Effect of Size and Shape on Electronic and Optical Properties of CdSe Quantum Dots

In this paper, we used the 8-band k$\cdot$p model with valence force field considerations to investigate the effect of size and shape on electronic and optical properties of cadmium selenide quantum dots. Major factors related to their properties including band mixing probabilities, spatial charge distributions, transition matrix elements and Fermi factors were studied. Volumetrically larger CdSe dots were found to have smaller band-gaps but higher transition matrix elements and Fermi factors. The maximum optical gain for dots was observed to have an initially positive and then negative correlation with their real-space size as a result of combined effects of various factors. For the shape effects, cubic dots were found to have smaller band-gaps, Fermi factors and transition matrix elements than spherical dots due to higher level of asymmetry and different surface effects. Consequently, cubic dots have lower emission energy, smaller amplification. The occurrence of near E1-H1 transition broadens the gain spectrum of cubic dots. Cubic and spherical dots are both proven to be promising candidates for optical devices under visible range. We have demonstrated that size and shape change could both effectively alter the properties of quantum dots and therefore recommend consideration of both when optimizing the performance for any desired application.Comment: Published in Optik - International Journal for Light and Electron Optics (8 pages, 10 figures), 201

Joint Resource Allocation for eICIC in Heterogeneous Networks

Interference coordination between high-power macros and low-power picos deeply impacts the performance of heterogeneous networks (HetNets). It should deal with three challenges: user association with macros and picos, the amount of almost blank subframe (ABS) that macros should reserve for picos, and resource block (RB) allocation strategy in each eNB. We formulate the three issues jointly for sum weighted logarithmic utility maximization while maintaining proportional fairness of users. A class of distributed algorithms are developed to solve the joint optimization problem. Our framework can be deployed for enhanced inter-cell interference coordination (eICIC) in existing LTE-A protocols. Extensive evaluation are performed to verify the effectiveness of our algorithms.Comment: Accepted by Globecom 201

Finite projective planes admitting a projective linear group PSL (2,q)

AbstractLet S be a projective plane, and let G⩽Aut(S) and PSL(2,q)⩽G⩽PΓL(2,q) with q>3. If G acts point-transitively on S, then q=7 and S is of order 2

The connected generalized Cayley graphs

In this paper, we provide the sufficient and necessary conditions for generalized Cayley graphs to be connected and bipartite, respectively. As a consequence, we determine the groups whose all generalized Cayley cubic graphs are connected and integral

WU-CRISPR: Characteristics of functional guide RNAs for the CRISPR/Cas9 system

The CRISPR/Cas9 system has been rapidly adopted for genome editing. However, one major issue with this system is the lack of robust bioinformatics tools for design of single guide RNA (sgRNA), which determines the efficacy and specificity of genome editing. To address this pressing need, we analyze CRISPR RNA-seq data and identify many novel features that are characteristic of highly potent sgRNAs. These features are used to develop a bioinformatics tool for genome-wide design of sgRNAs with improved efficiency. These sgRNAs as well as the design tool are freely accessible via a web server, WU-CRISPR (http://crispr.wustl.edu). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0784-0) contains supplementary material, which is available to authorized users

Overlooked Video Classification in Weakly Supervised Video Anomaly Detection

Current weakly supervised video anomaly detection algorithms mostly use multiple instance learning (MIL) or their varieties. Almost all recent approaches focus on how to select the correct snippets for training to improve the performance. They overlook or do not realize the power of video classification in boosting the performance of anomaly detection. In this paper, we study explicitly the power of video classification supervision using a BERT or LSTM. With this BERT or LSTM, CNN features of all snippets of a video can be aggregated into a single feature which can be used for video classification. This simple yet powerful video classification supervision, combined into the MIL framework, brings extraordinary performance improvement on all three major video anomaly detection datasets. Particularly it improves the mean average precision (mAP) on the XD-Violence from SOTA 78.84\% to new 82.10\%. The source code is available at https://github.com/wjtan99/BERT_Anomaly_Video_Classification.Comment: arXiv admin note: text overlap with arXiv:2101.10030 by other author