Query: Minimally Supervised Number Normalization

Relevant passages:
Reducibility Among Combinatorial Problems <=SEP=> A large class of computational problems involve the determination of properties of graphs, digraphs, integers, arrays of integers, finite families of finite sets, boolean formulas and elements of other countable domains. Through simple encodings from such domains into the set of words over a finite alphabet these problems can be converted into language recognition problems, and we can inquire into their computational complexity. It is reasonable to consider such a problem satisfactorily solved when an algorithm for its solution is found which terminates within a number of steps bounded by a polynomial in the length of the input. Many problems with wide applicability – e.g., set cover, knapsack, hitting set, max cut, and satisfiability – lack a polynomial algorithm for solving them, but also lack a proof that no such polynomial algorithm exists. Hence, they remain “open problems.” This paper references the recent work, “On the Reducibility of Combinatorial Problems” [1]. BODY A large class of open problems are mutually convertible via poly-time reductions. Hence, either all can be solved in poly-time, or none can. REFERENCES [1] R. Karp. Reducibility Among Combinatorial Problems. In Complexity of Computer Computations, 1972. ∗With apologies to Professor Richard Karp. Volume X of Tiny Transactions on Computer Science This content is released under the Creative Commons Attribution-NonCommercial ShareAlike License. Permission to make digital or hard copies of all or part of this work is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. CC BY-NC-SA 3.0: http://creativecommons.org/licenses/by-nc-sa/3.0/.

Grapheme-to-phoneme conversion using Long Short-Term Memory recurrent neural networks <=SEP=> Grapheme-to-phoneme (G2P) models are key components in speech recognition and text-to-speech systems as they describe how words are pronounced. We propose a G2P model based on a Long Short-Term Memory (LSTM) recurrent neural network (RNN). In contrast to traditional joint-sequence based G2P approaches, LSTMs have the flexibility of taking into consideration the full context of graphemes and transform the problem from a series of grapheme-to-phoneme conversions to a word-to-pronunciation conversion. Training joint-sequence based G2P require explicit grapheme-to-phoneme alignments which are not straightforward since graphemes and phonemes don't correspond one-to-one. The LSTM based approach forgoes the need for such explicit alignments. We experiment with unidirectional LSTM (ULSTM) with different kinds of output delays and deep bidirectional LSTM (DBLSTM) with a connectionist temporal classification (CTC) layer. The DBLSTM-CTC model achieves a word error rate (WER) of 25.8% on the public CMU dataset for US English. Combining the DBLSTM-CTC model with a joint n-gram model results in a WER of 21.3%, which is a 9% relative improvement compared to the previous best WER of 23.4% from a hybrid system.

Text-To-Speech Synthesis <=SEP=> 

Long Short-Term Memory <=SEP=> Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

Speech recognition with deep recurrent neural networks <=SEP=> Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.

A comparative analysis of data center network architectures <=SEP=> Advances in data intensive computing and high performance computing facilitate rapid scaling of data center networks, resulting in a growing body of research exploring new network architectures that enhance scalability, cost effectiveness and performance. Understanding the tradeoffs between these different network architectures could not only help data center operators improve deployments, but also assist system designers to optimize applications running on top of them. In this paper, we present a comparative analysis of several well known data center network architectures using important metrics, and present our results on different network topologies. We show the tradeoffs between these topologies and present implications on practical data center implementations.

Tuning metaheuristics: A data mining based approach for particle swarm optimization <=SEP=> The paper is concerned with practices for tuning the parameters of metaheuristics. Settings such as, e.g., the cooling factor in simulated annealing, may greatly affect a metaheuristic’s efficiency as well as effectiveness in solving a given decision problem. However, procedures for organizing parameter calibration are scarce and commonly limited to particular metaheuristics. We argue that the parameter selection task can appropriately be addressed by means of a data mining based approach. In particular, a hybrid system is devised, which employs regression models to learn suitable parameter values from past moves of a metaheuristic in an online fashion. In order to identify a suitable regression method and, more generally, to demonstrate the feasibility of the proposed approach, a case study of particle swarm optimization is conducted. Empirical results suggest that characteristics of the decision problem as well as search history data indeed embody information that allows suitable parameter values to be determined, and that this type of information can successfully be extracted by means of nonlinear regression models. 2011 Elsevier Ltd. All rights reserved.

