Template Adaptation for Face Verification and Identification

Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild dataset for imagery and the YouTubeFaces dataset for videos. In contrast, the newly released IJB-A face recognition dataset unifies evaluation of one-to-many face identification with one-to-one face verification over templates, or sets of imagery and videos for a subject. In this paper, we study the problem of template adaptation, a form of transfer learning to the set of media in a template. Extensive performance evaluations on IJB-A show a surprising result, that perhaps the simplest method of template adaptation, combining deep convolutional network features with template specific linear SVMs, outperforms the state-of-the-art by a wide margin. We study the effects of template size, negative set construction and classifier fusion on performance, then compare template adaptation to convolutional networks with metric learning, 2D and 3D alignment. Our unexpected conclusion is that these other methods, when combined with template adaptation, all achieve nearly the same top performance on IJB-A for template-based face verification and identification

Learning Object-Independent Modes of Variation with Feature Flow Fields

We present a unifying framework in which "object-independent" modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as "generators" to produce a manifold of images of a new object from a single example of that object. We develop the framework in the context of a well-known example: analyzing the modes of spatial deformations of a scene under camera movement. Our method learns a close approximation to the standard affine deformations that are expected from the geometry of the situation, and does so in a completely unsupervised (i.e. ignorant of the geometry of the situation) fashion. We stress that it is learning a "parameterization", not just the parameter values, of the data. We then demonstrate how we have used the same framework to derive a novel data-driven model of joint color change in images due to common lighting variations. The model is superior to previous models of color change in describing non-linear color changes due to lighting

Effect of practical layered dielectric loads on SAR patterns from dual concentric conductor microstrip antennas

Radiation patterns of 2 and 4cm square Dual Concentric Conductor (DCC) microstrip antennas were studied theoretically with Finite Difference Time Domain (FDTD) analysis and compared with experimental measurements of power deposition (SAR) in layered lossy dielectric loads. Single and array configurations were investigated with 915 MHz excitation applied across either one, two or four sides, or four corners of the square apertures. FDTD simulations were carried out for realistic models of a muscle tissue load coupled to the DCC antennas with a 5 mm thick bolus of either distilled water or low loss Silicone Oil. This study characterizes the effect on SAR of adding three additional thin dielectric layers which are necessary for clinical use of the applicator. These layers consist of a 0.1 mm thick dielectric coating on the array surface to provide electrical isolation of DCC apertures, and 0.15 mm thick plastic layers above and below the bolus to contain the liquid. Experimental measurements of SAR in a plane 1 cm deep in muscle phantom agree well with theoretical FDTD simulations in the multi-layered tissue models. These studies reveal significant changes in SAR for applicator configurations involving low dielectric constant (Er) layers on either side of a high Er water bolus layer. Prominent changes include a broadening and centring of the SAR under each aperture as well as increased SAR penetration in muscle. No significant differences are noted between the simple and complete load configurations for the low Er Silicone Oil bolus. Both theoretical and measured data demonstrate relatively uniform SAR distributions with50% of maximum SAR extending to the perimeter of single and multi-aperture array configurations of DCC applicators when using a thin 5 mm water or Silicone Oil bolus

Managing active pharmaceutical ingredient raw material variability during twin-screw blend feeding

Continuous powder feeding is a critical step in continuous manufacturing of solid dosage forms, as this unit operation should ensure the mass flow consistency at the desired powder feed rate to guarantee the process throughput and final product consistency. In this study, twin-screw feeding of a pharmaceutical formulation (i.e., blend) existing of a highly dosed very poorly flowing active pharmaceutical ingredient (API) leading to insufficient feeding capacity was investigated. Furthermore, the API showed very high batch-to-batch variability in raw material properties dominating the formulation blend properties. Formulation changes were evaluated to improve the flowability of the blends and to mitigate the impact of API batch-to-batch variability on the twin-screw feeding. Herewith, feeding evaluation tests and an extensive material characterization of the reformulated blends were performed to assess the impact of the formulation changes upon continuous twin-screw feeding. The transfer of the glidant from extra-granular to intra-granular phase allowed to improve the flowability of the blends. A sufficient feeding capacity for the downstream process and a mitigation of the impact of batch-to-batch variability of the API upon twin-screw feeding of the blends could be achieved. No effect of the formulation or of the API properties on the feeding stability was observed. The material characterization of the blends allowed identifying the material attributes which were critical for continuous twin-screw feeding (i.e., bulk density, mass charge and powder cohesiveness)

