As a competitive alternative to the Markowitz mean-variance portfolio, the Kelly growth optimal portfolio has drawn sufficient attention in investment science. While the growth optimal portfolio is theoretically guaranteed to dominate any other portfolio with probability 1 in the long run, it practically tends to be highly risky in the short term. Moreover, empirical analysis and performance enhancement studies under practical settings are surprisingly short. In particular, how to handle the challenging but realistic condition with insufficient training data has barely been investigated. In order to fill voids, especially grappling with the difficulty from small samples, in this paper, we propose a growth optimal portfolio strategy equipped with ensemble learning. We synergically leverage the bootstrap aggregating algorithm and the random subspace method into portfolio construction to mitigate estimation error. We analyze the behavior and hyperparameter selection of the proposed strategy by simulation, and then corroborate its effectiveness by comparing its out-of-sample performance with those of 10 competing strategies on four datasets. Experimental results lucidly confirm that the new strategy has superiority in extensive evaluation criteria.

Radio frequency identification technology (RFID) is widely used in identity authentication and payment, and it also becomes an indispensable part of daily life. Cloud-based RFID systems have broad application prospects, and can be provided as a service to individuals or organisations. For example, RFID cards can be used for cash-less payment, physical access control, temporary rights and identification in cloud environment. When an RFID card is used, there is a wireless transaction between the card and its reader, which could be attacked by several methods, including a relay attack. Relay attacks are difficult to completely prevent and a serious threat to RFID systems security. An attacker could use limited resources to build up this kind of attack and may need little knowledge of the underlying protocol. In recent years, researchers have proposed solutions using second channels to resist relay attack, such as using environmental measurements including noise, light and temperature. This paper describes research on the defence techniques for relay attacks in cloudbased RFID systems. The cloud-based architecture for RFID systems typically consists of RFID tags, card readers (fixed or mobile) and cloud-based server functionality.

In addition to only considering stocks' price series, utilizing short and instant texts from social medias like Twitter has potential to yield better stock market prediction. While some previous approaches have explored this direction, their results are still far from satisfactory due to their reliance on performance of sentiment analysis and limited capabilities of learning direct relations between target stock trends and their daily social texts. To bridge this gap, we propose a novel Cross-modal attention based Hybrid Recurrent Neural Network (CH-RNN), which is inspired by the recent proposed DA-RNN model. Specifically, CH-RNN consists of two essential modules. One adopts DA-RNN to gain stock trend representations for different stocks. The other utilizes recurrent neural network to model daily aggregated social texts. These two modules interact seamlessly by the following two manners: 1) daily representations of target stock trends from the first module are leveraged to select trend-related social texts through a cross-modal attention mechanism, and 2) representations of text sequences and trend series are further integrated. The comprehensive experiments on the real dataset we build demonstrate the effectiveness of CH-RNN and benefit of considering social texts.

As the cornerstone of the modern portfolio theory, Markowitz's mean-variance optimization is a major model adopted in portfolio management. However, the estimation errors in its input parameters substantially deteriorate its performance in practice. Specifically, loss could be huge when the number of assets for investment is not much smaller than the sample size of historical data. To hasten the applicability of Markowitz's portfolio optimization to large portfolios, in this paper, we propose a new portfolio strategy via subset resampling. Through resampling subsets of the original large universe of assets, we construct the associated subset portfolios with more accurately estimated parameters without requiring additional data. By aggregating a number of constructed subset portfolios, we attain a well-diversified portfolio of all assets. To investigate its performance, we first analyze its corresponding efficient frontiers by simulation, provide analysis on the hyperparameter selection, and then empirically compare its out-of-sample performance with those of various competing strategies on diversified datasets. Experimental results corroborate that the proposed portfolio strategy has marked superiority in extensive evaluation criteria.

In order to grapple with the conundrum in the scalability of kernel-based learning algorithms, the method of approximating nonlinear kernels via random feature maps has attracted wide attention in large-scale learning systems. Specifically, the associated sampling procedure is one critical component that dictates the quality of random feature maps. However, for high-dimensional features, the standard Monte Carlo sampling method has been shown to be less effective in producing low-variance random samples. In consequence, it demands constructing a large number of features to attain the desired accuracy for downstream use. In this paper, we present a novel sampling algorithm powered by moment matching techniques to reduce the variance of random features. Our extensive empirical studies and comparisons with several highly competitive peer methods verify the superiority of the proposed algorithm in Gram matrix approximation and generalization errors in regression. Our rigorous theoretical proofs justify that the proposed algorithm is guaranteed achieving lower variance than the standard Monte Carlo method in high dimensional settings.

Early and accurate diagnosis of Alzheimer's disease is beneficial to both preserve daily functioning and test possible new treatments. However, current diagnosis depends on dozens of factors, including the family member, past medical problems, tests of memory, blood and urine tests, brain scans and even cerebrospinal fluid specimens. Among them, regular features (e.g., blood and urine tests, brain scans) are simple and accurate. Privileged features (e.g., tests of memory, cerebrospinal fluid specimens) are inconvenient and uncomfortable to acquire, and thus incomplete in most cases. In this work, we propose a two-stage learning framework to predict merely using the regular feature with the aid of incomplete privileged data in the training time. In particular, we first complement the missing data of privileged features by exploring the relationship with regular features and labels. The recovered privileged features, regular features, as well as labels, are combined as a new set of privileged features. Then privileged learning via matching logits is applied to boost the diagnosis accuracy. Our experiments and comparison studies with competing techniques on both synthetic data and real benchmarks have corroborated the effectiveness and superiority of the proposed framework for biomedical applications.

