Iterative Mode-Dropping for the Sum Capacity of MIMO-MAC with Per-Antenna Power Constraint

We propose an iterative mode-dropping algorithm that optimizes input signals to achieve the sum capacity of the MIMO-MAC with per-antenna power constraint. The algorithm successively optimizes each user's input covariance matrix by applying mode-dropping to the equivalent single-user MIMO rate maximization problem. Both analysis and simulation show fast convergence. We then use the algorithm to briefly highlight the difference in MIMO-MAC capacities under sum and per-antenna power constraints.Comment: 6 pages double-column, 5 figure

Antibiotics Time Machine is NP-hard

The antibiotics time machine is an optimization question posed by Mira \latin{et al.} on the design of antibiotic treatment plans to minimize antibiotic resistance. The problem is a variation of the Markov decision process. These authors asked if the problem can be solved efficiently. In this paper, we show that this problem is NP-hard in general.Comment: 5 page

Quality-Gated Convolutional LSTM for Enhancing Compressed Video

The past decade has witnessed great success in applying deep learning to enhance the quality of compressed video. However, the existing approaches aim at quality enhancement on a single frame, or only using fixed neighboring frames. Thus they fail to take full advantage of the inter-frame correlation in the video. This paper proposes the Quality-Gated Convolutional Long Short-Term Memory (QG-ConvLSTM) network with bi-directional recurrent structure to fully exploit the advantageous information in a large range of frames. More importantly, due to the obvious quality fluctuation among compressed frames, higher quality frames can provide more useful information for other frames to enhance quality. Therefore, we propose learning the "forget" and "input" gates in the ConvLSTM cell from quality-related features. As such, the frames with various quality contribute to the memory in ConvLSTM with different importance, making the information of each frame reasonably and adequately used. Finally, the experiments validate the effectiveness of our QG-ConvLSTM approach in advancing the state-of-the-art quality enhancement of compressed video, and the ablation study shows that our QG-ConvLSTM approach is learnt to make a trade-off between quality and correlation when leveraging multi-frame information. The project page: https://github.com/ryangchn/QG-ConvLSTM.git.Comment: Accepted to IEEE International Conference on Multimedia and Expo (ICME) 201

Multi-Frame Quality Enhancement for Compressed Video

The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the quality of a single frame, ignoring the similarity between consecutive frames. In this paper, we investigate that heavy quality fluctuation exists across compressed video frames, and thus low quality frames can be enhanced using the neighboring high quality frames, seen as Multi-Frame Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach for compressed video, as a first attempt in this direction. In our approach, we firstly develop a Support Vector Machine (SVM) based detector to locate Peak Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame Convolutional Neural Network (MF-CNN) is designed to enhance the quality of compressed video, in which the non-PQF and its nearest two PQFs are as the input. The MF-CNN compensates motion between the non-PQF and PQFs through the Motion Compensation subnet (MC-subnet). Subsequently, the Quality Enhancement subnet (QE-subnet) reduces compression artifacts of the non-PQF with the help of its nearest PQFs. Finally, the experiments validate the effectiveness and generality of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video. The code of our MFQE approach is available at https://github.com/ryangBUAA/MFQE.gitComment: to appear in CVPR 201

The leadership experiences of successful minority American women in financial services : a participatory study

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Psycho-Social Variables Contributing to Disparities of Hmong Students in Postsecondary Education

Statistics show that only 14.4% of Hmong students age 25 and older held bachelor’s degrees ( US Census Bureau, 2012) and 27% of Hmong live in poverty in the United States (UNPO, 2014). These are alarming statistics when compared to other racial and ethnic groups. The following systematic review was conducted to answer the research question: What psycho-social variables contributing to disparities of Hmong students in postsecondary education? Previous research articles included peer reviews and dissertations that were published after the year 2000. The databases used to collect relevant research were EBSCOhost (Academic Search Premier), Google Scholars, and JSTOR using search terms: “Hmong students” and “academic success” or “factors contributing to Hmong student educational success” or Hmong students’ experience in American educational system.” Twelve articles met inclusion criteria and were used for the final review. Four overarching categories were established to ensure a comprehensive review: acculturation, cultural expectations, educational experience, and socioeconomic status. Underneath these overarching categories, eight sub-themes surfaced from the synthesis regarding psycho-social factors that contribute to disparities of Hmong students in postsecondary education. These sub-themes are: 1) generational conflict, 2) ethnic identity, 3) family support, 4) family obligations, 5) academic support programs, 6) teacher/student interactions, 7) parental involvement, and 8) financial resource. These themes suggests that Hmong students are faced 1 Psycho-Social factors to Disparities of Hmong Students in Postsecondary Education with challenges related to their historical context, ethnic culture, and experiences in the United States. Future research is required to further understand the unique challenges Hmong students encounter as they move through American society