In recent years, reinforcement learning (RL) has achieved a remarkable achievement and it has attracted researchers' attention in modeling real-life scenarios by expanding its research beyond conventional complex games. Prediction of optimal treatment regimens from observational real clinical data is being popularized, and more advanced versions of RL algorithms are being implemented in the literature. However, RL-generated medications still need careful supervision of expertise parties or doctors in healthcare. Hence, in this paper, a Supervised Optimal Chemotherapy Regimen (SOCR) approach to investigate optimal chemotherapy-dosing schedule for cancer patients was presented by using Offline Reinforcement Learning. The optimal policy suggested by the RL approach was supervised by incorporating previous treatment decisions of oncologists, which could add clinical expertise knowledge on algorithmic results. Presented SOCR approach followed a model-based architecture using conservative Q-Learning (CQL) algorithm. The developed model was tested using a manually constructed database of forty Stage-IV colon cancer patients, receiving line-1 chemotherapy treatments, who were clinically classified as 'Bevacizumab based patient' and 'Cetuximab based patient'. Experimental results revealed that the supervision from the oncologists has considered the effect to stabilize chemotherapy regimen and it was suggested that the proposed framework could be successfully used as a supportive model for oncologists in deciding their treatment decisions.

The vector graphics with gradient mesh can be attributed to their compactness and scalability; however, they tend to fall short when it comes to real-time editing due to a lack of real-time rasterization and an efficient editing tool for image details. In this paper, we encode global manipulation geometries and local image details within a hybrid vector structure, using parametric patches and detailed features for localized and parallelized thin-plate spline interpolation in order to achieve good compressibility, interactive expressibility, and editability. The proposed system then automatically extracts an optimal set of detailed color features while considering the compression ratio of the image as well as reconstruction error and its characteristics applicable to the preservation of structural and irregular saliency of the image. The proposed real-time vector representation makes it possible to construct an interactive editing system for detail-maintained image magnification and color editing as well as material replacement in cross mapping, without maintaining spatial and temporal consistency while editing in a raster space. Experiments demonstrate that our representation method is superior to several state-of-the-art methods and as good as JPEG, while providing real-time editability and preserving structural and irregular saliency information.

Human factor causes most of the road accidents; for improving traffic safety, advanced driver assistance system and autonomous driving have become active research. The most significant challenge of these systems is the development of a reliable obstacle detection which can recognize pedestrians and pedestrian-like objects on the road from color images and thermal images. Consequently, the object detection based on neural networks, such as YOLO [1] , is emerging as a solution for identifying obstacles with various appearance. Although the convolutional neural network provides a robust and reliable result, it still involves substantial computational costs and hard to port to an embedded device. This work intends to improve the performance of YOLO and aims at presenting a real-time obstacle detection system on an integrated device.

Introducing motion into existing static paintings is becoming a field that is gaining momentum. This effort facilitates keeping artworks current and translating them to different forms for diverse audiences. Chinese ink paintings and Japanese Sumies are well recognized in Western cultures, yet not easily practiced due to the years of training required. We are motivated to develop an interactive system for artists, non-artists, Asians, and non-Asians to enjoy the unique style of Chinese paintings. In this paper, our focus is on replacing static water flow scenes with animations. We include flow patterns, surface ripples, and water wakes which are challenging not only artistically but also algorithmically. We develop a data-driven system that procedurally computes a flow field based on stroke properties extracted from the painting, and animate water flows artistically and stylishly. Technically, our system first extracts water-flow-portraying strokes using their locations, oscillation frequencies, brush patterns, and ink densities. We construct an initial flow pattern by analyzing stroke structures, ink dispersion densities, and placement densities. We cluster extracted strokes as stroke pattern groups to further convey the spirit of the original painting. Then, the system automatically computes a flow field according to the initial flow patterns, water boundaries, and flow obstacles. Finally, our system dynamically generates and animates extracted stroke pattern groups with the constructed field for controllable smoothness and temporal coherence. The users can interactively place the extracted stroke patterns through our adapted Poisson-based composition onto other paintings for water flow animation. In conclusion, our system can visually transform a static Chinese painting to an interactive walk-through with seamless and vivid stroke-based flow animations in its original dynamic spirits without flickering artifacts.

In this paper, the orthogonal frequency-division multiplexing signal without inserting guard subintervals (NG-OFDM) is considered and combined with spectral precoding to achieve high spectrum compactness. In order to reject interblock interference among received NG-OFDM blocks over dispersive channels, a guard subinterval in each received block is removed at the receiver and this turns the resultant receiver to be nonorthogonal over subcarrier subchannels and thus subject to artificial intercarrier interference. It is shown that the proposed spectrally precoded NG-OFDM system can provide channel capacity very close to nonprecoded and spectrally precoded OFDM using cyclic prefix (CP-OFDM) when the removal guard ratio is small, and much higher channel capacity per unit bandwidth than spectrally precoded CP- OFDM conveying the same data rate provided that a small out-of-band power proportion is required.

It is a necessary but challenging task for creative producers to have an idea of the depth perception of the target audience when watching a stereoscopic film in a cinema during production. This paper proposes a novel metric, geometric perceived depth percentage (GPDP), to numerate and depict the depth perception of a scene before rendering. In addition to the geometric relationship between the object depth and focal distance, GPDP takes the screen width and viewing distance into account. As a result, it provides a more intuitive means for predicting stereoscopy and is universal across different viewing conditions. Based on GPDP, we design a practical tool to visualize the stereoscopic perception without the need for any 3-D device or special environment. The tool utilizes the stereoscopic comfort volume, GPDP-based shading schemes, depth perception markers, and GPDP histograms as visual cues so that animators can set stereoscopic parameters more easily. The tool is easily implemented in any modern rendering pipeline, including interactive Autodesk Maya and offline Pixar's RenderMan renderer. It has been used in several production projects including commercial ones. Finally, two user studies show that GPDP is a proper depth perception indicator and the proposed tool can make the stereoscopic parameter setting process easier and more efficient.

This paper is concerned with minimizing the delay due to data communication during the execution of a parallel algorithm on an SIMD computer with a two-way circular unit-shift interconnection network. Algorithms are developed which determine, for a given parallel procedure, the order of computation within that procedure, for every parallel arithmetic expression, the alignment of operands for every binary operation, and the mapping and remapping of data into physical memories so that the communication cost is minimized. The proposed algorithms are applicable to array variables with special types of index functions.