DEVCOM Army Research Laboratory

ADVERTISEMENT RETURN TO ISSUEPREVarticleNEXTNonaqueous Liquid Electrolytes for Lithium-Based Rechargeable BatteriesKang XuKang XuElectrochemistry Branch, Sensor and Electron Devices Directorate, U.S. Army Research Laboratory, Adelphi, Maryland 20783-1197 More by Kang XuCite this: Chem. Rev. 2004, 104, 10, 4303–4418Publication Date (Web):September 16, 2004Publication History Received3 November 2003Published online16 September 2004Published inissue 1 October 2004https://pubs.acs.org/doi/10.1021/cr030203ghttps://doi.org/10.1021/cr030203gresearch-articleACS PublicationsCopyright © 2004 American Chemical SocietyRequest reuse permissionsArticle Views89913Altmetric-Citations5770LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Electrodes,Electrolytes,Ions,Lithium,Solvents Get e-Alerts

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTElectrolytes and Interphases in Li-Ion Batteries and BeyondKang Xu*View Author Information Electrochemistry Branch, Energy and Power Division, Sensor and Electronics Directorate, U.S. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, Maryland 20783-1197, United States*E-mail: [email protected], [email protected]Cite this: Chem. Rev. 2014, 114, 23, 11503–11618Publication Date (Web):October 29, 2014Publication History Received2 January 2014Published online29 October 2014Published inissue 10 December 2014https://pubs.acs.org/doi/10.1021/cr500003whttps://doi.org/10.1021/cr500003wreview-articleACS PublicationsCopyright © This article not subject to U.S. Copyright. Published 2014 by the American Chemical SocietyRequest reuse permissionsArticle Views102735Altmetric-Citations3895LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Electrodes,Electrolytes,Salts,Solvents,Surface chemistry Get e-Alerts

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.

OBJECTIVE: Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional neural networks (CNNs), which have been used in computer vision and speech recognition to perform automatic feature extraction and classification, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible. APPROACH: In this work we introduce EEGNet, a compact convolutional neural network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI. We compare EEGNet, both for within-subject and cross-subject classification, to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR). MAIN RESULTS: We show that EEGNet generalizes across paradigms better than, and achieves comparably high performance to, the reference algorithms when only limited training data is available across all tested paradigms. In addition, we demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features. SIGNIFICANCE: Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks. Our models can be found at: https://github.com/vlawhern/arl-eegmodels.

Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfections in the training phase of deep neural networks make them vulnerable to adversarial samples: inputs crafted by adversaries with the intent of causing deep neural networks to misclassify. In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs. In an application to computer vision, we show that our algorithms can reliably produce samples correctly classified by human subjects but misclassified in specific targets by a DNN with a 97% adversarial success rate while only modifying on average 4.02% of the input features per sample. We then evaluate the vulnerability of different sample classes to adversarial perturbations by defining a hardness measure. Finally, we describe preliminary work outlining defenses against adversarial samples by defining a predictive measure of distance between a benign input and a target classification.

Procedures are given in the report for determining statistically whether the highest observation, or the lowest observation, or the highest and lowest observations, or the two highest observations, or the two lowest observations, or perhaps more of the observations in the sample may be considered to be outlying observations or discrepant values. Statistical tests of significance are useful in this connection either in the absence of assignable physical causes or to support a practical judgement that some of the experimental observations are aberrant. Both the statistical formulae and illustrative applications of the procedures to practical examples are given, thus representing a rather complete treatment of significance tests for outliers in single univariate samples.

A concentrated effort for battery safety Aqueous electrolytes are limited to run below 1.23 V to avoid degradation. Suo et al. smash through this limit with an aqueous salt solution containing lithium (Li) bis(trifluoromethane sulfonyl)imide to create an electrolyte that has an electrochemical window of 3 V (see the Perspective by Smith and Dunn). They used extremely high-concentration solutions, which suppressed hydrogen evolution and electrode oxidation. At these concentrations, the Li solvation shell changes because there simply is not enough water to neutralize the Li + charge. Thus, flammable organic electrolytes could potentially be replaced with a safer aqueous alternative. Science , this issue p. 938 ; see also p. 918

Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious content like malware identified as legitimate or controlling vehicle behavior. Yet, all existing adversarial example attacks require knowledge of either the model internals or its training data. We introduce the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge. Indeed, the only capability of our black-box adversary is to observe labels given by the DNN to chosen inputs. Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN. We use the local substitute to craft adversarial examples, and find that they are misclassified by the targeted DNN. To perform a real-world and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online deep learning API. We find that their DNN misclassifies 84.24% of the adversarial examples crafted with our substitute. We demonstrate the general applicability of our strategy to many ML techniques by conducting the same attack against models hosted by Amazon and Google, using logistic regression substitutes. They yield adversarial examples misclassified by Amazon and Google at rates of 96.19% and 88.94%. We also find that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder.

