21,538 research outputs found
A Darwinian approach to control-structure design
Genetic algorithms (GA's), as introduced by Holland (1975), are one form of directed random search. The form of direction is based on Darwin's 'survival of the fittest' theories. GA's are radically different from the more traditional design optimization techniques. GA's work with a coding of the design variables, as opposed to working with the design variables directly. The search is conducted from a population of designs (i.e., from a large number of points in the design space), unlike the traditional algorithms which search from a single design point. The GA requires only objective function information, as opposed to gradient or other auxiliary information. Finally, the GA is based on probabilistic transition rules, as opposed to deterministic rules. These features allow the GA to attack problems with local-global minima, discontinuous design spaces and mixed variable problems, all in a single, consistent framework
Justice and predictability in torts
Recent reexaminations of the principles of tort liability have entertained two possible
rationales for the fault principle, one "moral" and the other economic. Neither is satisfactory.
I propose here a third rationale and show how it suffices to refute at least some of the
challenges to the negligence system. The character of this rationale is causal, and the central
thesis of this paper is that in as much as the tort system should aim to place the costs of
accidents on the source of those accidents, then we have not yet found an acceptable
alternative to the negligence system. This thesis is defended and developed through a
reexamination of some recent theories of strict liability and reflection on some of what has
been said about the role of causation in torts. A backdrop to the entire discussion is the
question of how one might best ensure that potential defendants will be able to predict with
reasonable certainty which courses of action will make them liable, should damages ensue
ATP: a Datacenter Approximate Transmission Protocol
Many datacenter applications such as machine learning and streaming systems
do not need the complete set of data to perform their computation. Current
approximate applications in datacenters run on a reliable network layer like
TCP. To improve performance, they either let sender select a subset of data and
transmit them to the receiver or transmit all the data and let receiver drop
some of them. These approaches are network oblivious and unnecessarily transmit
more data, affecting both application runtime and network bandwidth usage. On
the other hand, running approximate application on a lossy network with UDP
cannot guarantee the accuracy of application computation. We propose to run
approximate applications on a lossy network and to allow packet loss in a
controlled manner. Specifically, we designed a new network protocol called
Approximate Transmission Protocol, or ATP, for datacenter approximate
applications. ATP opportunistically exploits available network bandwidth as
much as possible, while performing a loss-based rate control algorithm to avoid
bandwidth waste and re-transmission. It also ensures bandwidth fair sharing
across flows and improves accurate applications' performance by leaving more
switch buffer space to accurate flows. We evaluated ATP with both simulation
and real implementation using two macro-benchmarks and two real applications,
Apache Kafka and Flink. Our evaluation results show that ATP reduces
application runtime by 13.9% to 74.6% compared to a TCP-based solution that
drops packets at sender, and it improves accuracy by up to 94.0% compared to
UDP
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