Life v. Death: Or Why the Death Penalty Should Marginally Deter

Econometric measures of the effect of capital punishment have increasingly provided evidence that it deters homicides. However, most researchers on both sides of the death penalty debate continue to rely on rather simple assumptions about criminal behavior. I attempt to provide a more nuanced and predictive rational choice model of the incentives and disincentives to kill, with the aim of assessing to what extent the statistical findings of deterrence are in line with theoretical expectations. In particular, I examine whether it is plausible to suppose there is a marginal increase in deterrence created by increasing the penalty from life imprisonment without parole to capital punishment. The marginal deterrence effect is shown to be a direct negative function of prison conditions as they are anticipated by the potential offender – the more tolerable someone perceives imprisonment to be, the less deterrent effect prison will have, and the greater the amount of marginal deterrence the threat of capital punishment will add. I then examine the empirical basis for believing there to be a subset of killers who are relatively unafraid of the prison environment, and who therefore may be deterred effectively only by the death penalty. Criminals, empirically, appear to fear a capital sentence, and are willing to sacrifice important procedural rights during plea bargaining to avoid this risk. This has the additional effect of increasing the mean expected term of years attached to a murder conviction, and may generate a secondary deterrent effect of capital punishment. At least for some offenders, the death penalty should induce greater caution in their use of lethal violence, and the deterrent effect seen statistically is possibly derived from the change in the behavior of these individuals. This identification of a particular group on whom the death penalty has the greatest marginal effect naturally suggests reforms in sentencing (and plea bargaining) which focus expensive capital prosecutions on those most resistant to alternative criminal sanctions

Cross-Examining The Brain: A Legal Analysis of Neural Imaging for Credibility Impeachment

The last decade has seen remarkable process in understanding ongoing psychological processes at the neurobiological level, progress that has been driven technologically by the spread of functional neuroimaging devices, especially magnetic resonance imaging, that have become the research tools of a theoretically sophisticated cognitive neuroscience. As this research turns to specification of the mental processes involved in interpersonal deception, the potential evidentiary use of material produced by devices for detecting deception, long stymied by the conceptual and legal limitations of the polygraph, must be re-examined. Although studies in this area are preliminary, and I conclude they have not yet satisfied the foundational requirements for the admissibility of scientific evidence, the potential for use – particularly as a devastating impeachment threat to encourage factual veracity – is a real one that the legal profession should seek to foster through structuring the correct incentives and rules for admissibility. In particular, neuroscience has articulated basic memory processes to a sufficient degree that contemporaneously neuroimaged witnesses would be unable to feign ignorance of a familiar item (or to claim knowledge of something unfamiliar). The brain implementation of actual lies, and deceit more generally, is of greater complexity and variability. Nevertheless, the research project to elucidate them is conceptually sound, and the law cannot afford to stand apart from what may ultimately constitute profound progress in a fundamental problem of adjudication

Description and simulation of an integrated power and attitude control system concept for space-vehicle application

An Integrated Power and Attitude Control System (IPACS) concept with potential application to a broad class of space missions is discussed. A description is given of the basic concept of combining the onboard energy storage and attitude control functions by storing energy in spinning flywheels which are used to provide control torques. A shuttle-launched Research and Applications Module (RAM) A303B solar-observatory mission having stringent pointing requirements (1.0 arc second) is selected to investigate possible interactions between energy storage and attitude control. A simulation of this spacecraft involving actual laboratory-model control-system hardware is presented. Simulation results are discussed which indicate that the IPACS concept, even in a failure-mode configuration, can readily meet the RAM A303B pointing requirements

Combined branch target and predicate prediction for instruction blocks

Embodiments provide methods, apparatus, systems, and computer readable media associated with predicting predicates and branch targets during execution of programs using combined branch target and predicate predictions. The predictions may be made using one or more prediction control flow graphs which represent predicates in instruction blocks and branches between blocks in a program. The prediction control flow graphs may be structured as trees such that each node in the graphs is associated with a predicate instruction, and each leaf associated with a branch target which jumps to another block. During execution of a block, a prediction generator may take a control point history and generate a prediction. Following the path suggested by the prediction through the tree, both predicate values and branch targets may be predicted. Other embodiments may be described and claimed.Board of Regents, University of Texas Syste

vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design

The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to study different machine learning algorithms, forcing them to either use a less desirable network architecture or parallelize the processing across multiple GPUs. We propose a runtime memory manager that virtualizes the memory usage of DNNs such that both GPU and CPU memory can simultaneously be utilized for training larger DNNs. Our virtualized DNN (vDNN) reduces the average GPU memory usage of AlexNet by up to 89%, OverFeat by 91%, and GoogLeNet by 95%, a significant reduction in memory requirements of DNNs. Similar experiments on VGG-16, one of the deepest and memory hungry DNNs to date, demonstrate the memory-efficiency of our proposal. vDNN enables VGG-16 with batch size 256 (requiring 28 GB of memory) to be trained on a single NVIDIA Titan X GPU card containing 12 GB of memory, with 18% performance loss compared to a hypothetical, oracular GPU with enough memory to hold the entire DNN.Comment: Published as a conference paper at the 49th IEEE/ACM International Symposium on Microarchitecture (MICRO-49), 201

Combined branch target and predicate prediction

Embodiments provide methods, apparatus, systems, and computer readable media associated with predicting predicates and branch targets during execution of programs using combined branch target and predicate predictions. The predictions may be made using one or more prediction control flow graphs which represent predicates in instruction blocks and branches between blocks in a program. The prediction control flow graphs may be structured as trees such that each node in the graphs is associated with a predicate instruction, and each leaf associated with a branch target which jumps to another block. During execution of a block, a prediction generator may take a control point history and generate a prediction. Following the path suggested by the prediction through the tree, both predicate values and branch targets may be predicted. Other embodiments may be described and claimed.Board of Regents, University of Texas Syste

Method and apparatus for congestion-aware routing in a computer interconnection network

The present disclosure relates to an example of a method for a first router to adaptively determine status within a network. The network may include the first router, a second router and a third router. The method for the first router may comprise determining status information regarding the second router located in the network, and transmitting the status information to the third router located in the network. The second router and the third router may be indirectly coupled to one another.Board of Regents, University of Texas Syste

Computing nodes for executing groups of instructions

A computation node according to various embodiments of the invention includes at least one input port capable of being coupled to at least one first other 5 computation node, a first store coupled to the input port(s) to store input data, a second store to receive and store instructions, an instruction wakeup unit to match the input data to the instructions, at least one execution unit to execute the instructions, using the input data to produce output data, and at least one output port capable of being coupled to at least one second other computation node. The node may also include a router to direct the output data from the output port(s) to the second other node. A system according to various embodiments of the invention includes and external instruction sequencer to fetch a group of instructions, and one or more interconnected, preselected computational nodes. An article according to an embodiment of the invention includes a medium having instructions which are capable of causing a machine to partition a program into a plurality of groups of instructions, assign one or more of the instruction groups to a plurality of interconnected preselected computation nodes, load the instruction groups on to the nodes, and execute the instruction groups as each instruction in each group receives all necessary associated operands for execution.Board of Regents, University of Texas Syste