Algorithmic Programming Language Identification

Motivated by the amount of code that goes unidentified on the web, we introduce a practical method for algorithmically identifying the programming language of source code. Our work is based on supervised learning and intelligent statistical features. We also explored, but abandoned, a grammatical approach. In testing, our implementation greatly outperforms that of an existing tool that relies on a Bayesian classifier. Code is written in Python and available under an MIT license.Comment: 11 pages. Code: https://github.com/simon-weber/Programming-Language-Identificatio

Absolutely Continuous Spectrum for Random Schroedinger Operators on the Bethe Strip

The Bethe Strip of width $m$ is the cartesian product \B\times\{1,...,m\}, where \B is the Bethe lattice (Cayley tree). We prove that Anderson models on the Bethe strip have "extended states" for small disorder. More precisely, we consider Anderson-like Hamiltonians \;H_\lambda=\frac12 \Delta \otimes 1 + 1 \otimes A + \lambda \Vv on a Bethe strip with connectivity $K \geq 2$, where $A$ is an $m\times m$ symmetric matrix, \Vv is a random matrix potential, and $\lambda$ is the disorder parameter. Given any closed interval $I\subset (-\sqrt{K}+a_{\mathrm{max}},\sqrt{K}+a_{\mathrm{min}})$, where $a_{\mathrm{min}}$ and $a_{\mathrm{max}}$ are the smallest and largest eigenvalues of the matrix $A$, we prove that for $\lambda$ small the random Schr\"odinger operator $\;H_\lambda$ has purely absolutely continuous spectrum in $I$ with probability one and its integrated density of states is continuously differentiable on the interval $I$

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks. Despite its success, for large datasets, training and validating a single configuration often takes hours, days, or even weeks, which limits the achievable performance. To accelerate hyperparameter optimization, we propose a generative model for the validation error as a function of training set size, which is learned during the optimization process and allows exploration of preliminary configurations on small subsets, by extrapolating to the full dataset. We construct a Bayesian optimization procedure, dubbed Fabolas, which models loss and training time as a function of dataset size and automatically trades off high information gain about the global optimum against computational cost. Experiments optimizing support vector machines and deep neural networks show that Fabolas often finds high-quality solutions 10 to 100 times faster than other state-of-the-art Bayesian optimization methods or the recently proposed bandit strategy Hyperband

Stabilizing discontinuous Galerkin methods using Dafermos' entropy rate criterion

A novel approach for the stabilization of the discontinuous Galerkin method based on the Dafermos entropy rate crition is presented. The approach is centered around the efficient solution of linear or nonlinear optimization problems in every timestep as a correction to the basic discontinuous Galerkin scheme. The thereby enforced Dafermos criterion results in improved stability compared to the basic method while retaining the order of the method in numerical experiments. Further modification of the optimization problem allows also to enforce classical entropy inequalities for the scheme. The proposed stabilization is therefore an alternative to flux-differencing, finite-volume subcells, artificial viscosity, modal filtering, and other shock capturing procedures

Stabilizing Discontinuous Galerkin Methods Using Dafermos' Entropy Rate Criterion: II -- Systems of Conservation Laws and Entropy Inequality Predictors

A novel approach for the stabilization of the Discontinuous Galerkin method based on the Dafermos entropy rate crition is presented. First, estimates for the maximal possible entropy dissipation rate of a weak solution are derived. Second, families of conservative Hilbert-Schmidt operators are identified to dissipate entropy. Steering these operators using the bounds on the entropy dissipation results in high-order accurate shock-capturing DG schemes for the Euler equations, satisfying the entropy rate criterion and an entropy inequality

Discovery-led refinement in e-discovery investigations: sensemaking, cognitive ergonomics and system design.

Given the very large numbers of documents involved in e-discovery investigations, lawyers face a considerable challenge of collaborative sensemaking. We report findings from three workplace studies which looked at different aspects of how this challenge was met. From a sociotechnical perspective, the studies aimed to understand how investigators collectively and individually worked with information to support sensemaking and decision making. Here, we focus on discovery-led refinement; specifically, how engaging with the materials of the investigations led to discoveries that supported refinement of the problems and new strategies for addressing them. These refinements were essential for tractability. We begin with observations which show how new lines of enquiry were recursively embedded. We then analyse the conceptual structure of a line of enquiry and consider how reflecting this in e-discovery support systems might support scalability and group collaboration. We then focus on the individual activity of manual document review where refinement corresponded with the inductive identification of classes of irrelevant and relevant documents within a collection. Our observations point to the effects of priming on dealing with these efficiently and to issues of cognitive ergonomics at the human–computer interface. We use these observations to introduce visualisations that might enable reviewers to deal with such refinements more efficiently

Comments: A Right to Testimony of Immunized Defense Witnesses

In 1980, the court of appeals for the Third Circuit, in Government of Virgin Islands v. Smith, held that a defendant\u27s right to evidence in a criminal trial included a right to immunity /or his witnesses. Since that time, the district court of Maryland has similarly upheld, in United States v. Lyon, a defendant\u27s right to testimony even though the witness may require immunity. This article discusses the competing interests weighing for and against defense witness immunity and suggests that once it is determined that a defendant has a right to certain testimony, it is proper to burden the government with a choice of alternatives to ensure the defendant that right

Comments: A Right to Testimony of Immunized Defense Witnesses

In 1980, the court of appeals for the Third Circuit, in Government of Virgin Islands v. Smith, held that a defendant\u27s right to evidence in a criminal trial included a right to immunity /or his witnesses. Since that time, the district court of Maryland has similarly upheld, in United States v. Lyon, a defendant\u27s right to testimony even though the witness may require immunity. This article discusses the competing interests weighing for and against defense witness immunity and suggests that once it is determined that a defendant has a right to certain testimony, it is proper to burden the government with a choice of alternatives to ensure the defendant that right

Stimulus-invariant processing and spectrotemporal reverse correlation in primary auditory cortex

The spectrotemporal receptive field (STRF) provides a versatile and integrated, spectral and temporal, functional characterization of single cells in primary auditory cortex (AI). In this paper, we explore the origin of, and relationship between, different ways of measuring and analyzing an STRF. We demonstrate that STRFs measured using a spectrotemporally diverse array of broadband stimuli -- such as dynamic ripples, spectrotemporally white noise, and temporally orthogonal ripple combinations (TORCs) -- are very similar, confirming earlier findings that the STRF is a robust linear descriptor of the cell. We also present a new deterministic analysis framework that employs the Fourier series to describe the spectrotemporal modulations contained in the stimuli and responses. Additional insights into the STRF measurements, including the nature and interpretation of measurement errors, is presented using the Fourier transform, coupled to singular-value decomposition (SVD), and variability analyses including bootstrap. The results promote the utility of the STRF as a core functional descriptor of neurons in AI.Comment: 42 pages, 8 Figures; to appear in Journal of Computational Neuroscienc