Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework that incorporates knowledge graphs for describing the relationships between multiple labels. Our model learns an information propagation mechanism from the semantic label space, which can be applied to model the interdependencies between seen and unseen class labels. With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks. Compared to state-of-the-art approaches, comparable or improved performances can be achieved by our method.Comment: CVPR 201

Transient thermal stress intensity factors of near surface cracks

An experimental study was performed to determine the stress intensity factors for cracks near a boundary when they experience a sudden temperature change. Different isochromatic fringe patterns were observed for the two tips of the cracks, i.e., one near the boundary and the other away from the boundary;A combination of modern digital image analysis and the idea of half-fringe photoelasticity was adopted to collect data from experimentally obtained photographs of the transient event. The photoelastic analysis of a hybrid multiparameter-multipoint approach was employed. Three power series were used to describe the effects due to opening mode, sliding mode and far-field stresses, respectively. The accuracy of fringe measurement was obtained by iteration of the measured results through pattern recognition software supported on a local image processing system. It was found that the use of up to four coefficients for each power series gave well-matched results for both tips between the experimentally obtained fringe patterns and the fringes regenerated from substituting the experimentally determined coefficients for the power series into the generalized Westergaard\u27s equations for crack tip stress fields;The variation of the stress intensity factors with time for a crack at five angular orientation is presented

Local network coding on packet erasure channels -- From Shannon capacity to stability region

Network Coding (NC) has emerged as a ubiquitous technique of communication networks and has extensive applications in both practical implementations and theoretical developments. While the Avalanche P2P file system from Microsoft, the MORE routing protocol, and the COPE coding architecture from MIT have implemented the idea of NC and exhibited promising performance improvements, a significant part of the success of NC stems from the continuing theoretic development of NC capacity, e.g., the Shannon capacity results for the single-flow multi-cast network and the packet erasure broadcast channel with feedback. However, characterizing the capacity for the practical wireless multi-flow network setting remains a challenging topic in NC. For example, the difficulties of finding the optimal NC strategy over multiple flows under varying-channel qualities and the rate adaption scenarios hinder any further advancement in this area. Despite the difficulty of characterizing the full capacity for large networks, there are evidences showing that even when using only local operations, NC can still recover substantial NC gain. We believe that a deeper understanding of multi-flow local network coding will play a key role in designing the next-generation high-throughput coding-based wireless network architecture. This thesis consists of three parts. In the first part, we characterize the full Shannon capacity region of the COPE principle when applied to a 2-flow wireless butterfly network with broadcast packet erasure channels. The capacity results allow for random overhearing probabilities, arbitrary scheduling policies, network-wide channel state information (CSI) feedback after each transmission, and potential use of non-linear network codes. We propose a theoretical outer bound and a new class of linear network codes, named the Space-Based Linear Network Coding (SBLNC), that achieves the capacity outer bound. Numerical experiments show that SBLNC provides close-to-optimal throughput even in the scenario with opportunistic routing. In the second part, we further consider the complete network dynamics of stochastic arrivals and queueing and study the corresponding stability region. Based on dynamic packet arrivals, the resulting solution would be one step closer to practical implementation, when compared to the previous block-code-based capacity study. For the 2-flow downlink scenario, we propose the first opportunistic INC + scheduling solution that is provably optimal for time-varying channels, i.e., the corresponding stability region matches the optimal Shannon capacity. Specifically, we first introduce a new binary INC operation, which is distinctly different from the traditional wisdom of XORing two overheard packets. We then develop a queue-length-based scheduling scheme, which, with the help of the new INC operation, can robustly and optimally adapt to time-varying channel quality. We then show that the proposed algorithm can be easily extended for rate adaptation and it again robustly achieves the optimal throughput. In the third part, we propose an 802.11-based MAC layer protocol which incorporates the rate adaption solution developed in the second part. The new MAC protocol realizes the promised intersession network coding gain for two-flow downlink traffic with short decoding delay. Furthermore, we delicately retain the CSMA-CA distributed contention mechanism with only 17 bits new header field changes, and carefully ensure the backward compatibility. In summary, the new solution demonstrates concrete throughput improvement without alternating the too much packet-by-packet traffic behavior. Such a feature is critical in practical implementation since it allows the network coding solution to be transparent to any arbitrary upper layer applications

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Minimisation of Non-periodic Preventive Maintenance Cost in Series-Parallel Systems

A new method to optimise the non-periodic preventive maintenance model of a series-parallel system is proposed. A two-stage algorithm that incorporates the failure limit policy to determine maintenance components, maintenance times, and total maintenance cost is suggested. When the reliability of the system  reaches a threshold value, preventive maintenance is performed. The first stage identifies the parallel subsystem required to be maintained, while the second stage determines the component required to be maintained in the parallel sub-system. A unit-cost life index (UCL) has been developed to evaluate the extent to which maintaining a component extends the life of a system for the parallel subsystem. Three simulated cases demonstrate the effectiveness and the practicality of the proposed method in optimising the non-periodic preventive maintenance model of a series-parallel system.Defence Science Journal, 2011, 61(1), pp.44-50, DOI:http://dx.doi.org/10.14429/dsj.61.6

Grey Situation Decision-making Algorithm to Optimize Silicon Wafer Slicing

The slicing of Silicon wafer is a complex manufacturing process in producing the raw materials for electronic chips and requires the efforts to effectively monitor the stability in production line and ensure the quality for the products composed of different shapes and materials. Human decision failure and other analytical errors are the most common source of management problems in such manufacturing stage. This paper presents a case regarding the silicon wafer manufacturing to examine the response to quality errors. The study has adopted the approach of grey situation decision-making algorithm for problem detection that suggests a technique to attain the quality control and reduce potential costs in production

Quantum Criticality from in-situ Density Imaging

We perform large-scale Quantum Monte Carlo (QMC) simulations for strongly interacting bosons in a 2D optical lattice trap, and confirm an excellent agreement with the benchmarking in-situ density measurements by the Chicago group [1]. We further present a general finite temperature phase diagram both for the uniform and the trapped systems, and demonstrate how the universal scaling properties near the superfluid(SF)-to-Mott insulator(MI) transition can be observed by analysing the in-situ density profile. The characteristic temperature to find such quantum criticality is estimated to be of the order of the single-particle bandwidth, which should be achievable in the present or near future experiments. Finally, we examine the validity regime of the local fluctuation-dissipation theorem (FDT), which can be a used as a thermometry in the strongly interacting regime.Comment: 4 page