Query: Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors

Relevant passages:
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.

Adam: A Method for Stochastic Optimization <=SEP=> We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

Deep Residual Learning for Image Recognition <=SEP=> Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Learning a Deep Convolutional Network for Image Super-Resolution <=SEP=> We propose a deep learning method for single image superresolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the lowresolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.

A Public Domain Dataset for Human Activity Recognition using Smartphones <=SEP=> Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context information about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repository. Results, obtained on the dataset by exploiting a multiclass Support Vector Machine (SVM), are also acknowledged.

On deep learning-based channel decoding <=SEP=> We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes. Although it is possible to achieve maximum a posteriori (MAP) bit error rate (BER) performance for both code families and for short codeword lengths, we observe that (i) structured codes are easier to learn and (ii) the neural network is able to generalize to codewords that it has never seen during training for structured, but not for random codes. These results provide some evidence that neural networks can learn a form of decoding algorithm, rather than only a simple classifier. We introduce the metric normalized validation error (NVE) in order to further investigate the potential and limitations of deep learning-based decoding with respect to performance and complexity.

Automated plant Watering system <=SEP=> In daily operations related to farming or gardening Watering is the most important cultural practice and the most labor-intensive task. No matter whichever weather it is, either too hot and dry or too cloudy and wet, you want to be able to control the amount of water that reaches your plants. Modern watering systems could be effectively used to water plants when they need it. But this manual process of watering requires two important aspects to be considered: when and how much to water. In order to replace manual activities and making gardener's work easier, we have create automatic plant watering system. By adding automated plant watering system to your garden or agricultural field, you will help all of your plants reach their fullest potential as well as conserving water. Using sprinklers drip emitters, or a combination of both, we can design a system that is ideal for every plant in our yard. For implementation of automatic plant watering system, we have used combination of sprinkler systems, pipes, and nozzles. This project uses the ATmega328 microcontroller. It is programmed to sense moisture level of plants at particular instance of time, if the moisture content is less than specified threshold which is predefined according to particular plant's water need then desired amount of water is supplied till it reaches threshold. Generally, plants need to be watered twice a day, morning and evening. Thus, the microcontroller is programmed to water plants two times per day. System is designed in such a way that it reports its current state as well as remind the user to add water to the tank. All this notifications are made through mobile application. We hope that through this prototype we all can enjoy having plants, without being worried about absent or forgetfulness.

Energy-Efficient I/O Thread Schedulers for NVMe SSDs on NUMA <=SEP=> Non-volatile memory express (NVMe) based SSDs and the NUMA platform are widely adopted in servers to achieve faster storage speed and more powerful processing capability. As of now, very little research has been conducted to investigate the performance and energy efficiency of the state-of-the-art NUMA architecture integrated with NVMe SSDs, an emerging technology used to host parallel I/O threads. As this technology continues to be widely developed and adopted, we need to understand the runtime behaviors of such systems in order to design software runtime systems that deliver optimal performance while consuming only the necessary amount of energy. This paper characterizes the runtime behaviors of a Linux-based NUMA system employing multiple NVMe SSDs. Our comprehensive performance and energy-efficiency study using massive numbers of parallel I/O threads shows that the penalty due to CPU contention is much smaller than that due to remote access of NVMe SSDs. Based on this insight, we develop a dynamic "lesser evil" algorithm called ESN, to minimize the impact of these two types of penalties. ESN is an energy-efficient profiling-based I/O thread scheduler for managing I/O threads accessing NVMe SSDs on NUMA systems. Our empirical evaluation shows that ESN can achieve optimal I/O throughput and latency while consuming up to 50% less energy and using fewer CPUs.

Cascade Mask Generation Framework for Fast Small Object Detection <=SEP=> Detecting small objects is a challenging task. Existing CNN-based objection detection pipeline faces such a dilemma: using a high-resolution image as input incurs high computational cost, but using a low-resolution image as input loses the feature representation of small objects and therefore leads to low accuracy. In this work, we propose a cascade mask generation framework to tackle this issue. The proposed framework takes in multi-scale images as input and processes them in ascending order of the scale. Each processing stage outputs object proposals as well as a region-of-interest (RoI) mask for the next stage. With RoI convolution, the masked regions can be excluded from the computation in the next stage. The procedure continues until the largest scale image is processed. Finally, the object proposals generated from multiple scales are classified by a post classifier. Extensive experiments on Tsinghua-Tencent 100K traffic sign benchmark demonstrate that our approach achieves state-of-the-art small object detection performance at a significantly improved speed-accuracy tradeoff compared with previous methods.

