Upper Bounds on the Capacities of Noncontrollable Finite-State Channels with/without Feedback

Noncontrollable finite-state channels (FSCs) are FSCs in which the channel inputs have no influence on the channel states, i.e., the channel states evolve freely. Since single-letter formulae for the channel capacities are rarely available for general noncontrollable FSCs, computable bounds are usually utilized to numerically bound the capacities. In this paper, we take the delayed channel state as part of the channel input and then define the {\em directed information rate} from the new channel input (including the source and the delayed channel state) sequence to the channel output sequence. With this technique, we derive a series of upper bounds on the capacities of noncontrollable FSCs with/without feedback. These upper bounds can be achieved by conditional Markov sources and computed by solving an average reward per stage stochastic control problem (ARSCP) with a compact state space and a compact action space. By showing that the ARSCP has a uniformly continuous reward function, we transform the original ARSCP into a finite-state and finite-action ARSCP that can be solved by a value iteration method. Under a mild assumption, the value iteration algorithm is convergent and delivers a near-optimal stationary policy and a numerical upper bound.Comment: 15 pages, Two columns, 6 figures; appears in IEEE Transaction on Information Theor

Accessible Capacity of Secondary Users

A new problem formulation is presented for the Gaussian interference channels (GIFC) with two pairs of users, which are distinguished as primary users and secondary users, respectively. The primary users employ a pair of encoder and decoder that were originally designed to satisfy a given error performance requirement under the assumption that no interference exists from other users. In the scenario when the secondary users attempt to access the same medium, we are interested in the maximum transmission rate (defined as {\em accessible capacity}) at which secondary users can communicate reliably without affecting the error performance requirement by the primary users under the constraint that the primary encoder (not the decoder) is kept unchanged. By modeling the primary encoder as a generalized trellis code (GTC), we are then able to treat the secondary link and the cross link from the secondary transmitter to the primary receiver as finite state channels (FSCs). Based on this, upper and lower bounds on the accessible capacity are derived. The impact of the error performance requirement by the primary users on the accessible capacity is analyzed by using the concept of interference margin. In the case of non-trivial interference margin, the secondary message is split into common and private parts and then encoded by superposition coding, which delivers a lower bound on the accessible capacity. For some special cases, these bounds can be computed numerically by using the BCJR algorithm. Numerical results are also provided to gain insight into the impacts of the GTC and the error performance requirement on the accessible capacity.Comment: 42 pages, 12 figures, 2 tables; Submitted to IEEE Transactions on Information Theory on December, 2010, Revised on November, 201

Mechanistic analysis of endothelial lipase G promotion of the occurrence and development of cervical carcinoma by activating the phosphatidylinositol-4,5-bisphosphate 3-kinase/protein kinase B/mechanistic target of rapamycin kinase signalling pathway

Lipase G, endothelial type (LIPG) is expressed abundantly in tissues with a high metabolic rate and vascularisation. Research on LIPG has focussed on metabolic syndromes. However, the role of LIPG in providing lipid precursors suggests that it might function in the metabolism of carcinoma cells. Analysis in The Cancer Genome Atlas indicated that patients with cervical carcinoma with high LIPG expression had a lower survival prognosis compared with patients with low LIPG expression. The mechanism underlying the effects of LIPG in cervical carcinoma is unclear. The present study aimed to determine the role of LIPG in cervical carcinoma and its mechanism. The results showed that the LIPG expression level was higher in cervical cancer. Downregulation of LIPG expression inhibited cell migration, invasion, proliferation, and the formation of cell colonies, but increased the rate of apoptosis. The Human papillomavirus E6 protein might reduce the expression of miR-148a-3p, relieve the inhibitory effect of miR-148a-3p on LIPG expression, and promote the progression of cervical cancer through the phosphatidylinositol-4,5-bisphosphate 3-kinase/protein kinase B/mechanistic target of rapamycin kinase signalling pathway.IMPACT STATEMENT What is already known on this subject? LIPG provides lipid precursors, suggesting that it might function in the metabolism of carcinoma cells What do the results of this study add? LIPG might be regulated by HPV16 E6/miR-148a-3p and promote cervical carcinoma progression via the PI3K/AKT/mTOR signalling pathway. What are the implications of these finding for clinical practice and/or further research? The results indicated that novel treatment and diagnosis strategies for cervical carcinoma could be developed related to LIPG. However, the detailed relationship between LIPG and cervical carcinoma remains to be fully determined

A Probabilistic Analysis of Sparse Coded Feature Pooling and Its Application for Image Retrieval.

Feature coding and pooling as a key component of image retrieval have been widely studied over the past several years. Recently sparse coding with max-pooling is regarded as the state-of-the-art for image classification. However there is no comprehensive study concerning the application of sparse coding for image retrieval. In this paper, we first analyze the effects of different sampling strategies for image retrieval, then we discuss feature pooling strategies on image retrieval performance with a probabilistic explanation in the context of sparse coding framework, and propose a modified sum pooling procedure which can improve the retrieval accuracy significantly. Further we apply sparse coding method to aggregate multiple types of features for large-scale image retrieval. Extensive experiments on commonly-used evaluation datasets demonstrate that our final compact image representation improves the retrieval accuracy significantly