How can Francis Bacon help forensic science? The four idols of human biases

Much debate has focused on whether forensic science is indeed a science. This paper is not aimed at answering, or even trying to contribute to, this question. Rather, in this paper I try to find ways to improve forensic science by identifying potential vulnerabilities. To this end I use Francis Bacon's doctrine of idols which distinguishes between different types of human biases that may prevent scientific and objective inquiry. Bacon’s doctrine contains four sources for such biases: Idols Tribus (of the 'tribe'), Idols Specus (of the 'den'/'cave'), Idols Fori (of the 'market'), and Idols Theatre (of the 'theatre'). While his 400 year old doctrine does not, of course, perfectly match up with our current world view, it still provides a productive framework for examining and cataloguing some of the potential weaknesses and limitations in our current approach to forensic science

The Paradox of Human Expertise: Why Experts Can Get It Wrong

Expertise is correctly, but one-sidedly, associated with special abilities and enhanced performance. The other side of expertise, however, is surreptitiously hidden. Along with expertise, performance may also be degraded, culminating in a lack of flexibility and error. Expertise is demystified by explaining the brain functions and cognitive architecture involved in being an expert. These information processing mechanisms, the very making of expertise, entail computational trade-offs that sometimes result in paradoxical functional degradation. For example, being an expert entails using schemas, selective attention, chunking information, automaticity, and more reliance on top-down information, all of which allow experts to perform quickly and efficiently; however, these very mechanisms restrict flexibility and control, may cause the experts to miss and ignore important information, introduce tunnel vision and bias, and can cause other effects that degrade performance. Such phenomena are apparent in a wide range of expert domains, from medical professionals and forensic examiners, to military fighter pilots and financial traders

Solitons supported by localized nonlinearities in periodic media

Nonlinear periodic systems, such as photonic crystals and Bose-Einstein condensates (BECs) loaded into optical lattices, are often described by the nonlinear Schr\"odinger/Gross-Pitaevskii equation with a sinusoidal potential. Here, we consider a model based on such a periodic potential, with the nonlinearity (attractive or repulsive) concentrated either at a single point or at a symmetric set of two points, which are represented, respectively, by a single {\delta}-function or a combination of two {\delta}-functions. This model gives rise to ordinary solitons or gap solitons (GSs), which reside, respectively, in the semi-infinite or finite gaps of the system's linear spectrum, being pinned to the {\delta}-functions. Physical realizations of these systems are possible in optics and BEC, using diverse variants of the nonlinearity management. First, we demonstrate that the single {\delta}-function multiplying the nonlinear term supports families of stable regular solitons in the self-attractive case, while a family of solitons supported by the attractive {\delta}-function in the absence of the periodic potential is completely unstable. We also show that the {\delta}-function can support stable GSs in the first finite gap in both the self-attractive and repulsive models. The stability analysis for the GSs in the second finite gap is reported too, for both signs of the nonlinearity. Alongside the numerical analysis, analytical approximations are developed for the solitons in the semi-infinite and first two finite gaps, with the single {\delta}-function positioned at a minimum or maximum of the periodic potential. In the model with the symmetric set of two {\delta}-functions, we study the effect of the spontaneous symmetry breaking of the pinned solitons. Two configurations are considered, with the {\delta}-functions set symmetrically with respect to the minimum or maximum of the potential

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC

Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over-fitting, Bayesian Matrix Factorization has not been widely adopted because of the prohibitive cost of inference. In this paper, we propose a scalable distributed Bayesian matrix factorization algorithm using stochastic gradient MCMC. Our algorithm, based on Distributed Stochastic Gradient Langevin Dynamics, can not only match the prediction accuracy of standard MCMC methods like Gibbs sampling, but at the same time is as fast and simple as stochastic gradient descent. In our experiments, we show that our algorithm can achieve the same level of prediction accuracy as Gibbs sampling an order of magnitude faster. We also show that our method reduces the prediction error as fast as distributed stochastic gradient descent, achieving a 4.1% improvement in RMSE for the Netflix dataset and an 1.8% for the Yahoo music dataset

