Composite CDMA - A statistical mechanics analysis

Code Division Multiple Access (CDMA) in which the spreading code assignment to users contains a random element has recently become a cornerstone of CDMA research. The random element in the construction is particular attractive as it provides robustness and flexibility in utilising multi-access channels, whilst not making significant sacrifices in terms of transmission power. Random codes are generated from some ensemble, here we consider the possibility of combining two standard paradigms, sparsely and densely spread codes, in a single composite code ensemble. The composite code analysis includes a replica symmetric calculation of performance in the large system limit, and investigation of finite systems through a composite belief propagation algorithm. A variety of codes are examined with a focus on the high multi-access interference regime. In both the large size limit and finite systems we demonstrate scenarios in which the composite code has typical performance exceeding sparse and dense codes at equivalent signal to noise ratio.Comment: 23 pages, 11 figures, Sigma Phi 2008 conference submission - submitted to J.Stat.Mec

Issue Brief: Immigration and Gender Issues

This brief examines the intersections between immigration and gender issues, and explores the implications for such issues as class-based distinctions, sexual trafficking, mail order brides, and child-rearing. It also discusses demographics and potential capacity for mobilization for immigrant women

The Explicit Coding Rate Region of Symmetric Multilevel Diversity Coding

It is well known that {\em superposition coding}, namely separately encoding the independent sources, is optimal for symmetric multilevel diversity coding (SMDC) (Yeung-Zhang 1999). However, the characterization of the coding rate region therein involves uncountably many linear inequalities and the constant term (i.e., the lower bound) in each inequality is given in terms of the solution of a linear optimization problem. Thus this implicit characterization of the coding rate region does not enable the determination of the achievability of a given rate tuple. In this paper, we first obtain closed-form expressions of these uncountably many inequalities. Then we identify a finite subset of inequalities that is sufficient for characterizing the coding rate region. This gives an explicit characterization of the coding rate region. We further show by the symmetry of the problem that only a much smaller subset of this finite set of inequalities needs to be verified in determining the achievability of a given rate tuple. Yet, the cardinality of this smaller set grows at least exponentially fast with $L$. We also present a subset entropy inequality, which together with our explicit characterization of the coding rate region, is sufficient for proving the optimality of superposition coding

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.67% with 1 false positive per scan and sensitivity of 94.19% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.Comment: Submitted to IEEE TM

Isolation and characterization of nine microsatellite loci for the tropical understory tree Miconia affinis Wurdack (Melastomataceae)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75161/1/j.1755-0998.2008.02428.x.pd

Sliding Secure Symmetric Multilevel Diversity Coding

Symmetric multilevel diversity coding (SMDC) is a source coding problem where the independent sources are ordered according to their importance. It was shown that separately encoding independent sources (referred to as ``\textit{superposition coding}") is optimal. In this paper, we consider an $(L,s)$ \textit{sliding secure} SMDC problem with security priority, where each source $X_{\alpha}~(s\leq \alpha\leq L)$ is kept perfectly secure if no more than $\alpha-s$ encoders are accessible. The reconstruction requirements of the $L$ sources are the same as classical SMDC. A special case of an $(L,s)$ sliding secure SMDC problem that the first $s-1$ sources are constants is called the $(L,s)$ \textit{multilevel secret sharing} problem. For $s=1$, the two problems coincide, and we show that superposition coding is optimal. The rate regions for the $(3,2)$ problems are characterized. It is shown that superposition coding is suboptimal for both problems. The main idea that joint encoding can reduce coding rates is that we can use the previous source $X_{\alpha-1}$ as the secret key of $X_{\alpha}$. Based on this idea, we propose a coding scheme that achieves the minimum sum rate of the general $(L,s)$ multilevel secret sharing problem. Moreover, superposition coding of the $s$ sets of sources $X_1$, $X_2$, $\cdots$, $X_{s-1}$, $(X_s, X_{s+1}, \cdots, X_L)$ achieves the minimum sum rate of the general sliding secure SMDC problem

Proving Information Inequalities by Gaussian Elimination

The proof of information inequalities and identities under linear constraints on the information measures is an important problem in information theory. For this purpose, ITIP and other variant algorithms have been developed and implemented, which are all based on solving a linear program (LP). In this paper, we develop a method with symbolic computation. Compared with the known methods, our approach can completely avoids the use of linear programming which may cause numerical errors. Our procedures are also more efficient computationally.Comment: arXiv admin note: text overlap with arXiv:2202.0278

Enabling Privacy-Preserving Prediction for Length of Stay in ICU - A Multimodal Federated-Learning-based Approach

While the proliferation of data-driven machine learning approaches has resulted in new opportunities for precision healthcare, there are a number of challenges associated with fully utilizing medical data, for example partly due to the heterogeneity of data modalities in electronic health records. Moreover, medical data often sits in data silos due to various regulatory, privacy, ethical, and legal considerations, which complicates efforts to fully utilize machine learning. Motivated by these challenges, we focus on clinical care—length of stay prediction and propose a Multimodal Federated Learning approach. The latter is designed to leverage both privacy-preserving federated learning and multimodal data to facilitate length of stay prediction. By applying this approach to a real-world medical dataset, we demonstrate the predictive power of our approach as well as how it can address the earlier discussed challenges. The findings also suggest the potential of the proposed multimodal federated learning approach for other similar healthcare settings

Cerebellar encoding of multiple candidate error cues in the service of motor learning

or learning to occur through trial and error, the nervous system must effectively detect and encode performance errors. To examine this process, we designed a set of oculomotor learning tasks with more than one visual object providing potential error cues, as would occur in a natural visual scene. A task-relevant visual target and a task-irrelevant visual background both influenced vestibulo-ocular reflex learning in rhesus monkeys. Thus, motor learning does not identify a single error cue based on behavioral relevance, but can be simultaneously influenced by more than one cue. Moreover, the relative weighting ofthe differentcues could vary. Ifthe speed ofthe visual target's motion on the retina was low (≪1°/s), background motion dominated learning, but if target speed was high, the effects of the background were suppressed. The target and background motion had similar, nonlinear effects on the putative neural instructive signals carried by cerebellar climbing fibers, but with a stronger influence ofthe backgroundon the climbing fibers than on learning. In contrast, putative neuralinstructive signals carriedby the simple spikes of Purkinje cells were influenced solely by the motion of the visual target. Because they are influenced by different cues during training, joint control of learning by the climbing fibers and Purkinje cells may expand the learning capacity of the cerebellar circuit