The latest video coding standard high-efficiency video coding (HEVC) provides 50% improvement in coding efficiency compared to H.264/AVC to meet the rising demands for video streaming, better video quality, and higher resolution. The deblocking filter (DF) and sample adaptive offset (SAO) play an important role in the HEVC encoder, and the SAO is newly adopted in HEVC. Due to the high throughput requirement in the video encoder, design challenges such as data dependence, external memory traffic, and on-chip memory area become even more critical. To solve these problems, we first propose an interlacing memory organization on the basis of quarter-LCU to resolve the data dependence between vertical and horizontal filtering of DF. The on-chip SRAM area is also reduced to about 25% on the basis of quarter-LCU scheme without throughput loss. We also propose a simplified bitrate estimation method of rate-distortion cost calculation to reduce the computational complexity in the mode decision of SAO. Our proposed hardware architecture of combined DF and SAO is designed for the HEVC intraencoder, and the proposed simplified bitrate estimation method of SAO can be applied to both intra- and intercoding. As a result, our design can support ultrahigh definition 7680 × 4320 at 40 f/s applications at merely 182 MHz working frequency. Total logic gate count is 103.3 K in 65 nm CMOS process.

Merton's portfolio optimization problem in the presence of transaction costs for multiple assets has been an important and challenging problem in both theory and practice. Most existing work suffers from curse of dimensionality and encounters with the difficulty of generalization. In this paper, we develop an approximate dynamic programing method of synergistically combining the Lowner-John ellipsoid approximation with conventional value function iteration to quantify the associated optimal trading policy. Through constructing Lowner-John ellipsoids to parameterize the optimal policy and taking Euclidean projections onto the constructed ellipsoids to implement the trading policy, the proposed algorithm has cut computational costs up to a factor of five hundred and meanwhile achieved near-optimal risk-adjusted returns across both synthetic and real-world market datasets.

Due to recent empirical success, machine learning algorithms have drawn sufficient attention and are becoming important analysis tools in financial industry. In particular, as the core engine of many financial services such as private wealth and pension fund management, portfolio management calls for the application of those novel algorithms. Most of portfolio allocation strategies do not account for costs from market frictions such as transaction costs and capital gain taxes, as the complexity of sensible cost models often causes the induced problem intractable. In this paper, we propose a doubly regularized portfolio that provides a modest but effective solution to the above difficulty. Specifically, as all kinds of trading costs primarily root in large transaction volumes, to reduce volumes we synergistically combine two penalty terms with classic risk minimization models to ensure: (1) only a small set of assets are selected to invest in each period; (2) portfolios in consecutive trading periods are similar. To assess the new portfolio, we apply standard evaluation criteria and conduct extensive experiments on well-known benchmarks and market datasets. Compared with various state-of-the-art portfolios, the proposed portfolio demonstrates a superior performance of having both higher risk-adjusted returns and dramatically decreased transaction volumes.

This brief describes a new method to implement the single-port SRAM-based transpose memory for large size discrete cosine transform (DCT)/indiscrete cosine transform (IDCT) which are used in the latest video coding standard, such as high efficiency video coding. Instead of shift-register array or multiport SRAM, only single-port SRAM is used in the proposed design. A new diagonal data mapping scheme is proposed to reduce the number of SRAM banks used to implement the transpose memory. This design can be flexibly extended to support DCT/IDCT of different transform sizes and different data throughput rates. To support larger size DCT/IDCT, only the depth of SRAM needs to be increased. To support different data throughput rate, multiple SRAM banks are well organized according to the required throughput. Row access and column access can be perfectly supported under single port SRAM. The equivalent gate count per bit (EGC) of proposed approach is less than two, which is much more efficient than the previous method. It is suitable for real-time processing of the video with the resolution up to 1080P HD or even higher.

As the next generation standard of video coding, the High Efficiency Video Coding (HEVC) aims to provide significantly improved compression performance in comparison with all existing video coding standards. We propose a four-stage pipeline hardware architecture on a quarter-LCU basis of deblocking filter in HEVC. Coupled with the novel filter order, a memory interlacing technique is adopted to increase the throughput, which can access the data in the process of both vertical and horizontal filtering efficiently. As a result, our design can support 4K×2K (4096×2048) at 30 fps applications with merely 28 MHz working frequency.

4 or 8-point IDCT are widely used in traditional video coding standards. However larger size (16/32-point) IDCT has been proposed in the next generation video standard such as HEVC. To fulfill this requirement, this work proposes a fast computational algorithm of large size integer IDCT. A unified VLSI architecture for 4/8/16/32-point integer IDCT is also proposed accordingly. It can support the following video standards: MPEG-2/4, H.264, AVS, VC-1 and HEVC. Multiplier less MCM (Multiple Constant Multiplication) is used for 4/8-point IDCT. The regular multipliers and sharing technique are used for 16/32-point IDCT. The transpose memory uses SRAM instead of the traditional register array in order to further reduce the hardware overhead. It can support real-time decoding of 4K×2K (4096×2048) 30fps video sequence at 191MHz working frequency, with 93K gate count and 18944-bit SRAM. We suggest a normalized criterion called design efficiency to compare with previous works. It shows that this design is 31% more efficient than previous work.

To address transmission problems encountered in WLAN environments, we propose a multi-user MIMO SDMA system based on the theory of convex optimization. With each user's BER as the cost function, we compute transmit and receive matrices to achieve optimal system performance. The proposed algorithm is assessed by simulations and is compared with conventional methods.