Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval

In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to lean a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship. To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further proposed upon the adversarial training to strengthen the correlations between inputs and corresponding outputs. Our approach is generative to learn hash functions such that the learned hash codes can maximally correlate each input-output correspondence, meanwhile can also regenerate the inputs so as to minimize the information loss. The learning to hash embedding is thus performed to jointly optimize the parameters of the hash functions across modalities as well as the associated generative models. Extensive experiments on a variety of large-scale cross-modal data sets demonstrate that our proposed method achieves better retrieval results than the state-of-the-arts.Comment: To appeared on IEEE Trans. Image Processing. arXiv admin note: text overlap with arXiv:1703.10593 by other author

Annuities and their Derivatives: The Recent Canadian Experience

This chapter surveys recent developments within the Canadian “income annuity” marketplace. We start by computing the Money’s Worth Ratio (MWR) using a unique dataset which includes a decade of Canadian annuity payouts. We then move-on to discuss the Guaranteed Lifetime Withdrawal Benefit (GLWB) product which has recently become available in Canada. This important innovation is extremely popular and shares many characteristics with a conventional income annuity. Finally, we conclude with thoughts on the optimal product allocation within the context of the Canadian retirement portfolio

The origin of the complex character of the ohmic impedance

The local and global Ohmic response for an electrode exhibiting geometry-induced potential and/or current distributions has recently been shown to be represented by a frequency-dependent complex impedance. A physical explanation for this result is provided in terms of the radial contribution to local current density and the decrease in current density along the current lines. Experiments performed with Cu/Al and Mg/Al galvanic couples show that, in regions where a radial current density does not exist, the local Ohmic impedance is independent of position; whereas, in regions where the radial current density cannot be neglected, the local Ohmic impedance is a function of position. Simulations performed on recessed electrodes show that, even in the absence of a radial current, an axial variation of current density gives rise to a complex Ohmic impedance. The complex character of the Ohmic impedance shows that an equivalent circuit, using the usual two-terminal resistor to represent the Ohmic contribution of the electrolyte, provides an inadequate representation of an electrode with geometry-induced current and potential distributions

GII Representation-Based Cross-View Gait Recognition by Discriminative Projection With List-Wise Constraints

Remote person identification by gait is one of the most important topics in the field of computer vision and pattern recognition. However, gait recognition suffers severely from the appearance variance caused by the view change. It is very common that gait recognition has a high performance when the view is fixed but the performance will have a sharp decrease when the view variance becomes significant. Existing approaches have tried all kinds of strategies like tensor analysis or view transform models to slow down the trend of performance decrease but still have potential for further improvement. In this paper, a discriminative projection with list-wise constraints (DPLC) is proposed to deal with view variance in cross-view gait recognition, which has been further refined by introducing a rectification term to automatically capture the principal discriminative information. The DPLC with rectification (DPLCR) embeds list-wise relative similarity measurement among intraclass and inner-class individuals, which can learn a more discriminative and robust projection. Based on the original DPLCR, we have introduced the kernel trick to exploit nonlinear cross-view correlations and extended DPLCR to deal with the problem of multiview gait recognition. Moreover, a simple yet efficient gait representation, namely gait individuality image (GII), based on gait energy image is proposed, which could better capture the discriminative information for cross view gait recognition. Experiments have been conducted in the CASIA-B database and the experimental results demonstrate the outstanding performance of both the DPLCR framework and the new GII representation. It is shown that the DPLCR-based cross-view gait recognition has outperformed the-state-of-the-art approaches in almost all cases under large view variance. The combination of the GII representation and the DPLCR has further enhanced the performance to be a new benchmark for cross-view gait recognition

