Ordinary Differential Equation-based Sparse Signal Recovery

This study investigates the use of continuous-time dynamical systems for sparse signal recovery. The proposed dynamical system is in the form of a nonlinear ordinary differential equation (ODE) derived from the gradient flow of the Lasso objective function. The sparse signal recovery process of this ODE-based approach is demonstrated by numerical simulations using the Euler method. The state of the continuous-time dynamical system eventually converges to the equilibrium point corresponding to the minimum of the objective function. To gain insight into the local convergence properties of the system, a linear approximation around the equilibrium point is applied, yielding a closed-form error evolution ODE. This analysis shows the behavior of convergence to the equilibrium point. In addition, a variational optimization problem is proposed to optimize a time-dependent regularization parameter in order to improve both convergence speed and solution quality. The deep unfolded-variational optimization method is introduced as a means of solving this optimization problem, and its effectiveness is validated through numerical experiments.Comment: 13 page

Gradient Flow Decoding for LDPC Codes

The power consumption of the integrated circuit is becoming a significant burden, particularly for large-scale signal processing tasks requiring high throughput. The decoding process of LDPC codes is such a heavy signal processing task that demands power efficiency and higher decoding throughput. A promising approach to reducing both power and latency of a decoding process is to use an analog circuit instead of a digital circuit. This paper investigates a continuous-time gradient flow-based approach for decoding LDPC codes, which employs a potential energy function similar to the objective function used in the gradient descent bit flipping (GDBF) algorithm. We experimentally demonstrate that the decoding performance of the gradient flow decoding is comparable to that of the multi-bit mode GDBF algorithm. Since an analog circuit of the gradient flow decoding requires only analog arithmetic operations and an integrator, future advancements in programmable analog integrated circuits may make practical implementation feasible.Comment: 6 page

A Model of Cancer Stem Cells Derived from Mouse Induced Pluripotent Stem Cells

Cancer stem cells (CSCs) are capable of continuous proliferation and self-renewal and are proposed to play significant roles in oncogenesis, tumor growth, metastasis and cancer recurrence. CSCs are considered derived from normal stem cells affected by the tumor microenvironment although the mechanism of development is not clear yet. In 2007, Yamanaka's group succeeded in generating Nanog mouse induced pluripotent stem (miPS) cells, in which green fluorescent protein (GFP) has been inserted into the 5′-untranslated region of the Nanog gene. Usually, iPS cells, just like embryonic stem cells, are considered to be induced into progenitor cells, which differentiate into various normal phenotypes depending on the normal niche. We hypothesized that CSCs could be derived from Nanog miPS cells in the conditioned culture medium of cancer cell lines, which is a mimic of carcinoma microenvironment. As a result, the Nanog miPS cells treated with the conditioned medium of mouse Lewis lung carcinoma acquired characteristics of CSCs, in that they formed spheroids expressing GFP in suspension culture, and had a high tumorigenicity in Balb/c nude mice exhibiting angiogenesis in vivo. In addition, these iPS-derived CSCs had a capacity of self-renewal and expressed the marker genes, Nanog, Rex1, Eras, Esg1 and Cripto, associated with stem cell properties and an undifferentiated state. Thus we concluded that a model of CSCs was originally developed from miPS cells and proposed the conditioned culture medium of cancer cell lines might perform as niche for producing CSCs. The model of CSCs and the procedure of their establishment will help study the genetic alterations and the secreted factors in the tumor microenvironment which convert miPS cells to CSCs. Furthermore, the identification of potentially bona fide markers of CSCs, which will help the development of novel anti-cancer therapies, might be possible though the CSC model

MMSE Signal Detection for MIMO Systems based on Ordinary Differential Equation

Motivated by emerging technologies for energy efficient analog computing and continuous-time processing, this paper proposes continuous-time minimum mean squared error estimation for multiple-input multiple-output (MIMO) systems based on an ordinary differential equation. Mean squared error (MSE) is a principal detection performance measure of estimation methods for MIMO systems. We derive an analytical MSE formula that indicates the MSE at any time. The MSE of the proposed method depends on a regularization parameter which affects the convergence property of the MSE. Furthermore, we extend the proposed method by using a time-dependent regularization parameter to achieve better convergence performance. Numerical experiments indicated excellent agreement with the theoretical values and improvement in the convergence performance owing to the use of the time-dependent parameter

Energy Efficient Over-the-Air Computation for Correlated Data in Wireless Sensor Networks

Over-the-air computation (AirComp) enables efficient wireless data aggregation in sensor networks by simultaneous processing of calculation and communication. This paper proposes a novel precoding method for AirComp that incorporates statistical properties of sensing data, spatial correlation and heterogeneous data correlation. The design of the proposed precoding matrix requires no iterative processes so that it can be realized with low computational costs. Moreover, this method provides dimensionality reduction of sensing data to reduce communication costs per sensor. We evaluate performance of the proposed method in terms of various system parameters. The results show the superiority of the proposed method to conventional non-iterative methods in cases where the number of receive antennas at the aggregator is less than that of the total transmit antennas at the sensors

Practical Liposomal Formulation for Taxanes with Polyethoxylated Castor Oil and Ethanol with Complete Encapsulation Efficiency and High Loading Efficiency

Taxanes including paclitaxel and docetaxel are effective anticancer agents preferably sufficient for liposomal drug delivery. However, the encapsulation of these drugs with effective amounts into conventional liposomes is difficult due to their high hydrophobicity. Therefore, an effective encapsulation strategy for liposomal taxanes has been eagerly anticipated. In this study, the mixture of polyethoxylated castor oil (Cremophor EL) and ethanol containing phosphate buffered saline termed as CEP was employed as a solvent of the inner hydrophilic core of liposomes where taxanes should be incorporated. Docetaxel-, paclitaxel-, or 7-oxacetylglycosylated paclitaxel-encapsulating liposomes were successfully prepared with almost 100% of encapsulation efficiency and 29.9, 15.4, or 29.1 mol% of loading efficiency, respectively. We then applied the docetaxel-encapsulating liposomes for targeted drug delivery. Docetaxel-encapsulating liposomes were successfully developed HER2-targeted drug delivery by coupling HER2-specific binding peptide on liposome surface. The HER2-targeting liposomes exhibited HER2-specific internalization and enhanced anticancer activity in vitro. Therefore, we propose the sophisticated preparation of liposomal taxanes using CEP as a promising formulation for effective cancer therapies