An approach to represent time series forecasting via fuzzy numbers

This paper introduces a new approach for estimating the uncertainty in the forecast through the construction of Triangular Fuzzy Numbers (TFNs). The interval of the proposed TFN presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE). Here, instead of the representing the forecast with a crisp value with a Prediction Interval (PI), the level of uncertainty associated with the point forecasts will be quantified by defining TFNs (linguistic terms) within the uncertainty interval provided by the FLUBE. This will give the opportunity to handle the forecast as linguistic terms which will increase the interpretability. Moreover, the proposed approach will provide valuable information about the accuracy of the forecast by providing a relative membership degree. The demonstrated results indicate that the proposed FLUBE based TFN representation is an efficient and useful approach to represent the uncertainty and the quality of the forecast

An enhanced fuzzy linguistic term generation and representation for time series forecasting

This paper introduces an enhancement to linguistic forecast representation using Triangular Fuzzy Numbers (TFNs) called Enhanced Linguistic Generation and Representation Approach (ElinGRA). Since there is always an error margin in the predictions, there is a need to define error bounds in the forecast. The interval of the proposed presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE) by getting the models of forecast errors. Thus, instead of a classical statistical approaches, the level of uncertainty associated with the point forecasts will be defined within the FLUBE bounds and these bound can be used for defining fuzzy linguistic terms for the forecasts. Here, ElinGRA is proposed to generate triangular fuzzy numbers (TFNs) for the predictions. In addition to opportunity to handle the forecast as linguistic terms which will increase the interpretability, ElinGRA improved forecast accuracy of constructed TFNs by adding an extra correction term. The results of the experiments, which are conducted on two data sets, show the benefit of using ElinGRA to represent the uncertainty and the quality of the forecast

The Importance of the Role that Turkish Ethnic Media Played in the Formation Process of Turkish Diaspora in America

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A Template-Based Design Methodology for Graph-Parallel Hardware Accelerators

Graph applications have been gaining importance in the last decade due to emerging big data analytics problems such as Web graphs, social networks, and biological networks. For these applications, traditional CPU and GPU architectures suffer in terms of performance and power consumption due to irregular communications, random memory accesses, and load balancing problems. It has been shown that specialized hardware accelerators can achieve much better power and energy efficiency compared to the general purpose CPUs and GPUs. In this paper, we present a template-based methodology specifically targeted for hardware accelerator design of big-data graph applications. Important architectural features that are key for energy efficient execution are implemented in a common template. The proposed template-based methodology is used to design hardware accelerators for different graph applications with little effort. Compared to an application-specific high-level synthesis methodology, we show that the proposed methodology can generate hardware accelerators with up to 18× better energy efficiency and requires less design effort

Hardware accelerator design for data centers

As the size of available data is increasing, it is becoming inefficient to scale the computational power of traditional systems. To overcome this problem, customized application-specific accelerators are becoming integral parts of modern system on chip (SOC) architectures. In this paper, we summarize existing hardware accelerators for data centers and discuss the techniques to implement and embed them along with the existing SOCs. © 2015 IEEE

Architectural requirements for energy efficient execution of graph analytics applications

Intelligent data analysis has become more important in the last decade especially because of the significant increase in the size and availability of data. In this paper, we focus on the common execution models and characteristics of iterative graph analytics applications. We show that the features that improve work efficiency can lead to significant overheads on existing systems. We identify the opportunities for custom hardware implementation, and outline the desired architectural features for energy efficient computation of graph analytics applications. © 2015 IEEE

Leptin and resistin levels in serum of patients with hematologic malignancies: correlation with clinical characteristic

