We consider opportunistic communication over multiple channels where the state ("good" or "bad") of each channel evolves as independent and identically distributed Markov processes. A user, with limited channel sensing and access capability, chooses one channel to sense and subsequently access (based on the sensed channel state) in each time slot. A reward is obtained whenever the user senses and accesses a "good" channel. The objective is to design an optimal channel selection policy that maximizes the expected total (discounted or average) reward accrued over a finite or infinite horizon. This problem can be cast as a Partially Observable Markov Decision Process (POMDP) or a restless multi-armed bandit process, to which optimal solutions are often intractable. We show in this paper that a myopic policy that maximizes the immediate one-step reward is always optimal when the state transitions are positively correlated over time. When the state transitions are negatively correlated, we show that the same policy is optimal when the number of channels is limited to 2 or 3, while presenting a counterexample for the case of 4 channels. This result finds applications in opportunistic transmission scheduling in a fading environment, cognitive radio networks for spectrum overlay, and resource-constrained jamming and anti-jamming.Preliminary version of this work was presented at

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply Adv-GAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under stateof-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge. 1

Spectrum is a critical yet scarce resource and it has been shown that dynamic spectrum access can significantly improve spectrum utilization. To achieve this, it is important to incentivize the primary license holders to open up their under-utilized spectrum for sharing. In this paper we present a secondary spectrum market where a primary license holder can sell access to its unused or under-used spectrum resources in the form of certain fine-grained spectrumspace-time unit. Secondary wireless service providers can purchase such contracts to deploy new service, enhance their existing service, or deploy ad hoc service to meet flash crowds demand. Within the context of this market, we investigate how to use auction mechanisms to allocate and price spectrum resources so that the primary license holder's revenue is maximized. We begin by classifying a number of alternative auction formats in terms of spectrum demand. We then study a specific auction format where secondary wireless service providers have demands for fixed locations (cells). We propose an optimal auction based on the concept of virtual valuation. Assuming the knowledge of valuation distributions, the optimal auction uses the Vickrey-Clarke-Groves (VCG) mechanism to maximize the expected revenue while enforcing truthfulness. To reduce the computational complexity, we further design a truthful suboptimal auction with polynomial time complexity. It uses a monotone allocation and critical value payment to enforce truthfulness. Simulation results show that this suboptimal auction can generate stable expected revenue.

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Abstract-In this study, we consider optimal opportunistic spectrum access (OSA) policies for a transmitter in a multichannel wireless system, where a channel can be in one of multiple states. In such systems, the transmitter typically does not have complete information on the channel states, but can learn by probing individual channels at the expense of certain resources, e.g., energy and time. The main goal is to derive optimal strategies for determining which channels to probe, in what sequence, and which channel to use for transmission. We consider two problems within this context and show that they are equivalent to different data maximization and throughput maximization problems. For both problems, we derive key structural properties of the corresponding optimal strategy. In particular, we show that it has a threshold structure and can be described by an index policy. We further show that the optimal strategy for the first problem can only take one of three structural forms. Using these results, we first present a dynamic program that computes the optimal strategy within a finite number of steps, even when the state space is uncountably infinite. We then present and examine a more efficient, but suboptimal, two-step look-ahead strategy for each problem. These strategies are shown to be optimal for a number of cases of practical interest. We examine their performance via numerical studies.

In this paper we study the online learning problem involving rested and restless multiarmed bandits with multiple plays. The system consists of a single player/user and a set of K finite-state discrete-time Markov chains (arms) with unknown state spaces and statistics. At each time step the player can play M , M ≤ K, arms. The objective of the user is to decide for each step which M of the K arms to play over a sequence of trials so as to maximize its long term reward. The restless multiarmed bandit is particularly relevant to the application of opportunistic spectrum access (OSA), where a (secondary) user has access to a set of K channels, each of time-varying condition as a result of random fading and/or certain primary users' activities.We first show that a logarithmic regret algorithm exists for the rested multiarmed bandit problem. We then construct an algorithm for the restless bandit problem which utilizes regenerative cycles of a Markov chain and computes a sample mean based index policy. We show that under mild conditions on the state transition probabilities of the Markov chains this algorithm achieves logarithmic regret uniformly over time, and that this regret bound is also optimal.

In this paper, we propose a simple yet effective method of identifying traffic conditions on surface streets given location traces collected from on-road vehicles-this requires only GPS location data, plus infrequent low-bandwidth cellular updates. Unlike other systems, which simply display vehicle speeds on the road, our system characterizes unique traffic patterns on each road segment and identifies unusual traffic states on a segment-by-segment basis. We developed and evaluated the system by applying it to two sets of location traces. Evaluation results show that higher than 90% accuracy in characterization can be achieved after ten or more traversals are collected on a given road segment. We also show that traffic patterns on a road are very consistent over time, provided that the underlying road conditions do not change. This allows us to use a longer history in identifying traffic conditions with higher accuracy.

Summary Aims Baicalin (BAI), a flavonoid compound isolated from the root of Scutellaria baicalensis Georgi, has been established to have potent anti‐inflammation and neuroprotective properties; however, its effects during Alzheimer's disease ( AD ) treatment have not been well studied. This study aimed to investigate the effects of BAI pretreatment on cognitive impairment and neuronal protection against microglia‐induced neuroinflammation and to explore the mechanisms underlying its anti‐inflammation effects. Methods To determine whether BAI plays a positive role in ameliorating the memory and cognition deficits in APP (amyloid beta precursor protein)/PS1 (presenilin‐1) mice, behavioral experiments were conducted. We assessed the effects of BAI on microglial activation, the production of proinflammatory cytokines, and neuroinflammation‐mediated neuron apoptosis in vivo and in vitro using Western blot, RT ‐ PCR , ELISA , immunohistochemistry, and immunofluorescence. Finally, to elucidate the anti‐inflammation mechanisms underlying the effects of BAI , the protein expression of NLRP 3 inflammasomes and the expression of proteins involved in the TLR 4/ NF ‐κB signaling pathway were measured using Western blot and immunofluorescence. Results The results indicated that BAI treatment attenuated spatial memory dysfunction in APP / PS 1 mice, as assessed by the passive avoidance test and the Morris water maze test. Additionally, BAI administration effectively decreased the number of activated microglia and proinflammatory cytokines, as well as neuroinflammation‐mediated neuron apoptosis, in APP / PS 1 mice and LPS (lipopolysaccharides)/Aβ‐stimulated BV 2 microglial cells. Lastly, the molecular mechanistic study revealed that BAI inhibited microglia‐induced neuroinflammation via suppression of the activation of NLRP 3 inflammasomes and the TLR 4/ NF ‐κB signaling pathway. Conclusion Overall, the results of the present study indicated that BAI is a promising neuroprotective compound for use in the prevention and treatment of microglia‐mediated neuroinflammation during AD progression.