Query: Strain Gauges Based on CVD Graphene Layers and Exfoliated Graphene Nanoplatelets with Enhanced Reproducibility and Scalability for Large Quantities

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Scalable production of large quantities of defect-free few-layer graphene by shear exfoliation in liquids. <=SEP=> To progress from the laboratory to commercial applications, it will be necessary to develop industrially scalable methods to produce large quantities of defect-free graphene. Here we show that high-shear mixing of graphite in suitable stabilizing liquids results in large-scale exfoliation to give dispersions of graphene nanosheets. X-ray photoelectron spectroscopy and Raman spectroscopy show the exfoliated flakes to be unoxidized and free of basal-plane defects. We have developed a simple model that shows exfoliation to occur once the local shear rate exceeds 10(4) s(-1). By fully characterizing the scaling behaviour of the graphene production rate, we show that exfoliation can be achieved in liquid volumes from hundreds of millilitres up to hundreds of litres and beyond. The graphene produced by this method performs well in applications from composites to conductive coatings. This method can be applied to exfoliate BN, MoS2 and a range of other layered crystals.

Large-area synthesis of high-quality and uniform graphene films on copper foils. <=SEP=> Graphene has been attracting great interest because of its distinctive band structure and physical properties. Today, graphene is limited to small sizes because it is produced mostly by exfoliating graphite. We grew large-area graphene films of the order of centimeters on copper substrates by chemical vapor deposition using methane. The films are predominantly single-layer graphene, with a small percentage (less than 5%) of the area having few layers, and are continuous across copper surface steps and grain boundaries. The low solubility of carbon in copper appears to help make this growth process self-limiting. We also developed graphene film transfer processes to arbitrary substrates, and dual-gated field-effect transistors fabricated on silicon/silicon dioxide substrates showed electron mobilities as high as 4050 square centimeters per volt per second at room temperature.

Measurement of the elastic properties and intrinsic strength of monolayer graphene. <=SEP=> We measured the elastic properties and intrinsic breaking strength of free-standing monolayer graphene membranes by nanoindentation in an atomic force microscope. The force-displacement behavior is interpreted within a framework of nonlinear elastic stress-strain response, and yields second- and third-order elastic stiffnesses of 340 newtons per meter (N m(-1)) and -690 Nm(-1), respectively. The breaking strength is 42 N m(-1) and represents the intrinsic strength of a defect-free sheet. These quantities correspond to a Young's modulus of E = 1.0 terapascals, third-order elastic stiffness of D = -2.0 terapascals, and intrinsic strength of sigma(int) = 130 gigapascals for bulk graphite. These experiments establish graphene as the strongest material ever measured, and show that atomically perfect nanoscale materials can be mechanically tested to deformations well beyond the linear regime.

Raman spectrum of graphene and graphene layers. <=SEP=> Graphene is the two-dimensional building block for carbon allotropes of every other dimensionality. We show that its electronic structure is captured in its Raman spectrum that clearly evolves with the number of layers. The D peak second order changes in shape, width, and position for an increasing number of layers, reflecting the change in the electron bands via a double resonant Raman process. The G peak slightly down-shifts. This allows unambiguous, high-throughput, nondestructive identification of graphene layers, which is critically lacking in this emerging research area.

Synthesis of graphene-based nanosheets via chemical reduction of exfoliated graphite oxide <=SEP=> Reduction of a colloidal suspension of exfoliated graphene oxide sheets in water with hydrazine hydrate results in their aggregation and subsequent formation of a high-surface-area carbon material which consists of thin graphene-based sheets. The reduced material was characterized by elemental analysis, thermo-gravimetric analysis, scanning electron microscopy, X-ray photoelectron spectroscopy, NMR spectroscopy, Raman spectroscopy, and by electrical conductivity measurements. 2007 Elsevier Ltd. All rights reserved.

