Training Big Random Forests with Little Resources

Without access to large compute clusters, building random forests on large datasets is still a challenging problem. This is, in particular, the case if fully-grown trees are desired. We propose a simple yet effective framework that allows to efficiently construct ensembles of huge trees for hundreds of millions or even billions of training instances using a cheap desktop computer with commodity hardware. The basic idea is to consider a multi-level construction scheme, which builds top trees for small random subsets of the available data and which subsequently distributes all training instances to the top trees' leaves for further processing. While being conceptually simple, the overall efficiency crucially depends on the particular implementation of the different phases. The practical merits of our approach are demonstrated using dense datasets with hundreds of millions of training instances.Comment: 9 pages, 9 Figure

Sharing visual features for multiclass and multiview object detection

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (run-time) computational complexity, and the (training-time) sample complexity, scales linearly with the number of classes to be detected. It seems unlikely that such an approach will scale up to allow recognition of hundreds or thousands of objects. We present a multi-class boosting procedure (joint boosting) that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required, and therefore the computational cost, is observed to scale approximately logarithmically with the number of classes. The features selected jointly are closer to edges and generic features typical of many natural structures instead of finding specific object parts. Those generic features generalize better and reduce considerably the computational cost of an algorithm for multi-class object detection

Pilot Sensitivity to Simulator Flight Dynamics Model Formulation for Stall Training

A piloted simulation study was performed in the Cockpit Motion Facility at the National Aeronautics and Space Administration Langley Research Center. The research was motivated by the desire to reduce the commercial transport airplane fatal accident rate due to in-flight loss of control. The purpose of this study, which focused on a generic T-tail transport airplane, was to assess pilot sensitivity to flight dynamics model formulation used during a simulator stall recognition and recovery training/demonstration profile. To accomplish this, the flight dynamics model was designed with many configuration options. The model options were based on recently acquired static and dynamic stability and control data from sources that included wind tunnel, water tunnel, and computational fluid dynamics. The results, which are specific to a transport airplane stall recognition and recovery guided demonstration scenario, showed the two most important aerodynamic effects (other than stick pusher) to model were stall roll- off and the longitudinal static stability characteristic associated with the pitch break

Contextual models for object detection using boosted random fields

We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and local evidence of a conditional random field (CRF). The graph structure is learned by assembling graph fragments in an additive model. The connections between individual pixels are not very informative, but by using dense graphs, we can pool information from large regions of the image; dense models also support efficient inference. We show how contextual information from other objects can improve detection performance, both in terms of accuracy and speed, by using a computational cascade. We apply our system to detect stuff and things in office and street scenes

Amphotericin B enhances the synthesis and release of the immunosuppressive agent gliotoxin from the pulmonary pathogen Aspergillus fumigatus

Exposure of the pulmonary pathogen Aspergillus fumigatus to amphotericin B alters membrane permeability as indicated by the escape of amino acids and protein from the mycelium. Amphotericin B exposure for periods of 2-4 h also leads to increased release of the immunosuppressive agent gliotoxin into the surrounding culture medium. Examination of the intracellular gliotoxin concentration following exposure to amphotericin B indicated elevated levels within the hyphae as well as in the culture medium - an effect which was also evident upon exposure of A. fumigatus to DMSO. These results indicate that in parallel with the ability of amphotericinBto act as a fungistatic agent it can also induce the synthesis of gliotoxin and facilitate its release by increasing the permeability of the fungal cell membrane. Increased synthesis of gliotoxin may result from the commencement of secondary metabolism in the presence of amphotericin B. The ability of amphotericin B to enhance the synthesis and release of gliotoxin may exacerbate the effects of the toxin and facilitate fungal invasion of pulmonary tissue

MVG Mechanism: Differential Privacy under Matrix-Valued Query

Differential privacy mechanism design has traditionally been tailored for a scalar-valued query function. Although many mechanisms such as the Laplace and Gaussian mechanisms can be extended to a matrix-valued query function by adding i.i.d. noise to each element of the matrix, this method is often suboptimal as it forfeits an opportunity to exploit the structural characteristics typically associated with matrix analysis. To address this challenge, we propose a novel differential privacy mechanism called the Matrix-Variate Gaussian (MVG) mechanism, which adds a matrix-valued noise drawn from a matrix-variate Gaussian distribution, and we rigorously prove that the MVG mechanism preserves $(\epsilon,\delta)$-differential privacy. Furthermore, we introduce the concept of directional noise made possible by the design of the MVG mechanism. Directional noise allows the impact of the noise on the utility of the matrix-valued query function to be moderated. Finally, we experimentally demonstrate the performance of our mechanism using three matrix-valued queries on three privacy-sensitive datasets. We find that the MVG mechanism notably outperforms four previous state-of-the-art approaches, and provides comparable utility to the non-private baseline.Comment: Appeared in CCS'1

Context-Based Vision System for Place and Object Recognition

While navigating in an environment, a vision system has to be able to recognize where it is and what the main objects in the scene are. In this paper we present a context-based vision system for place and object recognition. The goal is to identify familiar locations (e.g., office 610, conference room 941, Main Street), to categorize new environments (office, corridor, street) and to use that information to provide contextual priors for object recognition (e.g., table, chair, car, computer). We present a low-dimensional global image representation that provides relevant information for place recognition and categorization, and how such contextual information introduces strong priors that simplify object recognition. We have trained the system to recognize over 60 locations (indoors and outdoors) and to suggest the presence and locations of more than 20 different object types. The algorithm has been integrated into a mobile system that provides real-time feedback to the user

Transcriptional responses in the adaptation to ischaemia-reperfusion injury: a study of the effect of ischaemic preconditioning in total knee arthroplasty patients

<p>Abstract</p> <p>Background</p> <p>Ischaemic preconditioning (IPC) has emerged as a method of reducing ischaemia-reperfusion injury. However, the complex mechanism through which IPC elicits this protection is not fully understood. The aim of this study was to investigate the genomic response induced by IPC in muscle biopsies taken from the operative leg of total knee arthroplasty patients in order to gain insight into the IPC mechanism.</p> <p>Methods</p> <p>Twenty patients, undergoing primary total knee arthroplasty, were randomly assigned to IPC (n = 10) and control (n = 10) groups. Patients in the IPC group received ischaemic preconditioning immediately prior to surgery. IPC was induced by three five-minute cycles of tourniquet insufflation interrupted by five-minute cycles of reperfusion. A muscle biopsy was taken from the operative knee of control and IPC-treated patients at the onset of surgery and, again, at one hour into surgery. The gene expression profile of muscle biopsies was determined using the Affymetrix Human U113 2.0 microarray system and validated using real-time polymerase chain reaction (RT-PCR). Measurements of C-reactive protein (CRP), erythrocyte sedimentation (ESR), white cell count (WCC), cytokines and haemoglobin were also made pre- and post-operatively.</p> <p>Results</p> <p>Microarray analysis revealed a significant increase in the expression of important oxidative stress defence genes, immediate early response genes and mitochondrial genes. Upregulation of pro-survival genes was also observed and correlated with a downregulation of pro-apoptotic gene expression. CRP, ESR, WCC, cytokine and haemoglobin levels were not significantly different between control and IPC patients.</p> <p>Conclusions</p> <p>The findings of this study suggest that IPC of the lower limb in total knee arthroplasty patients induces a protective genomic response, which results in increased expression of immediate early response genes, oxidative stress defence genes and pro-survival genes. These findings indicate that ischaemic preconditioning may be of potential benefit in knee arthroplasty and other musculoskeletal conditions.</p