A Unified Approach to the Change of Resolution: Space and Gray-Level <=SEP=> Multiple resolution analysis of images is a current trend in computer vision. In most cases, only spatial resolution has been considered. However, image resolution has an additional aspect: gray level, or color, resolution. Color resolution has traditionally been considered in the area of computer graphics. By defining a suitable measure for the comparison of images, changes in resolution can be treated with the same tools as changes in color resolution. A gray tone image, for example, can be compared to a halftone image having only two colors (black and white), but of higher spatial resolution. An important application can be in pyramids, one of the most commonly used multiple (spatial) resolution schemes, where this approach provides a tool to change the color resolution as well. Increasing color resolution while reducing spatial resolution to retain more image details and prevent aliasing is an example of the possibility to find optimal combinations of resolution reduction, spatial and color, to best fit an application.

PaperVis: Literature Review Made Easy <=SEP=> Reviewing literatures for a certain research field is always important for academics. One could use Google-like information seeking tools, but oftentimes he/she would end up obtaining too many possibly related papers, as well as the papers in the associated citation network. During such a process, a user may easily get lost after following a few links for searching or cross-referencing. It is also difficult for the user to identify relevant/important papers from the resulting huge collection of papers. Our work, called PaperVis, endeavors to provide a user-friendly interface to help users quickly grasp the intrinsic complex citation-reference structures among a specific group of papers. We modify the existing Radial Space Filling (RSF) and Bullseye View techniques to arrange involved papers as a node-link graph that better depicts the relationships among them while saving the screen space at the same time. PaperVis applies visual cues to present node attributes and their transitions among interactions, and it categorizes papers into semantically meaningful hierarchies to facilitate ensuing literature exploration. We conduct experiments on the InfoVis 2004 Contest Dataset to demonstrate the effectiveness of PaperVis.

Front-End Factor Analysis for Speaker Verification <=SEP=> This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems are proposed which use this new representation. The first system is a support vector machine-based system that uses the cosine kernel to estimate the similarity between the input data. The second system directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate analysis (LDA), and nuisance attribute projection (NAP). We found that the best results are obtained when LDA is followed by WCCN. We achieved an equal error rate (EER) of 1.12% and MinDCF of 0.0094 using the cosine distance scoring on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation dataset. We also obtained 4% absolute EER improvement for both-gender trials on the 10 s-10 s condition compared to the classical joint factor analysis scoring.

PASCAL Boundaries: A Semantic Boundary Dataset with a Deep Semantic Boundary Detector <=SEP=> In this paper, we address the task of instance-level semantic boundary detection. To this end, we generate a large database consisting of more than 10k images (which is 20 bigger than existing edge detection databases) along with ground truth boundaries between 459 semantic classes including instances from both foreground objects and different types of background, and call it the PASCAL Boundaries dataset. This is a timely introduction of the dataset since results on the existing standard dataset for measuring edge detection performance, i.e. BSDS500, have started to saturate. In addition to generating a new dataset, we propose a deep network-based multi-scale semantic boundary detector and name it Multi-scale Deep Semantic Boundary Detector (M-DSBD). We evaluate M-DSBD on PASCAL boundaries and compare it to baselines. We test the transfer capabilities of our model by evaluating on MS-COCO and BSDS500 and show that our model transfers well.

Service-Oriented Architecture for Big Data Analytics in Smart Cities <=SEP=> A smart city has recently become an aspiration for many cities around the world. These cities are looking to apply the smart city concept to improve sustainability, quality of life for residents, and economic development. The smart city concept depends on employing a wide range of advanced technologies to improve the performance of various services and activities such as transportation, energy, healthcare, and education, while at the same time improve the city's resources utilization and initiate new business opportunities. One of the promising technologies to support such efforts is the big data technology. Effective and intelligent use of big data accumulated over time in various sectors can offer many advantages to enhance decision making in smart cities. In this paper we identify the different types of decision making processes involved in smart cities. Then we propose a service-oriented architecture to support big data analytics for decision making in smart cities. This architecture allows for integrating different technologies such as fog and cloud computing to support different types of analytics and decision-making operations needed to effectively utilize available big data. It provides different functions and capabilities to use big data and provide smart capabilities as services that the architecture supports. As a result, different big data applications will be able to access and use these services for varying proposes within the smart city.

