Query: Exploiting the Reactive Power in Deep Neural Models for Non-Intrusive Load Monitoring

Relevant passages:
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation <=SEP=> This paper considers additive factorial hidden Markov models, an extension to HMMs where the state factors into multiple independent chains, and the output is an additive function of all the hidden states. Although such models are very powerful, accurate inference is unfortunately difficult: exact inference is not computationally tractable, and existing approximate inference techniques are highly susceptible to local optima. In this paper we propose an alternative inference method for such models, which exploits their additive structure by 1) looking at the observed difference signal of the observation, 2) incorporating a “robust” mixture component that can account for unmodeled observations, and 3) constraining the posterior to allow at most one hidden state to change at a time. Combining these elements we develop a convex formulation of approximate inference that is computationally efficient, has no issues of local optima, and which performs much better than existing approaches in practice. The method is motivated by the problem of energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances; applied to this task, our algorithm achieves state-of-the-art performance, and is able to separate many appliances almost perfectly using just the total aggregate signal.

On the importance of initialization and momentum in deep learning <=SEP=> Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful models that were considered to be almost impossible to train using stochastic gradient descent with momentum. In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs (on datasets with long-term dependencies) to levels of performance that were previously achievable only with Hessian-Free optimization. We find that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum and well-initialized networks perform markedly worse when the momentum is absent or poorly tuned. Our success training these models suggests that previous attempts to train deep and recurrent neural networks from random initializations have likely failed due to poor initialization schemes. Furthermore, carefully tuned momentum methods su ce for dealing with the curvature issues in deep and recurrent network training objectives without the need for sophisticated second-order methods.

Recent approaches to non-intrusive load monitoring techniques in residential settings <=SEP=> The concept of Smart Grids is closely related to energy conservation and load shedding concepts. However, it is difficult to quantify the effectiveness of energy conservation efforts in residential settings without any sort of end-use energy information as feedback. In order to achieve that, load monitoring methods are normally used. In recent years, non-intrusive load monitoring (NILM) approaches are gaining popularity due to their minimal installation requirements and cost effectiveness. For a NILM system to work, only one sensor at the entry point to a home is required. Fluctuations in the aggregate power consumption signals are used to mathematically estimate the composition of operation of appliances. This approach eliminates the requirement of installing plug-meters for every appliance in the house. In this paper, we provide a review of recent research efforts on state-of-the-art NILM algorithms before concluding with a baseline and overall vision for our future research direction.

Toward Non-Intrusive Load Monitoring via Multi-Label Classification <=SEP=> Demand-side management technology is a key element of the proposed smart grid, which will help utilities make more efficient use of their generation assets by reducing consumers' energy demand during peak load periods. However, although some modern appliances can respond to price signals from the utility companies, there is a vast stock of older appliances that cannot. For such appliances, utilities must infer what appliances are operating in a home, given only the power signals on the main feeder to the home (i.e., the home's power consumption must be disaggregated into individual appliances). We report on an in-depth investigation of multi-label classification algorithms for disaggregating appliances in a power signal. A systematic review of this research topic shows that this class of algorithms has received little attention in the literature, even though it is arguably a more natural fit to the disaggregation problem than the traditional single-label classifiers used to date. We examine a multi-label meta-classification framework (RAkEL), and a bespoke multi-label classification algorithm (MLkNN), employing both time-domain and wavelet-domain feature sets. We test these classifiers on two real houses from the Reference Energy Disaggregation Dataset. We found that the multilabel algorithms are effective and competitive with published results on the datasets.

ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home <=SEP=> This paper presents ElectriSense, a new solution for automatically detecting and classifying the use of electronic devices in a home from a single point of sensing. ElectriSense relies on the fact that most modern consumer electronics and fluorescent lighting employ switch mode power supplies (SMPS) to achieve high efficiency. These power supplies continuously generate high frequency electromagnetic interference (EMI) during operation that propagates throughout a home's power wiring. We show both analytically and by in-home experimentation that EMI signals are stable and predictable based on the device's switching frequency characteristics. Unlike past transient noise-based solutions, this new approach provides the ability for EMI signatures to be applicable across homes while still being able to differentiate between similar devices in a home. We have evaluated our solution in seven homes, including one six-month deployment. Our results show that ElectriSense can identify and classify the usage of individual devices with a mean accuracy of 93.82%.

