Fixed-Lag Smoothing for Low-Delay Predictive Coding with Noise Shaping for Lossy Networks

We consider linear predictive coding and noise shaping for coding and transmission of auto-regressive (AR) sources over lossy networks. We generalize an existing framework to arbitrary filter orders and propose use of fixed-lag smoothing at the decoder, in order to further reduce the impact of transmission failures. We show that fixed-lag smoothing up to a certain delay can be obtained without additional computational complexity by exploiting the state-space structure. We prove that the proposed smoothing strategy strictly improves performance under quite general conditions. Finally, we provide simulations on AR sources, and channels with correlated losses, and show that substantial improvements are possible

Model for Estimation of Bounds in Digital Coding of Seabed Images

This paper proposes the novel model for estimation of bounds in digital coding of images. Entropy coding of images is exploited to measure the useful information content of the data. The bit rate achieved by reversible compression using the rate-distortion theory approach takes into account the contribution of the observation noise and the intrinsic information of hypothetical noise-free image. Assuming the Laplacian probability density function of the quantizer input signal, SQNR gains are calculated for image predictive coding system with non-adaptive quantizer for white and correlated noise, respectively. The proposed model is evaluated on seabed images. However, model presented in this paper can be applied to any signal with Laplacian distribution

Colored-Gaussian Multiple Descriptions: Spectral and Time-Domain Forms

It is well known that Shannon's rate-distortion function (RDF) in the colored quadratic Gaussian (QG) case can be parametrized via a single Lagrangian variable (the "water level" in the reverse water filling solution). In this work, we show that the symmetric colored QG multiple-description (MD) RDF in the case of two descriptions can be parametrized in the spectral domain via two Lagrangian variables, which control the trade-off between the side distortion, the central distortion, and the coding rate. This spectral-domain analysis is complemented by a time-domain scheme-design approach: we show that the symmetric colored QG MD RDF can be achieved by combining ideas of delta-sigma modulation and differential pulse-code modulation. Specifically, two source prediction loops, one for each description, are embedded within a common noise shaping loop, whose parameters are explicitly found from the spectral-domain characterization.Comment: Accepted for publications in the IEEE Transactions on Information Theory. Title have been shortened, abstract clarified, and paper significantly restructure

Predictive validity of the START for unauthorised leave and substance abuse in a secure mental health setting:a pseudo-prospective cohort study

Background Risk assessment and management is central to the nursing role in forensic mental health settings. The Short Term Assessment of Risk and Treatability (START) aims to support assessment through identification of risk and protective factors. It has demonstrated predictive validity for aggression; it also aims to aid risk assessment for unauthorised leave and substance abuse where its performance is relatively untested. Objectives To test the predictive validity of the START for unauthorised leave and substance abuse. Design A naturalistic, pseudo-prospective cohort study. Settings Four centres of a large UK provider of secure inpatient mental health services. Participants Inpatients resident between May 2011 and October 2013 who remained in the service for 3-months following assessment with the START by their clinical team. Exclusion criteria were missing assessment data in excess of prorating guidelines. Of 900 eligible patients 73 were excluded leaving a final sample size of n = 827 (response rate 91.9%). Mean age was 38.5 years (SD = 16.7); most participants (72.2%) were male; common diagnoses were schizophrenia-type disorders, personality disorders, organic disorders, developmental disorders and intellectual disability. Methods Routinely conducted START assessments were gathered. Subsequent incidents of substance abuse and unauthorised leave were coded independently. Positive and negative predictive values of low and elevated risk were calculated. Receiver Operating Characteristic analysis was conducted to ascertain the predictive accuracy of the assessments based on their sensitivity and specificity. Results Patient-based rates of unauthorised leave (2.4%) and substance abuse (1.6%) were low. The positive and negative predictive values for unauthorised leave were 5.9% and 98.4%; and for substance abuse 8.1% and 99.0%. The START specific risk estimate for unauthorised leave predicted its associated outcome (Area under the curve = .659, p < .05, 95% CI .531, .786); the substance abuse risk estimate predicted its outcome with a large effect size (Area under the curve = .723, p < .01, 95% CI .568, .879). Conclusions The study provides limited support for the START by demonstrating the predictive validity of its specific risk estimates for substance abuse and unauthorised leave. High negative predictive values suggest the tool may be of most utility in screening out low risk individuals from unnecessary restrictive interventions; very low positive predictive values suggest caution before implementing restrictive interventions in those rated at elevated risk. Researchers should investigate how multidisciplinary teams formulate risk assessments for these outcomes since they outperform the quantitative element of this tool

The Landscape of Long Non-Coding RNA Dysregulation and Clinical Relevance in Muscle Invasive Bladder Urothelial Carcinoma.

Bladder cancer is one of the most common cancers in the United States, but few advancements in treatment options have occurred in the past few decades. This study aims to identify the most clinically relevant long non-coding RNAs (lncRNAs) to serve as potential biomarkers and treatment targets for muscle invasive bladder cancer (MIBC). Using RNA-sequencing data from 406 patients in The Cancer Genome Atlas (TCGA) database, we identified differentially expressed lncRNAs in MIBC vs. normal tissues. We then associated lncRNA expression with patient survival, clinical variables, oncogenic signatures, cancer- and immune-associated pathways, and genomic alterations. We identified a panel of 20 key lncRNAs that were most implicated in MIBC prognosis after differential expression analysis and prognostic correlations. Almost all lncRNAs we identified are correlated significantly with oncogenic processes. In conclusion, we discovered previously undescribed lncRNAs strongly implicated in the MIBC disease course that may be leveraged for diagnostic and treatment purposes in the future. Functional analysis of these lncRNAs may also reveal distinct mechanisms of bladder cancer carcinogenesis