Learning Visual Patterns: Imposing Order on Objects, Trajectories and Networks

Fundamental to many tasks in the field of computer vision, this work considers the understanding of observed visual patterns in static images and dynamic scenes . Within this broad domain, we focus on three particular subtasks, contributing novel solutions to: (a) the subordinate categorization of objects (avian species specifically), (b) the analysis of multi-agent interactions using the agent trajectories, and (c) the estimation of camera network topology. In contrast to object recognition, where the presence or absence of certain parts is generally indicative of basic-level category, the problem of subordinate categorization rests on the ability to establish salient distinctions amongst the characteristics of those parts which comprise the basic-level category. Focusing on an avian domain due to the fine-grained structure of the category taxonomy, we explore a pose-normalized appearance model based on a volumetric poselet scheme. The variation in shape and appearance properties of these parts across a taxonomy provides the cues needed for subordinate categorization. Our model associates the underlying image pattern parameters used for detection with corresponding volumetric part location, scale and orientation parameters. These parameters implicitly define a mapping from the image pixels into a pose-normalized appearance space, removing view and pose dependencies, facilitating fine-grained categorization with relatively few training examples. We next examine the problem of leveraging trajectories to understand interactions in dynamic multi-agent environments. We focus on perceptual tasks, those for which an agent's behavior is governed largely by the individuals and objects around them. We introduce kinetic accessibility, a model for evaluating the perceived, and thus anticipated, movements of other agents. This new model is then applied to the analysis of basketball footage. The kinetic accessibility measures are coupled with low-level visual cues and domain-specific knowledge for determining which player has possession of the ball and for recognizing events such as passes, shots and turnovers. Finally, we present two differing approaches for estimating camera network topology. The first technique seeks to partition a set of observations made in the camera network into individual object trajectories. As exhaustive consideration of the partition space is intractable, partitions are considered incrementally, adding observations while pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model, balancing the consistency of appearances across a hypothesized trajectory with the latest predictions of camera adjacency. A primarily benefit of estimating object trajectories is that higher-order statistics, as opposed to just first-order adjacency, can be derived, yielding resilience to camera failure and the potential for improved tracking performance between cameras. Unlike the former centralized technique, the latter takes a decentralized approach, estimating the global network topology with local computations using sequential Bayesian estimation on a modified multinomial distribution. Key to this method is an information-theoretic appearance model for observation weighting. The inherently distributed nature of the approach allows the simultaneous utilization of all sensors as processing agents in collectively recovering the network topology

Learning Higher-order Transition Models in Medium-scale Camera Networks

We present a Bayesian framework for learning higherorder transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the network and have a greater predictive power for multi-camera tracking than camera adjacency alone. These models also provide inherent resilience to camera failure, filling in gaps left by single or even multiple non-adjacent camera failures. Our approach to estimating higher-order transition models relies on the accurate assignment of camera observations to the underlying trajectories of objects moving through the network. We addresses this data association problem by gathering the observations and evaluating alternative partitions of the observation set into individual object trajectories. Searching the complete partition space is intractable, so an incremental approach is taken, iteratively adding observations and pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model. When the algorithm has considered all observations, the most likely (MAP) partition is taken as the true object trajectories. From these recovered trajectories, the higher-order statistics we seek can be derived and employed for tracking. The partitioning algorithm we present is parallel in nature and can be readily extended to distributed computation in medium-scale smart camera networks. 1

Understanding the Organization, Operation, and Victimization Process of Labor Trafficking in the United States

This study examines the organization, operation, and victimization process of labor trafficking across multiple industries in the United States. It examines labor trafficking victim abuse and exploitation along a continuum, from victims' recruitment for work in the United States; through their migration experiences (if any), employment victimization experiences, and efforts to seek help; to their ultimate escape and receipt of services. Data for this study came from a sample of 122 closed labor trafficking victim service records from service providers in four US cities. In addition, interviews were conducted with labor trafficking survivors, local and federal law enforcement officials, legal advocates, and service providers in each site to better understand the labor trafficking victimization experience, the networks involved in labor trafficking and the escape and removal process, and the barriers to investigation and prosecution of labor trafficking cases

