Clustering-based Source-aware Assessment of True Robustness for Learning Models

We introduce a novel validation framework to measure the true robustness of learning models for real-world applications by creating source-inclusive and source-exclusive partitions in a dataset via clustering. We develop a robustness metric derived from source-aware lower and upper bounds of model accuracy even when data source labels are not readily available. We clearly demonstrate that even on a well-explored dataset like MNIST, challenging training scenarios can be constructed under the proposed assessment framework for two separate yet equally important applications: i) more rigorous learning model comparison and ii) dataset adequacy evaluation. In addition, our findings not only promise a more complete identification of trade-offs between model complexity, accuracy and robustness but can also help researchers optimize their efforts in data collection by identifying the less robust and more challenging class labels.Comment: Submitted to UAI 201

Finite Horizon Online Lazy Scheduling with Energy Harvesting Transmitters over Fading Channels

Lazy scheduling, i.e. setting transmit power and rate in response to data traffic as low as possible so as to satisfy delay constraints, is a known method for energy efficient transmission.This paper addresses an online lazy scheduling problem over finite time-slotted transmission window and introduces low-complexity heuristics which attain near-optimal performance.Particularly, this paper generalizes lazy scheduling problem for energy harvesting systems to deal with packet arrival, energy harvesting and time-varying channel processes simultaneously. The time-slotted formulation of the problem and depiction of its offline optimal solution provide explicit expressions allowing to derive good online policies and algorithms

Next Generation M2M Cellular Networks: Challenges and Practical Considerations

In this article, we present the major challenges of future machine-to-machine (M2M) cellular networks such as spectrum scarcity problem, support for low-power, low-cost, and numerous number of devices. As being an integral part of the future Internet-of-Things (IoT), the true vision of M2M communications cannot be reached with conventional solutions that are typically cost inefficient. Cognitive radio concept has emerged to significantly tackle the spectrum under-utilization or scarcity problem. Heterogeneous network model is another alternative to relax the number of covered users. To this extent, we present a complete fundamental understanding and engineering knowledge of cognitive radios, heterogeneous network model, and power and cost challenges in the context of future M2M cellular networks

On the Performance of MIMO FSO Communications over Double Generalized Gamma Fading Channels

A major performance degrading factor in free space optical communication (FSO) systems is atmospheric turbulence. Spatial diversity techniques provide a promising approach to mitigate turbulence-induced fading. In this paper, we study the error rate performance of FSO links with spatial diversity over atmospheric turbulence channels described by the Double Generalized Gamma distribution which is a new generic statistical model covering all turbulence conditions. We assume intensity modulation/direct detection with on-off keying and present the BER performance of single-input multiple-output (SIMO), multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) FSO systems over this new channel model.Comment: 6 Pages, 4 figure, IEEE ICC conference 201

New Goods and the Transition to a New Economy

The U.S. went through a remarkable structural transformation between 1800 and 2000. In 1800 the majority of people worked in agriculture. Barely anyone did by 2000. What caused the rapid demise of agriculture in the economy? The analysis here concentrates on the development of new consumer goods associated with technological progress. The introduction of new goods into the framework lessens the need to rely on satiation points, subsistence levels of consumption, and the like. The analysis suggests that between 1800 and 2000 economic welfare grew by at least 1.5 percent a year, and maybe as much as 10 percent annually, the exact number depending upon the metric preferred.