Thickness dependent interlayer transport in vertical MoS2 Josephson junctions

We report on observations of thickness dependent Josephson coupling and multiple Andreev reflections (MAR) in vertically stacked molybdenum disulfide (MoS2) - molybdenum rhenium (MoRe) Josephson junctions. MoRe, a chemically inert superconductor, allows for oxide free fabrication of high transparency vertical MoS2 devices. Single and bilayer MoS2 junctions display relatively large critical currents (up to 2.5 uA) and the appearance of sub-gap structure given by MAR. In three and four layer thick devices we observe orders of magnitude lower critical currents (sub-nA) and reduced quasiparticle gaps due to proximitized MoS2 layers in contact with MoRe. We anticipate that this device architecture could be easily extended to other 2D materials.Comment: 18 pages, 6 figures including Supporting Informatio

Participation anticipating in elections using data mining methods

Anticipating the political behavior of people will be considerable help for election candidates to assess the possibility of their success and to be acknowledged about the public motivations to select them. In this paper, we provide a general schematic of the architecture of participation anticipating system in presidential election by using KNN, Classification Tree and Na\"ive Bayes and tools orange based on crisp which had hopeful output. To test and assess the proposed model, we begin to use the case study by selecting 100 qualified persons who attend in 11th presidential election of Islamic republic of Iran and anticipate their participation in Kohkiloye & Boyerahmad. We indicate that KNN can perform anticipation and classification processes with high accuracy in compared with two other algorithms to anticipate participation

Real-time deep hair matting on mobile devices

Augmented reality is an emerging technology in many application domains. Among them is the beauty industry, where live virtual try-on of beauty products is of great importance. In this paper, we address the problem of live hair color augmentation. To achieve this goal, hair needs to be segmented quickly and accurately. We show how a modified MobileNet CNN architecture can be used to segment the hair in real-time. Instead of training this network using large amounts of accurate segmentation data, which is difficult to obtain, we use crowd sourced hair segmentation data. While such data is much simpler to obtain, the segmentations there are noisy and coarse. Despite this, we show how our system can produce accurate and fine-detailed hair mattes, while running at over 30 fps on an iPad Pro tablet.Comment: 7 pages, 7 figures, submitted to CRV 201

Compiling symbolic attacks to protocol implementation tests

Recently efficient model-checking tools have been developed to find flaws in security protocols specifications. These flaws can be interpreted as potential attacks scenarios but the feasability of these scenarios need to be confirmed at the implementation level. However, bridging the gap between an abstract attack scenario derived from a specification and a penetration test on real implementations of a protocol is still an open issue. This work investigates an architecture for automatically generating abstract attacks and converting them to concrete tests on protocol implementations. In particular we aim to improve previously proposed blackbox testing methods in order to discover automatically new attacks and vulnerabilities. As a proof of concept we have experimented our proposed architecture to detect a renegotiation vulnerability on some implementations of SSL/TLS, a protocol widely used for securing electronic transactions.Comment: In Proceedings SCSS 2012, arXiv:1307.802

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Department of Energy Engineering (Battery Science and Technology)The continuous throng in demand for high energy density rechargeable batteries innovatively drives technological development in cell design as well as electrochemically active materials. In that perspective metal-free batteries consisting of a flowing seawater as a cathode active material were introduced. However, the electrochemical performance of the seawater battery was restrained by NASICON (Na3Zr2Si2PO12) ceramic solid electrolyte. Here, we demonstrate a new class of fibrous nanomat hard-carbon (FNHC) anode/1D (one-dimensional) bucky paper (1DBP) cathode hybrid electrode architecture in seawater battery based on 1D building block-interweaved hetero-nanomat frameworks. Differently from conventional slurry-cast electrodes, exquisitely designed hybrid hetero-nanomat electrodes are fabricated through concurrent dual electrospraying and electrospinning for the anode, vacuum-assisted infiltration for the cathode. HC nanoparticles are closely embedded in the spatially reinforced polymeric nanofiber/CNT hetero-nanomat skeletons that play a crucial role in constructing 3D-bicontinuous ion/electron transport pathways and allow to eliminate heavy metallic aluminum foil current collectors. Eventually the FNHC/1DBP seawater full cell, driven by aforementioned physicochemical uniqueness, shows exceptional improvement in electrochemical performance (Energy density = 693 Wh kg-1), (Power density = 3341 W kg-1) removing strong stereotype of ceramic solid electrolyte, which beyond those achievable with innovative next generation battery technologies.ope

Progressive growing of self-organized hierarchical representations for exploration

Designing agent that can autonomously discover and learn a diversity of structures and skills in unknown changing environments is key for lifelong machine learning. A central challenge is how to learn incrementally representations in order to progressively build a map of the discovered structures and re-use it to further explore. To address this challenge, we identify and target several key functionalities. First, we aim to build lasting representations and avoid catastrophic forgetting throughout the exploration process. Secondly we aim to learn a diversity of representations allowing to discover a "diversity of diversity" of structures (and associated skills) in complex high-dimensional environments. Thirdly, we target representations that can structure the agent discoveries in a coarse-to-fine manner. Finally, we target the reuse of such representations to drive exploration toward an "interesting" type of diversity, for instance leveraging human guidance. Current approaches in state representation learning rely generally on monolithic architectures which do not enable all these functionalities. Therefore, we present a novel technique to progressively construct a Hierarchy of Observation Latent Models for Exploration Stratification, called HOLMES. This technique couples the use of a dynamic modular model architecture for representation learning with intrinsically-motivated goal exploration processes (IMGEPs). The paper shows results in the domain of automated discovery of diverse self-organized patterns, considering as testbed the experimental framework from Reinke et al. (2019)

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning. While semantic segmentation algorithms enable 3D image analysis and quantification in many applications, the design of respective specialised solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We propose nnU-Net, a deep learning framework that condenses the current domain knowledge and autonomously takes the key decisions required to transfer a basic architecture to different datasets and segmentation tasks. Without manual tuning, nnU-Net surpasses most specialised deep learning pipelines in 19 public international competitions and sets a new state of the art in the majority of the 49 tasks. The results demonstrate a vast hidden potential in the systematic adaptation of deep learning methods to different datasets. We make nnU-Net publicly available as an open-source tool that can effectively be used out-of-the-box, rendering state of the art segmentation accessible to non-experts and catalyzing scientific progress as a framework for automated method design.Comment: * Fabian Isensee and Paul F. J\"ager share the first authorshi

High-Efficient Parallel CAVLC Encoders on Heterogeneous Multicore Architectures

This article presents two high-efficient parallel realizations of the context-based adaptive variable length coding (CAVLC) based on heterogeneous multicore processors. By optimizing the architecture of the CAVLC encoder, three kinds of dependences are eliminated or weaken, including the context-based data dependence, the memory accessing dependence and the control dependence. The CAVLC pipeline is divided into three stages: two scans, coding, and lag packing, and be implemented on two typical heterogeneous multicore architectures. One is a block-based SIMD parallel CAVLC encoder on multicore stream processor STORM. The other is a component-oriented SIMT parallel encoder on massively parallel architecture GPU. Both of them exploited rich data-level parallelism. Experiments results show that compared with the CPU version, more than 70 times of speedup can be obtained for STORM and over 50 times for GPU. The implementation of encoder on STORM can make a real-time processing for 1080p @30fps and GPU-based version can satisfy the requirements for 720p real-time encoding. The throughput of the presented CAVLC encoders is more than 10 times higher than that of published software encoders on DSP and multicore platforms