Evaluating search and matching models using experimental data

This paper introduces an innovative test of search and matching models using the exogenous variation available in experimental data. We take an off-the-shelf Pissarides matching model and calibrate it to data on the control group from a randomized social experiment. We then simulate a program group from a randomized experiment within the model. As a measure of the performance of the model, we compare the outcomes of the program groups from the model and from the randomized experiment. We illustrate our methodology using the Canadian Self-Sufficiency Project (SSP), a social experiment providing a time limited earnings supplement for Income Assistance recipients who obtain full time employment within a 12 month period. We find two features of the model are consistent with the experimental results: endogenous search intensity and exogenous job destruction. We find mixed evidence in support of the assumption of fixed hours of labor supply. Finally, we find a constant job destruction rate is not consistent with the experimental data in this context

Attributes and action recognition based on convolutional neural networks and spatial pyramid VLAD encoding

© Springer International Publishing AG 2017.Determination of human attributes and recognition of actions in still images are two related and challenging tasks in computer vision, which often appear in fine-grained domains where the distinctions between the different categories are very small. Deep Convolutional Neural Network (CNN) models have demonstrated their remarkable representational learning capability through various examples. However, the successes are very limited for attributes and action recognition as the potential of CNNs to acquire both of the global and local information of an image remains largely unexplored. This paper proposes to tackle the problem with an encoding of a spatial pyramid Vector of Locally Aggregated Descriptors (VLAD) on top of CNN features. With region proposals generated by Edgeboxes, a compact and efficient representation of an image is thus produced for subsequent prediction of attributes and classification of actions. The proposed scheme is validated with competitive results on two benchmark datasets: 90.4% mean Average Precision (mAP) on the Berkeley Attributes of People dataset and 88.5% mAP on the Stanford 40 action dataset

Wearable EEG: what is it, why is it needed and what does it entail?

Published versio

Decreased dopamine activity predicts relapse in methamphetamine abusers.

Studies in methamphetamine (METH) abusers showed that the decreases in brain dopamine (DA) function might recover with protracted detoxification. However, the extent to which striatal DA function in METH predicts recovery has not been evaluated. Here we assessed whether striatal DA activity in METH abusers is associated with clinical outcomes. Brain DA D2 receptor (D2R) availability was measured with positron emission tomography and [(11)C]raclopride in 16 METH abusers, both after placebo and after challenge with 60 mg oral methylphenidate (MPH) (to measure DA release) to assess whether it predicted clinical outcomes. For this purpose, METH abusers were tested within 6 months of last METH use and then followed up for 9 months of abstinence. In parallel, 15 healthy controls were tested. METH abusers had lower D2R availability in caudate than in controls. Both METH abusers and controls showed decreased striatal D2R availability after MPH and these decreases were smaller in METH than in controls in left putamen. The six METH abusers who relapsed during the follow-up period had lower D2R availability in dorsal striatum than in controls, and had no D2R changes after MPH challenge. The 10 METH abusers who completed detoxification did not differ from controls neither in striatal D2R availability nor in MPH-induced striatal DA changes. These results provide preliminary evidence that low striatal DA function in METH abusers is associated with a greater likelihood of relapse during treatment. Detection of the extent of DA dysfunction may be helpful in predicting therapeutic outcomes

The James Clerk Maxwell telescope dense gas survey of the Perseus molecular cloud

This is the final version of the article. Available from the publisher via the DOI in this record.We present the results of a large-scale survey of the very dense (n > 106 cm-3) gas in the Perseus molecular cloud using HCO+ and HCN (J = 4 → 3) transitions. We have used this emission to trace the structure and kinematics of gas found in pre- and protostellar cores, as well as in outflows. We compare the HCO+/HCN data, highlighting regions where there is a marked discrepancy in the spectra of the two emission lines. We use the HCO+ to identify positively protostellar outflows and their driving sources, and present a statistical analysis of the outflow properties that we derive from this tracer. We find that the relations we calculate between the HCO+ outflow driving force and the Menv and Lbol of the driving source are comparable to those obtained from similar outflow analyses using 12CO, indicating that the two molecules give reliable estimates of outflow properties. We also compare the HCO+ and the HCN in the outflows, and find that the HCN traces only the most energetic outflows, the majority of which are driven by young Class 0 sources. We analyse the abundances of HCN and HCO+ in the particular case of the IRAS 2A outflows, and find that the HCN is much more enhanced than the HCO+ in the outflow lobes. We suggest that this is indicative of shock enhancement of HCN along the length of the outflow; this process is not so evident for HCO+, which is largely confined to the outflow base. © 2014 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.SLW-S and JH are funded by the Science and Technology Facilities Council of the UK. The James Clerk Maxwell Telescope is operated by the Joint Astronomy Centre on behalf of the Science and Technology Facilities Council of the United Kingdom, the National Research Council of Canada and (until 2013 March 31) the Netherlands Organisation for Scientific Researc

Multi-Objective Demand Side Scheduling Considering the Operational Safety of Appliances

The safe operation of appliances is of great concern to users. The safety risk increases when the appliances are in operation during periods when users are not at home or when they are asleep. In this paper, multi-objective demand side scheduling is investigated with consideration to the appliances’ operational safety together with the electricity cost and the operational delay. The formulation of appliances’ operational safety is proposed based on users’ at-home status and awake status. Then the relationships between the operational safety and the other two objectives are investigated through the approach of finding the Pareto-optimal front. Moreover, this approach is compared with the Weigh and Constraint approaches. As the Pareto-optimal front consists of a set of optimal solutions, this paper proposes a method to make the final scheduling decision based on the relationships among the multiple objectives. Simulation results demonstrate that the operational safety is improved with the sacrifice of the electricity cost and the operational delay, and that the approach of finding the Pareto-optimal front is effective in presenting comprehensive optimal solutions of the multi-objective demand side scheduling