Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection

Facial Action Unit (AU) detection is a crucial task in affective computing and social robotics as it helps to identify emotions expressed through facial expressions. Anatomically, there are innumerable correlations between AUs, which contain rich information and are vital for AU detection. Previous methods used fixed AU correlations based on expert experience or statistical rules on specific benchmarks, but it is challenging to comprehensively reflect complex correlations between AUs via hand-crafted settings. There are alternative methods that employ a fully connected graph to learn these dependencies exhaustively. However, these approaches can result in a computational explosion and high dependency with a large dataset. To address these challenges, this paper proposes a novel self-adjusting AU-correlation learning (SACL) method with less computation for AU detection. This method adaptively learns and updates AU correlation graphs by efficiently leveraging the characteristics of different levels of AU motion and emotion representation information extracted in different stages of the network. Moreover, this paper explores the role of multi-scale learning in correlation information extraction, and design a simple yet effective multi-scale feature learning (MSFL) method to promote better performance in AU detection. By integrating AU correlation information with multi-scale features, the proposed method obtains a more robust feature representation for the final AU detection. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on widely used AU detection benchmark datasets, with only 28.7\% and 12.0\% of the parameters and FLOPs of the best method, respectively. The code for this method is available at \url{https://github.com/linuxsino/Self-adjusting-AU}.Comment: 13pages, 7 figure

Characterization and cytotoxicity of PAHs in PM2.5 emitted from residential solid fuel burning in the Guanzhong Plain, China

The emission factors (EFs) of polycyclic aromatic hydrocarbons (PAHs) in PM2.5 were measured from commonly used stoves and fuels in the rural Guanzhong Plain, China. The toxicity of the PM2.5 also was measured using in vitro cellular tests. EFs of PAHs varied from 0.18 mg kg(-1) (maize straw charcoal burning in a clean stove) to 83.3 mg kg(-1) (maize straw burning in Heated Kang). The two largest influencing factors on PAH EFs were air supply and volatile matter proportion in fuel. Improvements in these two factors could decrease not only EFs of PAHs but also the proportion of 3-ring to 5-ring PAHs. Exposure to PM2.5 extracts caused a concentration-dependent decline in cell viability but an increase in reactive oxygen species (ROS), tumor necrosis factor alpha (TNF-alpha) and interleukin 6 (IL-6). PM2.5 emitted from maize burning in Heated Kang showed the highest cytotoxicity, and EFs of ROS and inflammatory factors were the highest as well. In comparison, maize straw charcoal burning in a clean stove showed the lowest cytotoxicity, which indicated a clean stove and fuel treatment were both efficient methods for reducing cytotoxicity of primary PM2.5. The production of these bioreactive factors were highly correlated with 3-ring and 4-ring PAHs. Specifically, pyrene, anthracene and benzo(a)anthracene had the highest correlations with ROS production (R = 0.85, 0.81 and 0.80, respectively). This study shows that all tested stoves emitted PM2.5 that was cytotoxic to human cells; thus, there may be no safe levels of exposure to PM2,5 emissions from cooking and heating stoves using solid fuels. The study may also provide a new approach for evaluating the cytotoxicity of primary emitted PM2.5 from solid fuel burning as well as other PM2.5 sources. (C) 2018 Elsevier Ltd. All rights reserved

On skew cyclic codes over M2(F2) M_{2}(\mathbb{F}_{2})

The algebraic structure of skew cyclic codes over $M_{2}$($\mathbb{F}_2$), using the $\mathbb{F}_4$-cyclic algebra, is studied in this work. We determine that a skew cyclic code with a polynomial of minimum degree $d(x)$ is a free code generated by $d(x)$. According to our findings, skew cyclic codes of odd and even lengths are cyclic and $2$-quasi-cyclic over $M_{2}(\mathbb{F}_{2})$, respectively. We provide the self-dual skew condition of Hermitian dual codes of skew cyclic codes. The generator polynomials of Euclidean dual codes are obtained. Furthermore, a spanning set of a double skew cyclic code over $M_{2}(\mathbb{F}_{2})$ is considered in this paper

Optimizing urban rail timetable under time-dependent demand and oversaturated conditions

This article focuses on optimizing a passenger train timetable in a heavily congested urban rail corridor. When peak-hour demand temporally exceeds the maximum loading capacity of a train, passengers may not be able to board the next arrival train, and they may be forced to wait in queues for the following trains. A binary integer programming model incorporated with passenger loading and departure events is constructed to provide a theoretic description for the problem under consideration. Based on time-dependent, origin-to-destination trip records from an automatic fare collection system, a nonlinear optimization model is developed to solve the problem on practically sized corridors, subject to the available train-unit fleet. The latest arrival time of boarded passengers is introduced to analytically calculate effective passenger loading time periods and the resulting time-dependent waiting times under dynamic demand conditions. A by-product of the model is the passenger assignment with strict capacity constraints under oversaturated conditions. Using cumulative input–output diagrams, we present a local improvement algorithm to find optimal timetables for individual station cases. A genetic algorithm is developed to solve the multi-station problem through a special binary coding method that indicates a train departure or cancellation at every possible time point. The effectiveness of the proposed model and algorithm are evaluated using a real-world data set

Coordinating assignment and routing decisions in transit vehicle schedules: A variable-splitting Lagrangian decomposition approach for solution symmetry breaking

This paper focuses on how to coordinate a critical set of assignment and routing decisions in a class of multiple-depot transit vehicle scheduling problems. The assignment decision aims to assign a set of transit vehicles from their current locations to trip tasks in a given timetable, where the routing decision needs to route different vehicles to perform the assigned tasks and return to the depot or designated layover locations. When applying the general purpose solvers and task-oriented Lagrangian relaxation framework for real world instances, a thorny issue is that different but indistinguishable vehicles from the same depot or similar locations could commit to the same set of tasks. This inherent solution symmetry property causes extremely difficult computational barriers for effectively eliminating identical solutions, and the lower bound solutions could contain many infeasible vehicle-to-task matches, leading to large optimality gaps. To systematically coordinate the assignment and routing decisions and further dynamically break symmetry during the solution search process, we adopt a variable-splitting approach to introduce task-specific and vehicle-distinguishable Lagrangian multipliers and then propose a sequential assignment process in order to enhance the solution quality for the augmented models with tight formulations. We conduct the numerical experiments to offer the managerial interpretation and examine solution quality of the proposed approach in a wider range of applications