Katz and Dogs: The Best Path Forward in Applying \u3cem\u3eUnited States v. Davis\u3c/em\u3e\u27 Good Faith Exception to the Exclusionary Rule and How the Seventh Circuit Has Gone Astray

Sometimes, law enforcement officers violate the Fourth Amendment and in the process find and seize evidence they wish to use in a subsequent criminal prosecution. In these circumstances, a question that has long troubled courts, and a question that is becoming more and more difficult to answer, is whether such evidence should be admissible at trial. In Weeks v. United States and Mapp v. Ohio, the Supreme Court established that evidence seized in violation of the Fourth Amendment was not admissible in federal and state prosecutions. This rule has become known as the exclusionary rule. However, in a line of cases beginning with United States v. Leon, the Court has held, in a variety of different circumstances, that evidence should not be excluded if officers are acting in good faith or objectively reasonably, even when those officers\u27 actions violate the Fourth Amendment. The most recent case in this line of good faith exception cases is Davis v. United States, where the Court held that [e]vidence obtained during a search conducted in reasonable reliance on binding precedent is not subject to the exclusionary rule. Because of the potential breadth of its holding, Davis is an incredibly important case in Fourth Amendment jurisprudence, and it has already led to a great variety of interpretations in lower courts. The first question that has led to a variety of different interpretations has been what exactly constitutes binding precedent? The second question has been, if there is binding precedent available, what are the limits of officers\u27 good faith reliance on that precedent? Or, as one federal court has phrased the issue, [t]he scope of [the] reasonable-reliance-on-precedent test turns on two subsidiary questions: what universe of cases can the police rely on? And how clearly must those cases govern the current case for that reliance to be objectively reasonable? This Comment examines the variety of different ways courts have applied Davis\u27 holding in answering these the above two questions. After this analysis, this Comment suggests the best path forward for courts when interpreting and applying Davis. Finally, this Comment discusses the Seventh Circuit\u27s interpretation of Davis in a 2014 case, United States v. Gutierrez, and how the court went astray from this best path

Katz and Dogs: The Best Path Forward in Applying \u3cem\u3eUnited States v. Davis\u3c/em\u3e\u27 Good Faith Exception to the Exclusionary Rule and How the Seventh Circuit Has Gone Astray

Sometimes, law enforcement officers violate the Fourth Amendment and in the process find and seize evidence they wish to use in a subsequent criminal prosecution. In these circumstances, a question that has long troubled courts, and a question that is becoming more and more difficult to answer, is whether such evidence should be admissible at trial. In Weeks v. United States and Mapp v. Ohio, the Supreme Court established that evidence seized in violation of the Fourth Amendment was not admissible in federal and state prosecutions. This rule has become known as the exclusionary rule. However, in a line of cases beginning with United States v. Leon, the Court has held, in a variety of different circumstances, that evidence should not be excluded if officers are acting in good faith or objectively reasonably, even when those officers\u27 actions violate the Fourth Amendment. The most recent case in this line of good faith exception cases is Davis v. United States, where the Court held that [e]vidence obtained during a search conducted in reasonable reliance on binding precedent is not subject to the exclusionary rule. Because of the potential breadth of its holding, Davis is an incredibly important case in Fourth Amendment jurisprudence, and it has already led to a great variety of interpretations in lower courts. The first question that has led to a variety of different interpretations has been what exactly constitutes binding precedent? The second question has been, if there is binding precedent available, what are the limits of officers\u27 good faith reliance on that precedent? Or, as one federal court has phrased the issue, [t]he scope of [the] reasonable-reliance-on-precedent test turns on two subsidiary questions: what universe of cases can the police rely on? And how clearly must those cases govern the current case for that reliance to be objectively reasonable? This Comment examines the variety of different ways courts have applied Davis\u27 holding in answering these the above two questions. After this analysis, this Comment suggests the best path forward for courts when interpreting and applying Davis. Finally, this Comment discusses the Seventh Circuit\u27s interpretation of Davis in a 2014 case, United States v. Gutierrez, and how the court went astray from this best path

ENTL: Embodied Navigation Trajectory Learner

We propose Embodied Navigation Trajectory Learner (ENTL), a method for extracting long sequence representations for embodied navigation. Our approach unifies world modeling, localization and imitation learning into a single sequence prediction task. We train our model using vector-quantized predictions of future states conditioned on current states and actions. ENTL's generic architecture enables the sharing of the the spatio-temporal sequence encoder for multiple challenging embodied tasks. We achieve competitive performance on navigation tasks using significantly less data than strong baselines while performing auxiliary tasks such as localization and future frame prediction (a proxy for world modeling). A key property of our approach is that the model is pre-trained without any explicit reward signal, which makes the resulting model generalizable to multiple tasks and environments

FLUID: A Unified Evaluation Framework for Flexible Sequential Data

Modern ML methods excel when training data is IID, large-scale, and well labeled. Learning in less ideal conditions remains an open challenge. The sub-fields of few-shot, continual, transfer, and representation learning have made substantial strides in learning under adverse conditions; each affording distinct advantages through methods and insights. These methods address different challenges such as data arriving sequentially or scarce training examples, however often the difficult conditions an ML system will face over its lifetime cannot be anticipated prior to deployment. Therefore, general ML systems which can handle the many challenges of learning in practical settings are needed. To foster research towards the goal of general ML methods, we introduce a new unified evaluation framework - FLUID (Flexible Sequential Data). FLUID integrates the objectives of few-shot, continual, transfer, and representation learning while enabling comparison and integration of techniques across these subfields. In FLUID, a learner faces a stream of data and must make sequential predictions while choosing how to update itself, adapt quickly to novel classes, and deal with changing data distributions; while accounting for the total amount of compute. We conduct experiments on a broad set of methods which shed new insight on the advantages and limitations of current solutions and indicate new research problems to solve. As a starting point towards more general methods, we present two new baselines which outperform other evaluated methods on FLUID. Project page: https://raivn.cs.washington.edu/projects/FLUID/.Comment: 27 pages, 6 figures. Project page: https://raivn.cs.washington.edu/projects/FLUID