Automatic C-to-CUDA Code Generation for Affine Programs <=SEP=> Graphics Processing Units (GPUs) offer tremendous computational power. CUDA (Compute Unified Device Architecture) provides a multi-threaded parallel programming model, facilitating high performance implementations of general-purpose computations. However, the explicitly managed memory hierarchy and multi-level parallel view make manual development of high-performance CUDA code rather complicated. Hence the automatic transformation of sequential input programs into efficient parallel CUDA programs is of considerable interest. This paper describes an automatic code transformation system that generates parallel CUDA code from input sequential C code, for regular (affine) programs. Using and adapting publicly available tools that have made polyhedral compiler optimization practically effective, we develop a C-to-CUDA transformation system that generates two-level parallel CUDA code that is optimized for efficient data access. The performance of automatically generated code is compared with manually optimized CUDA code for a number of benchmarks. The performance of the automatically generated CUDA code is quite close to hand-optimized CUDA code and considerably better than the benchmarks’ performance on a multicore CPU.

An Experimental Evaluation of Apple Siri and Google Speech Recognition <=SEP=> We perform an experimental evaluation of two popular cloud-based speech recognition systems. Cloudbased speech recognition systems enhances Web surfing, transportation, health care, etc. Using voice commands helps drivers stay connected to the Internet by avoiding traffic safety risks. The performance of these type of applications should be robust under difficult network conditions. User frustration with network traffic problems can affect the usability of these applications. We evaluate the performance of two popular cloud-based speech recognition applications, Apple Siri and Google Speech Recognition (GSR) under various network conditions. We evaluate transcription delay and accuracy of transcription of each application under different packet loss and jitter values. Results of our study show that performance of cloud-based speech recognition systems can be affected by jitter and packet loss; which are commonly occurring over WiFi and cellular network connections. keywords: Cloud Speech Recognition, Quality of Experience, Software Measurement, Streaming Media, Real-time Systems.

Interactive evolution for the procedural generation of tracks in a high-end racing game <=SEP=> We present a framework for the procedural generation of tracks for a high-end car racing game (TORCS) using interactive evolution. The framework maintains multiple populations and allow users to work both on their own population (in single-user mode) or to collaborate with other users on a shared population. Our architecture comprises a web frontend and an evolutionary backend. The former manages the interaction with users (e.g., logs registered and anonymous users, collects evaluations, provides access to all the evolved populations) and maintains the database server that stores all the present/past populations. The latter runs all the tasks related to evolution (selection, recombination and mutation) and all the tasks related to the target racing game (e.g., the track generation). We performed two sets of experiments involving five human subjects to evolve racing tracks alone (in a single-user mode) or cooperatively. Our preliminary results on five human subjects show that, in all the experiments, there is an increase of users' satisfaction as the evolution proceeds. Users stated that they perceived improvements in the quality of the individuals between subsequent populations and that, at the end, the process produced interesting tracks.

Bandwidth-Enhanced Low-Profile Cavity-Backed Slot Antenna by Using Hybrid SIW Cavity Modes <=SEP=> A bandwidth enhanced method of a low-profile substrate integrated waveguide (SIW) cavity-backed slot antenna is presented in this paper. Bandwidth enhancement is achieved by simultaneously exciting two hybrid modes in the SIW-backed cavity and merging them within the required frequency range. These two hybrid modes, whose dominant fields are located in different half parts of the SIW cavity, are two different combinations of the and resonances. This design method has been validated by experiments. Compared with those of a previously presented SIW cavity-backed slot antenna, fractional impedance bandwidth of the proposed antenna is enhanced from 1.4% to 6.3%, its gain and radiation efficiency are also slightly improved to 6.0 dBi and 90%, and its SIW cavity size is reduced about 30%. The proposed antenna exhibits low cross polarization level and high front to back ratio. It still retains advantages of low-profile, low fabrication cost, and easy integration with planar circuits.

Usefulness of altmetrics for measuring the broader impact of research: A case study using data from PLOS and F1000Prime <=SEP=> Purpose: The present case study investigates the usefulness of altmetrics for measuring the broad impact of research. Methods: This case study is based on a sample of 1,082 PLOS (the Public Library of Science) journal articles recommended in F1000. The dataset includes altmetrics which were provided by PLOS. The F1000 dataset contains tags on papers which were assigned by experts to characterise them. Findings: Results from the Facebook and Twitter models show higher predicted numbers of counts for "good for teaching" papers than for those papers where the tag is not set. Further model estimations show that saves by Mendeley users are particularly to be expected when a paper introduces a new practical/ theoretical technique (tag: "technical advance"). The tag "New finding" is statistically significant in the model with which the Facebook counts are evaluated. Conclusions: The "good for teaching" is assigned to papers which could be of interest to a wider circle of readers than the peers in a specialized area. Thus, the results of the current study indicate that Facebook and Twitter, but not Figshare or Mendeley, might provide an indication of which papers are of interest to a broader circle of readers (and not only for the peers in a specialist area), and could therefore be useful for the measurement of the social impact of research.