Compounding the confusion is the use of the broad term cognitive radio as a synonym for dynamic spectrum access. As an initial attempt at unifying the terminology, the taxonomy of dynamic spectrum access is provided. In this article, an overview of challenges and recent developments in both technological and regulatory aspects of opportunistic spectrum access (OSA). The three basic components of OSA are discussed. Spectrum opportunity identification is crucial to OSA in order to achieve nonintrusive communication. The basic functions of the opportunity identification module are identified

Lithium metal is an ideal battery anode. However, dendrite growth and limited Coulombic efficiency during cycling have prevented its practical application in rechargeable batteries. Herein, we report that the use of highly concentrated electrolytes composed of ether solvents and the lithium bis(fluorosulfonyl)imide salt enables the high-rate cycling of a lithium metal anode at high Coulombic efficiency (up to 99.1%) without dendrite growth. With 4 M lithium bis(fluorosulfonyl)imide in 1,2-dimethoxyethane as the electrolyte, a lithium|lithium cell can be cycled at 10 mA cm(-2) for more than 6,000 cycles, and a copper|lithium cell can be cycled at 4 mA cm(-2) for more than 1,000 cycles with an average Coulombic efficiency of 98.4%. These excellent performances can be attributed to the increased solvent coordination and increased availability of lithium ion concentration in the electrolyte. Further development of this electrolyte may enable practical applications for lithium metal anode in rechargeable batteries.

The evolution of a single hairpin vortex-like structure in the mean turbulent field of a low-Reynolds-number channel flow is studied by direct numerical simulation. The structure of the initial three-dimensional vortex is extracted from the two-point spatial correlation of the velocity field by linear stochastic estimation given a second-quadrant ejection event vector. Initial vortices having vorticity that is weak relative to the mean vorticity evolve gradually into omega-shaped vortices that persist for long times and decay slowly. As reported in Zhou, Adrian & Balachandar (1996), initial vortices that exceed a threshold strength relative to the mean flow generate new hairpin vortices upstream of the primary vortex. The detailed mechanisms for this upstream process are determined, and they are generally similar to the mechanisms proposed by Smith et al . (1991), with some notable differences in the details. It has also been found that new hairpins generate downstream of the primary hairpin, thereby forming, together with the upstream hairpins, a coherent packet of hairpins that propagate coherently. This is consistent with the experimental observations of Meinhart & Adrian (1995). The possibility of autogeneration above a critical threshold implies that hairpin vortices in fully turbulent fields may occur singly, but they more often occur in packets. The hairpins also generate quasi-streamwise vortices to the side of the primary hairpin legs. This mechanism bears many similarities to the mechanisms found by Brooke & Hanratty (1993) and Bernard, Thomas & Handler (1993). It provides a means by which new quasi-streamwise vortices, and, subsequently, new hairpin vortices can populate the near-wall layer.

We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.

Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent-based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

Problems at the intersection of vision and language are of significant importance both as challenging research questions and for the rich set of applications they enable. However, inherent structure in our world and bias in our language tend to be a simpler signal for learning than visual modalities, resulting in models that ignore visual information, leading to an inflated sense of their capability. We propose to counter these language priors for the task of Visual Question Answering (VQA) and make vision (the V in VQA) matter! Specifically, we balance the popular VQA dataset (Antol et al., ICCV 2015) by collecting complementary images such that every question in our balanced dataset is associated with not just a single image, but rather a pair of similar images that result in two different answers to the question. Our dataset is by construction more balanced than the original VQA dataset and has approximately twice the number of image-question pairs. Our complete balanced dataset is publicly available at http://visualqa.org/ as part of the 2nd iteration of the Visual Question Answering Dataset and Challenge (VQA v2.0). We further benchmark a number of state-of-art VQA models on our balanced dataset. All models perform significantly worse on our balanced dataset, suggesting that these models have indeed learned to exploit language priors. This finding provides the first concrete empirical evidence for what seems to be a qualitative sense among practitioners. Finally, our data collection protocol for identifying complementary images enables us to develop a novel interpretable model, which in addition to providing an answer to the given (image, question) pair, also provides a counter-example based explanation. Specifically, it identifies an image that is similar to the original image, but it believes has a different answer to the same question. This can help in building trust for machines among their users.

This Review covers a sequence of key discoveries and technical achievements that eventually led to the birth of the lithium-ion battery. In doing so, it not only sheds light on the history with the advantage of contemporary hindsight but also provides insight and inspiration to aid in the ongoing quest for better batteries of the future. A detailed retrospective on ingenious designs, accidental discoveries, intentional breakthroughs, and deceiving misconceptions is given: from the discovery of the element lithium to its electrochemical synthesis; from intercalation host material development to the concept of dual-intercalation electrodes; and from the misunderstanding of intercalation behavior into graphite to the comprehension of interphases. The onerous demands of bringing all critical components (anode, cathode, electrolyte, solid-electrolyte interphases), each of which possess unique chemistries, into a sophisticated electrochemical device reveal that the challenge of interfacing these originally incongruent components often outweighs the individual merits and limits in their own properties. These important lessons are likely to remain true for the more aggressive battery chemistries of future generations, ranging from a revisited Li-metal anode, to conversion-reaction type chemistries such as Li/sulfur, Li/oxygen, and metal fluorides, and to bivalent cation intercalations.

Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

Repeated measures correlation (rmcorr) is a statistical technique for determining the common within-individual association for paired measures assessed on two or more occasions for multiple individuals. Simple regression/correlation is often applied to non-independent observations or aggregated data; this may produce biased, specious results due to violation of independence and/or differing patterns between-participants versus within-participants. Unlike simple regression/correlation, rmcorr does not violate the assumption of independence of observations. Also, rmcorr tends to have much greater statistical power because neither averaging nor aggregation is necessary for an intra-individual research question. Rmcorr estimates the common regression slope, the association shared among individuals. To make rmcorr accessible, we provide background information for its assumptions and equations, visualization, power, and tradeoffs with rmcorr compared to multilevel modeling. We introduce the R package (rmcorr) and demonstrate its use for inferential statistics and visualization with two example datasets. The examples are used to illustrate research questions at different levels of analysis, intra-individual, and inter-individual. Rmcorr is well-suited for research questions regarding the common linear association in paired repeated measures data. All results are fully reproducible.

OBJECTIVE: We evaluate and quantify the effects of human, robot, and environmental factors on perceived trust in human-robot interaction (HRI). BACKGROUND: To date, reviews of trust in HRI have been qualitative or descriptive. Our quantitative review provides a fundamental empirical foundation to advance both theory and practice. METHOD: Meta-analytic methods were applied to the available literature on trust and HRI. A total of 29 empirical studies were collected, of which 10 met the selection criteria for correlational analysis and 11 for experimental analysis. These studies provided 69 correlational and 47 experimental effect sizes. RESULTS: The overall correlational effect size for trust was r = +0.26,with an experimental effect size of d = +0.71. The effects of human, robot, and environmental characteristics were examined with an especial evaluation of the robot dimensions of performance and attribute-based factors. The robot performance and attributes were the largest contributors to the development of trust in HRI. Environmental factors played only a moderate role. CONCLUSION: Factors related to the robot itself, specifically, its performance, had the greatest current association with trust, and environmental factors were moderately associated. There was little evidence for effects of human-related factors. APPLICATION: The findings provide quantitative estimates of human, robot, and environmental factors influencing HRI trust. Specifically, the current summary provides effect size estimates that are useful in establishing design and training guidelines with reference to robot-related factors of HRI trust. Furthermore, results indicate that improper trust calibration may be mitigated by the manipulation of robot design. However, many future research needs are identified.

Two-dimensional (2D) materials, such as molybdenum disulfide (MoS(2)), have been shown to exhibit excellent electrical and optical properties. The semiconducting nature of MoS(2) allows it to overcome the shortcomings of zero-bandgap graphene, while still sharing many of graphene's advantages for electronic and optoelectronic applications. Discrete electronic and optoelectronic components, such as field-effect transistors, sensors, and photodetectors made from few-layer MoS(2) show promising performance as potential substitute of Si in conventional electronics and of organic and amorphous Si semiconductors in ubiquitous systems and display applications. An important next step is the fabrication of fully integrated multistage circuits and logic building blocks on MoS(2) to demonstrate its capability for complex digital logic and high-frequency ac applications. This paper demonstrates an inverter, a NAND gate, a static random access memory, and a five-stage ring oscillator based on a direct-coupled transistor logic technology. The circuits comprise between 2 to 12 transistors seamlessly integrated side-by-side on a single sheet of bilayer MoS(2). Both enhancement-mode and depletion-mode transistors were fabricated thanks to the use of gate metals with different work functions.

The hydrous ruthenium oxide has been formed by a sol‐gel process. The precursor was obtained by mixing aqueous solutions of and alkalis. The hydrous ruthenium oxide powder was obtained by annealing the precursor at low temperatures. The crystalline structure and the electrochemical properties of the powder have been studied as a function of the annealing temperature. At lower annealing temperatures the powder is in an amorphous phase with a high specific capacitance. Specific capacitance as high as 720 F/g was measured for the powder formed at 150°C. When the annealing temperature exceeded 175°C, the crystalline phase was formed, and the specific capacitance dropped rapidly. The surface area of the powder and the resistivity of the pellet made from these powders have also been studied. The specific surface area and the resistivity decreased as the annealing temperature increased. A capacitor was made with electrodes comprised of hydrous ruthenium oxide and electrolyte. The energy density of 96 J/g (or 26.7 Wh/kg), based on electrode material only, was measured for the cell using hydrous ruthenium oxide electrodes. It was also found that hydrous ruthenium oxide is stable in electrolyte.