Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation

© 2019 IEEE. We consider the problem of unsupervised domain adaptation in semantic segmentation. The key in this campaign consists in reducing the domain shift, i.e., enforcing the data distributions of the two domains to be similar. A popular strategy is to align the marginal distribution in the feature space through adversarial learning. However, this global alignment strategy does not consider the local category-level feature distribution. A possible consequence of the global movement is that some categories which are originally well aligned between the source and target may be incorrectly mapped. To address this problem, this paper introduces a category-level adversarial network, aiming to enforce local semantic consistency during the trend of global alignment. Our idea is to take a close look at the category-level data distribution and align each class with an adaptive adversarial loss. Specifically, we reduce the weight of the adversarial loss for category-level aligned features while increasing the adversarial force for those poorly aligned. In this process, we decide how well a feature is category-level aligned between source and target by a co-training approach. In two domain adaptation tasks, i.e., GTA5-> Cityscapes and SYNTHIA-> Cityscapes, we validate that the proposed method matches the state of the art in segmentation accuracy

DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks

IEEE Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis,we prove that DP-LTOD scheme can guarantee \epsilon-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking

Mapping the feel of the arm with the sight of the object: on the embodied origins of infant reaching

For decades, the emergence and progression of infant reaching was assumed to be largely under the control of vision. More recently, however, the guiding role of vision in the emergence of reaching has been downplayed. Studies found that young infants can reach in the dark without seeing their hand and that corrections in infants\u27 initial hand trajectories are not the result of visual guidance of the hand, but rather the product of poor movement speed calibration to the goal. As a result, it has been proposed that learning to reach is an embodied process requiring infants to explore proprioceptively different movement solutions, before they can accurately map their actions onto the intended goal. Such an account, however, could still assume a preponderant (or prospective) role of vision, where the movement is being monitored with the scope of approximating a future goal-location defined visually. At reach onset, it is unknown if infants map their action onto their vision, vision onto their action, or both. To examine how infants learn to map the feel of their hand with the sight of the object, we tracked the object-directed looking behavior (via eye-tracking) of three infants followed weekly over an 11-week period throughout the transition to reaching. We also examined where they contacted the object. We find that with some objects, infants do not learn to align their reach to where they look, but rather learn to align their look to where they reach. We propose that the emergence of reaching is the product of a deeply embodied process, in which infants first learn how to direct their movement in space using proprioceptive and haptic feedback from self-produced movement contingencies with the environment. As they do so, they learn to map visual attention onto these bodily centered experiences, not the reverse. We suggest that this early visuo-motor mapping is critical for the formation of visually-elicited, prospective movement control

Exploration and Research on the Mixed Mode Curriculum of “Competition, Training and Teaching”

In response to the many problems encountered in the teaching of the “SSM Framework” course, a project-driven hybrid teaching model is proposed. The reform integrates “Competition, Training and Teaching” into one, utilizes online teaching platforms to arrange pre-class activities, carry out interactive teaching in class, and improve post-class practice. In teaching, the roles of teachers and students should be exchanged to fully mobilize students’ learning initiative and cultivate their ability to solve and analyze problems. In the assessment, a process evaluation mechanism is introduced to incorporate project construction into the assessment scope and improve practical application capabilities. The practical results indicate that the application of the new model in curriculum significantly enhances students’ learning interest and practical abilities, which is feasible for promotion

Macro-micro adversarial network for human parsing

© Springer Nature Switzerland AG 2018. In human parsing, the pixel-wise classification loss has drawbacks in its low-level local inconsistency and high-level semantic inconsistency. The introduction of the adversarial network tackles the two problems using a single discriminator. However, the two types of parsing inconsistency are generated by distinct mechanisms, so it is difficult for a single discriminator to solve them both. To address the two kinds of inconsistencies, this paper proposes the Macro-Micro Adversarial Net (MMAN). It has two discriminators. One discriminator, Macro D, acts on the low-resolution label map and penalizes semantic inconsistency, e.g., misplaced body parts. The other discriminator, Micro D, focuses on multiple patches of the high-resolution label map to address the local inconsistency, e.g., blur and hole. Compared with traditional adversarial networks, MMAN not only enforces local and semantic consistency explicitly, but also avoids the poor convergence problem of adversarial networks when handling high resolution images. In our experiment, we validate that the two discriminators are complementary to each other in improving the human parsing accuracy. The proposed framework is capable of producing competitive parsing performance compared with the state-of-the-art methods, i.e., mIoU = 46.81% and 59.91% on LIP and PASCAL-Person-Part, respectively. On a relatively small dataset PPSS, our pre-trained model demonstrates impressive generalization ability. The code is publicly available at https://github.com/RoyalVane/MMAN

Peculiar torsion dynamical response of spider dragline silk

This work was supported by the NSFC (No. 11472114), the Natural Science Foundation of Hubei Province (No. 2015CFB394), and the Young Elite Scientist Sponsorship Program by CAST (No. 2016QNRC001). D.L. and D.J.D. thank the support from the EU's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 704292

The interplay of proactive personality and internship quality in Chinese university graduates' job search success: The role of career adaptability

Based on the job characteristics model and career construction theory, this study examined the role of internship quality in the employment success of Chinese university students. A four-wave survey study was conducted in a sample of university graduates (N = 207) and the results showed that after the effects of baseline career adaptability (Time 1) were controlled, internship quality (Time 2) and proactive personality (Time 2) positively were both associated with subsequent career adaptability (Time 3), which was further related to indicators of employment success (number of job offers, starting salary, and job search efficiency) at Time 4. In addition, internship quality was also found to be a significant moderator of the relationship between proactive personality and career adaptability as well as employment success, such that when internship quality was lower, the indirect effect of proactive personality on job search success through career adaptability was stronger. The corresponding moderated mediation model was also supported by the results. These findings carry implications for future studies on school-to-work transition and organizational recruitment practices