Large-scale Graphitic Thin Films Synthesized on Ni and Transferred to Insulators: Structural and Electronic Properties

We present a comprehensive study of the structural and electronic properties of ultrathin films containing graphene layers synthesized by chemical vapor deposition (CVD) based surface segregation on polycrystalline Ni foils then transferred onto insulating SiO2/Si substrates. Films of size up to several mm's have been synthesized. Structural characterizations by atomic force microscopy (AFM), scanning tunneling microscopy (STM), cross-sectional transmission electron microscopy (XTEM) and Raman spectroscopy confirm that such large scale graphitic thin films (GTF) contain both thick graphite regions and thin regions of few layer graphene. The films also contain many wrinkles, with sharply-bent tips and dislocations revealed by XTEM, yielding insights on the growth and buckling processes of the GTF. Measurements on mm-scale back-gated transistor devices fabricated from the transferred GTF show ambipolar field effect with resistance modulation ~50% and carrier mobilities reaching ~2000 cm^2/Vs. We also demonstrate quantum transport of carriers with phase coherence length over 0.2 $\mu$m from the observation of 2D weak localization in low temperature magneto-transport measurements. Our results show that despite the non-uniformity and surface roughness, such large-scale, flexible thin films can have electronic properties promising for device applications.Comment: This version (as published) contains additional data, such as cross sectional TEM image

Typestate-Guided Fuzzer for Discovering Use-after-Free Vulnerabilities

© 2020 Association for Computing Machinery. Existing coverage-based fuzzers usually use the individual control flow graph (CFG) edge coverage to guide the fuzzing process, which has shown great potential in finding vulnerabilities. However, CFG edge coverage is not effective in discovering vulnerabilities such as use-after-free (UaF). This is because, to trigger UaF vulnerabilities, one needs not only to cover individual edges, but also to traverse some (long) sequence of edges in a particular order, which is challenging for existing fuzzers. To this end, we propose to model UaF vulnerabilities as typestate properties, and develop a typestateguided fuzzer, named UAFL, for discovering vulnerabilities violating typestate properties. Given a typestate property, we first perform a static typestate analysis to find operation sequences potentially violating the property. Our fuzzing process is then guided by the operation sequences in order to progressively generate test cases triggering property violations. In addition, we also employ an information flow analysis to improve the efficiency of the fuzzing process. We have performed a thorough evaluation of UAFL on 14 widely-used real-world programs. The experiment results show that UAFL substantially outperforms the state-of-the-art fuzzers, including AFL, AFLFast, FairFuzz, MOpt, Angora and QSYM, in terms of the time taken to discover vulnerabilities. We have discovered 10 previously unknown vulnerabilities, and received 5 new CVEs

Direct Imaging of Graphene Edges: Atomic Structure and Electronic Scattering

We report an atomically-resolved scanning tunneling microscopy (STM) investigation of the edges of graphene grains synthesized on Cu foils by chemical vapor deposition (CVD). Most of the edges are macroscopically parallel to the zigzag directions of graphene lattice. These edges have microscopic roughness that is found to also follow zigzag directions at atomic scale, displaying many ~120 degree turns. A prominent standing wave pattern with periodicity ~3a/4 (a being the graphene lattice constant) is observed near a rare-occurring armchair-oriented edge. Observed features of this wave pattern are consistent with the electronic intervalley backscattering predicted to occur at armchair edges but not at zigzag edges

Minimizing Queue Length Regret Under Adversarial Network Models

Stochastic models have been dominant in network optimization theory for over two decades, due totheir analytical tractability. However, these models fail to capture non-stationary or even adversarialnetwork dynamics which are of increasing importance for modeling the behavior of networksunder malicious attacks or characterizing short-term transient behavior. In this paper, we focuson minimizing queue length regret under adversarial network models, which measures the finite-time queue length difference between a causal policy and an “oracle” that knows the future. Twoadversarial network models are developed to characterize the adversary’s behavior. We provide lowerbounds on queue length regret under these adversary models and analyze the performance of twocontrol policies (i.e., the MaxWeight policy and the Tracking Algorithm). We further characterizethe stability region under adversarial network models, and show that both the MaxWeight policyand the Tracking Algorithm are throughput-optimal even in adversarial settings.National Science Foundation (U.S.) (Grant CNS-1524317)United States. Defense Advanced Research Projects Agency. Information Innovation Office (Contract HROO l l-l 5-C-0097