Electric and magnetic signatures of dust devils from the 2000–2001 MATADOR desert tests

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94763/1/jgre1728.pd

Comparing Constant and Variable Rate Applications of Solid Cattle Manure on Greenhouse Gas Emissions From Dark Brown Chernozems

Non-Peer ReviewedAlthough field application of solid cattle manure (SCM) is an alternative, low-cost nitrogen (N) source to conventional synthetic fertilizers, gaseous losses of manure-N, occurring via volatilization and denitrification, are well documented. However, the effect of variable rate application of SCM on gaseous N emissions at a landscape-scale has received less attention. The objective of this study was to compare the nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) fluxes from watershed basins within the same field, with and without the addition of fresh feedlot SCM applied at either constant blanket or variable landscape-adjusted rates. Gas samples were collected in 2019 and 2020 with gas sampling locations further classified according to their catchment area size. The non-manured watershed basins had low cumulative N2O and CO2 emissions, and were strong CH4 sinks compared to manured basins. Additionally, basins receiving the Variable Rate manure application had lower N2O emissions than those receiving the Constant Rate manure application. The low elevation, larger catchment area landscape positions contributed proportionally more to cumulative N2O and CO2 emissions, along with reduced CH4 consumption, compared to the smaller catchment areas higher in the landscape, due to greater soil moisture and organic matter content within those depressional soils

Effects of rhythm on memory for spoken sequences : a model and tests of its stimulus-driven mechanism

Immediate memory for spoken sequences depends on their rhythm – different levels of accuracy and patterns of error are seen according to the way in which items are spaced in time. Current models address these phenomena only partially or not at all. We investigate the idea that temporal grouping effects are an emergent property of a general serial ordering mechanism based on a population of oscillators locally-sensitive to amplitude modulations on different temporal scales. Two experiments show that the effects of temporal grouping are independent of the predictability of the grouping pattern, consistent with this model’s stimulus-driven mechanism and inconsistent with alternative accounts in terms of top-down processes. The second experiment reports detailed and systematic differences in the recall of irregularly grouped sequences that are broadly consistent with predictions of the new model. We suggest that the bottom-up multi-scale population oscillator (or BUMP) mechanism is a useful starting point for a general account of serial order in language processing more widely

The effectiveness, safety and cost-effectiveness of cytisine versus varenicline for smoking cessation in an Australian population: a study protocol for a randomized controlled non-inferiority trial

Smoking cessation medications are effective but often underutilised because of costs and side effects. Cytisine is a plant-based smoking cessation medication with over 50 years of use in Central and Eastern Europe. While cytisine has been found to be well-tolerated and more effective than nicotine replacement therapy, direct comparison with varenicline have not been conducted. This study evaluates the effectiveness, safety and cost-effectiveness of cytisine compared with varenicline.Two arm, parallel group, randomised, non-inferiority trial, with allocation concealment and blinded outcome assessment.Australian population-based study.Adult daily smokers (N=1266) interested in quitting will be recruited through advertisements and Quitline telephone-based cessation support services.Eligible participants will be randomised (1:1 ratio) to receive either cytisine capsules (25-day supply) or varenicline tablets (12-week supply), prescribed in accordance with the manufacturer's recommended dosing regimen. The medication will be mailed to each participant's nominated residential address. All participants will also be offered standard Quitline behavioural support (up to six 10-12 minute sessions).Assessments will be undertaken by telephone at baseline, 4- and 7-months post-randomisation. Participants will also be contacted twice (two and four weeks post-randomisation) to ascertain adverse events, treatment adherence and smoking status. The primary outcome will be self-reported 6-month continuous abstinence from smoking, verified by carbon monoxide at 7-month follow-up. We will also evaluate the relative safety and cost-effectiveness of cytisine compared with varenicline. Secondary outcomes will include self-reported continuous and 7-day point prevalence abstinence and cigarette consumption at each follow-up interview.If cytisine is as effective as varenicline, its lower cost and natural plant-based composition may make it an acceptable and affordable smoking cessation medication that could save millions of lives worldwide