Query: A Review of Machine Learning Applied to Medical Time Series

Relevant passages:
A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data <=SEP=> The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC).

A Critical Review of Recurrent Neural Networks for Sequence Learning <=SEP=> Countless learning tasks require dealing with sequential data. Image captioning, speech synthesis, and music generation all require that a model produce outputs that are sequences. In other domains, such as time series prediction, video analysis, and musical information retrieval, a model must learn from inputs that are sequences. Interactive tasks, such as translating natural language, engaging in dialogue, and controlling a robot, often demand both capabilities. Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes. Unlike standard feedforward neural networks, recurrent networks retain a state that can represent information from an arbitrarily long context window. Although recurrent neural networks have traditionally been difficult to train, and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled successful large-scale learning with them. In recent years, systems based on long short-term memory (LSTM) and bidirectional (BRNN) architectures have demonstrated ground-breaking performance on tasks as varied as image captioning, language translation, and handwriting recognition. In this survey, we review and synthesize the research that over the past three decades first yielded and then made practical these powerful learning models. When appropriate, we reconcile conflicting notation and nomenclature. Our goal is to provide a selfcontained explication of the state of the art together with a historical perspective and references to primary research.

Machine learning - a probabilistic perspective <=SEP=> “Kevin Murphy excels at unraveling the complexities of machine learning methods while motivating the reader with a stream of illustrated examples and real-world case studies. The accompanying software package includes source code for many of the figures, making it both easy and very tempting to dive in and explore these methods for yourself. A must-buy for anyone interested in machine learning or curious about how to extract useful knowledge from big data.” John Winn, Microsoft Research

Machine Learning for Sequential Data: A Review <=SEP=> Statistical learning problems in many fields involve sequential data. This paper formalizes the principal learning tasks and describes the methods that have been developed within the machine learning research community for addressing these problems. These methods include sliding window methods, recurrent sliding windows, hidden Markov models, conditional random fields, and graph transformer networks. The paper also discusses some open research issues.

Lung cancer cell identification based on artificial neural network ensembles <=SEP=> An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.

Cover your ACKs: pitfalls of covert channel censorship circumvention <=SEP=> In response to increasingly sophisticated methods of blocking access to censorship circumvention schemes such as Tor, recently proposed systems such as Skypemorph, FreeWave, and CensorSpoofer have used voice and video conferencing protocols as "cover channels" to hide proxy connections. We demonstrate that even with perfect emulation of the cover channel, these systems can be vulnerable to attacks that detect or disrupt the covert communications while having no effect on legitimate cover traffic. Our attacks stem from differences in the channel requirements for the cover protocols, which are peer-to-peer and loss tolerant, and the covert traffic, which is client-proxy and loss intolerant. These differences represent significant limitations and suggest that such protocols are a poor choice of cover channel for general censorship circumvention schemes.

Multiple View Geometry in Computer Vision <=SEP=> 

Towards Online, Accurate, and Scalable QoS Prediction for Runtime Service Adaptation <=SEP=> Service-based cloud applications are typically built on component services to fulfill certain application logic. To meet quality-of-service (QoS) guarantees, these applications have to become resilient against the QoS variations of their component services. Runtime service adaptation has been recognized as a key solution to achieve this goal. To make timely and accurate adaptation decisions, effective QoS prediction is desired to obtain the QoS values of component services. However, current research has focused mostly on QoS prediction of the working services that are being used by a cloud application, but little on QoS prediction of candidate services that are also important for making adaptation decisions. To bridge this gap, in this paper, we propose a novel QoS prediction approach, namely adaptive matrix factorization (AMF), which is inspired from the collaborative filtering model used in recommender systems. Specifically, our AMF approach extends conventional matrix factorization into an online, accurate, and scalable model by employing techniques of data transformation, online learning, and adaptive weights. Comprehensive experiments have been conducted based on a real-world large-scale QoS dataset of Web services to evaluate our approach. The evaluation results provide good demonstration for our approach in achieving accuracy, efficiency, and scalability.

