Scalable Projection-Free Optimization

As a projection-free algorithm, Frank-Wolfe (FW) method, also known as conditional gradient, has recently received considerable attention in the machine learning community. In this dissertation, we study several topics on the FW variants for scalable projection-free optimization. We first propose 1-SFW, the first projection-free method that requires only one sample per iteration to update the optimization variable and yet achieves the best known complexity bounds for convex, non-convex, and monotone DR-submodular settings. Then we move forward to the distributed setting, and develop Quantized Frank-Wolfe (QFW), ageneral communication-efficient distributed FW framework for both convex and non-convex objective functions. We study the performance of QFW in two widely recognized settings: 1) stochastic optimization and 2) finite-sum optimization. Finally, we propose Black-Box Continuous Greedy, a derivative-free and projection-free algorithm, that maximizes a monotone continuous DR-submodular function over a bounded convex body in Euclidean space

TWO-HANDED TYPING METHOD ON AN ARBITRARY SURFACE

A computing device may detect user input, such as finger movements resembling typing on an invisible virtual keyboard in the air or on any surface, to enable typing. The computing device may use sensors (e.g., accelerometers, cameras, piezoelectric sensors, etc.) to detect the user’s finger movements, such as the user’s fingers moving through the air and/or contacting a surface. The computing device may then decode (or, in other words, convert, interpret, analyze, etc.) the detected finger movements to identify corresponding inputs representative of characters (e.g., alphanumeric characters, national characters, special characters, etc.). To reduce input errors, the computing device may decode the detected finger movements, at least in part, based on contextual information, such as preceding characters, words, and/or the like entered via previously detected user inputs. Similarly, the computing device may apply machine learning techniques and adjust parameters, such as a signal-to-noise ratio, to improve the accuracy of input-entry. In some examples, the computing device may implement specific recognition, prediction, and correction algorithms to improve the accuracy of input-entry. In this way, the computing device may accommodate biasing in finger movements that may be specific to a user entering the input

STUDY ON THE PERFORMANCE OF HIGH-MODULUS ASPHALT CONCRETE PAVEMENT IN EXTREME CURVES OR STEEP SLOPES OF TRUNK HIGHWAY

With the purpose of the project, we determined the performance of high modulus asphalt concrete (HMAC) pavement in sharp curves or steep slopes of the trunk highway. We selected bending of road surface, bending and stretching strain at the bottom of surface layer, vertical compressive strain at the bottom of surface layer as research parameter index. By using the three-dimensional model analysis function of the finite element software ANSYS, the mechanical models of asphalt pavement with three different structures under the action of steep slope and heavy traffic are established. Firstly, the conventional asphalt pavement consists of 4cmAC-13 bituminous pavement (the top layer) and 6cmAC-20 bituminous pavement (the bottom layer). Then, the HMAC pavement 1 consists of 4cmAC-13 bituminous pavement (the top layer) and 6cmAC-EME14 bituminous pavement (the bottom layer).The HMAC pavement 2 consists of 6cmAC-EME14 bituminous pavement (the top layer) and 4cmAC-13 bituminous pavement (the bottom layer). Then we tried it out that for the deflection value, the HMAC pavement 1 was 5.34 percentage point reduced than the conventional asphalt pavement. At the same time, the HMAC pavement 2 was 6.95 percentage point reduced than the conventional asphalt pavement. So, it can significantly reduce the bending strain at the bottom of the surface layer by using HMAC as asphalt pavement structure. For the resistance to shear strain and vertical compressive strain at the bottom of the surface layer, the HMAC pavement 1 is the best. Then the HMAC pavement 2 follows and then the conventional asphalt pavement. The results show that the HMAC can significantly improve the overall stiffness of the pavement and reduce the bending, shearing and vertical strain. Meanwhile, it can also reduce the occurrence of wheel rut, upheaval, fatigue crack and other common diseases