Biostratigraphic Study of the Gurpi Formation Based on Planktonic Foraminifera In Lar Area (Kuh-e-kurdeh Section)

The study of planktonic foraminifera of the Gurpi formations at Lar area (Kuh-e-kurdeh section) enables me to find the most standard biozones defined in mediterranean regions, especially Tethysian domain. Five biozones were determined. Biozones I (Globotruncanita elevata zone) and II (Globotruncana ventricosa zone) and III (Radotruncana calcarata zone) indicate the Early Campanian and Middle and Late Campanian, respectively. Biozones IV (Globotruncanita stuarti zone) and V (Gansserina gansseri zone) suggest the Early and Middle Maastrichtian, respectively. In the Late Maastrichtian, due to decreasing in water depth at the study area, Abathomphalus mayaroensis zone defined in Tethysian domain was not recognised.

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Doctor of Philosophy

dissertationDeep Neural Networks (DNNs) are the state-of-art solution in a growing number of tasks including computer vision, speech recognition, and genomics. However, DNNs are computationally expensive as they are carefully trained to extract and abstract features from raw data using multiple layers of neurons with millions of parameters. In this dissertation, we primarily focus on inference, e.g., using a DNN to classify an input image. This is an operation that will be repeatedly performed on billions of devices in the datacenter, in self-driving cars, in drones, etc. We observe that DNNs spend a vast majority of their runtime to runtime performing matrix-by-vector multiplications (MVM). MVMs have two major bottlenecks: fetching the matrix and performing sum-of-product operations. To address these bottlenecks, we use in-situ computing, where the matrix is stored in programmable resistor arrays, called crossbars, and sum-of-product operations are performed using analog computing. In this dissertation, we propose two hardware units, ISAAC and Newton.In ISAAC, we show that in-situ computing designs can outperform DNN digital accelerators, if they leverage pipelining, smart encodings, and can distribute a computation in time and space, within crossbars, and across crossbars. In the ISAAC design, roughly half the chip area/power can be attributed to the analog-to-digital conversion (ADC), i.e., it remains the key design challenge in mixed-signal accelerators for deep networks. In spite of the ADC bottleneck, ISAAC is able to out-perform the computational efficiency of the state-of-the-art design (DaDianNao) by 8x. In Newton, we take advantage of a number of techniques to address ADC inefficiency. These techniques exploit matrix transformations, heterogeneity, and smart mapping of computation to the analog substrate. We show that Newton can increase the efficiency of in-situ computing by an additional 2x. Finally, we show that in-situ computing, unfortunately, cannot be easily adapted to handle training of deep networks, i.e., it is only suitable for inference of already-trained networks. By improving the efficiency of DNN inference with ISAAC and Newton, we move closer to low-cost deep learning that in turn will have societal impact through self-driving cars, assistive systems for the disabled, and precision medicine

MemZip: exploring unconventional benefits from memory compression

pre-printMemory compression has been proposed and deployed in the past to grow the capacity of a memory system and reduce page fault rates. Compression also has secondary benefits: it can reduce energy and bandwidth demands. However, most prior mechanisms have been designed to focus on the capacity metric and few prior works have attempted to explicitly reduce energy or bandwidth. Further, mechanisms that focus on the capacity metric also require complex logic to locate the requested data in memory. In this paper, we design a highly simple compressed memory architecture that does not target the capacity metric. Instead, it focuses on complexity, energy, bandwidth, and reliability. It relies on rank subsetting and a careful placement of compressed data and metadata to achieve these benefits. Further, the space made available via compression is used to boost other metrics - the space can be used to implement stronger error correction codes or energy-efficient data encodings. The best performing MemZip configuration yields a 45% performance improvement and 57% memory energy reduction, compared to an uncompressed non-sub-ranked baseline. Another energy-optimized configuration yields a 29.8% performance improvement and a 79% memory energy reduction, relative to the same baseline

Evaluation of Wind Resources and the Effect of Market Price Components on Wind-Farm Income: A Case Study of Ørland in Norway

