The Dual Feminisation of HIV/AIDS

This is an Accepted Manuscript of an article published by Taylor & Francis in Globalizations on 2011, available online: http://wwww.tandfonline.com/10.1080/14747731.2010.49302

Examining Trust and Reliance in Collaborations between Humans and Automated Agents

Human trust and reliance in artificial agents is critical to effective collaboration in mixed human computer teams. Understanding the conditions under which humans trust and rely upon automated agent recommendations is important as trust is one of the mechanisms that allow people to interact effectively with a variety of teammates. We conducted exploratory research to investigate how personality characteristics and uncertainty conditions affect human-machine interactions. Participants were asked to determine if two images depicted the same or different people, while simultaneously considering the recommendation of an automated agent. Results of this effort demonstrated a correlation between judgements of agent expertise and user trust. In addition, we found that in conditions of high and low uncertainty, the decision outcomes of participants moved significantly in the direction of the agent’s recommendation. Differences in reported trust in the agent were observed in individuals with low and high levels of extraversion

Vertical Distribution, Persistence, and Activity of Entomopathogenic Nematodes (Nematoda: Heterorhabditidae and Steinernematidae) in Alfalfa Snout Beetle- (Coleoptera: Curculionidae) Infested Fields

The vertical movement, persistence, and activity of four isolates of entomopathogenic nematodes, Heterorhabditis bacteriophora Poinar (Oswego), Heterorhabditis bacteriophora Poinar (NC), Steinernema carpocapsae (Weiser) (NY001),and an undescribed Steinernema species (NY008-2E), were evaluated for 24 mo at field locations in northern New York. Nematodes were released into three alfalfa fields naturally infested with alfalfa snout beetle, Otiorhynchus ligustici (L.). Each field differed in soil type and soil textural composition: silt loam, sandy loam, and loamy sand. Nematodes were recovered from soil using trap insects, Galleria mellonella larvae, and their vertical distribution was monitored at 5-cm intervals to depths of 20 cm for Steinernena species and 35 cm for Heterorhabditis species. All nematodes persisted (no significant reduction in percentage of infection of G. mellonella) for 6 mo after the initial application in all soil types. However, by the end of the second growing season (17 mo after application), all nematodes showed significant reductions in infection rates of G. mellonella except H. bacteriophora (Oswego) which showed high levels of infection for 24 mo. Nematode vertical movement was affected by soil type and varied by isolate. S. carpocapsae (NY00l)and Steinernema sp. (NY008-2E) remained primarily in soil depths <15 cm, whereas both heterorhabditids dispersed to soil depths of 35 cm. Vertical movement of H. bacteriophera (Oswego) was greatest in loamy sand and vertical movement of Steinernema sp. (NY008-2E) was greatest in sandy loam. Percentage of infection of G. mellonella by H. bacteriophora (Oswego) and S. carpocapsae (NY00l)was significantly correlated with rising soil temperatures in early spring. H. bacteriophora (Oswego) and S. carpocapsae (NYOOl)infected G. mellonella larvae in the field at soil temperatures between 15 and 18°C. Steinernema sp. (NY008-2E)infected G. mellonella larvae in the field at soil temperatures between 13 and 15°

H-Net: unsupervised attention-based stereo depth estimation leveraging epipolar geometry

Depth estimation from a stereo image pair has become one of the most explored applications in computer vision, with most previous methods relying on fully supervised learning settings. However, due to the difficulty in acquiring accurate and scalable ground truth data, the training of fully supervised methods is challenging. As an alternative, self-supervised methods are becoming more popular to mitigate this challenge. In this paper, we introduce the H-Net, a deep-learning framework for unsupervised stereo depth estimation that leverages epipolar geometry to refine stereo matching. For the first time, a Siamese autoencoder architecture is used for depth estimation which allows mutual information between rectified stereo images to be extracted. To enforce the epipolar constraint, the mutual epipolar attention mechanism has been designed which gives more emphasis to correspondences of features that lie on the same epipolar line while learning mutual information between the input stereo pair. Stereo correspondences are further enhanced by incorporating semantic information to the proposed attention mechanism. More specifically, the optimal transport algorithm is used to suppress attention and eliminate outliers in areas not visible in both cameras. Extensive experiments on KITTI2015 and Cityscapes show that the proposed modules are able to improve the performance of the unsupervised stereo depth estimation methods while closing the gap with the fully supervised approaches

Quantified HI Morphology II : Lopsidedness and Interaction in WHISP Column Density Maps

Lopsidedness of the gaseous disk of spiral galaxies is a common phenomenon in disk morphology, profile and kinematics. Simultaneously, the asymmetry of a galaxy's stellar disk, in combination with other morphological parameters, has seen extensive use as an indication of recent merger or interaction in galaxy samples. Quantified morphology of stellar spiral disks is one avenue to determine the merger rate over much of the age of the Universe. In this paper, we measure the quantitative morphology parameters for the HI column density maps from the Westerbork observations of neutral Hydrogen in Irregular and SPiral galaxies (WHISP). These are Concentration, Asymmetry, Smoothness, Gini, M20, and one addition of our own, the Gini parameter of the second order moment (GM). Our aim is to determine if lopsided or interacting disks can be identified with these parameters. Our sample of 141 HI maps have all previous classifications on their lopsidedness and interaction. We find that the Asymmetry, M20, and our new GM parameter correlate only weakly with the previous morphological lopsidedness quantification. These three parameters may be used to compute a probability that an HI disk is morphologically lopsided but not unequivocally to determine it. However, we do find that that the question whether or not an HI disk is interacting can be settled well using morphological parameters. Parameter cuts from the literature do not translate from ultraviolet to HI directly but new selection criteria using combinations of Asymmetry and M20 or Concentration and M20, work very well. We suggest that future all-sky HI surveys may use these parameters of the column density maps to determine the merger fraction and hence rate in the local Universe with a high degree of accuracy.Comment: 12 pages, 5 figures, 1 table, accepted by MNRAS, appendix not include

Towards autonomous control of surgical instruments using adaptive-fusion tracking and robot self-calibration

The ability to track surgical instruments in realtime is crucial for autonomous Robotic Assisted Surgery (RAS). Recently, the fusion of visual and kinematic data has been proposed to track surgical instruments. However, these methods assume that both sensors are equally reliable, and cannot successfully handle cases where there are significant perturbations in one of the sensors' data. In this paper, we address this problem by proposing an enhanced fusion-based method. The main advantage of our method is that it can adjust fusion weights to adapt to sensor perturbations and failures. Another problem is that before performing an autonomous task, these robots have to be repetitively recalibrated by a human for each new patient to estimate the transformations between the different robotic arms. To address this problem, we propose a self-calibration algorithm that empowers the robot to autonomously calibrate the transformations by itself in the beginning of the surgery. We applied our fusion and selfcalibration algorithms for autonomous ultrasound tissue scanning and we showed that the robot achieved stable ultrasound imaging when using our method. Our performance evaluation shows that our proposed method outperforms the state-of-art both in normal and challenging situations