Updatable Block-Level Message-Locked Encryption <=SEP=> Deduplication is a widely used technique for reducing storage space of cloud service providers. Yet, it is unclear how to support deduplication of encrypted data securely until the study of Bellareetal on message-locked encryption (Eurocrypt 2013). Since then, there are many improvements such as strengthening its security, reducing client storage, etc. While updating a (shared) file is common, there is little attention on how to efficiently update large encrypted files in a remote storage with deduplication. To modify even a single bit, existing solutions require the trivial and expensive way of downloading and decrypting the large ciphertext.
 We initiate the study of updatable block-level message-locked encryption. We propose a provably secure construction that is efficiently updatable with O(log|F|) computational cost, where |F| is the file size. It also supports proof-of-ownership, a nice feature which protects storage providers from being abused as a free content distribution network.

LIGHTNETs: Smart LIGHTing and Mobile Optical Wireless NETworks — A Survey <=SEP=> Recently, rapid increase of mobile devices pushed the radio frequency (RF)-based wireless technologies to their limits. Free-space-optical (FSO), a.k.a. optical wireless, communication has been considered as one of the viable solutions to respond to the ever-increasing wireless capacity demand. Particularly, Visible Light Communication (VLC) which uses light emitting diode (LED) based smart lighting technology provides an opportunity and infrastructure for the high-speed low-cost wireless communication. Though stemming from the same core technology, the smart lighting and FSO communication have inherent tradeoffs amongst each other. In this paper, we present a tutorial and survey of advances in these two technologies and explore the potential for integration of the two as a single field of study: LIGHTNETs. We focus our survey to the context of mobile communications given the recent pressing needs in mobile wireless networking. We deliberate on key challenges involved in designing technologies jointly performing the two functions simultaneously: LIGHTing and NETworking.

Deep Image Segmentation by Quality Inference <=SEP=> Traditionally, convolutional neural networks are trained for semantic segmentation by having an image given as input and the segmented mask as output. In this work, we propose a neural network trained by being given an image and mask pair, with the output being the quality of that pairing. The segmentation is then created afterwards through backpropagation on the mask. This allows enriching training with semi-supervised synthetic variations on the ground-truth. The proposed iterative segmentation technique allows improving an existing segmentation or creating one from scratch. We compare the performance of the proposed methodology with state-of-the-art deep architectures for image segmentation and achieve competitive results, being able to improve their segmentations.

Automatic Caption Generation for News Images <=SEP=> This paper is concerned with the task of automatically generating captions for images, which is important for many image-related applications. Examples include video and image retrieval as well as the development of tools that aid visually impaired individuals to access pictorial information. Our approach leverages the vast resource of pictures available on the web and the fact that many of them are captioned and colocated with thematically related documents. Our model learns to create captions from a database of news articles, the pictures embedded in them, and their captions, and consists of two stages. Content selection identifies what the image and accompanying article are about, whereas surface realization determines how to verbalize the chosen content. We approximate content selection with a probabilistic image annotation model that suggests keywords for an image. The model postulates that images and their textual descriptions are generated by a shared set of latent variables (topics) and is trained on a weakly labeled dataset (which treats the captions and associated news articles as image labels). Inspired by recent work in summarization, we propose extractive and abstractive surface realization models. Experimental results show that it is viable to generate captions that are pertinent to the specific content of an image and its associated article, while permitting creativity in the description. Indeed, the output of our abstractive model compares favorably to handwritten captions and is often superior to extractive methods.

