Phase retrieval from power spectra of masked signals

In diffraction imaging, one is tasked with reconstructing a signal from its power spectrum. To resolve the ambiguity in this inverse problem, one might invoke prior knowledge about the signal, but phase retrieval algorithms in this vein have found limited success. One alternative is to create redundancy in the measurement process by illuminating the signal multiple times, distorting the signal each time with a different mask. Despite several recent advances in phase retrieval, the community has yet to construct an ensemble of masks which uniquely determines all signals and admits an efficient reconstruction algorithm. In this paper, we leverage the recently proposed polarization method to construct such an ensemble. We also present numerical simulations to illustrate the stability of the polarization method in this setting. In comparison to a state-of-the-art phase retrieval algorithm known as PhaseLift, we find that polarization is much faster with comparable stability.Comment: 18 pages, 3 figure

Improving Continuous Sign Language Recognition with Cross-Lingual Signs

This work dedicates to continuous sign language recognition (CSLR), which is a weakly supervised task dealing with the recognition of continuous signs from videos, without any prior knowledge about the temporal boundaries between consecutive signs. Data scarcity heavily impedes the progress of CSLR. Existing approaches typically train CSLR models on a monolingual corpus, which is orders of magnitude smaller than that of speech recognition. In this work, we explore the feasibility of utilizing multilingual sign language corpora to facilitate monolingual CSLR. Our work is built upon the observation of cross-lingual signs, which originate from different sign languages but have similar visual signals (e.g., hand shape and motion). The underlying idea of our approach is to identify the cross-lingual signs in one sign language and properly leverage them as auxiliary training data to improve the recognition capability of another. To achieve the goal, we first build two sign language dictionaries containing isolated signs that appear in two datasets. Then we identify the sign-to-sign mappings between two sign languages via a well-optimized isolated sign language recognition model. At last, we train a CSLR model on the combination of the target data with original labels and the auxiliary data with mapped labels. Experimentally, our approach achieves state-of-the-art performance on two widely-used CSLR datasets: Phoenix-2014 and Phoenix-2014T.Comment: Accepted by ICCV 202

The Relationship Between Teachers’ Perception Towards Administrative Support and Their Job Satisfaction in a Secondary Vocational School, Kunming, China

The main purpose of this study was to determine the relationship between teachers’ perceptions towards administrative support and their job satisfaction in a secondary vocational school, Kunming, China. 83 full-time teachers from the selected school were surveyed in this research. The researcher used Mean and Standard Deviation to analysis the teachers’ perceptions toward the level of administrative support the perceived and their job satisfaction. Pearson Product Moment Coefficient of Correlation was used to test the relationship between the two variables. The result of this study showed that teachers in the target school perceived a high level of administrative support, also, teachers’ perceptions toward job satisfaction in this school regarded as moderate. Pearson correlation tested that there was a relationship between teachers’ perceptions toward administrative support and their job satisfaction. Teachers in the target school perceived. The teachers in this school feel most satisfied with the instrumental support that provides by the principal and feel more satisfied with the intrinsic aspect of their work

Joint prediction of travel mode choice and purpose from travel surveys: A multitask deep learning approach

The prediction and behavioural analysis of travel mode choice and purpose are critical for transport planning and have attracted increasing interest in research. Traditionally, the prediction of travel mode choice and trip purpose has been tackled separately, which fail to fully leverage the shared information between travel mode and purpose. This study addresses this gap by proposing a multitask learning deep neural network framework (MTLDNN) to jointly predict mode choice and purpose. We empirically evaluate and validate this framework using the household travel survey data in Greater London, UK. The results show that this framework has significantly lower cross-entropy loss than multinomial logit models (MNL) and single-task-learning deep neural network models (STLDNN). On the other hand, the predictive accuracy of MTLDNN is similar to STLDNN and is significantly higher than MNL. Moreover, in terms of behaviour analysis, the substitution pattern and choice probability of MTLDNN regarding input variables largely agree with MNL and STLDNN. This work demonstrates that MTLDNN is efficient in utilising the information shared by travel mode choice and purpose, and is capable of producing behaviourally reasonable substitution patterns across travel modes. Future research would develop more advanced MTLDNN frameworks for travel behaviour analysis and generalise MTLDNN to other travel behaviour topics

Computerized Stock Trading System

This IQP combines fundamental and technical analysis by adding value to the stocks selected by the Guru Stock Screener through utilizing the concept of complete trading strategies. We utilize TradeStation trading platform to evaluate four additional screening criteria: Moving Average Crossover, Average Directional Index, Volatility and Efficiency indicators on 100 stocks that are selected by the Guru Stock Screener to further refine the stock list down to only five stocks plus GOOG and X that satisfy certain criteria

Freestanding dielectric nanohole array metasurface for mid-infrared wavelength applications

We designed and simulated freestanding dielectric optical metasurfaces based on arrays of etched nanoholes in a silicon membrane. We showed $2\pi$ phase control and high forward transmission at mid-infrared wavelengths by tuning the dimensions of the holes. We also identified the mechanisms responsible for high forward scattering efficiency and showed that these conditions are connected with the well-known Kerker conditions already proposed for isolated scatterers. A beam deflector was designed and optimized through sequential particle swarm and gradient descent optimization to maximize transmission efficiency and reduce unwanted grating orders. Such freestanding silicon nanohole array metasurfaces are promising for the realization of silicon based mid-infrared optical elements