Rate and distortion redundancies for universal source coding with respect to a fidelity criterion

Rissanen has shown that there exist universal noiseless codes for {Xi} with per-letter rate redundancy as low as (K log N)/2N, where N is the blocklength and K is the number of source parameters. we derive an analogous result for universal source coding with respect to the squared error fidelity criterion: there exist codes with per-letter rate redundancy as low as (K log N)/2N and per-letter distortion (averaged over X^N and θ) at most D(R)[1 + K/N], where D(r) is an average distortion-rate function and K is now the number of parameters in the code

Weighted universal transform coding: universal image compression with the Karhunen-Loève transform

We introduce a two-stage universal transform code for image compression. The code combines Karhunen-Loève transform coding with weighted universal bit allocation (WUBA) in a two-stage algorithm analogous to the algorithm for weighted universal vector quantization (WUVQ). The encoder uses a collection of transform/bit allocation pairs rather than a single transform/bit allocation pair (as in JPEG) or a single transform with a variety of bit allocations (as in WUBA). We describe both an encoding algorithm for achieving optimal compression using a collection of transform/bit allocation pairs and a technique for designing locally optimal collections of transform/bit allocation pairs. We demonstrate the performance using the mean squared error distortion measure. On a sequence of combined text and gray scale images, the algorithm achieves up to a 2 dB improvement over a JPEG style coder using the discrete cosine transform (DCT) and an optimal collection of bit allocations, up to a 3 dB improvement over a JPEG style coder using the DCT and a single (optimal) bit allocation, up to 6 dB over an entropy constrained WUVQ with first- and second-stage vector dimensions equal to 16 and 4 respectively, and up to a 10 dB improvement over an entropy constrained vector quantizer (ECVQ) with a vector dimension of 4

Weighted universal bit allocation: optimal multiple quantization matrix coding

We introduce a two-stage bit allocation algorithm analogous to the algorithm for weighted universal vector quantization (WUVQ). The encoder uses a collection of possible bit allocations (typically in the form of a collection of quantization matrices) rather than a single bit allocation (or single quantization matrix). We describe both an encoding algorithm for achieving optimal compression using a collection of bit allocations and a technique for designing locally optimal collections of bit allocations. We demonstrate performance on a JPEG style coder using the mean squared error (MSE) distortion measure. On a sequence of medical brain scans, the algorithm achieves up to 2.5 dB improvement over a single bit allocation system, up to 5 dB improvement over a WUVQ with first- and second-stage vector dimensions equal to 16 and 4 respectively, and up to 12 dB improvement over an entropy constrained vector quantizer (ECVQ) using 4 dimensional vectors

Enhancement of charged macromolecule capture by nanopores in a salt gradient

Nanopores spanning synthetic membranes have been used as key components in proof-of-principle nanofluidic applications, particularly those involving manipulation of biomolecules or sequencing of DNA. The only practical way of manipulating charged macromolecules near nanopores is through a voltage difference applied across the nanopore-spanning membrane. However, recent experiments have shown that salt concentration gradients applied across nanopores can also dramatically enhance charged particle capture from a low concentration reservoir of charged molecules at one end of the nanopore. This puzzling effect has hitherto eluded a physically consistent theoretical explanation. Here, we propose an electrokinetic mechanism of this enhanced capture that relies on the electrostatic potential near the pore mouth. For long pores with diameter much greater than the local screening length, we obtain accurate analytic expressions showing how salt gradients control the local conductivity which can lead to increased local electrostatic potentials and charged analyte capture rates. We also find that the attractive electrostatic potential may be balanced by an outward, repulsive electroosmotic flow (EOF) that can in certain cases conspire with the salt gradient to further enhance the analyte capture rate.Comment: 10 pages, 6 Figure

Hydrodynamic mean field solutions of 1D exclusion processes with spatially varying hopping rates

We analyze the open boundary partially asymmetric exclusion process with smoothly varying internal hopping rates in the infinite-size, mean field limit. The mean field equations for particle densities are written in terms of Ricatti equations with the steady-state current $J$ as a parameter. These equations are solved both analytically and numerically. Upon imposing the boundary conditions set by the injection and extraction rates, the currents $J$ are found self-consistently. We find a number of cases where analytic solutions can be found exactly or approximated. Results for $J$ from asymptotic analyses for slowly varying hopping rates agree extremely well with those from extensive Monte Carlo simulations, suggesting that mean field currents asymptotically approach the exact currents in the hydrodynamic limit, as the hopping rates vary slowly over the lattice. If the forward hopping rate is greater than or less than the backward hopping rate throughout the entire chain, the three standard steady-state phases are preserved. Our analysis reveals the sensitivity of the current to the relative phase between the forward and backward hopping rate functions.Comment: 12 pages, 4 figure

A Large-Scale CNN Ensemble for Medication Safety Analysis

Revealing Adverse Drug Reactions (ADR) is an essential part of post-marketing drug surveillance, and data from health-related forums and medical communities can be of a great significance for estimating such effects. In this paper, we propose an end-to-end CNN-based method for predicting drug safety on user comments from healthcare discussion forums. We present an architecture that is based on a vast ensemble of CNNs with varied structural parameters, where the prediction is determined by the majority vote. To evaluate the performance of the proposed solution, we present a large-scale dataset collected from a medical website that consists of over 50 thousand reviews for more than 4000 drugs. The results demonstrate that our model significantly outperforms conventional approaches and predicts medicine safety with an accuracy of 87.17% for binary and 62.88% for multi-classification tasks