Pileus: protecting user resources from vulnerable cloud services <=SEP=> Cloud computing platforms are now constructed as distributed, modular systems of cloud services, which enable cloud users to manage their cloud resources. However, in current cloud platforms, cloud services fully trust each other, so a malicious user may exploit a vulnerability in a cloud service to obtain unauthorized access to another user's data. To date, over 150 vulnerabilities have been reported in cloud services in the OpenStack cloud. Research efforts in cloud security have focused primarily on attacks originating from user VMs or compromised operating systems rather than threats caused by the compromise of distributed cloud services, leaving cloud users open to attacks from these vulnerable cloud services. In this paper, we propose the Pileus cloud service architecture, which isolates each user's cloud operations to prevent vulnerabilities in cloud services from enabling malicious users to gain unauthorized access. Pileus deploys stateless cloud services "on demand" to service each user's cloud operations, limiting cloud services to the permissions of individual users. Pileus leverages the decentralized information flow control (DIFC) model for permission management, but the Pileus design addresses special challenges in the cloud environment to: (1) restrict how cloud services may be allowed to make security decisions; (2) select trustworthy nodes for access enforcement in a dynamic, distributed environment; and (3) limit the set of nodes a user must trust to service each operation. We have ported the OpenStack cloud platform to Pileus, finding that we can systematically prevent compromised cloud services from attacking other users' cloud operations with less than 3% additional latency for the operation. Application of the Pileus architecture to Open-Stack shows that confined cloud services can service users' cloud operations effectively for a modest overhead.

Novelty and Reinforcement Learning in the Value System of Developmental Robots <=SEP=> The value system of a developmental robot signals the occurrence of salient sensory inputs, modulates the mapping from sensory inputs to action outputs, and evaluates candidate actions. In the work reported here, a low level value system is modeled and implemented. It simulates the non-associative animal learning mechanism known as habituation e ect. Reinforcement learning is also integrated with novelty. Experimental results show that the proposed value system works as designed in a study of robot viewing angle selection.

A Linear Permanent-Magnet Motor for Active Vehicle Suspension <=SEP=> Traditionally, automotive suspension designs with passive components have been a compromise between the three conflicting demands of road holding, load carrying, and passenger comfort. Linear electromagnetic motor-based active suspension has superior controllability and bandwidth, provides shock load isolation between the vehicle chassis and wheel, and, therefore, has great potential. It also has the ability to recover energy that is dissipated in the shock absorber in the passive systems and results in a much more energy-efficient suspension system. This paper describes the issues pertinent to the design of a high force density tubular permanent-magnet (PM) motor for active suspension in terms of performance optimization, the use of a solid stator core for low-cost production and its impact on thrust force, and the assessment of demagnetization risk.

A linguistic ontology of space for natural language processing <=SEP=> a r t i c l e i n f o a b s t r a c t We present a detailed semantics for linguistic spatial expressions supportive of computational processing that draws substantially on the principles and tools of ontological engineering and formal ontology. We cover language concerned with space, actions in space and spatial relationships and develop an ontological organization that relates such expressions to general classes of fixed semantic import. The result is given as an extension of a linguistic ontology, the Generalized Upper Model, an organization which has been used for over a decade in natural language processing applications. We describe the general nature and features of this ontology and show how we have extended it for working particularly with space. Treaitng the semantics of natural language expressions concerning space in this way offers a substantial simplification of the general problem of relating natural spatial language to its contextualized interpretation. Example specifications based on natural language examples are presented, as well as an evaluation of the ontology's coverage, consistency, predictive power, and applicability.

Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-Supervised Object and Action Localization <=SEP=> We propose ‘Hide-and-Seek’, a weakly-supervised framework that aims to improve object localization in images and action localization in videos. Most existing weakly-supervised methods localize only the most discriminative parts of an object rather than all relevant parts, which leads to suboptimal performance. Our key idea is to hide patches in a training image randomly, forcing the network to seek other relevant parts when the most discriminative part is hidden. Our approach only needs to modify the input image and can work with any network designed for object localization. During testing, we do not need to hide any patches. Our Hide-and-Seek approach obtains superior performance compared to previous methods for weakly-supervised object localization on the ILSVRC dataset. We also demonstrate that our framework can be easily extended to weakly-supervised action localization.

Externalising the autobiographical self: sharing personal memories online facilitated memory retention. <=SEP=> Internet technology provides a new means of recalling and sharing personal memories in the digital age. What is the mnemonic consequence of posting personal memories online? Theories of transactive memory and autobiographical memory would make contrasting predictions. In the present study, college students completed a daily diary for a week, listing at the end of each day all the events that happened to them on that day. They also reported whether they posted any of the events online. Participants received a surprise memory test after the completion of the diary recording and then another test a week later. At both tests, events posted online were significantly more likely than those not posted online to be recalled. It appears that sharing memories online may provide unique opportunities for rehearsal and meaning-making that facilitate memory retention.

