Berkemer Revisited: Uncovering the Middle Ground Between Miranda and the New Terry

Over the past twenty-five years, appellate courts have significantly expanded the scope of police authority to stop and frisk potential suspects without probable cause, a power originally granted to law enforcement by the Supreme Court in Terry v. Ohio. This development has led Terry’s once limited licensing of police searches to run into conflict with a defendant’s right against compulsory self-incrimination while in police custody, as articulated by Miranda v. Arizona. This Note explores the contours of this unforeseen collision between two core constitutional doctrines and the solutions generated by appellate courts to resolve the conflict. Courts today are generally divided as to whether Miranda should apply during a valid, but intrusive Terry stop. This Note argues that a distinct overlap now exists between Miranda and Terry; one that should compel courts to invoke Miranda where police detain and question a suspect in a manner analogous to custodial interrogation. However, this Note also stresses that courts should be vigilant in enforcing the public safety exception to Miranda, particularly in light of Terry’s inherent unpredictability and extemporaneous nature

Diasporic consciousness in contemporary Colombia

published or submitted for publicationis peer reviewe

The Ensemble Kalman Filter: A Signal Processing Perspective

The ensemble Kalman filter (EnKF) is a Monte Carlo based implementation of the Kalman filter (KF) for extremely high-dimensional, possibly nonlinear and non-Gaussian state estimation problems. Its ability to handle state dimensions in the order of millions has made the EnKF a popular algorithm in different geoscientific disciplines. Despite a similarly vital need for scalable algorithms in signal processing, e.g., to make sense of the ever increasing amount of sensor data, the EnKF is hardly discussed in our field. This self-contained review paper is aimed at signal processing researchers and provides all the knowledge to get started with the EnKF. The algorithm is derived in a KF framework, without the often encountered geoscientific terminology. Algorithmic challenges and required extensions of the EnKF are provided, as well as relations to sigma-point KF and particle filters. The relevant EnKF literature is summarized in an extensive survey and unique simulation examples, including popular benchmark problems, complement the theory with practical insights. The signal processing perspective highlights new directions of research and facilitates the exchange of potentially beneficial ideas, both for the EnKF and high-dimensional nonlinear and non-Gaussian filtering in general

Detail-Preserving Pooling in Deep Networks

Most convolutional neural networks use some method for gradually downscaling the size of the hidden layers. This is commonly referred to as pooling, and is applied to reduce the number of parameters, improve invariance to certain distortions, and increase the receptive field size. Since pooling by nature is a lossy process, it is crucial that each such layer maintains the portion of the activations that is most important for the network's discriminability. Yet, simple maximization or averaging over blocks, max or average pooling, or plain downsampling in the form of strided convolutions are the standard. In this paper, we aim to leverage recent results on image downscaling for the purposes of deep learning. Inspired by the human visual system, which focuses on local spatial changes, we propose detail-preserving pooling (DPP), an adaptive pooling method that magnifies spatial changes and preserves important structural detail. Importantly, its parameters can be learned jointly with the rest of the network. We analyze some of its theoretical properties and show its empirical benefits on several datasets and networks, where DPP consistently outperforms previous pooling approaches.Comment: To appear at CVPR 201