Microphone Array Processing for Distant Speech Recognition: From Close-Talking Microphones to Far-Field Sensors <=SEP=> Distant speech recognition (DSR) holds the promise of the most natural human computer interface because it enables man-machine interactions through speech, without the necessity of donning intrusive body- or head-mounted microphones. Recognizing distant speech robustly, however, remains a challenge. This contribution provides a tutorial overview of DSR systems based on microphone arrays. In particular, we present recent work on acoustic beam forming for DSR, along with experimental results verifying the effectiveness of the various algorithms described here; beginning from a word error rate (WER) of 14.3% with a single microphone of a linear array, our state-of-the-art DSR system achieved a WER of 5.3%, which was comparable to that of 4.2% obtained with a lapel microphone. Moreover, we present an emerging technology in the area of far-field audio and speech processing based on spherical microphone arrays. Performance comparisons of spherical and linear arrays reveal that a spherical array with a diameter of 8.4 cm can provide recognition accuracy comparable or better than that obtained with a large linear array with an aperture length of 126 cm.

The Impact on Family Functioning of Social Media Use by Depressed Adolescents: A Qualitative Analysis of the Family Options Study <=SEP=> BACKGROUND
Adolescent depression is a prevalent mental health problem, which can have a major impact on family cohesion. In such circumstances, excessive use of the Internet by adolescents may exacerbate family conflict and lack of cohesion. The current study aims to explore these patterns within an intervention study for depressed adolescents.


METHOD
The current study draws upon data collected from parents within the family options randomized controlled trial that examined family based interventions for adolescent depression (12-18 years old) in Melbourne, Australia (2012-2014). Inclusion in the trial required adolescents to meet diagnostic criteria for a major depressive disorder via the Structured Clinical Interview for DSM-IV Childhood Disorders. The transcripts of sessions were examined using qualitative thematic analysis. The transcribed sessions consisted of 56 h of recordings in total from 39 parents who took part in the interventions.


RESULTS
The thematic analysis explored parental perceptions of their adolescent's use of social media (SM) and access to Internet content, focusing on the possible relationship between adolescent Internet use and the adolescent's depressive disorder. Two overarching themes emerged as follows: the sense of loss of parental control over the family environment and parents' perceived inability to protect their adolescent from material encountered on the Internet and social interactions via SM.


CONCLUSION
Parents within the context of family based treatments felt that prolonged exposure to SM exposed their already vulnerable child to additional stressors and risks. The thematic analysis uncovered a sense of parental despair and lack of control, which is consistent with their perception of SM and the Internet as relentless and threatening to their parental authority and family cohesion.

A Type-Based Compiler for Standard ML <=SEP=> Compile-time type information should be valuable in efficient compilation of statically typed functional languages such as Standard ML. But how should type-directed compilation work in real compilers, and how much performance gain will type-based optimizations yield? In order to support more efficient data representations and gain more experience about type-directed compilation, we have implemented a new type-based middle end and back end for the Standard ML of New Jersey compiler. We describe the basic design of the new compiler, identify a number of practical issues, and then compare the performance of our new compiler with the old non-type-based compiler. Our measurement shows that a combination of several simple type-based optimizations reduces heap allocation by 36%; and improves the already-efficient code generated by the old non-type-based compiler by about 19% on a DECstation 500.

Using Neural Networks to Generate Inferential Roles for Natural Language <=SEP=> Neural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be able to do in order to provide an account of how the meanings of complex linguistic expressions, such as sentences, are understood. We argue that one thing such models need to be able to do is generate predictions about which further sentences are likely to follow from a given sentence; these define the sentence's "inferential role." We then show that it is possible to train a tree-structured neural network model to generate very simple examples of such inferential roles using the recently released Stanford Natural Language Inference (SNLI) dataset. On an empirical front, we evaluate the performance of this model by reporting entailment prediction accuracies on a set of test sentences not present in the training data. We also report the results of a simple study that compares human plausibility ratings for both human-generated and model-generated entailments for a random selection of sentences in this test set. On a more theoretical front, we argue in favor of a revision to some common assumptions about semantics: understanding a linguistic expression is not only a matter of mapping it onto a representation that somehow constitutes its meaning; rather, understanding a linguistic expression is mainly a matter of being able to draw certain inferences. Inference should accordingly be at the core of any model of semantic cognition.