The Role of the Immune Response in the Pathogenesis of Thyroid Eye Disease: A Reassessment

Background Although thyroid eye disease is a common complication of Graves’ disease, the pathogenesis of the orbital disease is poorly understood. Most authorities implicate the immune response as an important causal factor. We sought to clarify pathogenesis by using gene expression microarray. Methods An international consortium of ocular pathologists and orbital surgeons contributed formalin fixed orbital biopsies. RNA was extracted from orbital tissue from 20 healthy controls, 25 patients with thyroid eye disease (TED), 25 patients with nonspecific orbital inflammation (NSOI), 7 patients with sarcoidosis and 6 patients with granulomatosis with polyangiitis (GPA). Tissue was divided into a discovery set and a validation set. Gene expression was quantified using Affymetrix U133 Plus 2.0 microarrays which include 54,000 probe sets. Results Principal component analysis showed that gene expression from tissue from patients with TED more closely resembled gene expression from healthy control tissue in comparison to gene expression characteristic of sarcoidosis, NSOI, or granulomatosis with polyangiitis. Unsupervised cluster dendrograms further indicated the similarity between TED and healthy controls. Heat maps based on gene expression for cytokines, chemokines, or their receptors showed that these inflammatory markers were associated with NSOI, sarcoidosis, or GPA much more frequently than with TED. Conclusion This is the first study to compare gene expression in TED to gene expression associated with other causes of exophthalmos. The juxtaposition shows that inflammatory markers are far less characteristic of TED relative to other orbital inflammatory diseases

Finding the optimal background subtraction algorithm for EuroHockey 2015 video

Background subtraction is a classic step in a vision-based localization and tracking workflow. Previous studies have compared background subtraction algorithms on publicly available datasets; however comparisons were made only with manually optimized parameters. The aim of this research was to identify the optimal background subtraction algorithm for a set of field hockey videos captured at EuroHockey 2015. Particle Swarm Optimization was applied to find the optimal background subtraction algorithm. The objective function was the F-score, i.e. the harmonic mean of precision and recall. The precision and recall were calculated using the output of the background subtraction algorithm and gold standard labeled images. The training dataset consisted of 15 x 13 second field hockey video segments. The test data consisted of 5 x 13 second field hockey video segments. The video segments were chosen to be representative of the teams present at the tournament, the times of day the matches were played and the weather conditions experienced. Each segment was 960 pixels x 540 pixels and had 10 ground truth labeled frames. Eight commonly used background subtraction algorithms were considered. Results suggest that a background subtraction algorithm must use optimized parameters for a valid comparison of performance. Particle Swarm Optimization is an appropriate method to undertake this optimization. The optimal algorithm, Temporal Median, achieved an F-score of 0.791 on the test dataset, suggesting it generalizes to the rest of the video footage captured at EuroHockey 2015

Voices Obscured in Complex Environmental Settings (VOICES) corpus

This paper introduces the Voices Obscured In Complex Environmental Settings (VOICES) corpus, a freely available dataset under Creative Commons BY 4.0. This dataset will promote speech and signal processing research of speech recorded by far-field microphones in noisy room conditions. Publicly available speech corpora are mostly composed of isolated speech at close-range microphony. A typical approach to better represent realistic scenarios, is to convolve clean speech with noise and simulated room response for model training. Despite these efforts, model performance degrades when tested against uncurated speech in natural conditions. For this corpus, audio was recorded in furnished rooms with background noise played in conjunction with foreground speech selected from the LibriSpeech corpus. Multiple sessions were recorded in each room to accommodate for all foreground speech-background noise combinations. Audio was recorded using twelve microphones placed throughout the room, resulting in 120 hours of audio per microphone. This work is a multi-organizational effort led by SRI International and Lab41 with the intent to push forward state-of-the-art distant microphone approaches in signal processing and speech recognition.Comment: Submitted to Interspeech 201