Efficient Pose Clustering Using a Randomized Algorithm

Pose clustering is a method to perform object recognition by determining hypothetical object poses and finding clusters of the poses in the space of legal object positions. An object that appears in an image will yield a large cluster of such poses close to the correct position of the object. If there are m model features and n image features, then there are O(m ³ n ³ ) hypothetical poses that can be determined from minimal information for the case of recognition of three-dimensional objects from feature points in two-dimensional images. Rather than clustering all of these poses, we show that pose clustering can have equivalent performance for this case when examining only O(mn) poses, due to correlation between the poses, if we are given two correct matches between model features and image features. Since we do not usually know two correct matches in advance, this property is used with randomization to decompose the pose clustering problem into O(n ² ) problems, each of which clusters O(mn) poses, for a total complexity of O(mn ³ ) . Further speedup can be achieved through the use of grouping techniques. This method also requires little memory and makes the use of accurate clustering algorithms less costly. We use recursive histograming techniques to perform clustering in time and space that is guaranteed to be linear in the number of poses. Finally, we present results demonstrating the recognition of objects in the presence of noise, clutter, and occlusion.     

Wind tunnel-based testing of a photoelectrochemical oxidative filter-based air purification unit in coronavirus and influenza aerosol removal and inactivation

Recirculating air purification technologies are employed as potential means of reducing exposure to aerosol particles and airborne viruses. Toward improved testing of recirculating air purification units, we developed and applied a medium-scale single-pass wind tunnel test to examine the size-dependent collection of particles and the collection and inactivation of viable bovine coronavirus (BCoV, a betacoronavirus), porcine respiratory coronavirus (PRCV, an alphacoronavirus), and influenza A virus (IAV), by a commercial air purification unit. The tested unit, the Molekule Air Mini, incorporates a MERV 16 filter as well as a photoelectrochemical oxidating layer. It was found to have a collection efficiency above 95.8% for all tested particle diameters and flow rates, with collection efficiencies above 99% for supermicrometer particles with the minimum collection efficiency for particles smaller than 100 nm. For all three tested viruses, the physical tracer-based log reduction was near 2.0 (99% removal). Conversely, the viable virus log reductions were found to be near 4.0 for IAV, 3.0 for BCoV, and 2.5 for PRCV, suggesting additional inactivation in a virus family- and genus-specific manner. In total, this work describes a suite of test methods which can be used to rigorously evaluate the efficacy of recirculating air purification technologies.     

Electrochemical impedance spectroscopic characterization of the oxide film formed over low modulus Ti–35.5Nb–7.3Zr–5.7Ta alloy in phosphate buffer saline at various potentials

Electrochemical impedance spectroscopic characterization of the low modulus Ti–35.5Nb–7.3Zr–5.7Ta alloy has been performed in phosphate buffer saline solution at 37 °C. Measurements were performed at various immersion intervals up to 720 h at OCP and also at various anodic potentials up to 2 V. The alloy exhibits a two time constant impedance response at the OCP and a one-time constant response at anodic potentials in the passive region. The thickness of the oxide film formed has been evaluated and the electrochemical interpretation of the results has been reported. Cyclic potentiodynamic profile of the alloy displays valve metal characteristics and the presence of a wide passive region that extends up to the maximum potential value of 2 V studied.     

Low Dietary c9t11-Conjugated Linoleic Acid Intake from Dairy Fat or Supplements Reduces Inflammation in Collagen-Induced Arthritis

Dietary cis‐9,trans‐11 (c9t11) conjugated linoleic acid (CLA) fed at 0.5 % w/w was previously shown to attenuate inflammation in the murine collagen‐induced (CA) arthritis model, and growing evidence implicates c9t11‐CLA as a major anti‐inflammatory component of dairy fat. To understand c9t11‐CLA's contribution to dairy fat's anti‐inflammatory action, the minimum amount of dietary c9t11‐CLA needed to reduce inflammation must be determined. This study had two objectives: (1) determine the minimum dietary anti‐inflammatory c9t11‐CLA intake level in the CA model, and (2) compare this to anti‐inflammatory effects of dairy fat (non‐enriched, naturally c9t11‐CLA‐enriched, or c9t11‐CLA‐supplemented). Mice received the following dietary fat treatments (w/w) post arthritis onset: corn oil (6 % CO), 0.125, 0.25, 0.375, and 0.5 % c9t11‐CLA, control butter (6 % CB), c9t11‐enriched butter (6 % EB), or c9t11‐CLA‐supplemented butter (6 % SB, containing 0.2 % c9t11‐CLA). Paw arthritic severity and pad swelling were scored and measured, respectively, over an 84‐day study period. All c9t11‐CLA and butter diets decreased the arthritic score (25–51 %, P < 0.01) and paw swelling (8–11 %, P < 0.01). Throughout the study, plasma tumor necrosis factor (TNFα) was elevated in CO‐fed arthritic mice compared to non‐arthritic (NA) mice but was reduced in 0.5 % c9t11‐CLA‐ and EB‐fed mice. Interleukin‐1β and IL‐6 were increased in arthritic CO‐fed mice compared to NA mice but were reduced in 0.5 % c9t11‐CLA‐ and EB‐fed mice through day 42. In conclusion, 0.125 % c9t11‐CLA reduced clinical arthritis as effectively as higher doses, and decreased arthritis in CB‐fed mice suggested that the minimal anti‐inflammatory levels of c9t11‐CLA might be below 0.125 %.