Aromas of rosemary and lavender essential oils differentially affect cognition and mood in healthy adults. <=SEP=> This study was designed to assess the olfactory impact of the essential oils of lavender (Lavandula angustifolia) and rosemary (Rosmarlnus officinalis) on cognitive performance and mood in healthy volunteers. One hundred and forty-four participants were randomly assigned to one of three independent groups, and subsequently performed the Cognitive Drug Research (CDR) computerized cognitive assessment battery in a cubicle containing either one of the two odors or no odor (control). Visual analogue mood questionnaires were completed prior to exposure to the odor, and subsequently after completion of the test battery. The participants were deceived as to the genuine aim of the study until the completion of testing to prevent expectancy effects from possibly influencing the data. The outcome variables from the nine tasks that constitute the CDR core battery feed into six factors that represent different aspects of cognitive functioning. Analysis of performance revealed that lavender produced a significant decrement in performance of working memory, and impaired reaction times for both memory and attention based tasks compared to controls. In contrast, rosemary produced a significant enhancement of performance for overall quality of memory and secondary memory factors, but also produced an impairment of speed of memory compared to controls. With regard to mood, comparisons of the change in ratings from baseline to post-test revealed that following the completion of the cognitive assessment battery, both the control and lavender groups were significantly less alert than the rosemary condition; however, the control group was significantly less content than both rosemary and lavender conditions. These findings indicate that the olfactory properties of these essential oils can produce objective effects on cognitive performance, as well as subjective effects on mood.

Sentiment analysis of a document using deep learning approach and decision trees <=SEP=> The given paper describes modern approach to the task of sentiment analysis of movie reviews by using deep learning recurrent neural networks and decision trees. These methods are based on statistical models, which are in a nutshell of machine learning algorithms. The fertile area of research is the application of Google's algorithm Word2Vec presented by Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean in 2013. The main idea of Word2Vec is the representations of words with the help of vectors in such manner that semantic relationships between words preserved as basic linear algebra operations. The extra advantage of the mentioned algorithm above the alternatives is computational efficiency. This paper focuses on using Word2Vec model for text classification by their sentiment type.

A Hardware Design Language for Efficient Control of Timing Channels <=SEP=> Information security can be compromised by leakage via low-level hardware features. One recently prominent example is cache probing attacks, which rely on timing channels created by caches. We introduce a hardware design language, SecVerilog, which makes it possible to statically analyze information flow at the hardware level. With SecVerilog, systems can be built with verifiable control of timing channels and other information channels. SecVerilog is Verilog, extended with expressive type annotations that enable precise reasoning about information flow. It also comes with rigorous formal assurance: we prove that SecVerilog enforces timing-sensitive noninterference and thus ensures secure information flow. By building a secure MIPS processor and its caches, we demonstrate that SecVerilog makes it possible to build complex hardware designs with verified security, yet with low overhead in time, space, and HW designer effort.

A comparison of Deep Learning methods for environmental sound detection <=SEP=> Environmental sound detection is a challenging application of machine learning because of the noisy nature of the signal, and the small amount of (labeled) data that is typically available. This work thus presents a comparison of several state-of-the-art Deep Learning models on the IEEE challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge task and data, classifying sounds into one of fifteen common indoor and outdoor acoustic scenes, such as bus, cafe, car, city center, forest path, library, train, etc. In total, 13 hours of stereo audio recordings are available, making this one of the largest datasets available.

A Tristate Rigid Reversible and Non-Back-Drivable Active Docking Mechanism for Modular Robotics <=SEP=> This paper proposes a new active bonding mechanism that achieves rigid, reversible, and nonback-drivable coupling between modular mobile robots in a chain formation. The first merit of this interface lies in its ability to operate in three independent modes. In the drive mode, the motor torque is routed to drive the module. In the clamp mode, the motor torque is redirected toward an active joint that enables one module to rotate relative to its neighbors in the formation. In the neutral mode, the motor's rotation achieves alignment between the interface's components prior to the initiation of the drive and clamp modes. The second merit stems from the dual-rod slider rocker (DRSR) mechanism, which toggles between the interface's three modes of operation. The design details of the interface are presented, as well as the optimal kinematic synthesis and dynamic analysis of the DRSR mechanism. Simulation and experimental results validate the DRSR's unique kinematics, as well as the rigidity and the three operation modes of the docking interface.