Architecture for the Internet of Things (IoT): API and Interconnect <=SEP=> The proposed architecture is structured into a secure API, a backbone, and separate device networks with standard interface to the backbone. The API decouples innovation of services and service logic from protocols and network elements. It also enables service portability between systems, i.e. a service may be allocated to end-systems or servers, with possible relocation and replication throughout its lifecycle. Ubiquitous service provisioning depends on interoperability, not only for provisioning of a standard QoS controlled IP bearer, but also for cross domain naming, security, mobility, multicast, location, routing and management, including fair compensation for utility provisioning. The proposed architecture not only includes these critical elements but also caters for multi-homing, mobile networks with dynamic membership, and third party persistent storage based on indirection.

Double Thompson Sampling for Dueling Bandits <=SEP=> In this paper, we propose a Double Thompson Sampling (D-TS) algorithm for dueling bandit problems. As its name suggests, D-TS selects both the first and the second candidates according to Thompson Sampling. Specifically, D-TS maintains a posterior distribution for the preference matrix, and chooses the pair of arms for comparison according to two sets of samples independently drawn from the posterior distribution. This simple algorithm applies to general Copeland dueling bandits, including Condorcet dueling bandits as a special case. For general Copeland dueling bandits, we show that D-TS achieves O(K log T ) regret. Moreover, using a back substitution argument, we refine the regret to O(K log T +K log log T ) in Condorcet dueling bandits and most practical Copeland dueling bandits. In addition, we propose an enhancement of D-TS, referred to as D-TS, to reduce the regret in practice by carefully breaking ties. Experiments based on both synthetic and real-world data demonstrate that D-TS and D-TS significantly improve the overall performance, in terms of regret and robustness.

Bio fl oc technology in aquaculture : Bene fi cial effects and future challenges <=SEP=> 

A Proposal for a Set of Level 3 Basic Linear Algebra Subprograms <=SEP=> This paper describes a proposal for Level 3 Basic Linear Algebra Subprograms (Level 3 BLAS). The Level 3 BLAS are targeted at matrix-matrix operations with the aim of providing more efficient, but portable, implementations of algorithms on high-performance computers, especially those with hierarchical memory and parallel processing capability.

Accurate vertical road profile estimation using v-disparity map and dynamic programming <=SEP=> Detecting obstacles on the road is crucial for the advanced driver assistance systems. Obstacle detection on the road can be greatly facilitated if we have a vertical road profile. Therefore, in this paper, we present a novel method that can estimate an accurate vertical road profile of the scene from the stereo images. Unlike conventional stereo-based road profile estimation methods that heavily rely on a parametric model of the road surface, our method can obtain a road profile for an arbitrary complicated road. To this end, an energy function that includes the stereo matching fidelity and spatio-temporal smoothness of the road profile is presented, and thus the road profile is extracted by maximizing the energy function via dynamic programming. The experimental results demonstrate the effectiveness of the proposed method.

Position control of Robotino mobile robot using fuzzy logic <=SEP=> The appearance of fuzzy logic theory led to new trends in robotics field and solves various typical problems. In fact, fuzzy logic is a good alternative for increasing the capabilities of autonomous mobile robots in an unknown dynamic environment by integrating human experience. The paper presents the design and implementation of the position control using fuzzy logic for an omni directional mobile robot. The position control of the mobile robot was tested remotely using the Festo Robotino platform and Matlab environment. The obstacle avoidance and mobile robot odometry were also studied in this work.

Phase III clinical trial of thalidomide plus dexamethasone compared with dexamethasone alone in newly diagnosed multiple myeloma: a clinical trial coordinated by the Eastern Cooperative Oncology Group. <=SEP=> PURPOSE
To determine if thalidomide plus dexamethasone yields superior response rates compared with dexamethasone alone as induction therapy for newly diagnosed multiple myeloma.


