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1. Transmission of Bursty Traffic over Fading Channels with Adaptive Decision Feedback

   Sayedana, Borna; Mahajan, Aditya; Yeh, Edmund (August 2020, Proceedings of the Inter- national Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt))

   In this paper, cross-layer design of transmitting data packets over AWGN fading channel with adaptive decision feedback is considered. The transmitter decides the number of packets to transmit and the threshold of the decision feedback based on the queue length and the channel state. The transmit power is chosen such that the probability of error is below a pre-specified threshold. We model the system as a Markov decision process and use ideas from lattice theory to establish qualitative properties of optimal transmission strategies. In particular, we show that: (i) if the channel state remains the same and the number of packets in the queue increase, then the optimal policy either transmits more packets or uses a smaller decision feedback threshold or both; and (ii) if the number of packets in the queue remain the same and the channel quality deteriorates, then the optimal policy either transmits fewer packets or uses a larger threshold for the decision feedback or both. We also show under rate constraints that if the channel gains for all channel states are above a threshold, then the “or” in the above characterization can be replaced by “and”. Finally, we present a numerical example showing that adaptive decision feedback significantly improves the power-delay trade-off as compared with the case of no feedback. 

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2. A Cooperative Jamming Game in Wireless Networks under Uncertainty

   https://doi.org/10.1007/978-3-030-63086-7_14  

   Xu, Z.; Baykal-Gürsoy, M. (December 2020, Security and Privacy in Communication Networks. SecureComm 2020. Lecture Notes)

   null (Ed.)

   Considered is a multi-channel wireless network for secret communication that uses the signal-to-interference-plus-noise ratio (SINR) as the performance measure. An eavesdropper can intercept encoded messages through a degraded channel of each legitimate transmitter-receiver communication pair. A friendly interferer, on the other hand, may send cooperative jamming signals to enhance the secrecy performance of the whole network. Besides, the state information of the eavesdropping channel may not be known completely. The transmitters and the friendly interferer have to cooperatively decide on the optimal jamming power allocation strategy that balances the secrecy performance with the cost of employing intentional interference, while the eavesdropper tries to maximize her eavesdropping capacity. To solve this problem, we propose and analyze a non-zero-sum game between the network defender and the eavesdropper who can only attack a limited number of channels. We show that the Nash equilibrium strategies for the players are of threshold type. We present an algorithm to find the equilibrium strategy pair. Numerical examples demonstrate the equilibrium and contrast it to baseline strategies. 

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3. PMGAN: Paralleled Mix-Generator Generative Adversarial Networks with Balance Control

   Xiao, Xia; Rajasekaran, Sanguthevar (January 2018, The 27th International Conference on Artificial Neural Networks)

   A Generative Adversarial Network (GAN) is an unsupervised generative framework to generate a sample distribution that is identical to the data distribution. Recently, mix strategy multi-generator/discriminator GANs have been shown to outperform single pair GANs. However, the mixed model suffers from the problem of linearly growing training time. Also, imbalanced training among generators makes it difficult to parallelize. In this paper, we propose a balanced mix-generator GAN that works in parallel by mixing multiple disjoint generators to approximate the real distribution. The weights of the discriminator and the classifier are controlled by a balance strategy. We also present an efficient loss function, to force each generator to embrace few modes with a high probability. Our model is naturally adaptive to large parallel computation frameworks. Each generator can be trained on multiple GPUs asynchronously. We have performed extensive experiments on synthetic datasets, MNIST1000, CIFAR-10, and ImageNet. The results establish that our model can achieve the state-of-the-art performance (in terms of the modes coverage and the inception score), with significantly reduced training time. We also show that the missing mode problem can be relieved with a growing number of generators. 

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4. CHAMMI: A benchmark for channel-adaptive models in microscopy imaging

   Chen, Zitong_Sam; Pham, Chau; Wang, Siqi; Doron, Michael; Moshkov, Nikita; Plummer, Bryan; Caicedo, Juan_C (February 2024, Advances in neural information processing systems)

   Most neural networks assume that input images have a fixed number of channels (three for RGB images). However, there are many settings where the number of channels may vary, such as microscopy images where the number of channels changes depending on instruments and experimental goals. Yet, there has not been a systemic attempt to create and evaluate neural networks that are invariant to the number and type of channels. As a result, trained models remain specific to individual studies and are hardly reusable for other microscopy settings. In this paper, we present a benchmark for investigating channel-adaptive models in microscopy imaging, which consists of 1) a dataset of varied-channel single-cell images, and 2) a biologically relevant evaluation framework. In addition, we adapted several existing techniques to create channel-adaptive models and compared their performance on this benchmark to fixed-channel, baseline models. We find that channel-adaptive models can generalize better to out-of-domain tasks and can be computationally efficient. We contribute a curated dataset and an evaluation API to facilitate objective comparisons in future research and applications. 

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5. Beyond Data and Model Parallelism for Deep Neural Networks

   Jia, Zhihao; Zaharia, Matei; Aiken, Alex (April 2019, SysML 2019)

   Existing deep learning systems commonly parallelize deep neural network (DNN) training using data or model parallelism, but these strategies often result in suboptimal parallelization performance. We introduce SOAP, a more comprehensive search space of parallelization strategies for DNNs that includes strategies to parallelize a DNN in the Sample, Operator, Attribute, and Parameter dimensions. We present FlexFlow, a deep learning engine that uses guided randomized search of the SOAP space to find a fast parallelization strategy for a specific parallel machine. To accelerate this search, FlexFlow introduces a novel execution simulator that can accurately predict a parallelization strategy’s performance and is three orders of magnitude faster than prior approaches that execute each strategy. We evaluate FlexFlow with six real-world DNN benchmarks on two GPU clusters and show that FlexFlow increases training throughput by up to 3.3× over state-of-the-art approaches, even when including its search time, and also improves scalability. 

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