This paper aims to present a detailed analysis of the performance of a wind-farm using the wind turbine power measurement standard IEC61400-12-1 (2017). Ten minutes averaged wind data are obtained from LIDAR over the period of twelve months and it is compared with the 38 years’ data from weather station with the objective of determining the wind resources at the wind-farm. The performance of one of the wind turbines located in the wind-farm is assessed by comparing the wind power potential of the wind turbine with its actual power production. Our analysis shows that the wind farm under study is rated as ‘good’ in terms of wind power production and has wind power density of 479 W/m2. The annual wind-farm’s income is estimated based on the real-data collected from the wind turbines. The effect of price of electricity and the spot prices of Norwegian-Swedish green certificate on the income will be illustrated by means of a Monte-Carlo Simulation (MCS) approach. Our study provides a different perspective of wind resource evaluation by analyzing LIDAR measurements using Windographer and combines it with the lesser explored effects of price components on the income using statistical tools

EFFECTIVENESS OF HYBRID-FLIPPED CLASSROOM IN IMPROVING EFL LEARNERS’ ARGUMENTATIVE WRITING SKILL

This study investigated the effectiveness of the hybrid-flipped classroom model in improving English-as-a-Foreign-Language (EFL) learners’ argumentative writing skill. Also, it evaluated the model as a means of learning argumentative writing skill. To these ends, the sample of 50 EFL learners from a language institute were selected after taking a language placement test and were assigned to control (conventional) and experimental (hybrid-flipped) groups. To collect the data, pretest and posttest argumentative essays as well as semistructured interviews were used. ANCOVA on the writing scores in the prestest and posttest phase showed that using the hybrid-flipped instruction had a significant effect on the learners’ argumentative writing performance. Moreover, thematic analysis on the qualitative interview data revealed several benefits of hybrid-flipped method such as learner-teacher and learner to learner interaction, increased learners’ responsibility, easy makeup for learners’ absence, and teacher assistance. The findings suggest the applicability of hybrid-flipped method for teaching L2 writing

Mimicking Classical Noise in Ion Channels by Quantum Decoherence

The mechanism of selectivity in ion channels is still an open question in biology. According to recent proposals, it seems that the selectivity filter of the ion channel, which plays a key role in the channel's function, may show quantum coherence, which can play a role in explaining the selection mechanism and conduction of ions. However, due to decoherence theory, the presence of environmental noise causes decoherence and loss of quantum effects. Sometimes we hope that the effect of calssical noise of the environment in ion channels can be modeled through a picture whose the quantum decoherence theory presents. In this paper, we simulated the behavior of the ion channel system in the Spin-Boson model using the unitary evolution of a stochastic Hamiltonian operator under the classical noise model. Also, in a different approach, we modeled the system evolution as a two-level Spin-Boson model with tunneling interacting with a bath of harmonic oscillators, using decoherence theory. The results of this system were discussed in different classical and quantum regimes. By examining the results it was found that the Spin-Boson model at a high hopping rate of Potassium ions can simulate the behavior of the system in the classical noise approach. This result is another proof for the fact that ion channels need high speed for high selectivity

Predictors of time to relapse in amphetamine-type substance users in the matrix treatment program in Iran : a Cox proportional hazard model application

Background: The aim of this study was to determine which predictors influence the risk of relapse among a cohort of amphetamine-type substance (ATS) users in Iran. Methods: A Cox proportional hazards model was conducted to determine factors associated with the relapse time in the Matrix treatment program provided by the Iranian National Center of Addiction Studies (INCAS) between March 2010 and October 2011. Results: Participating in more treatment sessions was associated with a lower probability of relapse. On the other hand, patients with less family support, longer dependence on ATS, and those with an experience of casual sex and a history of criminal offenses were more likely to relapse. Conclusion: This study broadens our understanding of factors influencing the risk of relapse in ATS use among an Iranian sample. The findings can guide practitioners during the treatment program