3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification

In this paper, we introduce a global video representation to video-based person re-identification (re-ID) that aggregates local 3D features across the entire video extent. Most of the existing methods rely on 2D convolutional networks (ConvNets) to extract frame-wise deep features which are pooled temporally to generate the video-level representations. However, 2D ConvNets lose temporal input information immediately after the convolution, and a separate temporal pooling is limited in capturing human motion in shorter sequences. To this end, we present a \textit{global} video representation (3D PersonVLAD), complementary to 3D ConvNets as a novel layer to capture the appearance and motion dynamics in full-length videos. However, encoding each video frame in its entirety and computing an aggregate global representation across all frames is tremendously challenging due to occlusions and misalignments. To resolve this, our proposed network is further augmented with 3D part alignment module to learn local features through soft-attention module. These attended features are statistically aggregated to yield identity-discriminative representations. Our global 3D features are demonstrated to achieve state-of-the-art results on three benchmark datasets: MARS \cite{MARS}, iLIDS-VID \cite{VideoRanking}, and PRID 2011Comment: Accepted to appear at IEEE Transactions on Neural Networks and Learning System

Local electrochemical impedance spectroscopy: A review and some recent developments

Local electrochemical impedance spectroscopy (LEIS), which provides a powerful tool for exploration of electrode heterogeneity, has its roots in the development of electrochemical techniques employing scanning of microelectrodes. The historical development of local impedance spectroscopy measurements is reviewed, and guidelines are presented for implementation of LEIS. The factors which control the limiting spatial resolution of the technique are identified. The mathematical foundation for the technique is reviewed, including definitions of interfacial and local Ohmic impedances on both local and global scales. Experimental results for the reduction of ferricyanide show the correspondence between local and global impedances. Simulations for a single Faradaic reaction on a disk electrode embedded in an insulator are used to show that the Ohmic contribution, traditionally considered to be a real value, can have complex character in certain frequency ranges

Multi-scale input-output analysis of consumption-based water resources: Method and application

This work develops a method of multi-scale input-output analysis for the embodied water accounting of an economy. This method can distinguish between the different virtual water contents of imported and local products and is therefore capable of accurately estimating the virtual water that is embodied in trade. As a simplified model rather than a multi-regional input-output analysis, this method substantially minimizes the data requirements. With the support of averaged Eora global embodied water intensity databases for the world and Chinese economies, a three-scale embodied water input-output analysis of the Beijing economy in 2007 has been conducted. Dozens of virtual water flows that relate to the Beijing economy have been identified and analyzed. Only 15% of the total water resources embodied in Beijing's local final demand were from local water withdrawal; 85% were from domestically and internationally imported products. The virtual water import is revealed to play a more important role than physical water transfer in easing Beijing's water shortage. Since the average water use efficiency of the Beijing economy is much higher than that of the Chinese economy but somewhat lower that of the rest of the world, Beijing is suggested to shifting its imports to foreign countries to optimize global water use. The method developed can be useful for water saving strategies for multiple responsible entities holding different opinions, and it can be easily applied to the embodied water accounting of a sub-national or even smaller economic community

Fully Composable and Adequate Verified Compilation with Direct Refinements between Open Modules (Technical Report)

Verified compilation of open modules (i.e., modules whose functionality depends on other modules) provides a foundation for end-to-end verification of modular programs ubiquitous in contemporary software. However, despite intensive investigation in this topic for decades, the proposed approaches are still difficult to use in practice as they rely on assumptions about the internal working of compilers which make it difficult for external users to apply the verification results. We propose an approach to verified compositional compilation without such assumptions in the setting of verifying compilation of heterogeneous modules written in first-order languages supporting global memory and pointers. Our approach is based on the memory model of CompCert and a new discovery that a Kripke relation with a notion of memory protection can serve as a uniform and composable semantic interface for the compiler passes. By absorbing the rely-guarantee conditions on memory evolution for all compiler passes into this Kripke Memory Relation and by piggybacking requirements on compiler optimizations onto it, we get compositional correctness theorems for realistic optimizing compilers as refinements that directly relate native semantics of open modules and that are ignorant of intermediate compilation processes. Such direct refinements support all the compositionality and adequacy properties essential for verified compilation of open modules. We have applied this approach to the full compilation chain of CompCert with its Clight source language and demonstrated that our compiler correctness theorem is open to composition and intuitive to use with reduced verification complexity through end-to-end verification of non-trivial heterogeneous modules that may freely invoke each other (e.g., mutually recursively)