Probabilistic Label Relation Graphs with Ising Models

We consider classification problems in which the label space has structure. A common example is hierarchical label spaces, corresponding to the case where one label subsumes another (e.g., animal subsumes dog). But labels can also be mutually exclusive (e.g., dog vs cat) or unrelated (e.g., furry, carnivore). To jointly model hierarchy and exclusion relations, the notion of a HEX (hierarchy and exclusion) graph was introduced in [7]. This combined a conditional random field (CRF) with a deep neural network (DNN), resulting in state of the art results when applied to visual object classification problems where the training labels were drawn from different levels of the ImageNet hierarchy (e.g., an image might be labeled with the basic level category "dog", rather than the more specific label "husky"). In this paper, we extend the HEX model to allow for soft or probabilistic relations between labels, which is useful when there is uncertainty about the relationship between two labels (e.g., an antelope is "sort of" furry, but not to the same degree as a grizzly bear). We call our new model pHEX, for probabilistic HEX. We show that the pHEX graph can be converted to an Ising model, which allows us to use existing off-the-shelf inference methods (in contrast to the HEX method, which needed specialized inference algorithms). Experimental results show significant improvements in a number of large-scale visual object classification tasks, outperforming the previous HEX model.Comment: International Conference on Computer Vision (2015

Cooperative Deep Reinforcement Learning for Multiple-Group NB-IoT Networks Optimization

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resources allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, the problem is to determine, in an online fashion at each Transmission Time Interval (TTI), the configurations that maximizes the long-term average number of IoT devices that are able to both access and deliver data. Given the complexity of optimal algorithms, a Cooperative Multi-Agent Deep Neural Network based Q-learning (CMA-DQN) approach is developed, whereby each DQN agent independently control a configuration variable for each group. The DQN agents are cooperatively trained in the same environment based on feedback regarding transmission outcomes. CMA-DQN is seen to considerably outperform conventional heuristic approaches based on load estimation.Comment: Submitted for conference publicatio

Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resource allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, there exists an important challenge in "how to determine the configuration that maximizes the long-term average number of served IoT devices at each Transmission Time Interval (TTI) in an online fashion". Given the complexity of searching for optimal configuration, we first develop real-time configuration selection based on the tabular Q-learning (tabular-Q), the Linear Approximation based Q-learning (LA-Q), and the Deep Neural Network based Q-learning (DQN) in the single-parameter single-group scenario. Our results show that the proposed reinforcement learning based approaches considerably outperform the conventional heuristic approaches based on load estimation (LE-URC) in terms of the number of served IoT devices. This result also indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve almost the same performance with much less training time. We further advance LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario, thereby solve the problem that Q-learning agents do not converge in high-dimensional configurations. In this scenario, the superiority of the proposed Q-learning approaches over the conventional LE-URC approach significantly improves with the increase of configuration dimensions, and the CMA-DQN approach outperforms the other approaches in both throughput and training efficiency

Simple reverse genetics approach to elucidating the biosynthetic pathway of complex thiopeptide nocathiacin

Biothythetic pathway of the most drugable thiopeptide nocathiacin has been elucidated by applying reverse genetics method based on its structural features. The present study provides an efficient approach for an easy access to the biosynthetic gene clusters of complex bioactive peptides that are ribosomally synthesized with extensive posttranslational modifications

A GaN-based wireless power and information transmission method using Dual-frequency Programmed Harmonic Modulation

Information transmission is often required in power transfer to implement control. In this paper, a Dual-Frequency Programmed Harmonic Modulation (DFPHM) method is proposed to transfer two frequencies carrying power and information with the single converter via a common inductive coil. The proposed method reduces the number of injection tightly coupled transformers used to transmit information, thereby simplifying the system structure and improving reliability. The performances of power and information transmission, and the method of information modulation and demodulation, as well as the principles of the control, are analyzed in detail. Then a simulation model is set up to verify the feasibility of the method. In addition, an experiment platform is established to verify that the single converter can transfer the power and information simultaneously via a common inductive coil without using tightly coupled transformers.Web of Science8498564984

Gravel Liquefaction Analysis of an Embankment Dam

Prior to 1970, the majority of earth and rockfill dams were constructed with little regard for earthquake resistant design- especially in the Pacific Northwest which, at that time, was considered an area of low to moderate seismic activity. Since the early 1970\u27s, and in particular since the near-catastrophic failure of the Lower San Fernando Dam in 1971, the vulnerability of hydraulic fill dams to pore pressure build-up and loss of strength as a result of earthquake shaking is well documented. In contrast, very few case histories exist on the liquefaction susceptibility of saturated gravel filters in zoned embankments. This paper summarizes a detailed finite-element, seismic stability analysis for a dam which has a saturated gravel filter of unknown relative density under the entire downstream shell of the embankment

Comparison of the ride performance of an integrated suspension model

Vehicle suspension is one of the important components to reduce vibration from the road. The vehicle seat suspension acts as another component to provide ride comfort, especially to reduce driver fatigues for long hour’s driving. In this paper, the ride comfort is therefore studied based on the integrated suspension model which includes vehicle chassis suspension, seat suspension and driver model. A four-DOF mathematical model is presented. The hydraulic actuator is introduced as well. Three controllers, including skyhook damper control, slide model control (SMC) and fuzzy logical control (FLC), are applied to the semi-active/active suspension with passive seat suspension. To improve the ride comfort further, combination the best performance of ride comfort from active chassis suspension, the semi-active seat suspension is then designed. The ride performance is evaluated based on driver deformation and acceleration