHOGDI

For the discovery of novel chemical matter generally endowed with bioactivity, strategies may be particularly efficient that combine previous insight about biological relevance, e.g., natural product (NP) structure, with methods that enable efficient coverage of chemical space, such as fragment-based design. We describe the de novo combination of different 5-membered NP-derived N-heteroatom fragments to structurally unprecedented “pseudo-natural products” in an efficient complexity-generating and enantioselective one-pot synthesis sequence. The pseudo-NPs inherit characteristic elements of NP structure but occupy areas of chemical space not covered by NP-derived chemotypes, and may have novel biological targets. Investigation of the pseudo-NPs in unbiased phenotypic assays and target identification led to the discovery of the first small-molecule ligand of the RHO GDP-dissociation inhibitor 1 (RHOGDI1), termed Rhonin. Rhonin inhibits the binding of the RHOGDI1 chaperone to GDP-bound RHO GTPases and alters the subcellular localization of RHO GTPases

The pseudo‐natural product rhonin targets RHOGDI

For the discovery of novel chemical matter generally endowed with bioactivity, strategies may be particularly efficient that combine previous insight about biological relevance, e.g., natural product (NP) structure, with methods that enable efficient coverage of chemical space, such as fragment-based design. We describe the de novo combination of different 5-membered NP-derived N-heteroatom fragments to structurally unprecedented “pseudo-natural products” in an efficient complexity-generating and enantioselective one-pot synthesis sequence. The pseudo-NPs inherit characteristic elements of NP structure but occupy areas of chemical space not covered by NP-derived chemotypes, and may have novel biological targets. Investigation of the pseudo-NPs in unbiased phenotypic assays and target identification led to the discovery of the first small-molecule ligand of the RHO GDP-dissociation inhibitor 1 (RHOGDI1), termed Rhonin. Rhonin inhibits the binding of the RHOGDI1 chaperone to GDP-bound RHO GTPases and alters the subcellular localization of RHO GTPases

The Effects of Music Therapy on Anxiety and Depression of Cancer Patients

Background and Purpose: Cancer patients often suffer from anxiety and depression. Various methods are used to alleviate anxiety and depression, but most of them have side effects. Music therapy can be used as a noninvasive method to reduce anxiety and depression. This study aimed to examine the effect of music therapy on anxiety and depression in patients with cancer. Materials and Methods: This quasi-experimental study was conducted attaching hospitals in Urmia city. A total number of sixty patients with depression and anxiety were recruited using random sampling method and divided into two groups of control and intervention. Patients in intervention group listened to light music at least 20 min per day for 3 days. The degree of patients' anxiety and depression was assessed by Hospital Anxiety and Depression Scale at baseline and 3 days after music therapy. Data were analyzed by SPSS version 13 using t-test, Pearson, and ANOVA tests. Results: The results showed no significant differences between demographic variable of intervention and control groups. Our findings indicated a significant decrease in the level of depression and anxiety among intervention group. There were significant relationships between anxiety, depression, and sex (P < 0.001, r = 0.42) as well as education (P = 0.003, r = 0.37). Conclusion: This study revealed positive effects of music therapy on decreasing level of depression and anxiety in patients with cancer. Therefore, it is recommended to include music therapy in the nursing care

DEVELOPING A MODULAR PORTFOLIO SELECTION MODEL FOR SHORT-TERM AND LONGTERM MARKET TRENDS AND MASS PSYCHOLOGY

&lt;p&gt;ENGLISH ABSTRACT: In an effort to model stock markets, many researchers have developed portfolio selection models to maximise investor satisfaction. However, this field still needs more accurate and comprehensive models. Development of these models is difficult because of unpredictable economic, social, and political variables that affect stock market behaviour. In this paper, a new model with three modules for portfolio optimisation is presented. The first module derives the efficient frontier through a new approach; the second presents an intelligent mechanism for emitting trading signals; while the third module integrates the outputs of the first two modules. Some important features of the model in comparison with others are: 1) consideration of investors’ emotions – the psychology of the market – that arises from the three above-mentioned factors; 2) significant loosening of simplifying assumptions about markets and stocks; and 3) greater sensitivity to new data.&lt;/p&gt;&lt;p&gt;AFRIKAANSE OPSOMMING: In ‘n poging om aandelemarkte te modelleer het verskeie navorsers portefeulje-seleksiemodelle ontwikkel om beleggers se tevredenheid te maksimiseer. Desnieteenstaande word meer akkurate en omvattende modelle benodig. Die ontwikkeling van hierdie modelle word bemoeilik deur die onvoorspelbare ekonomiese, sosiale en politiese veranderlikes wat aandelemarkte se gedrag raak. In hierdie artikel word ‘n nuwe model voorgehou wat bestaan uit drie modules vir portefeulje-optimisering. Die eerste module bepaal die doelmatigheidsgrens op ‘n nuwe metode; die tweede hou ‘n intelligente meganisme voor om transaksieseine te lewer terwyl die derde module die uitsette van die eerste twee modules integreer. Sommige van die belangrike eienskappe van die model wat dit van ander onderskei is: 1) konsiderasie van die beleggers se emosies – die sielkunde van die mark – wat ontstaan vanweë die genoemde faktore; 2) betekenisvolle verslapping van die vereenvoudigende aannames oor market en aandele; en 3) verhoogde sensitiwiteit tot nuwe data.&lt;/p&gt

Reliable and Energy Efficient MLC STT-RAM Buffer for CNN Accelerators

We propose a lightweight scheme where the formation of a data block is changed in such a way that it can tolerate soft errors significantly better than the baseline. The key insight behind our work is that CNN weights are normalized between -1 and 1 after each convolutional layer, and this leaves one bit unused in half-precision floating-point representation. By taking advantage of the unused bit, we create a backup for the most significant bit to protect it against the soft errors. Also, considering the fact that in MLC STT-RAMs the cost of memory operations (read and write), and reliability of a cell are content-dependent (some patterns take larger current and longer time, while they are more susceptible to soft error), we rearrange the data block to minimize the number of costly bit patterns. Combining these two techniques provides the same level of accuracy compared to an error-free baseline while improving the read and write energy by 9% and 6%, respectively