Sound-model-based acoustic source localization using distributed microphone arrays <=SEP=> Acoustic source localization and sound recognition are common acoustic scene analysis tasks that are usually considered separately. In this paper, a new source localization technique is proposed that works jointly with an acoustic event detection system. Given the identities and the end-points of simultaneous sounds, the proposed technique uses the statistical models of those sounds to compute a likelihood score for each model and for each signal at the output of a set of null-steering beamformers per microphone array. Those scores are subsequently combined to find the MAP-optimal event source positions in the room. Experimental work is reported for a scenario consisting of meeting-room acoustic events, either isolated or overlapped with speech. From the localization results, which are compared with those from the SRP-PHAT technique, it seems that the proposed model-based approach can be an alternative to current techniques for event-based localization.

Learning Sparsely Used Overcomplete Dictionaries <=SEP=> We consider the problem of learning sparsely used overcomplete dictionaries, where each observation is a sparse combination of elements from an unknown overcomplete dictionary. We establish exact recovery when the dictionary elements are mutually incoherent. Our method consists of a clustering-based initialization step, which provides an approximate estimate of the true dictionary with guaranteed accuracy. This estimate is then refined via an iterative algorithm with the following alternating steps: 1) estimation of the dictionary coefficients for each observation through l1 minimization, given the dictionary estimate, and 2) estimation of the dictionary elements through least squares, given the coefficient estimates. We establish that, under a set of sufficient conditions, our method converges at a linear rate to the true dictionary as well as the true coefficients for each observation.

Graph Convolutional Neural Networks for ADME Prediction in Drug Discovery <=SEP=> ADME in-silico methods have grown increasingly powerful over the past twenty years, driven by advances in machine learning and the abundance of high-quality training data generated by laboratory automation. Meanwhile, in the technology industry, deep-learning has taken o↵, driven by advances in topology design, computation, and data. The key premise of these methods is that the model is able to pass gradients back into the feature structure, engineering its own problem-specific representation for the data. Graph Convolutional Networks (GC-DNNs), a variation of neural fingerprints, allow for true deep-learning in the chemistry domain. We use this new approach to build human plasma protein binding, lipophilicty, and humans clearance models that significantly outperform random-forests and support-vector-regression.

System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems <=SEP=> If your wanted solutions manual ins't on this list, also can ask me if _ is available. Theory of Applied Robotics: Kinematics, Dynamics and Control (Reza N. _ Jazar) System Dynamics: Modeling and Simulation of Mechatronic Systems (4th _. Solution Manual Dynamics of Mechanical Systems (C.T.F. Ross) Solution Manual Thermodynamics : An Integrated Learning System (Schmidt, Ezekoye, System Dynamics : Modeling, Simulation, and Control of Mechatronic Systems (5th.

A Universal-Input Single-Stage High-Power-Factor Power Supply for HB-LEDs Based on Integrated Buck-Flyback Converter <=SEP=> Due to the high rise in luminous efficiency that HB-LEDs have experienced in the last recent years, many new applications have been researched. In this paper, a streetlight LED application will be covered, using the Integrated Buck-Flyback Converter developed in previous works, which performs power factor correction (PFC) from a universal ac source, as well as a control loop using the LM3524 IC, which allows PWM dimming operation mode. Firstly, the LED load will be linearized and modeled in order to calculate the IBFC topology properly. Afterwards, the converter will be calculated, presenting the one built in the lab. Secondly, the converter will be modeled in order to build the closed loop system, modeling the current sensor as well in order to develop an adequate controller. Finally, experimental results obtained from the lab tests will be presented.

Parsing Natural Scenes and Natural Language with Recursive Neural Networks <=SEP=> Recursive structure is commonly found in the inputs of different modalities such as natural scene images or natural language sentences. Discovering this recursive structure helps us to not only identify the units that an image or sentence contains but also how they interact to form a whole. We introduce a max-margin structure prediction architecture based on recursive neural networks that can successfully recover such structure both in complex scene images as well as sentences. The same algorithm can be used both to provide a competitive syntactic parser for natural language sentences from the Penn Treebank and to outperform alternative approaches for semantic scene segmentation, annotation and classification. For segmentation and annotation our algorithm obtains a new level of state-of-theart performance on the Stanford background dataset (78.1%). The features from the image parse tree outperform Gist descriptors for scene classification by 4%.