Coulomb Drag of Edge Excitations in the Chern-Simons Theory of the Fractional Quantum Hall Effect

Long range Coulomb interaction between the edges of a Hall bar changes the nature of the gapless edge excitations. Instead of independent modes propagating in opposite directions on each edge as expected for a short range interaction one finds elementary excitations living simultaneously on both edges, i.e. composed of correlated density waves propagating in the same direction on opposite edges. We discuss the microscopic features of this Coulomb drag of excitations in the fractional quantum Hall regime within the framework of the bosonic Chern-Simons Landau-Ginzburg theory. The dispersion law of these novel excitations is non linear and depends on the distance between the edges as well as on the current that flows through the sample. The latter dependence indicates a possibility of parametric excitation of these modes. The bulk distributions of the density and currents of the edge excitations differ significantly for short and long range interactions.Comment: 11 pages, REVTEX, 2 uuencoded postscript figure

Stochastic Vehicle Routing with Recourse

We study the classic Vehicle Routing Problem in the setting of stochastic optimization with recourse. StochVRP is a two-stage optimization problem, where demand is satisfied using two routes: fixed and recourse. The fixed route is computed using only a demand distribution. Then after observing the demand instantiations, a recourse route is computed -- but costs here become more expensive by a factor lambda. We present an O(log^2 n log(n lambda))-approximation algorithm for this stochastic routing problem, under arbitrary distributions. The main idea in this result is relating StochVRP to a special case of submodular orienteering, called knapsack rank-function orienteering. We also give a better approximation ratio for knapsack rank-function orienteering than what follows from prior work. Finally, we provide a Unique Games Conjecture based omega(1) hardness of approximation for StochVRP, even on star-like metrics on which our algorithm achieves a logarithmic approximation.Comment: 20 Pages, 1 figure Revision corrects the statement and proof of Theorem 1.

From the Chern-Simons theory for the fractional quantum Hall effect to the Luttinger model of its edges

The chiral Luttinger model for the edges of the fractional quantum Hall effect is obtained as the low energy limit of the Chern-Simons theory for the two dimensional system. In particular we recover the Kac-Moody algebra for the creation and annihilation operators of the edge density waves and the bosonization formula for the electronic operator at the edge.Comment: 4 pages, LaTeX, 1 Postscript figure include

Optimizing traffic signs and lights visibility for the teleoperation of autonomous vehicles through ROI compression

Autonomous vehicles are a promising solution to traffic congestion, air pollution, accidents, and wasted time and resources. However, remote driver intervention may be necessary for extreme situations to ensure safe roadside parking or complete remote takeover. In such cases, high-quality real-time video streaming is crucial for practical remote driving. In a preliminary study, we already presented a region of interest (ROI) HEVC data compression where the image was segmented into two categories of ROI and background, allocating more bandwidth to the ROI, yielding an improvement in the visibility of the classes that essential for driving while transmitting the background with lesser quality. However, migrating bandwidth to the large ROI portion of the image doesn't substantially improve the quality of traffic signs and lights. This work categorized the ROIs into either background, weak ROI, or strong ROI. The simulation-based approach uses a photo-realistic driving scenario database created with the Cognata self-driving car simulation platform. We use semantic segmentation to categorize the compression quality of a Coding Tree Unit (CTU) according to each pixel class. A background CTU can contain only sky, trees, vegetation, or building classes. Essentials for remote driving include significant classes such as roads, road marks, cars, and pedestrians. And most importantly, traffic signs and traffic lights. We apply thresholds to decide if the number of pixels in a CTU of a particular category is enough to declare it as belonging to the strong or weak ROI. Then, we allocate the bandwidth according to the CTU categories. Our results show that the perceptual quality of traffic signs, especially textual signs and traffic lights, improves significantly by up to 5.5 dB compared to the only background and foreground partition, while the weak ROI classes at least retain their original quality.Comment: 14 pages, 7 figure