PATIENTS AND METHODS
Patients were randomly assigned to receive thalidomide plus dexamethasone or dexamethasone alone. Patients in arm A received thalidomide 200 mg orally for 4 weeks; dexamethasone was administered at a dose of 40 mg orally on days 1 to 4, 9 to 12, and 17 to 20. Cycles were repeated every 4 weeks. Patients in arm B received dexamethasone alone at the same schedule as in arm A.


RESULTS
Two hundred seven patients were enrolled: 103 were randomly assigned to thalidomide plus dexamethasone and 104 were randomly assigned to dexamethasone alone; eight patients were ineligible. The response rate with thalidomide plus dexamethasone was significantly higher than with dexamethasone alone (63% v 41%, respectively; P = .0017). The response rate allowing for use of serum monoclonal protein levels when a measurable urine monoclonal protein was unavailable at follow-up was 72% v 50%, respectively. The incidence rates of grade 3 or higher deep vein thrombosis (DVT), rash, bradycardia, neuropathy, and any grade 4 to 5 toxicity in the first 4 months were significantly higher with thalidomide plus dexamethasone compared with dexamethasone alone (45% v 21%, respectively; P < .001). DVT was more frequent in arm A than in arm B (17% v 3%); grade 3 or higher peripheral neuropathy was also more frequent (7% v 4%, respectively).


CONCLUSION
Thalidomide plus dexamethasone demonstrates significantly superior response rates in newly diagnosed myeloma compared with dexamethasone alone. However, this must be balanced against the greater toxicity seen with the combination.

The impact of visual attributes on online image diffusion <=SEP=> Little is known on how visual content affects the popularity on social networks, despite images being now ubiquitous on the Web, and currently accounting for a considerable fraction of all content shared. Existing art on image sharing focuses mainly on non-visual attributes. In this work we take a complementary approach, and investigate resharing from a mainly visual perspective. Two sets of visual features are proposed, encoding both aesthetical properties (brightness, contrast, sharpness, etc.), and semantical content (concepts represented by the images). We collected data from a large image-sharing service (Pinterest) and evaluated the predictive power of different features on popularity (number of reshares). We found that visual properties have low predictive power compared that of social cues. However, after factoring-out social influence, visual features show considerable predictive power, especially for images with higher exposure, with over 3:1 accuracy odds when classifying highly exposed images between very popular and unpopular.

Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation <=SEP=> For more than one decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search focuses on finding shape-based similarity. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the “bag of words” approach that is widely accepted by the text mining and information retrieval communities. We show that our approach outperforms the existing methods in clustering, classification, and anomaly detection on several real datasets.

A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality <=SEP=> Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.

An Evaluation of Techniques for Grabbing and Manipulating Remote Objects in Immersive Virtual Environments <=SEP=> Grabbing and manipulating virtual objects is an important user interaction for immersive virtual environments. We present implementations and discussion of six techniques which allow manipulation of remote objects. A user study of these techniques was performed which revealed their characteristics and deficiencies, and led to the development of a new class of techniques. These hybrid techniques provide istinct advantages in terms of ease of use and efficiency because they consider the tasks of grabbing and manipulation separately. CR Categories and Subject Descr iptors: I.3.7 [Computer Graphics]:Three-Dimensional Graphics and Realism Virtual Reality; I.3.6 [Computer Graphics]:Methodology and Techniques Interaction Techniques.

Maladaptive and adaptive emotion regulation through music: a behavioral and neuroimaging study of males and females <=SEP=> Music therapists use guided affect regulation in the treatment of mood disorders. However, self-directed uses of music in affect regulation are not fully understood. Some uses of music may have negative effects on mental health, as can non-music regulation strategies, such as rumination. Psychological testing and functional magnetic resonance imaging (fMRI) were used explore music listening strategies in relation to mental health. Participants (n = 123) were assessed for depression, anxiety and Neuroticism, and uses of Music in Mood Regulation (MMR). Neural responses to music were measured in the medial prefrontal cortex (mPFC) in a subset of participants (n = 56). Discharge, using music to express negative emotions, related to increased anxiety and Neuroticism in all participants and particularly in males. Males high in Discharge showed decreased activity of mPFC during music listening compared with those using less Discharge. Females high in Diversion, using music to distract from negative emotions, showed more mPFC activity than females using less Diversion. These results suggest that the use of Discharge strategy can be associated with maladaptive patterns of emotional regulation, and may even have long-term negative effects on mental health. This finding has real-world applications in psychotherapy and particularly in clinical music therapy.

Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey <=SEP=> The vulnerability of face recognition systems to presentation attacks (also known as direct attacks or spoof attacks) has received a great deal of interest from the biometric community. The rapid evolution of face recognition systems into real-time applications has raised new concerns about their ability to resist presentation attacks, particularly in unattended application scenarios such as automated border control. The goal of a presentation attack is to subvert the face recognition system by presenting a facial biometric artifact. Popular face biometric artifacts include a printed photo, the electronic display of a facial photo, replaying video using an electronic display, and 3D face masks. These have demonstrated a high security risk for state-of-the-art face recognition systems. However, several presentation attack detection (PAD) algorithms (also known as countermeasures or antispoofing methods) have been proposed that can automatically detect and mitigate such targeted attacks. The goal of this survey is to present a systematic overview of the existing work on face presentation attack detection that has been carried out. This paper describes the various aspects of face presentation attacks, including different types of face artifacts, state-of-the-art PAD algorithms and an overview of the respective research labs working in this domain, vulnerability assessments and performance evaluation metrics, the outcomes of competitions, the availability of public databases for benchmarking new PAD algorithms in a reproducible manner, and finally a summary of the relevant international standardization in this field. Furthermore, we discuss the open challenges and future work that need to be addressed in this evolving field of biometrics.

Deep Fusion Net for Multi-atlas Segmentation: Application to Cardiac MR Images <=SEP=> Atlas selection and label fusion are two major challenges in multi-atlas segmentation. In this paper, we propose a novel deep fusion net for better solving these challenges. Deep fusion net is a deep architecture by concatenating a feature extraction subnet and a non-local patchbased label fusion (NL-PLF) subnet in a single network. This network is trained end-to-end for automatically learning deep features achieving optimal performance in a NL-PLF framework. The learned deep features are further utilized in defining a similarity measure for atlas selection. Experimental results on Cardiac MR images for left ventricular segmentation demonstrate that our approach is effective both in atlas selection and multi-atlas label fusion, and achieves state of the art in performance.

A New Distance Measure for Face Recognition System <=SEP=> This paper proposes a new powerful distance measure called Normalized Unmatched Points (NUP). This measure can be used in a face recognition system to discriminate facial images. It works by counting the number of unmatched pixels between query and database images. A face recognition system has been proposed which makes use of this proposed distance measure for taking the decision on matching. This system has been tested on four publicly available databases, viz. ORL, YALE, BERN and CALTECH databases. Experimental results show that the proposed measure achieves recognition rates more than 98.66% for the first five likely matched faces. It is observed that the NUP distance measure performs better than other existing similar variants on these databases.

A near-optimal algorithm for computing the entropy of a stream <=SEP=> We describe a simple algorithm for approximating the empirical entropy of a stream of <i>m</i> values in a single pass, using <i>O</i>(ε<sup>-2</sup> log(Δ<sup>-1</sup>) log <i>m</i>) words of space. Our algorithm is based upon a novel extension of a method introduced by Alon, Matias, and Szegedy [1]. We show a space lower bound of Ω(ε<sup>-2</sup> / log(ε<sup>-1</sup>)), meaning that our algorithm is near-optimal in terms of its dependency on ε. This improves over previous work on this problem [8, 13, 17, 5]. We show that generalizing to <i>k</i>th order entropy requires close to linear space for all <i>k</i> ≥ 1, and give additive approximations using our algorithm. Lastly, we show how to compute a multiplicative approximation to the entropy of a random walk on an undirected graph.