Ontology semantic approach to extraction of knowledge from holy quran <=SEP=> With the continued demand for Islamic knowledge, which is mainly based on the Quran as a source of knowledge and wisdom, systems that facilitate an easy search of the content of the Quran remain a considerable challenge. Although in recent years there have been tools for Quran search, most of these tools are based on keyword search, meaning that the user needs to know the correct keywords before being able to retrieve the content of al-Quran. In this paper, we propose a system that supports the end user in querying and exploring the Quran ontology. The system comprises user query reformulation against the Quran ontology stored and annotated in the knowledge base. The Quran ontology consists of noun concepts identified in al-Quran, and the relationship that exists between these concepts. The user writes a query in the natural language and the proposed system reformulates the query to match the content found in the knowledge base in order to retrieve the relevant answer. The answer is represented by the Quranic verse related to the user query.

A survey on text mining techniques <=SEP=> text mining is a technique to find meaningful patterns from the available text documents. The pattern discovery from the text and document organization of document is a well-known problem in data mining. Analysis of text content and categorization of the documents is a complex task of data mining. In order to find an efficient and effective technique for text categorization, various techniques of text categorization and classification is recently developed. Some of them are supervised and some of them unsupervised manner of document arrangement. This presented paper discusses different method of text categorization and cluster analysis for text documents. In addition of that a new text mining technique is proposed for future implementation. Keywords— text mining, classification, cluster analysis, survey

Bullying among young adolescents: the strong, the weak, and the troubled. <=SEP=> OBJECTIVES
Bullying and being bullied have been recognized as health problems for children because of their association with adjustment problems, including poor mental health and more extreme violent behavior. It is therefore important to understand how bullying and being bullied affect the well-being and adaptive functioning of youth. We sought to use multiple data sources to better understand the psychological and social problems exhibited by bullies, victims, and bully-victims.


DESIGN, SETTING, AND PARTICIPANTS
Analysis of data from a community sample of 1985 mostly Latino and black 6th graders from 11 schools in predominantly low socioeconomic status urban communities (with a 79% response rate).


MAIN OUTCOME MEASURES
Peer reports of who bullies and who is victimized, self-reports of psychological distress, and peer and teacher reports of a range of adjustment problems.


RESULTS
Twenty-two percent of the sample was classified as involved in bullying as perpetrators (7%), victims (9%), or both (6%). Compared with other students, these groups displayed school problems and difficulties getting along with classmates. Despite increased conduct problems, bullies were psychologically strongest and enjoyed high social standing among their classmates. In contrast, victims were emotionally distressed and socially marginalized among their classmates. Bully-victims were the most troubled group, displaying the highest level of conduct, school, and peer relationship problems.


CONCLUSIONS
To be able to intervene with bullying, it is important to recognize the unique problems of bullies, victims, and bully-victims. In addition to addressing these issues directly with their patients, pediatricians can recommend school-wide antibullying approaches that aim to change peer dynamics that support and maintain bullying.

