Coglianese, Cary, Lai, Alicia · eYLS (Yale Law School) · 2022
Critics raise alarm bells about governmental use of digital algorithms, charging that they are too complex, inscrutable, and prone to bias. A realistic assessment of digital algorithms, though, must acknowledge that government is already driven by algorithms of arguably greater complexity and potential for abuse: the algorithms implicit in human decision-making. The human brain operates algorithmically through complex neural networks. And when humans make collective decisions, they operate via algorithms too—those reflected in legislative, judicial, and administrative processes. Yet these human algorithms undeniably fail and are far from transparent. On an individual level, human decision-making suffers from memory limitations, fatigue, cognitive biases, and racial prejudices, among other problems. On an organizational level, humans succumb to groupthink and free riding, along with other collective dysfunctionalities. As a result, human decisions will in some cases prove far more problematic than their digital counterparts. Digital algorithms, such as machine learning, can improve governmental performance by facilitating outcomes that are more accurate, timely, and consistent. Still, when deciding whether to deploy digital algorithms to perform tasks currently completed by humans, public officials should proceed with care on a case-by-case basis. They should consider both whether a particular use would satisfy the basic preconditions for successful machine learning and whether it would in fact lead to demonstrable improvements over the status quo. The question about the future of public administration is not whether digital algorithms are perfect. Rather, it is a question about what will work better: human algorithms or digital ones.
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Coglianese, Cary, Lai, Alicia · eYLS (Yale Law School) · 2022
Critics raise alarm bells about governmental use of digital algorithms, charging that they are too complex, inscrutable, and prone to bias. A realistic assessment of digital algorithms, though, must acknowledge that government is already driven by algorithms of arguably greater complexity and potential for abuse: the algorithms implicit in human decision-making. The human brain operates algorithmically through complex neural networks. And when humans make collective decisions, they operate via algorithms too—those reflected in legislative, judicial, and administrative processes. Yet these human algorithms undeniably fail and are far from transparent. On an individual level, human decision-making suffers from memory limitations, fatigue, cognitive biases, and racial prejudices, among other problems. On an organizational level, humans succumb to groupthink and free-riding, along with other collective dysfunctionalities. As a result, human decisions will in some cases prove far more problematic than their digital counterparts. Digital algorithms, such as machine learning, can improve governmental performance by facilitating outcomes that are more accurate, timely, and consistent. Still, when deciding whether to deploy digital algorithms to perform tasks currently completed by humans, public officials should proceed with care on a case-by-case basis. They should consider both whether a particular use would satisfy the basic preconditions for successful machine learning and whether it would in fact lead to demonstrable improvements over the status quo. The question about the future of public administration is not whether digital algorithms are perfect. Rather, it is a question about what will work better: human algorithms or digital ones.
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Wenjun Xie, Sai Sravan, Bharadawaj Karri et al. · 2012
Axonal transport is related to neuron function and neuro-degradation; usually a large number of transport data need to be analyzed to get statistically significant result.
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Extinction Algorithm · Bulletin of Miscellaneous Information (Royal Gardens Kew)
Extinction Algorithm is the first solo product of Mr.S, the mastermind of Satanochio. This is more than just a simple record, it is an artistic protest, regarding the dehumanization procees of the nowadays society. This is the alarm signal, born from the depression of the artist, regarding individualityâs extinction. The human soul is an endagered species but we can change itâs extinction algorithm. How? Remembering. That is the main purpose of this somehow unexplainable black metal rooted , dark cinematic anthem collection. Do yourself a favor and share this record with as much people you can, maybe this way youâll not reach that day when youâll live alone on this dying planet surrounded by smiling monsters. Recommended especially for dreamers. This medicine can help you recover your native soul capacities. â Dungeon chamber guitars sessions lead by Mr.S Artwork by Costin Chioreanu â Twilight13media âUnder these stars my journey has begun Above this earth I keep my shadow flat Until Iâll become a part of one space, equilibrium shall be the purpose of my raceâ
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Changji Wang (631905), Yuan Li (67017), Xiaonan Xia (631906) et al. · Figshare · 2015
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Meng-Tsung Tsai (238271), Tsurng-Chen Chern (238272), Jen-Hsiang Chuang (210836) et al. · Figshare · 2015
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Amr M. T. Ali-Eldin (13170684) · Figshare · 2022
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Amr M. T. Ali-Eldin (13170684) · Figshare · 2022
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Yunpeng Liu (379288), Daniel A. Tennant (206938), Zexuan Zhu (296894) et al. · Figshare · 2015
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Yun Chi, Shenghuo Zhu, Y. Chi et al. · 2013
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Meng-Tsung Tsai (238271), Tsurng-Chen Chern (238272), Jen-Hsiang Chuang (210836) et al. · Figshare · 2015
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Ritu Verma, Sunita Dhingra · 2011
Multiprocessor task scheduling (MPTS) is an important and computationally difficult problem. Multiprocessors have emerged as a powerful computing means for running real-time applications especially due to limitation of uni-processor syste m for not having sufficient enough capability to execute all the tas ks. This paper describes multiprocessor task scheduling in the for m of permutation flow shop scheduling, which has an objective function for minimizing the makespan. Here, we will conclude how the performance of genetic algorithms (value of the makespan of the schedule) varies with the variation of Genet ic Algorithm (GA) control parameters (population size, crossover probability and mutation probability).
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Notes Taken Shizhong Li, Input A B · 2005
Summary: In the spring of 1979, the Soviet mathematician L.G.Khachian discovered a polynomial algorithm for LP called Ellipsoid Algorithm. This discovery classifies the linear programming problem in class P for the first time. After the high level introduction of the algorithm in the last class, we turn to discuss it in details in this lecture. Ellipsoid Algorithm.
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C.A.R. Hoare · Oxford University Research Archive (ORA) (University of Oxford) · 1961
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Hanan Alolaiyan (18536236), Laila Latif (19751417), Umer Shuaib (19751420) et al. · Figshare · 2024
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Megscammell · Zenodo (CERN European Organization for Nuclear Research) · 2021
Multistart with early termination of descents (METOD) algorithm source code, tests and examples.
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Raghvendra Mall (581171), Rocco Langone (581172), Johan A. K. Suykens (175304) · Figshare · 2015
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DKhan123 · Zenodo (CERN European Organization for Nuclear Research) · 2021
No description provided.
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Nam P. Nguyen (310988), Thang N. Dinh (550024), Yilin Shen (550025) et al. · Figshare · 2015
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LIzengnan · Zenodo (CERN European Organization for Nuclear Research) · 2026
This project includes the full processing workflow implemented in Google Earth Engine (GEE), covering cloud masking, NBR calculation, annual optimal pixel compositing, and time series disturbance detection ands results using LandTrendr algorithms.
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