Fusing Iris, Palmprint and Fingerprint in a Multi-biometric Recognition System <=SEP=> This paper presents a trimodal biometric recognition system based on iris, palmprint and fingerprint. Wavelet transform and Gabor filter are used to extract features in different scales and orientations from iris and palmprint. Minutiae extraction and alignment is used for fingerprint matching. Six different fusion algorithms including score-based, rank-based and decision-based methods are used to combine the results of three modules. We also propose a new rank-based fusion algorithm Maximum Inverse Rank (MIR) which is robust with respect to variations in scores and also bad ranking from a module. CASIA datasets for iris, palmprint and fingerprint are used in this study. The experiments show the effectiveness of our fusion method and our trimodal biometric recognition system in comparison to existing multi-modal recognition systems.

Dermoscopy in differentiating palmar syphiloderm from palmar papular psoriasis. <=SEP=> Palmar syphiloderm is one of the most common presentations of secondary syphilis and its recognition is of utmost importance in order to promptly identify such a disease and initiate appropriate workup/management. However, the differential diagnosis with palmar papular psoriasis often poses some difficulties, with consequent possible diagnostic errors/delays and prescription of improper therapies. In this report, we underline the role of dermoscopy as a supportive tool to facilitate the non-invasive recognition of palmar syphiloderm and its distinction from palmar papular psoriasis.

Semantically Guided Depth Upsampling <=SEP=> We present a novel method for accurate and efficient upsampling of sparse depth data, guided by high-resolution imagery. Our approach goes beyond the use of intensity cues only and additionally exploits object boundary cues through structured edge detection and semantic scene labeling for guidance. Both cues are combined within a geodesic distance measure that allows for boundary-preserving depth interpolation while utilizing local context. We model the observed scene structure by locally planar elements and formulate the upsampling task as a global energy minimization problem. Our method determines globally consistent solutions and preserves fine details and sharp depth boundaries. In our experiments on several public datasets at different levels of application, we demonstrate superior performance of our approach over the state-of-the-art, even for very sparse measurements.

A master-slave blockchain paradigm and application in digital rights management <=SEP=> Upon flaws of current blockchain platforms of heavyweight, large capacity of ledger, and time-consuming of synchronization of data, in this paper, we proposed a new paradigm of master-slave blockchain scheme (MSB) for pervasive computing that suitable for general PC, mobile device such as smart phones or PADs to participants in the working of mining and verification, in which we separated traditional blockchain model in 2 layer defined as master node layer and a series of slavery agents layer, then we proposed 2 approaches for partially computing model(P-CM) and non-computing of model(NCM) in the MSB blockchain, Finally large amounts of simulations manifest the proposed master-slave blockchain scheme is feasible, extendible and suitable for pervasive computing especially in the 5G generation environment, and can apply in the DRM-related applications.

Analysis of GDI Technique for Digital Circuit Design <=SEP=> Power Dissipation of Digital circuits can be reduced by 15% 25% by using appropriate logic restructuring and also it can be reduced by 40% 60% by lowering switching activity. Here, Gate Diffusion Input Technique which is based on a Shannon expansion is analyzed for minimizing the power consumption and delay of static digital circuits. This technique as compare to other currently used logic design style, allows less power consumption and reduced propagation delay for low-power design of combinatorial digital circuits with minimum number of transistors. In this paper, basic building blocks of digital system and few combinational circuits are analyzed using GDI and other CMOS techniques. All circuits are designed at 180nm technology in CADENCE and simulate using VIRTUOSO SPECTRE simulator at 100 MHz frequency. Comparative analysis has been done among GDI and other parallel design styles for designing ripple adder, CLA adder and bit magnitude comparator. Simulation result shows GDI technique saves 53. 3%, 55. 6% and 75. 6% power in ripple adder, CLA adder and bit magnitude comparator respectively as compare to CMOS. Also delay is reduced with 25. 2%, 3. 4% and 6. 9% as compare to CMOS. Analysis conclude that GDI is revolutionary high speed and low power consumption technique.