Hasil untuk "Modern"

G. Greaves, G. Greaves, A. L. Greer et al.

T. Herbert, K. Lawrence, A. Tzanova et al.

M. Ehsani, Yimin Gao, A. Emadi

S. Marshall

S. Kay, S. Marple

B. Amable

Christopher F. Baum

J. Orr

S. Freud

J. Ramsay, Martin I. Huber

William Safran

In most scholarly discussions of ethnic communities, immigrants, and aliens, and in most treatments of relationships between minorities and majorities, little if any attention has been devoted to diasporas. In the most widely read books on nationalism and ethnonationalism. the phenomenon is not considered worthy of discussion, let alone index entries. This omission is not surprising, for through the ages, the Diaspora had a very specific meaning: the exile of the Jews from their historic homeland and their dispersion throughout many lands, signifying as well the oppression and moral degradation implied by that dispersion. But a unique phenomenon is not very useful for social scientists attempting to make generalizations. Today, “diaspora” and, more specifically, “diaspora community” seem increasingly to be used as metaphoric designations for several categories of people—expatriates, expellees, political refugees, alien residents, immigrants, and ethnic and racial minorities tout court—in much the same way that “ghetto” has come to designate all kinds of crowded, constricted, and disprivileged urban environments, and “holocaust” has come to be applied to all kinds of mass murder.

J. B. Mcconahay

Kaivan Munshi

J. Shervais

Bodero Luciano, S. Ravi, Korotezki Waldemar

R. Kamien

Di Cao, Weihao Hu, Junbo Zhao et al.

With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed.

Aishwarya Kumar, P. Gupta, Ankita Srivastava

Objective Science and technology sector constituting of data science, machine learning and artificial intelligence are contributing towards COVID-19. The aim of the present study is to discuss the various aspects of modern technology used to fight against COVID-19 crisis at different scales, including medical image processing, disease tracking, prediction outcomes, computational biology and medicines. Methods A progressive search of the database related to modern technology towards COVID-19 is made. Further, a brief review is done on the extracted information by assessing the various aspects of modern technologies for tackling COVID-19 pandemic. Results We provide a window of thoughts on review of the technology advances used to decrease and smother the substantial impact of the outburst. Though different studies relating to modern technology towards COVID-19 have come up, yet there are still constrained applications and contributions of technology in this fight. Conclusions On-going progress in the modern technology has contributed in improving people’s lives and hence there is a solid conviction that validated research plans including artificial intelligence will be of significant advantage in helping people to fight this infection.

Esra Özyürek

O. Mutlu, Saugata Ghose, Juan G'omez-Luna et al.

Modern computing systems are overwhelmingly designed to move data to computation. This design choice goes directly against at least three key trends in computing that cause performance, scalability and energy bottlenecks: (1) data access is a key bottleneck as many important applications are increasingly data-intensive, and memory bandwidth and energy do not scale well, (2) energy consumption is a key limiter in almost all computing platforms, especially server and mobile systems, (3) data movement, especially off-chip to on-chip, is very expensive in terms of bandwidth, energy and latency, much more so than computation. These trends are especially severely-felt in the data-intensive server and energy-constrained mobile systems of today. At the same time, conventional memory technology is facing many technology scaling challenges in terms of reliability, energy, and performance. As a result, memory system architects are open to organizing memory in different ways and making it more intelligent, at the expense of higher cost. The emergence of 3D-stacked memory plus logic, the adoption of error correcting codes inside the latest DRAM chips, proliferation of different main memory standards and chips, specialized for different purposes (e.g., graphics, low-power, high bandwidth, low latency), and the necessity of designing new solutions to serious reliability and security issues, such as the RowHammer phenomenon, are an evidence of this trend. This chapter discusses recent research that aims to practically enable computation close to data, an approach we call processing-in-memory (PIM). PIM places computation mechanisms in or near where the data is stored (i.e., inside the memory chips, in the logic layer of 3D-stacked memory, or in the memory controllers), so that data movement between the computation units and memory is reduced or eliminated.