Availability : A Heuristic for Judging Frequency and Probability 122 <=SEP=> This paper explores a judgmental heuristic in which a person evaluates the frequency of classes or the probability of events by availability, i.e., by the ease with which relevant instances come to mind. In general, availability is correlated with ecological frequency, but it is also affected by other factors. Consequently, the reliance on the availability heuristic leads to systematic biases. Such biases are demonstrated in the judged frequency of classes of words, of combinatorial outcomes, and of repeated events. The phenomenon of illusory correlation is explained as an availability bias. The effects of the availability of incidents and scenarios on subjective probability are discussed.

Correcting the Document Layout: A Machine Learning Approach <=SEP=> In this paper, a machine learning approach to support the user during the correction of the layout analysis is proposed. Layout analysis is the process of extracting a hierarchical structure describing the layout of a page. In our approach, the layout analysis is performed in two steps: firstly, the global analysis determines possible areas containing paragraphs, sections, columns, figures and tables, and secondly, the local analysis groups together blocks that possibly fall within the same area. The result of the local analysis process strongly depends on the quality of the results of the first step. We investigate the possibility of supporting the user during the correction of the results of the global analysis. This is done by allowing the user to correct the results of the global analysis and then by learning rules for layout correction from the sequence of user actions. Experimental results on a set of multi-page documents are reported and commented. 1. Background and motivation Strategies for the extraction of layout analysis have been traditionally classified as top-down or bottom-up [10]. In top-down methods, the document image is repeatedly decomposed into smaller and smaller components, while in bottom-up methods, basic layout components are extracted from bitmaps and then grouped together into larger blocks on the basis of their characteristics. In WISDOM++, a document image analysis system that can transform paper documents into XML format [1], the applied page decomposition method is hybrid, since it combines a top-down approach to segment the document image, and a bottom-up layout analysis method to assemble basic blocks into frames. Some attempts to learn the layout structure from a set of training examples have also been reported in the literature [2,3,4,7,11]. They are based on ad-hoc learning algorithms, which learn particular data structures, such as geometric trees and tree grammars. Results are promising, although it has been proven that good layout structures could also be obtained by exploiting generic knowledge on typographic conventions [5]. This is the case of WISDOM++, which analyzes the layout in two steps: 1. A global analysis, in order to determine possible areas containing paragraphs, sections, columns, figures and tables. This step is based on an iterative process, in which the vertical and horizontal histograms of text blocks are alternately analyzed, in order to detect columns and sections/paragraphs, respectively. 2. A local analysis to group together blocks that possibly fall within the same area. Generic knowledge on west-style typesetting conventions is exploited to group blocks together, such as “the first line of a paragraph can be indented” and “in a justified text, the last line of a paragraph can be shorter than the previous one”. Experimental results proved the effectiveness of this knowledge-based approach on images of the first page of papers published in conference proceedings and journals [1]. However, performance degenerates when the system is tested on intermediate pages of multi-page articles, where the structure is much more variable, due to the presence of formulae, images, and drawings that can stretch over more than one column, or are quite close. The majority of errors made by the layout analysis module were in the global analysis step, while the local analysis step performed satisfactorily when the result of the global analysis was correct. In this paper, we investigate the possibility of supporting the user during the correction of the results of the global analysis. This is done by allowing the user to correct the results of the global analysis and then by learning rules for layout correction from his/her sequence of actions. This approach is different from those that learn the layout structure from scratch, since we try to correct the result of a global analysis returned by a bottom-up algorithm. Furthermore, we intend to capture knowledge on correcting actions performed by the user of the document image processing system. Other document processing systems allow users to correct the result of the layout analysis; nevertheless WISDOM++ is the only one that tries to learn correcting actions from user interaction with the system. Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003) 0-7695-1960-1/03 $17.00 © 2003 IEEE In the following section, we describe the layout correction operations. The automated generation of training examples is explained in Section 3. Section 4 introduces the learning strategy, while Section 5 presents some experimental results. 2. Correcting the layout Global analysis aims at determining the general layout structure of a page and operates on a tree-based representation of nested columns and sections. The levels of columns and sections are alternated (Figure 1), which means that a column contains sections, while a section contains columns. At the end of the global analysis, the user can only see the sections and columns that have been considered atomic, that is, not subject to further decomposition (Figure 2). The user can correct this result by means of three different operations: • Horizontal splitting: a column/section is cut horizontally. • Vertical splitting: a column/section is cut vertically. • Grouping: two sections/columns are merged together. The cut point in the two splitting operations is automatically determined by computing either the horizontal or the vertical histogram on the basic blocks returned by the segmentation algorithm. The horizontal (vertical) cut point corresponds to the largest gap between two consecutive bins in the horizontal (vertical) histogram. Therefore, splitting operations can be described by means of a unary function, split(X), where X represents the column/section to be split and the range is the set {horizontal, vertical, no_split}. The grouping operation, which can be described by means of a binary predicate group(A,B), is applicable to two sections (columns) A and B and returns a new section (column) C, whose boundary is determined as follows. Let (leftX, topX) and (bottomX, rightX) be the coordinates of the top-left and bottom-right vertices of a column/section X, respectively. Then: leftC= min(leftA, leftB), rightC=max(rightA,rightB), topC=min(topA,topB), bottomC=max(bottomA,bottomB). Grouping is possible only if the following two conditions are satisfied: 1. C does not overlap another section (column) in the document. 2. A and B are nested in the same column (section). After each splitting/grouping operation, WISDOM++ recomputes the result of the local analysis process, so that the user can immediately perceive the final effect of the requested correction and can decide whether to confirm the correction or not. 3. Representing corrections From the user interaction, WISDOM++ implicitly generates some training observations describing when and how the user intended to correct the result of the global analysis. These training observations are used to learn correction rules of the result of the global analysis, as explained in the next section. The simplest representation describes, for each training observation, the page layout at the i-th correction step and the correcting operation performed by the user on that layout. Therefore, if the user performs n-1 correcting operations, n observations are generated. The last one corresponds to the page layout accepted by the user. In the learning phase, this representation may lead the system to generate rules which strictly take into account the exact user correction sequence. However, several alternative correction sequences, which lead to the same result, may be also possible. If they are not considered, the learning strategy will suffer from data overfitting problems. This issue was already discussed in a preliminary work [9]. A more sophisticated representation, which takes into account alternative correction sequences, is based on the Column level

Building Lifecycle Management System for Enhanced Closed Loop Collaboration <=SEP=> In the past few years, the architecture, engineering and construction (AEC) industry has carried out efforts to develop BIM (Building Information Modelling) facilitating tools and standards for enhanced collaborative working and information sharing. Lessons learnt from other industries and tools such as PLM (Product Lifecycle Management) – established tool in manufacturing to manage the engineering change process – revealed interesting potential to manage more efficiently the building design and construction processes. Nonetheless, one of the remaining challenges consists in closing the information loop between multiple building lifecycle phases, e.g. by capturing information from middle-of-life processes (i.e., use and maintenance) to re-use it in end-of-life processes (e.g., to guide disposal decision making). Our research addresses this lack of closed-loop system in the AEC industry by proposing an open and interoperable Web-based building lifecycle management system. This paper gives (i) an overview of the requirement engineering process that has been set up to integrate efforts, standards and directives of both the AEC and PLM industries, and (ii) first proofs-of-concept of our system implemented on two distinct campus.

Bridge Resonant Inverter For Induction Heating Applications <=SEP=> Induction heating is a well-known technique to produce very high temperature for applications. A large number of topologies have been developed in this area such as voltage and current source inverter. Recent developments in switching schemes and control methods have made the voltage-source resonant inverters widely used in several applications that require output power control. The series-resonant inverter needs an output transformer for matching the output power to the load but it carry high current as a result additional real power loss is occur and overall efficiency also is reduced. This project proposes a high efficiency LLC resonant inverter for induction heating applications by using asymmetrical voltage cancellation control .The proposed control method is implemented in a full-bridge topology for induction heating application. The output power is controlled using the asymmetrical voltage cancellation technique. The LLC resonant tank is designed without the use of output transformer. This results in an increase of the net efficiency of the induction heating system. The circuit is simulated using MATLAB .The circuit is implemented using PIC controller. Both simulation and hardware results are compared.

Development and Initial Validation of a Multidimensional Scale Assessing Subjective Well-Being: The Well-Being Scale (WeBS). <=SEP=> Numerous scales currently exist that assess well-being, but research on measures of well-being is still advancing. Conceptualization and measurement of subjective well-being have emphasized intrapsychic over psychosocial domains of optimal functioning, and disparate research on hedonic, eudaimonic, and psychological well-being lacks a unifying theoretical model. Lack of systematic investigations on the impact of culture on subjective well-being has also limited advancement of this field. The goals of this investigation were to (1) develop and validate a self-report measure, the Well-Being Scale (WeBS), that simultaneously assesses overall well-being and physical, financial, social, hedonic, and eudaimonic domains of this construct; (2) evaluate factor structures that underlie subjective well-being; and (3) examine the measure's psychometric properties. Three empirical studies were conducted to develop and validate the 29-item scale. The WeBS demonstrated an adequate five-factor structure in an exploratory structural equation model in Study 1. Confirmatory factor analyses showed that a bifactor structure best fit the WeBS data in Study 2 and Study 3. Overall WeBS scores and five domain-specific subscale scores demonstrated adequate to excellent internal consistency reliability and construct validity. Mean differences in overall well-being and its five subdomains are presented for different ethnic groups. The WeBS is a reliable and valid measure of multiple aspects of well-being that are considered important to different ethnocultural groups.

Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network <=SEP=> A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

Adoption of Mobile Internet Services: An Exploratory Study of Mobile Commerce Early Adopters <=SEP=> Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

Literary Fiction Improves Theory of Mind <=SEP=> 

Soft decoding of JPEG 2000 compressed images using bit-rate-driven deep convolutional neural networks <=SEP=> Lossy image compression methods always introduce various unpleasant artifacts into the compressed results, especially at low bit-rates. In recent years, many effective soft decoding methods for JPEG compressed images have been proposed. However, to the best of our knowledge, very few works have been done on soft decoding of JPEG 2000 compressed images. Inspired by the outstanding performance of Convolution Neural Network (CNN) in various computer vision tasks, we presents a soft decoding method for JPEG 2000 by using multiple bit-rate-driven deep CNNs. More specifically, in training stage, we train a series of deep CNNs using lots of high quality training images and the corresponding JPEG 2000 compressed images at different coding bit-rates. In testing stage, for an input compressed image, the CNN trained with the nearest coding bit-rate is selected to perform soft decoding. Extensive experiments demonstrate the effectiveness of the presented soft decoding framework, which greatly improves the visual quality and objective scores of JPEG 2000 compressed images.

EvalAI: Towards Better Evaluation Systems for AI Agents <=SEP=> We introduce EvalAI, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence algorithms (AI) at scale. EvalAI is built to provide a scalable solution to the research community to fulfill the critical need of evaluating machine learning models and agents acting in an environment against annotations or with a human-in-the-loop. This will help researchers, students, and data scientists to create, collaborate, and participate in AI challenges organized around the globe. By simplifying and standardizing the process of benchmarking these models, EvalAI seeks to lower the barrier to entry for participating in the global scientific effort to push the frontiers of machine learning and artificial intelligence, thereby increasing the rate of measurable progress in this domain. Our code is available here.

Hype and Heavy Tails: A Closer Look at Data Breaches <=SEP=> Recent widely publicized data breaches have exposed the personal information of hundreds of millions of people. Some reports point to alarming increases in both the size and frequency of data breaches, spurring institutions around the world to address what appears to be a worsening situation. But, is the problem actually growing worse? In this paper, we study a popular public dataset and develop Bayesian Generalized Linear Models to investigate trends in data breaches. Analysis of the model shows that neither size nor frequency of data breaches has increased over the past decade. We find that the increases that have attracted attention can be explained by the heavy-tailed statistical distributions underlying the dataset. Specifically, we find that data breach size is log-normally distributed and that the daily frequency of breaches is described by a negative binomial distribution. These distributions may provide clues to the generative mechanisms that are responsible for the breaches. Additionally, our model predicts the likelihood of breaches of a particular size in the future. For example, we find that in the next year there is only a 31% chance of a breach of 10 million records or more in the US. Regardless of any trend, data breaches are costly, and we combine the model with two different cost models to project that in the next three years breaches could cost up to $55 billion.