Broadband Bent Triangular Omnidirectional Antenna for RF Energy Harvesting <=SEP=> In this letter, a broadband bent triangular omnidirectional antenna is presented for RF energy harvesting. The antenna has a bandwidth for VSWR ≤ 2 from 850 MHz to 1.94 GHz. The antenna is designed to receive both horizontal and vertical polarized waves and has a stable radiation pattern over the entire bandwidth. Antenna has also been optimized for energy harvesting application and it is designed for 100 Ω input impedance to provide a passive voltage amplification and impedance matching to the rectifier. A peak efficiency of 60% and 17% is obtained for a load of 500 Ω at 980 and 1800 MHz, respectively. At a cell site while harvesting all bands simultaneously a voltage of 3.76 V for open circuit and 1.38 V across a load of 4.3 k Ω is obtained at a distance of 25 m using an array of two elements of the rectenna.

Graph Indexing: Tree + Delta >= Graph <=SEP=> Recent scientific and technological advances have witnessed an abundance of structural patterns modeled as graphs. As a result, it is of special interest to process graph containment queries effectively on large graph databases. Given a graph database G, and a query graph q, the graph containment query is to retrieve all graphs in G which contain q as subgraph(s). Due to the vast number of graphs in G and the nature of complexity for subgraph isomorphism testing, it is desirable to make use of high-quality graph indexing mechanisms to reduce the overall query processing cost. In this paper, we propose a new cost-effective graph indexing method based on frequent tree-features of the graph database. We analyze the effectiveness and efficiency of tree as indexing feature from three critical aspects: feature size, feature selection cost, and pruning power. In order to achieve better pruning ability than existing graph-based indexing methods, we select, in addition to frequent tree-features (Tree), a small number of discriminative graphs (∆) on demand, without a costly graph mining process beforehand. Our study verifies that (Tree+∆) is a better choice than graph for indexing purpose, denoted (Tree+∆ ≥Graph), to address the graph containment query problem. It has two implications: (1) the index construction by (Tree+∆) is efficient, and (2) the graph containment query processing by (Tree+∆) is efficient. Our experimental studies demonstrate that (Tree+∆) has a compact index structure, achieves an order of magnitude better performance in index construction, and most importantly, outperforms up-to-date graphbased indexing methods: gIndex and C-Tree, in graph containment query processing.

Deep learning based human action recognition: A survey <=SEP=> Human action recognition has attracted much attentions because of its great potential applications. With the rapid development of computer performance and Internet, the methods of human action recognition based on deep learning become mainstream and develop at a breathless pace. This paper will give a novel reasonable taxonomy and a review of deep learning human action recognition methods based on color videos, skeleton sequences and depth maps. In addition, some datasets and effective tricks in action recognition deep learning methods will be introduced, and also the development trend is discussed.

A compact dual-band (L1/L2) GPS antenna design <=SEP=> A small slot-loaded patch antenna design developed for receiving both L1 and L2 bands GPS signals is discussed. The dual band coverage is achieved by using a patch mode at L2 band and a slot mode at L1 band. High dielectric material and meandered slot line are employed to reduce the antenna size down to 25.4 mm in diameter. The RHCP is achieved by combining two orthogonal modes via a small 0°-90° hybrid chip. Both patch and slot modes share a single proximity probe conveniently located on the side of the antenna (Fig.1). This paper discusses about the design procedure as well as simulated antenna performance.

PROBABILISTIC NEURAL NETWORK <=SEP=> This paper reports results of artificial neural network for robot navigation tasks. Machine learning methods have proven usability in many complex problems concerning mobile robots control. In particular we deal with the well-known strategy of navigating by “wall-following”. In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks. The PNN result was compared with the results of the Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman neural networks and the results of the previous studies reported focusing on robot navigation tasks and using same dataset. It was observed the PNN is the best classification accuracy with 99,635% accuracy using same dataset.

Trends and Challenges in CMOS Design for Emerging 60 GHz WPAN Applications <=SEP=> The extensive growth of wireless communications industry is creating a big market opportunity. Wireless operators are currently searching for new solutions which would be implemented into the existing wireless communication networks to provide the broader bandwidth, the better quality and new value-added services. In the last decade, most commercial efforts were focused on the 1-10 GHz spectrum for voice and data applications for mobile phones and portable computers (Niknejad & Hashemi, 2008). Nowadays, the interest is growing in applications that use high rate wireless communications. Multigigabit-per-second communication requires a very large bandwidth. The Ultra-Wide Band (UWB) technology was basically used for this issue. However, this technology has some shortcomings including problems with interference and a limited data rate. Furthermore, the 3–5 GHz spectrum is relatively crowded with many interferers appearing in the WiFi bands (Niknejad & Hashemi, 2008). The use of millimeter wave frequency band is considered the most promising technology for broadband wireless. In 2001, the Federal Communications Commission (FCC) released a set of rules governing the use of spectrum between 57 and 66 GHz (Baldwin, 2007). Hence, a large bandwidth coupled with high allowable transmit power equals high possible data rates. Traditionally the implementation of 60 GHz radio technology required expensive technologies based on III-V compound semiconductors such as InP and GaAs (Smulders et al., 2007). The rapid progress of CMOS technology has enabled its application in millimeter wave applications. Currently, the transistors became small enough, consequently fast enough. As a result, the CMOS technology has become one of the most attractive choices in implementing 60 GHz radio due to its low cost and high level of integration (Doan et al., 2005). Despite the advantages of CMOS technology, the design of 60 GHz CMOS transceiver exhibits several challenges and difficulties that the designers must overcome. This chapter aims to explore the potential of the 60 GHz band in the use for emergent generation multi-gigabit wireless applications. The chapter presents a quick overview of the state-of-the-art of 60 GHz radio technology and its potentials to provide for high data rate and short range wireless communications. The chapter is organized as follows. Section 2 presents an overview about 60 GHz band. The advantages are presented to highlight the performance characteristics of this band. The opportunities of the physical layer of the IEEE

Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system <=SEP=> Advances in bio-telemetry technology have made it possible to automatically monitor and classify behavioural activities in many animals, including domesticated species such as dairy cows. Automated behavioural classification has the potential to improve health and welfare monitoring processes as part of a Precision Livestock Farming approach. Recent studies have used accelerometers and pedometers to classify behavioural activities in dairy cows, but such approaches often cannot discriminate accurately between biologically important behaviours such as feeding, lying and standing or transition events between lying and standing. In this study we develop a decision-tree algorithm that uses tri-axial accelerometer data from a neck-mounted sensor to both classify biologically important behaviour in dairy cows and to detect transition events between lying and standing. Data were collected from six dairy cows that were monitored continuously for 36 h. Direct visual observations of each cow were used to validate the algorithm. Results show that the decision-tree algorithm is able to accurately classify three types of biologically relevant behaviours: lying (77.42 % sensitivity, 98.63 % precision), standing (88.00 % sensitivity, 55.00 % precision), and feeding (98.78 % sensitivity, 93.10 % precision). Transitions between standing and lying were also detected accurately with an average sensitivity of 96.45 % and an average precision of 87.50 %. The sensitivity and precision of the decision-tree algorithm matches the performance of more computationally intensive algorithms such as hidden Markov models and support vector machines. Biologically important behavioural activities in housed dairy cows can be classified accurately using a simple decision-tree algorithm applied to data collected from a neck-mounted tri-axial accelerometer. The algorithm could form part of a real-time behavioural monitoring system in order to automatically detect dairy cow health and welfare status.

Different models for model matching: An analysis of approaches to support model differencing <=SEP=> Calculating differences between models is an important and challenging task in Model Driven Engineering. Model differencing involves a number of steps starting with identifying matching model elements, calculating and representing their differences, and finally visualizing them in an appropriate way. In this paper, we provide an overview of the fundamental steps involved in the model differencing process and summarize the advantages and shortcomings of existing approaches for identifying matching model elements. To assist potential users in selecting one of the existing methods for the problem at stake, we investigate the trade-offs these methods impose in terms of accuracy and effort required to implement each one of them.

Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour <=SEP=> Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial.

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science <=SEP=> As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning--pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.

Brain basis of early parent-infant interactions: psychology, physiology, and in vivo functional neuroimaging studies. <=SEP=> Parenting behavior critically shapes human infants' current and future behavior. The parent-infant relationship provides infants with their first social experiences, forming templates of what they can expect from others and how to best meet others' expectations. In this review, we focus on the neurobiology of parenting behavior, including our own functional magnetic resonance imaging (fMRI) brain imaging experiments of parents. We begin with a discussion of background, perspectives and caveats for considering the neurobiology of parent-infant relationships. Then, we discuss aspects of the psychology of parenting that are significantly motivating some of the more basic neuroscience research. Following that, we discuss some of the neurohormones that are important for the regulation of social bonding, and the dysregulation of parenting with cocaine abuse. Then, we review the brain circuitry underlying parenting, proceeding from relevant rodent and nonhuman primate research to human work. Finally, we focus on a study-by-study review of functional neuroimaging studies in humans. Taken together, this research suggests that networks of highly conserved hypothalamic-midbrain-limbic-paralimbic-cortical circuits act in concert to support aspects of parent response to infants, including the emotion, attention, motivation, empathy, decision-making and other thinking that are required to navigate the complexities of parenting. Specifically, infant stimuli activate basal forebrain regions, which regulate brain circuits that handle specific nurturing and caregiving responses and activate the brain's more general circuitry for handling emotions, motivation, attention, and empathy--all of which are crucial for effective parenting. We argue that an integrated understanding of the brain basis of parenting has profound implications for mental health.

Learning to Fuse Things and Stuff <=SEP=> We propose an end-to-end learning approach for panoptic segmentation, a novel task unifying instance (things) and semantic (stuff) segmentation. Our model, TASCNet, uses feature maps from a shared backbone network to predict in a single feed-forward pass both things and stuff segmentations. We explicitly constrain these two output distributions through a global things and stuff binary mask to enforce cross-task consistency. Our proposed unified network is competitive with the state of the art on several benchmarks for panoptic segmentation as well as on the individual semantic and instance segmentation tasks.

How to get mature global virtual teams: a framework to improve team process management in distributed software teams <=SEP=> Managing global software development teams is not an easy task because of the additional problems and complexities that have to be taken into account. This paper defines VTManager, a methodology that provides a set of efficient practices for global virtual team management in software development projects. These practices integrate software development techniques in global environments with others such as explicit practices for global virtual team management, definition of skills and abilities needed to work in these teams, availability of collaborative work environments and shared knowledge management practices. The results obtained and the lessons learned from implementing VTManager in a pilot project to develop software tools for collaborative work in rural environments are also presented. This project was carried out by geographically distributed teams involving people from seven countries with a high level of virtualness.

An iterative maximum-likelihood polychromatic algorithm for CT <=SEP=> A new iterative maximum-likelihood reconstruction algorithm for X-ray computed tomography is presented. The algorithm prevents beam hardening artifacts by incorporating a polychromatic acquisition model. The continuous spectrum of the X-ray tube is modeled as a number of discrete energies. The energy dependence of the attenuation is taken into account by decomposing the linear attenuation coefficient into a photoelectric component and a Compton scatter component. The relative weight of these components is constrained based on prior material assumptions. Excellent results are obtained for simulations and for phantom measurements. Beam-hardening artifacts are effectively eliminated. The relation with existing algorithms is discussed. The results confirm that improving the acquisition model assumed by the reconstruction algorithm results in reduced artifacts. Preliminary results indicate that metal artifact reduction is a very promising application for this new algorithm.

Optimal Charging in Wireless Rechargeable Sensor Networks <=SEP=> Recent years have witnessed several new promising technologies to power wireless sensor networks, which motivate some key topics to be revisited. By integrating sensing and computation capabilities to the traditional radio-frequency identification (RFID) tags, the Wireless Identification and Sensing Platform (WISP) is an open-source platform acting as a pioneering experimental platform of wireless rechargeable sensor networks. Different from traditional tags, an RFID-based wireless rechargeable sensor node needs to charge its onboard energy storage above a threshold to power its sensing, computation, and communication components. Consequently, such charging delay imposes a unique design challenge for deploying wireless rechargeable sensor networks. In this paper, we tackle this problem by planning the optimal movement strategy of the mobile RFID reader, such that the time to charge all nodes in the network above their energy threshold is minimized. We first propose an optimal solution using the linear programming (LP) method. To further reduce the computational complexity, we then introduce a heuristic solution with a provable approximation ratio of (1 + θ)/(1 - ε) by discretizing the charging power on a 2-D space. Through extensive evaluations, we demonstrate that our design outperforms the set-cover-based design by an average of 24.7%, whereas the computational complexity is O((N/ε)2). Finally, we consider two practical issues in system implementation and provide guidelines for parameter setting.

CPW to waveguide transition with tapered slotline probe <=SEP=> A new CPW to waveguide transition is developed based on the concept of tapered slot antenna and E-plane probe coupling. The transition consists of a tapered slotline probe and a slotline to CPW matching section. The current design has the advantages of broad bandwidth, compact size, low fabrication cost, and high reliability. The characteristics of a prototype transition are investigated by numerical simulation on Duroid substrate and WR-90 waveguide. The back-to-back combination is measured to verify the agreement with the simulated results and realization of this design.

A novel secure hash algorithm for public key digital signature schemes <=SEP=> Hash functions are the most widespread among all cryptographic primitives, and are currently used in multiple cryptographic schemes and in security protocols. This paper presents a new Secure Hash Algorithm called (SHA-192). It uses a famous secure hash algorithm given by the National Institute of Standard and Technology (NIST).The basic design of SHA192 is to have the output length of 192.The SHA-192 has been designed to satisfy the different level of enhanced security and to resist the advanced SHA attacks. The security analysis of the SHA-192 is compared to the old one given by NIST and gives more security and excellent results as shown in our discussion. In this paper the digital signature algorithm which is given by NIST has been modified using the proposed algorithms SHA-192. Using proposed SHA-192 hash algorithm a new digital signature schemes is also proposed. The SHA-192 can be used in many applications such s public key cryptosystem, digital signcryption, message authentication code, random generator and in security architecture of upcoming wireless devices like software defined radio etc.

Ethical Challenges in Data-Driven Dialogue Systems <=SEP=> The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.

Speed-Security Tradeo s in Blockchain Protocols <=SEP=> Transaction processing speed is one of the major considerations in cryptocurrencies that are based on proof of work (POW) such as Bitcoin. At an intuitive level it is widely understood that processing speed is at odds with the security aspects of the underlying POW based consensus mechanism of such protocols, nevertheless the tradeo between the two properties is still not well understood. In this work, motivated by recent work [9] in the formal analysis of the Bitcoin backbone protocol, we investigate the tradeo between provable security and transaction processing speed viewing the latter as a function of the block generation rate. We introduce a new formal property of blockchain protocols, called chain growth, and we show it is fundamental for arguing the security of a robust transaction ledger. We strengthen the results of [9] showing for the rst time that reasonable security bounds hold even for the faster (than Bitcoin's) block generation rates that have been adopted by several major alt-coins (including Litecoin, Dogecoin etc.). We then provide a rst formal security proof of the GHOST rule for blockchain protocols. The GHOST rule was put forth in [14] as a mechanism to improve transaction processing speed and a variant of the rule is adopted by Ethereum. Our security analysis of the GHOST backbone matches our new analysis for Bitcoin in terms of the common pre x property but falls short in terms of chain growth where we provide an attack that substantially reduces the chain speed compared to Bitcoin. While our results establish the GHOST variant as a provably secure alternative to standard Bitcoin-like transaction ledgers they also highlight potential shortcomings in terms of processing speed compared to Bitcoin. We nally present attacks and simulation results against blockchain protocols (both for Bitcoin and GHOST) that present natural upper barriers for the speed-security tradeo . By combining our positive and negative results we map the speed/security domain for blockchain protocols and list open problems for future work.

Classification of teeth in cone-beam CT using deep convolutional neural network <=SEP=> Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification.

Harvesting rib cartilage grafts for secondary rhinoplasty. <=SEP=> BACKGROUND
Reconstruction of the nasal osseocartilaginous framework is the foundation of successful secondary rhinoplasty.


METHODS
Achieving this often requires large quantities of cartilage to correct both contour deformities and functional problems caused by previous procedures. Satisfactory and consistent long-term results rely on using grafts with low resorption rates and sufficient strength to offer adequate support. Auricular cartilage, irradiated cartilage, and alloplastic materials have all been used as implantable grafts with limited success.


RESULTS
In the senior author's experience (J.P.G.), rib cartilage has proven to be a reliable, abundant, and relatively accessible donor with which to facilitate successful secondary rhinoplasty surgery.


CONCLUSIONS
: The authors describe in detail the techniques that they have found to be integral in harvesting rib cartilage grafts for secondary rhinoplasty.