Probabilistic Analysis of Plug-In Electric Vehicles Impact on Electrical Grid Through Homes and Parking Lots <=SEP=> Plug-in electric vehicles in the future will possibly emerge widely in city areas. Fleets of such vehicles in large numbers could be regarded as considerable stochastic loads in view of the electrical grid. Moreover, they are not stabled in unique positions to define their impact on the grid. Municipal parking lots could be considered as important aggregators letting these vehicles interact with the utility grid in certain positions. A bidirectional power interface in a parking lot could link electric vehicles with the utility grid or any storage and dispersed generation. Such vehicles, depending on their need, could transact power with parking lots. Considering parking lots equipped with power interfaces, in more general terms, parking-to-vehicle and vehicle-to-parking are propose here instead of conventional grid-to-vehicle and vehicle-to-grid concepts. Based on statistical data and adopting general regulations on vehicles (dis)charging, a novel stochastic methodology is presented to estimate total daily impact of vehicles aggregated in parking lots on the grid. Different scenarios of plug-in vehicles' penetration are suggested in this paper and finally, the scenarios are simulated on standard grids that include several parking lots. The results show acceptable penetration level margins in terms of bus voltages and grid power loss.

Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback <=SEP=> This paper presents a visual-inertial odometry framework which tightly fuses inertial measurements with visual data from one or more cameras, by means of an iterated extended Kalman filter (IEKF). By employing image patches as landmark descriptors, a photometric error is derived, which is directly integrated as an innovation term in the filter update step. Consequently, the data association is an inherent part of the estimation process and no additional feature extraction or matching processes are required. Furthermore, it enables the tracking of non-corner shaped features, such as lines, and thereby increases the set of possible landmarks. The filter state is formulated in a fully robocentric fashion, which reduces errors related to nonlinearities. This also includes partitioning of a landmark’s location estimate into a bearing vector and distance and thereby allows an undelayed initialization of landmarks. Overall, this results in a compact approach which exhibits a high level of robustness with respect to low scene texture and motion blur. Furthermore, there is no time-consuming initialization procedure and pose estimates are available starting at the second image frame. We test the filter on different real datasets and compare it to other state-of-the-art visual-inertial frameworks. The experimental results show that robust localization with high accuracy can be achieved with this filterbased framework.

MatrixWave: Visual Comparison of Event Sequence Data <=SEP=> Event sequence data analysis is common in many domains, including web and software development, transportation, and medical care. Few have investigated visualization techniques for comparative analysis of multiple event sequence datasets. Grounded in the real-world characteristics of web clickstream data, we explore visualization techniques for comparison of two clickstream datasets collected on different days or from users with different demographics. Through iterative design with web analysts, we designed MatrixWave, a matrix-based representation that allows analysts to get an overview of differences in traffic patterns and interactively explore paths through the website. We use color to encode differences and size to offer context over traffic volume. User feedback on MatrixWave is positive. Our study participants made fewer errors with MatrixWave and preferred it over the more familiar Sankey diagram.

CareLog: a selective archiving tool for behavior management in schools <=SEP=> Identifying the function of problem behavior can lead to the development of more effective interventions. One way to identify the function is through functional behavior assessment (FBA). Teachers conduct FBA in schools. However, the task load of recording the data manually is high, and the challenge of accurately identifying antecedents and consequences is significant while interacting with students. These issues often result in imperfect information capture. CareLog allows teachers more easily to conduct FBAs and enhances the capture of relevant information. In this paper, we describe the design process that led to five design principles that governed the development of CareLog. We present results from a five-month, quasi-controlled study aimed at validating those design principles. We reflect on how various constraints imposed by special education settings impact the design and evaluation process for HCI practitioners and researchers.