Coflow scheduling in input-queued switches: Optimal delay scaling and algorithms

A coflow is a collection of parallel flows belonging to the same job. It has the all-or-nothing property: a coflow is not complete until the completion of all its constituent flows. In this paper, we focus on optimizing coflow-level delay, i.e., the time to complete all the flows in a coflow, in the context of an N × N input-queued switch. In particular, we develop a throughput-optimal scheduling policy that achieves the best scaling of coflow-level delay as N → ∞. We first derive lower bounds on the coflow-level delay that can be achieved by any scheduling policy. It is observed that these lower bounds critically depend on the variability of flow sizes. Then we analyze the coflow-level performance of some existing coflow-agnostic scheduling policies and show that none of them achieves provably optimal performance with respect to coflow-level delay. Finally, we propose the Coflow-Aware Batching (CAB) policy which achieves the optimal scaling of coflow-level delay under some mild assumptions.National Science Foundation (U.S.) (Grant CNS-1116209)National Science Foundation (U.S.) (Grant CNS-1617091)United States. Defense Advanced Research Projects Agency. Information Innovation Office (I20)Raytheon CompanyBBN Technologies (Contract No. HROO l l-l 5-C-0097

Optimal Network Control in Partially-Controllable Networks

The effectiveness of many optimal network control algorithms (e.g., BackPressure) relies on the premise that all of the nodes are fully controllable. However, these algorithms may yield poor performance in a partially-controllable network where a subset of nodes are uncontrollable and use some unknown policy. Such a partially-controllable model is of increasing importance in real-world networked systems such as overlay-underlay networks. In this paper, we design optimal network control algorithms that can stabilize a partially-controllable network. We first study the scenario where uncontrollable nodes use a queue-agnostic policy, and propose a low-complexity throughput-optimal algorithm, called Tracking-MaxWeight (TMW), which enhances the original MaxWeight algorithm with an explicit learning of the policy used by uncontrollable nodes. Next, we investigate the scenario where uncontrollable nodes use a queue-dependent policy and the problem is formulated as an MDP with unknown queueing dynamics. We propose a new reinforcement learning algorithm, called Truncated Upper Confidence Reinforcement Learning (TUCRL), and prove that TUCRL achieves tunable three-way tradeoffs between throughput, delay and convergence rate.National Science Foundation (U.S.) (Grant CNS-1524317)United States. Defense Advanced Research Projects Agency (Contract HROO l l-l 5-C-0097

Plumbago scandens L.

Stochastic models have been dominant in network optimization theory for over two decades, due to their analytical tractability. However, these models fail to capture non-stationary or even adversarial network dynamics which are of increasing importance for modeling the behavior of networks under malicious attacks or characterizing short-term transient behavior. In this paper, we consider the network utility maximization problem in adversarial network settings. In particular, we focus on the tradeoffs between total queue length and utility regret which measures the difference in network utility between a causal policy and an 'oracle' that knows the future within a finite time horizon. Two adversarial network models are developed to characterize the adversary's behavior. We provide lower bounds on the tradeoff between utility regret and queue length under these adversarial models, and analyze the performance of two control policies (i.e., the Drift-plus-Penalty algorithm and the Tracking Algorithm).National Science Foundation (U.S.) (Grant CNS-1524317)United States. Defense Advanced Research Projects Agency (Contract HROO l l-l 5-C-0097

Amelioration des performances en portee et en precision de localisation angulaire, des systemes de navigation sous-marine

SIGLECNRS TD Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc