L2L_2-Box Optimization for Green Cloud-RAN via Network Adaptation

In this paper, we propose a reformulation for the Mixed Integer Programming (MIP) problem into an exact and continuous model through using the $\ell_2$-box technique to recast the binary constraints into a box with an $\ell_2$ sphere constraint. The reformulated problem can be tackled by a dual ascent algorithm combined with a Majorization-Minimization (MM) method for the subproblems to solve the network power consumption problem of the Cloud Radio Access Network (Cloud-RAN), and which leads to solving a sequence of Difference of Convex (DC) subproblems handled by an inexact MM algorithm. After obtaining the final solution, we use it as the initial result of the bi-section Group Sparse Beamforming (GSBF) algorithm to promote the group-sparsity of beamformers, rather than using the weighted $\ell_1 / \ell_2$-norm. Simulation results indicate that the new method outperforms the bi-section GSBF algorithm by achieving smaller network power consumption, especially in sparser cases, i.e., Cloud-RANs with a lot of Remote Radio Heads (RRHs) but fewer users.Comment: 4 pages, 4 figure

A Mathematical Model of Tumor Volume Changes during Radiotherapy

Purpose. To develop a clinically viable mathematical model that quantitatively predicts tumor volume change during radiotherapy in order to provide treatment response assessment for prognosis, treatment plan optimization, and adaptation. Method and Materials. The correction factors containing hypoxia, DNA single strand breaks, potentially lethal damage, and other factors were used to develop an improved cell survival model based on the popular linear-quadratic model of cell survival in radiotherapy. The four-level cell population model proposed by Chvetsov et al. was further simplified by removing the initial hypoxic fraction and reoxygenation parameter, which are hard to obtain in routine clinics, such that an easy-to-use model can be developed for clinical applications. The new model was validated with data of nine lung and cervical cancer patients. Results. Out of the nine cases, the new model can predict tumor volume change in six cases with a correlation index R2 greater than 0.9 and the rest of three with R2 greater than 0.85. Conclusion. Based on a four-level cell population model, a more practical and simplified cell survival curve was proposed to model the tumor volume changes during radiotherapy. Validation study with patient data demonstrated feasibility and clinical usefulness of the new model in predicting tumor volume change in radiotherapy

Image-Plane Radio Transients on Short Timescales with ASKAP

Short bursts, flares, scintillation, and other radio time-domain phenomena usually imply extreme astrophysical environments (e.g., strong magnetic fields). Therefore, these objects can be used as a laboratory to study extreme physics that cannot be studied on Earth. This time-domain parameter space, however, is relatively unexplored historically, mainly limited by instrumental sensitivity and field-of-view. In this thesis, I present results from untargeted radio transient surveys, focusing on short-timescales transients in the image plane. The surveys were conducted using new techniques with the ASKAP telescope. Firstly, I present the discovery of a group of rapidly scintillating galaxies arranged linearly on the sky and spanning approximately 2 degrees. This unlikely sky distribution reveals the existence of a nearby, straight, and high-pressured plasma filament, which produces extreme scintillation. More generally, I demonstrate the potential of identifying intra-observation transients (i.e., on the timescale of 15 min) from hours-long observations with ASKAP. This survey (using ASKAP pilot survey data) led to the discovery of 38 highly variable and transient sources, including pulsars, radio flaring stars, and extreme scintillators. Finally, I demonstrate the possibility of identifying new pulsars (especially unusual pulsars) in the image plane through their variable behaviours and polarisation. Based on this approach, I discovered a new, bright pulsar in the Large Magellanic Cloud. These discoveries have filled gaps in this poorly-explored time-domain parameter space. The presence of a nearby, high-pressured plasma filament also changes our understanding of the origins of extreme scintillation, and requires new models to explain the underlying phenomenon. Using the same technique we expect to discover ~1 highly variable source per day on the full ASKAP surveys

Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks

Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wireless networks and ignore the impact of downlink/uplink inter-cell interference on the learning performance. In this paper, we investigate FL over a multi-cell wireless network, where each cell performs a different FL task and over-the-air computation (AirComp) is adopted to enable fast uplink gradient aggregation. We conduct convergence analysis of AirComp-assisted FL systems, taking into account the inter-cell interference in both the downlink and uplink model/gradient transmissions, which reveals that the distorted model/gradient exchanges induce a gap to hinder the convergence of FL. We characterize the Pareto boundary of the error-induced gap region to quantify the learning performance trade-off among different FL tasks, based on which we formulate an optimization problem to minimize the sum of error-induced gaps in all cells. To tackle the coupling between the downlink and uplink transmissions as well as the coupling among multiple cells, we propose a cooperative multi-cell FL optimization framework to achieve efficient interference management for downlink and uplink transmission design. Results demonstrate that our proposed algorithm achieves much better average learning performance over multiple cells than non-cooperative baseline schemes.Comment: This work has been accepted by IEEE Journal on Selected Areas in Communication

Nonvesicular Inhibitory Neurotransmission via Reversal of the GABA Transporter GAT-1

SummaryGABA transporters play an important but poorly understood role in neuronal inhibition. They can reverse, but this is widely thought to occur only under pathological conditions. Here we use a heterologous expression system to show that the reversal potential of GAT-1 under physiologically relevant conditions is near the normal resting potential of neurons and that reversal can occur rapidly enough to release GABA during simulated action potentials. We then use paired recordings from cultured hippocampal neurons and show that GABAergic transmission is not prevented by four methods widely used to block vesicular release. This nonvesicular neurotransmission was potently blocked by GAT-1 antagonists and was enhanced by agents that increase cytosolic [GABA] or [Na+] (which would increase GAT-1 reversal). We conclude that GAT-1 regulates tonic inhibition by clamping ambient [GABA] at a level high enough to activate high-affinity GABAA receptors and that transporter-mediated GABA release can contribute to phasic inhibition