Criminal responsibility without alternative possibilities? The dilemma of freedom and the structure of ascription

This essay is about some connections between the idea according to which free will and determinism are mutually compatible and the fundamentals of criminal imputation. It is sustained that the Principle of Alternative Possibilities remains indispensable as a starting point, without damage to its integration into a broader model, able to cover those situations where the moral agent intentionally (at least by negligence) produces (or do not avoid) the conditions of his own lack of liability in ordinary or general terms.Este artigo trata de algumas conexões entre a tese de que o livre arbítrio e o determinismo são mutuamente compatíveis e os fundamentos da imputação criminal. Sustenta-se que o princípio das possibilidades alternativas continua a ser um ponto de partida indispensável, sem prejuízo da sua integração num modelo mais amplo, capaz de abranger as situações em que o agente moral intencionalmente (pelo menos a título de negligência) produz (ou não evita) as condições da sua própria falta de responsabilidade em termos gerais

Large Deviations Performance of Consensus+Innovations Distributed Detection with Non-Gaussian Observations

We establish the large deviations asymptotic performance (error exponent) of consensus+innovations distributed detection over random networks with generic (non-Gaussian) sensor observations. At each time instant, sensors 1) combine theirs with the decision variables of their neighbors (consensus) and 2) assimilate their new observations (innovations). This paper shows for general non-Gaussian distributions that consensus+innovations distributed detection exhibits a phase transition behavior with respect to the network degree of connectivity. Above a threshold, distributed is as good as centralized, with the same optimal asymptotic detection performance, but, below the threshold, distributed detection is suboptimal with respect to centralized detection. We determine this threshold and quantify the performance loss below threshold. Finally, we show the dependence of the threshold and performance on the distribution of the observations: distributed detectors over the same random network, but with different observations' distributions, for example, Gaussian, Laplace, or quantized, may have different asymptotic performance, even when the corresponding centralized detectors have the same asymptotic performance.Comment: 30 pages, journal, submitted Nov 17, 2011; revised Apr 3, 201

Consensus and Products of Random Stochastic Matrices: Exact Rate for Convergence in Probability

Distributed consensus and other linear systems with system stochastic matrices $W_k$ emerge in various settings, like opinion formation in social networks, rendezvous of robots, and distributed inference in sensor networks. The matrices $W_k$ are often random, due to, e.g., random packet dropouts in wireless sensor networks. Key in analyzing the performance of such systems is studying convergence of matrix products $W_kW_{k-1}... W_1$. In this paper, we find the exact exponential rate $I$ for the convergence in probability of the product of such matrices when time $k$ grows large, under the assumption that the $W_k$'s are symmetric and independent identically distributed in time. Further, for commonly used random models like with gossip and link failure, we show that the rate $I$ is found by solving a min-cut problem and, hence, easily computable. Finally, we apply our results to optimally allocate the sensors' transmission power in consensus+innovations distributed detection

Distributed Detection over Random Networks: Large Deviations Performance Analysis

We study the large deviations performance, i.e., the exponential decay rate of the error probability, of distributed detection algorithms over random networks. At each time step $k$ each sensor: 1) averages its decision variable with the neighbors' decision variables; and 2) accounts on-the-fly for its new observation. We show that distributed detection exhibits a "phase change" behavior. When the rate of network information flow (the speed of averaging) is above a threshold, then distributed detection is asymptotically equivalent to the optimal centralized detection, i.e., the exponential decay rate of the error probability for distributed detection equals the Chernoff information. When the rate of information flow is below a threshold, distributed detection achieves only a fraction of the Chernoff information rate; we quantify this achievable rate as a function of the network rate of information flow. Simulation examples demonstrate our theoretical findings on the behavior of distributed detection over random networks.Comment: 30 pages, journal, submitted on December 3rd, 201

Desvalor da conduta e desvalor do resultado no ilícito penal: ao mesmo tempo, sobre o sentido de um injusto genuinamente “pessoal”

Abstract: The article analyzes the discussion about the relevance of the devalue of conduct and the devalue of result as elements of the criminal offense. In conclusion, it is asserted that the devalue of result is the matrix element of the criminal wrongdoing and that a truly “personal” wrongdoing must be found in an onto-anthropological understanding of Criminal Law.   Keywords: Criminal law; criminal wrongdoing; devalue of conduct; devalue of result; “personal” wrongdoing

Autoselecció de la velocitat de marxa dels adults amb sobrepès. és suficient la intensitat escollida per potenciar els beneficis de la salut?

Caminar és la modalitat d’activitat física sovint més indicada per augmentar el nivell d’activitat física de la població amb l’objectiu de millorar-ne l’estat de salut. No obstant això, es desconeix com seleccionen la intensitat de la pròpia velocitat de marxa els adults amb sobrepès. L’objectiu d’aquest estudi fou avaluar l’autoselecció de la velocitat de marxa dels adults amb sobrepès

Implementação eficiente do Shared Nearest Neighbour em dados espaciais

A taxa de colecta de dados espaciais está a aumentar e os algoritmos de agrupamento tornam-se cada vez mais populares, pois não necessitam de informação a priori. Contudo, estes algoritmos requerem um tempo de execução significativo e várias corridas para alcançar os melhores resultados. O Shared Nearest Neighbour (SNN) é um algoritmo de agrupamento cuja complexidade temporal no pior caso é O(n2), comprometendo a sua escalabilidade. Neste artigo, conjuga-se o SNN com estruturas de dados métricas que dão suporte à procura dos K vizinhos mais próximos, permitindo melhorar a sua complexidade temporal no caso esperado para O(n _ log(n)), com conjuntos de dados espaciais. Propomos, ainda, uma estratégia de reaproveitamento entre corridas do cálculo dos K vizinhos mais próximos, atingindo a complexidade de O(n). Através dos resultados experimentais, que avaliam a escalabilidade desta solução e a comparam com uma versão original do SNN, são obtidos ganhos muito significativos