Naturally-occurring sleep choice and time of day effects on p-beauty contest outcomes

We explore the behavioral consequences of sleep loss and time-of-day (circadian) effects on a particular type of decision making. Subject sleep is monitored for the week prior to a decision experiment, which is then conducted at 8 a.m. or 8 p.m. A validated circadian preference instrument allows us to randomly assign subjects to a more or less preferred time-of-day session. The well-known p-beauty contest (a.k.a., the guessing game) is administered to examine how sleep loss and circadian mismatch affect subject reasoning and learning. We find that the subject responses are consistent with significantly lower levels of iterative reasoning when ‘sleep deprived’ or at non-optimal times-of-day. A non-linear effect is estimated to indicate that too much sleep also leads to choices consistent with lower levels of reasoning, with an apparent optimum at close to 7 hours sleep per night. However, repeated play shows that sleep loss and non-optimal times-of-day do not affect learning or adaptation in response to information feedback. Our results apply to environments where anticipation is important, such as in coordination games, stock trading, driving, etc. These findings have important implications for the millions of adults considered sleep deprived, as well as those employed in shift work occupations. Key Words:

Robust Estimation of 3D Human Poses from a Single Image

Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector. 3D pose estimation is challenging because multiple 3D poses may correspond to the same 2D pose after projection due to the lack of depth information. Moreover, current 2D pose estimators are usually inaccurate which may cause errors in the 3D estimation. We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons. (ii) We enforce limb length constraints to eliminate anthropomorphically implausible skeletons. (iii) We estimate a 3D pose by minimizing the $L_1$-norm error between the projection of the 3D pose and the corresponding 2D detection. The $L_1$-norm loss term is robust to inaccurate 2D joint estimations. We use the alternating direction method (ADM) to solve the optimization problem efficiently. Our approach outperforms the state-of-the-arts on three benchmark datasets

Vehicle routing under time-dependent travel times: the impact of congestion avoidance

Daily traffic congestions form major problems for businesses such\ud as logistical service providers and distribution firms. They cause\ud late arrivals at customers and additional hiring costs for the truck\ud drivers. The additional costs of traffic congestions can be reduced\ud by taking into account and avoid well-predictable traffic congestions\ud within off-line vehicle route plans. In the literature, various strategies\ud are proposed to avoid traffic congestions, such as selecting alternative routes, changing the customer visit sequences, and changing the\ud vehicle-customer assignments. We investigate the impact of these and\ud other congestion avoidance strategies in off-line vehicle route plans on\ud the performance of these plans in reality. For this purpose, we develop\ud a set of VRP instances on real road networks, and a speed model that\ud inhabits the main characteristics of peak hour congestion. The instances are solved for different levels of congestion avoidance using a\ud modified Dijkstra algorithm and a restricted dynamic programming\ud heuristic. Computational experiments show that 99% of late arrivals\ud at customers can be eliminated if traffic congestions are accounted for\ud off-line. On top of that, almost 70% of the extra duty times caused by\ud the traffic congestions can be eliminated by clever avoidance strategies