A Bayesian Approach toward Active Learning for Collaborative Filtering

Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the performance of collaborative filtering depends on the number of rated examples given by the active user. The more the number of rated examples given by the active user, the more accurate the predicted ratings will be. Active learning provides an effective way to acquire the most informative rated examples from active users. Previous work on active learning for collaborative filtering only considers the expected loss function based on the estimated model, which can be misleading when the estimated model is inaccurate. This paper takes one step further by taking into account of the posterior distribution of the estimated model, which results in more robust active learning algorithm. Empirical studies with datasets of movie ratings show that when the number of ratings from the active user is restricted to be small, active learning methods only based on the estimated model don't perform well while the active learning method using the model distribution achieves substantially better performance.Comment: Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004

Evaluation of high density polyethylene plastic bag performance towards edge and point stresses using taguchi method

Plastic bag are widely used due to it is low cost and convenience for packaging items. The problem with the strength of the plastic bag tends to tear easily and perforated. This study aims to validate the simulation results of High Density Polyethylene (HDPE) plastic towards HDPE plastic bags manufactured in UTHM and thus to evaluate the performance of plastic bag towards mass, edge and point stresses. The tensile test simulation was conducted using Solidworks 2017 to validate the HDPE plastic material properties by comparing the tensile test performed according to ASTM D882-18. The real life application was conducted to validate the simulation result by comparing plastic film’s displacement with different mass applied. Taguchi Method was used to arrange the edge and point stress test parameter with varied angle, mass, length and distance between the loads. The result showed that the error percentage for all loads was lower than 10.00 % for simulation compared to experimental tensile test. It also showed that error percentage was less than 5.00 % by comparing real life application and simulation results for displacement of plastic film. For mass stress test, the loads with 5.0 kg square base has the highest stress acted on the plastic film’s surface which is 22.399 MPa. For edge stress test, sample D with 1.0 kg, 20 mm of edge’s length and 20 ° of edge’s angle have highest maximum stress and displacement acted on plastic film’s surface which are 34.086 MPa and 84.94 mm respectively. For point stress test, sample G with 1.0 kg, 10 ° of angle and 30 mm of the distance between the point load have highest maximum stress and displacement acted on surface of plastic film which are 50.676 MPa and 63.64 mm accordingly. Both sample D and G were perforated since the maximum stress acted was exceed the tensile strength of HDPE plastic which is 28.4 MPa. The validation of HDPE plastic towards HDPE plastic bag manufactured in UTHM was proven from the result obtained. The plastic bag’s performance towards mass, edge and point stresses was successfully evaluated by using Finite Element Analysis and Taguchi Method

Performance analysis of spatial modulation aided NOMA with full-duplex relay

A spatial modulation aided non-orthogonal multiple access with full-duplex relay (SM-NOMA-FDR) scheme is proposed for the coordinated direct and relay transmission in this paper. Specifically, the signal of the near user is mapped to an M-ary modulated symbol and the signal of the far user is mapped to an SM symbol. The base station first transmits signals to the near user and relay via SM-NOMA, and then the relay decodes and retransmits the signal of the far user. An SM-assisted FDR is used in this scheme to improve the spectral efficiency while reducing energy consumption and making full use of the antenna resources at the relay, since SM only activates one antenna in each transmission. We derive the ergodic capacity and bit error rate of the proposed scheme over independent Rayleigh fading channels. Numerical results validate the accuracy of the theoretical analysis and show the superior performance of the proposed SM-NOMA-FDR scheme

Rooting Depth and Extreme Precipitation Regulate Groundwater Recharge in the Thick Unsaturated Zone: A Case Study

Many modeling efforts have been made for shallow soil, but little has been done in deep-rooted ecosystems, especially on the long-term impact of deep-rooted vegetation to understand the impact of vegetation type on hydrological processes. In this study, we used the Community Land Model (CLM) version 4.0 to simulate the soil water dynamics and groundwater recharge with shallow-rooted and deep-rooted vegetation cover in the critical soil zone of 100 m thickness. We selected winter wheat and summer maize to represent shallow-rooted vegetation and apple trees as deep-rooted vegetation growing in the semi-humid Loess Plateau of China over the period of 1901–2015. Our results show that the rooting depth and precipitation dictate the occurrence of disconnected recharge. This occurred in soil depths that were below 75 m in wet years with annual precipitation of over 650, 730, and 1000 mm for the winter wheat field, summer maize field, and apple orchard, respectively. Connected recharge was the major component of groundwater recharge for all three crop fields. The transit time of precipitation ranged from several to hundreds of years via the disconnected recharge that is caused by extreme precipitation and the connected recharge that is generated by other precipitation. Therefore, both rooting depth and growth period of vegetation affect the groundwater recharge and transit time, as well as precipitation

Adaptive Temporal Encoding Network for Video Instance-level Human Parsing

Beyond the existing single-person and multiple-person human parsing tasks in static images, this paper makes the first attempt to investigate a more realistic video instance-level human parsing that simultaneously segments out each person instance and parses each instance into more fine-grained parts (e.g., head, leg, dress). We introduce a novel Adaptive Temporal Encoding Network (ATEN) that alternatively performs temporal encoding among key frames and flow-guided feature propagation from other consecutive frames between two key frames. Specifically, ATEN first incorporates a Parsing-RCNN to produce the instance-level parsing result for each key frame, which integrates both the global human parsing and instance-level human segmentation into a unified model. To balance between accuracy and efficiency, the flow-guided feature propagation is used to directly parse consecutive frames according to their identified temporal consistency with key frames. On the other hand, ATEN leverages the convolution gated recurrent units (convGRU) to exploit temporal changes over a series of key frames, which are further used to facilitate the frame-level instance-level parsing. By alternatively performing direct feature propagation between consistent frames and temporal encoding network among key frames, our ATEN achieves a good balance between frame-level accuracy and time efficiency, which is a common crucial problem in video object segmentation research. To demonstrate the superiority of our ATEN, extensive experiments are conducted on the most popular video segmentation benchmark (DAVIS) and a newly collected Video Instance-level Parsing (VIP) dataset, which is the first video instance-level human parsing dataset comprised of 404 sequences and over 20k frames with instance-level and pixel-wise annotations.Comment: To appear in ACM MM 2018. Code link: https://github.com/HCPLab-SYSU/ATEN. Dataset link: http://sysu-hcp.net/li