Aim:To evaluate leptin and resistin levels in patients with various hematologic malignancies. Methods: We included 21 patients with lymphoma, 14 with multiple myeloma (MM), 14 with acute leukemia, 13 with chronic lymphocytic leukemia (CLL), and 25 healthy control subjects into our study. The subjects’ body mass indexes (BMI) were calculated; hematological and acute phase response parameters, serum lipid were determined; serum leptin and resistin levels were determined by ELISA. Results: Serum leptin level was significantly increased in CLL and MM groups when compared to the control group (p < 0.01). Resistin level was significantly higher in lymphoma patients than in CLL, acute leukemia and control groups (p < 0.01). In the control group, leptin level was negatively correlated with hemoglobin level (r = –0.44, p = 0.047); and in all patients with hematologic malignancies, leptin level was correlated with BMI (r = 0.32, p = 0.02). Leptin in lymphoma subjects correlated with hemoglobin level (r = 0.64, p = 0.005), resistin level correlated with the platelet count in patients with hematologic malignancies (r = 0.26, p = 0.044). In addition, leptin level had negative correlations with international prognostic score (IPS) in Hodgkin lymphoma (r = –0.9, p = 0.002) and with international prognostic index (IPI) in non-Hodgkin lymphoma (r = –0.77, p = 0.03). In CLL patients, leptin level had a correlation with the poor prognostic marker — CD38 level (r = 0.68, p = 0.03). Conclusion: We found higher leptin levels in MM and CLL patients, and higher resistin levels in lymphoma patients: this fact demonstrates that changes in adipose tissue and metabolism occur in these disease states.Цель: определить уровни содержания лептина и резистина в сыворотке крови больных с различными онкогематологическими заболеваниями. Методы: обследован 21 больной лимфомой, 14 — множественной миеломой (ММ), 14 — острой лейкемией, 13 — хронической лимфоцитарной лейкемией (ХЛЛ), и 25 здоровых доноров. У пациентов определены такие характеристики: индекс массы тела (ИМТ), гематологические параметры, содержание липидов в сыворотке крови. Содержание лептина и резистина в сыворотке крови определяли иммуноферментным методом. Результаты: уровень лептина в сыворотке крови был значительно выше у больных с ХЛЛ и ММ, чем таковой у контрольной группы (р < 0,01). Уровень резистина был значительно выше в группе больных с лимфомами по сравнению с ХЛЛ, острой лейкемией и контрольной группами (р < 0,01). В контрольной группе уровень лептина отрицательно коррелировал с уровнем гемоглобина (r = –0,44, р = 0,047), а во всех группах больных уровень лептина коррелировал с ИМТ (r = 0,32, р = 0,02). Уровень лептина при лимфомах коррелировал с уровнем гемоглобина (r = 0,64, р = 0,005), уровень резистина коррелировал с количеством тромбоцитов у больных всех групп (r = 0,26, р = 0,044). При лимфоме Ходжкина выявлена отрицательная корреляция между уровнем лептина и величиной международной прогностической шкалы (r = -0,9, р = 0,002), при неходжкинской лимфоме — величиной международного прогностического индекса (r = –0,77, р = 0,03), у больных ХЛЛ — с уровнем экспрессии CD38 (r = 0,68, р = 0,03). Выводы: у больных ММ и ХЛЛ выявлен высокий уровень лептина, а с лимфомами — высокий уровень резистина: этот факт указывает на то, что у больных указанными онкогематологическими заболеваниями могут возникать изменения в структуре жировой ткани и обмене веществ

Energy Efficient Architecture for Graph Analytics Accelerators

Specialized hardware accelerators can significantly improve the performance and power efficiency of compute systems. In this paper, we focus on hardware accelerators for graph analytics applications and propose a configurable architecture template that is specifically optimized for iterative vertex-centric graph applications with irregular access patterns and asymmetric convergence. The proposed architecture addresses the limitations of the existing multi-core CPU and GPU architectures for these types of applications. The SystemC-based template we provide can be customized easily for different vertex-centric applications by inserting application-level data structures and functions. After that, a cycle-accurate simulator and RTL can be generated to model the target hardware accelerators. In our experiments, we study several graph-parallel applications, and show that the hardware accelerators generated by our template can outperform a 24 core high end server CPU system by up to 3x in terms of performance. We also estimate the area requirement and power consumption of these hardware accelerators through physical-aware logic synthesis, and show up to 65x better power consumption with significantly smaller area. © 2016 IEEE

Graph Analytics Accelerators for Cognitive Systems

Hardware accelerators are known to be performance and power efficient. This article focuses on accelerator design for graph analytics applications, which are commonly used kernels for cognitive systems. The authors propose a templatized architecture that is specifically optimized for vertex-centric graph applications with irregular memory access patterns, asynchronous execution, and asymmetric convergence. The proposed architecture addresses the limitations of existing CPU and GPU systems while providing a customizable template. The authors' experiments show that the generated accelerators can outperform a high-end CPU system with up to 3 times better performance and 65 times better power efficiency. © 1981-2012 IEEE