The MagicBook - Moving Seamlessly between Reality and Virtuality <=SEP=> or more than a decade researchers have tried to create intuitive computer interfaces by blending reality and virtual reality. The goal is for people to interact with the digital domain as easily as with the real world. Various approaches help us achieve this—in the area of tangible interfaces, we use real objects as interface widgets; in augmented reality, researchers overlay 3D virtual imagery onto the real world; and in VR interfaces , we entirely replace the real world with a computer generated environment. As Milgram pointed out, 1 these types of computer interfaces can be placed along a continuum according to how much of the user's environment is computer generated (Figure 1). Tangible interfaces lie far to the left on this reality–virtuality line, while immersive virtual environments are at the right extremity. Most current user interfaces exist as discrete points along this continuum. However, human activity can't always be broken into discrete components and for many tasks users may prefer to move seamlessly along the reality–virtuality continuum. This proves true when interacting with 3D graphical content, either creating virtual models or viewing them. For example, if people want to experience a virtual scene from different scales, then immer-sive virtual reality may be ideal. If they want to have a face-to-face discussion while viewing the virtual scene, an augmented reality interface may be best. 2 The MagicBook project is an early attempt to explore how we can use a physical object to smoothly transport users between reality and virtuality. Young children often fantasize about flying into the pages of a fairy tale and becoming part of the story. The MagicBook project makes this fantasy a reality using a normal book as the main interface object. People can turn the pages of the book, look at the pictures, and read the text without any additional technology (Figure 2a). However, if a person looks at the pages through an augmented reality display, they see 3D virtual models appearing out of the pages (Figure 2b). The models appear attached to the real page so users can see the augmented reality scene from any perspective by moving themselves or the book. The virtual content can be any size and is animated, so the augmented reality view is an enhanced version of a traditional 3D pop-up book. Users can change the virtual models by turning the book pages. When they see a scene they particularly like, …

Three stage 6–18 GHz high gain and high power amplifier based on GaN technology <=SEP=> A monolithic three stage HPA has been developed for wide band applications. This MMIC is fabricated on UMS 0.25 µm GaN technology based on SiC substrate. At 18GHz, the MMIC achieved in CW mode 10W of output power with 20dB linear gain and 20% power added efficiency. The HPA provided 6 to 10W output power over 6 to 18GHz with minimum small signal gain of 18dB. These obtained performances are very promising and very close to the simulations; this will allow a very short term further improvement. This demonstration is the first MMIC on the UMS 0.25µm GaN technology.

Emotion regulation for frustrating driving contexts <=SEP=> Driving is a challenging task because of the physical, attentional, and emotional demands. When drivers become frustrated by events their negative emotional state can escalate dangerously. This study examines behavioral and attitudinal effects of cognitively reframing frustrating events. Participants (N = 36) were asked to navigate a challenging driving course that included frustrating events such as long lights and being cut-off. Drivers were randomly assigned to three conditions. After encountering a frustrating event, drivers in a reappraisal-down condition heard voice prompts that reappraised the event in an effort to deflate negative reactions. Drivers in the second group, reappraisal-up, heard voice prompts that brought attention to the negative actions of vehicles and pedestrians. Drivers in a silent condition drove without hearing any voice prompts. Participants in the reappraisal-down condition had better driving behavior and reported less negative emotions than participants in the other conditions.

Facial Age Group Classification <=SEP=> Estimating human age group automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, It is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human age group. The aging process is determined by not only the person’s gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. An age group classification system for facial images is proposed in this paper. Five age groups including babies, children, young adults, middle-aged adults, and old adults, are used in the classification system. The process of the system is divided into threephases: location, feature extraction, and age classification. Geometric features are used to distinguish whether the face is baby or child. Wrinkle features are used to classify the image into one of three adult groupsyoung adults, middle-aged adults, and old adults.

Reflections on craft: probing the creative process of everyday knitters <=SEP=> Crafters today blend age-old techniques such as weaving and pottery with new information and communication technologies such as podcasts, online instructions, and blogs. This intersection of tradition and modernity provides an interesting site for understanding the adoption of new technology. We present a qualitative study of seven knitters introduced to Spyn - a system that enables the association of digitally recorded messages with physical locations on knit fabric. We gave knitters Spyn in order to elicit their reflections on their craft practices and learn from their interactions with material, people, and technology. While creating artifacts for friends and loved ones, knitters expanded the creative and communicative potential of their craftwork: knitters envisioned travel journals in knitted potholders and sung lullabies in knitted hats. We describe how these unusual craft activities provide a useful lens onto contemporary technological appropriation.

W-net: Bridged U-net for 2D Medical Image Segmentation <=SEP=> In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to train when convolutional layers increase even though a deeper network usually has a better generalization ability because of more learnable parameters. Secondly, the exponential ReLU (ELU), as an alternative of ReLU, is not much different from ReLU when the network of interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss functions for medical image segmentation, is not effective when the prediction is close to ground truth and will cause oscillation during training. To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size. Meanwhile, we propose a new loss function to accelerate the learning process and a combination of different activation functions to improve the network performance. Our experimental results suggest that our network is comparable or superior to state-of-the-art methods.

