Low-Complexity Detection/Equalization in Large-Dimension MIMO-ISI Channels Using Graphical Models

In this paper, we deal with low-complexity near-optimal detection/equalization in large-dimension multiple-input multiple-output inter-symbol interference (MIMO-ISI) channels using message passing on graphical models. A key contribution in the paper is the demonstration that near-optimal performance in MIMO-ISI channels with large dimensions can be achieved at low complexities through simple yet effective simplifications/approximations, although the graphical models that represent MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1) use of Markov Random Field (MRF) based graphical model with pairwise interaction, in conjunction with {\em message/belief damping}, and 2) use of Factor Graph (FG) based graphical model with {\em Gaussian approximation of interference} (GAI). The per-symbol complexities are $O(K^2n_t^2)$ and $O(Kn_t)$ for the MRF and the FG with GAI approaches, respectively, where $K$ and $n_t$ denote the number of channel uses per frame, and number of transmit antennas, respectively. These low-complexities are quite attractive for large dimensions, i.e., for large $Kn_t$. From a performance perspective, these algorithms are even more interesting in large-dimensions since they achieve increasingly closer to optimum detection performance for increasing $Kn_t$. Also, we show that these message passing algorithms can be used in an iterative manner with local neighborhood search algorithms to improve the reliability/performance of $M$-QAM symbol detection

Improving dialysis adherence for high risk patients using automated messaging: Proof of concept

AbstractComorbidities and socioeconomic barriers often limit patient adherence and self-management with hemodialysis. Missed sessions, often associated with communication barriers, can result in emergency dialysis and avoidable hospitalizations. This proof of concept study explored using a novel digital-messaging platform, EpxDialysis, to improve patient-to-dialysis center communication via widely available text messaging and telephone technology. A randomized controlled trial was conducted through Washington University-affiliated hemodialysis centers involving ESRD patients with poor attendance, defined as missing 2–6 sessions over the preceding 12 weeks. A cross-over study design evaluated appointment adherence between intervention and control groups. Comparing nonadherence rates eight weeks prior to enrollment, median appointment adherence after using the system increased by 75%, and median number of unintended hospitalization days fell by 31%. A conservative cost-benefit analysis of EpxDialysis demonstrates a 1:36 savings ratio from appointment adherence. EpxDialysis is a low-risk, cost-effective, intervention for increasing hemodialysis adherence in high-risk patients, especially at centers caring for vulnerable and low-income patients.</jats:p

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome‐scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network‐level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field

Codon Distributions in DNA

The codons, sixtyfour in number, are distributed over the coding parts of DNA sequences. The distribution function is the plot of frequency-versus-rank of the codons. These distributions are characterised by parameters that are almost universal, i.e., gene independent. There is but a small part that depends on the gene. We present the theory to calculate the universal (gene-independent) part. The part that is gene-specific, however, has undetermined overlaps and fluctuations.Comment: 31 pages, 5 figure

Condensation of Excitons in Cu2O at Ultracold Temperatures: Experiment and Theory

We present experiments on the luminescence of excitons confined in a potential trap at milli-Kelvin bath temperatures under cw-excitation. They reveal several distinct features like a kink in the dependence of the total integrated luminescence intensity on excitation laser power and a bimodal distribution of the spatially resolved luminescence. Furthermore, we discuss the present state of the theoretical description of Bose-Einstein condensation of excitons with respect to signatures of a condensate in the luminescence. The comparison of the experimental data with theoretical results with respect to the spatially resolved as well as the integrated luminescence intensity shows the necessity of taking into account a Bose-Einstein condensed excitonic phase in order to understand the behaviour of the trapped excitons.Comment: 41 pages, 23 figure