Multimodal imaging of optic disc drusen. <=SEP=> PURPOSE
To evaluate optic disc drusen, extracellular protein deposits known to contain numerous aggregates of mitochondria, using multimodal modalities featuring optical coherence tomography (OCT) and autofluorescence imaging.


DESIGN
Retrospective observational case series.


METHODS
Eyes with optic nerve drusen were examined with enhanced depth imaging (EDI)-OCT, swept source OCT, and fundus autofluorescence using a fundus camera.


RESULTS
Twenty-six eyes of 15 patients with optic disc drusen were evaluated. EDI-OCT and swept source OCT showed multiple optic disc drusen at different levels; most were located immediately anterior to the lamina cribrosa. The drusen were ovoid regions of lower reflectivity that were bordered by hyperreflective material, and in 12 eyes (46.2%) there were internal hyperreflective foci. The mean diameter of the optic disc drusen as measured in OCT images was 686.8 (standard deviation ± 395.2) μm. There was a significant negative correlation between the diameter of the optic disc drusen and the global retinal nerve fiber layer thickness (r = -0.61, P = .001). There was a significant negative correlation between proportion of the optic disc drusen area occupied by optic nerve drusen as detected by autofluorescence imaging and the global retinal nerve fiber layer thickness (r = -0.63, P = .001).


CONCLUSIONS
Deeper-penetration OCT imaging demonstrated the internal characteristics of optic disc drusen and their relationship with the lamina cribrosa in vivo. This study also showed that both the larger the drusen and the more area of the optic canal occupied by drusen, the greater the associated retinal nerve fiber layer abnormalities.

A Compact Fractal Loop Rectenna for RF Energy Harvesting <=SEP=> This letter presents a compact fractal loop rectenna for RF energy harvesting at GSM1800 bands. First, a fractal loop antenna with novel in-loop ground-plane impedance matching is proposed for the rectenna design. Also, a high-efficiency rectifier is designed in the loop antenna to form a compact rectenna. Measured results show that an efficiency of 61% and an output dc voltage of 1.8 V have been achieved over 12-kΩ resistor for 10 μW/cm2 power density at 1.8 GHz. The rectenna is able to power up a battery-less LCD watch at a distance of 10 m from the cell tower. The proposed rectenna is compact, easy to fabricate, and useful for various energy harvesting applications.

Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds <=SEP=> We describe a method to use discrete human feedback to enhance the performance of deep learning agents in virtual three-dimensional environments by extending deep-reinforcement learning to model the confidence and consistency of human feedback. This enables deep reinforcement learning algorithms to determine the most appropriate time to listen to the human feedback, exploit the current policy model, or explore the agent’s environment. Managing the trade-off between these three strategies allows DRL agents to be robust to inconsistent or intermittent human feedback. Through experimentation using a synthetic oracle, we show that our technique improves the training speed and overall performance of deep reinforcement learning in navigating three-dimensional environments using Minecraft. We further show that our technique is robust to highly innacurate human feedback and can also operate when no human feedback is given.

Making sense of implementation theories, models and frameworks <=SEP=> BACKGROUND
Implementation science has progressed towards increased use of theoretical approaches to provide better understanding and explanation of how and why implementation succeeds or fails. The aim of this article is to propose a taxonomy that distinguishes between different categories of theories, models and frameworks in implementation science, to facilitate appropriate selection and application of relevant approaches in implementation research and practice and to foster cross-disciplinary dialogue among implementation researchers.


DISCUSSION
Theoretical approaches used in implementation science have three overarching aims: describing and/or guiding the process of translating research into practice (process models); understanding and/or explaining what influences implementation outcomes (determinant frameworks, classic theories, implementation theories); and evaluating implementation (evaluation frameworks). This article proposes five categories of theoretical approaches to achieve three overarching aims. These categories are not always recognized as separate types of approaches in the literature. While there is overlap between some of the theories, models and frameworks, awareness of the differences is important to facilitate the selection of relevant approaches. Most determinant frameworks provide limited "how-to" support for carrying out implementation endeavours since the determinants usually are too generic to provide sufficient detail for guiding an implementation process. And while the relevance of addressing barriers and enablers to translating research into practice is mentioned in many process models, these models do not identify or systematically structure specific determinants associated with implementation success. Furthermore, process models recognize a temporal sequence of implementation endeavours, whereas determinant frameworks do not explicitly take a process perspective of implementation.