A Note on the Convergence of SOR for the PageRank Problem <=SEP=> A curious phenomenon when it comes to solving the linear system formulation of the PageRank problem is that while the convergence rate of Gauss–Seidel shows an improvement over Jacobi by a factor of approximately two, successive overrelaxation (SOR) does not seem to offer a meaningful improvement over Gauss–Seidel. This has been observed experimentally and noted in the literature, but to the best of our knowledge there has been no analytical explanation for this thus far. This convergence behavior is surprising because there are classes of matrices for which Gauss–Seidel is faster than Jacobi by a similar factor of two, and SOR accelerates convergence by an order of magnitude compared to Gauss–Seidel. In this short paper we prove analytically that the PageRank model has the unique feature that there exist PageRank linear systems for which SOR does not converge outside a very narrow interval depending on the damping factor, and that in such situations Gauss–Seidel may be the best choice among the relaxation parameters. Conversely, we show that within that narrow interval, there exists no PageRank problem for which SOR does not converge. Our result may give an analytical justification for the popularity of Gauss–Seidel as a solver for the linear system formulation of PageRank.

A Card Game Description Language <=SEP=> We present initial research regarding a system capable of generating novel card games. We furthermore propose a method for computationally analysing existing games of the same genre. Ultimately, we present a formalisation of card game rules, and a context-free grammar Gcardgame capable of expressing the rules of a large variety of card games. Example derivations are given for the poker variant Texas hold ’em, Blackjack and UNO. Stochastic simulations are used both to verify the implementation of these well-known games, and to evaluate the results of new game rules derived from the grammar. In future work, this grammar will be used to evolve completely novel card games using a grammarguided genetic program.

Chapter 7 Towards Automatically-Tuned Deep Neural Networks <=SEP=> Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks. In this work, we present two versions of Auto-Net, which provide automatically-tuned deep neural networks without any human intervention. The first version, Auto-Net 1.0, builds upon ideas from the competition-winning system Auto-sklearn by using the Bayesian Optimization method SMAC and uses Theano as the underlying deep learning (DL) framework. The more recent Auto-Net 2.0 builds upon a recent combination of Bayesian Optimization and HyperBand, called BOHB, and uses PyTorch as DL framework. To the best of our knowledge, Auto-Net 1.0 was the first automatically-tuned neural network to win competition datasets against human experts (as part of the first AutoML challenge). Further empirical results show that ensembling Auto-Net 1.0 with Auto-sklearn can perform better than either approach alone, and that Auto-Net 2.0 can perform better yet.

A Hexanucleotide Repeat Expansion in C9ORF72 Is the Cause of Chromosome 9p21-Linked ALS-FTD <=SEP=> The chromosome 9p21 amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) locus contains one of the last major unidentified autosomal-dominant genes underlying these common neurodegenerative diseases. We have previously shown that a founder haplotype, covering the MOBKL2b, IFNK, and C9ORF72 genes, is present in the majority of cases linked to this region. Here we show that there is a large hexanucleotide (GGGGCC) repeat expansion in the first intron of C9ORF72 on the affected haplotype. This repeat expansion segregates perfectly with disease in the Finnish population, underlying 46.0% of familial ALS and 21.1% of sporadic ALS in that population. Taken together with the D90A SOD1 mutation, 87% of familial ALS in Finland is now explained by a simple monogenic cause. The repeat expansion is also present in one-third of familial ALS cases of outbred European descent, making it the most common genetic cause of these fatal neurodegenerative diseases identified to date.

Use of Web 2 . 0 technologies in K-12 and higher education : The search for evidence-based practice <=SEP=> Evidence-based practice in education entails making pedagogical decisions that are informed by relevant empirical research evidence. The main purpose of this paper is to discuss evidence-based pedagogical approaches related to the use of Web 2.0 technologies in both K-12 and higher education settings. The use of such evidence-based practice would be useful to educators interested in fostering student learning through Web 2.0 tools. A comprehensive literature search across the Academic Search Premier, Education Research Complete, ERIC, and PsycINFO databases was conducted. Empirical studies were included for review if they specifically examined the impact of Web 2.0 technologies on student learning. Articles that merely described anecdotal studies such as student perception or feeling toward learning using Web 2.0, or studies that relied on student self-report data such as student questionnaire survey and interview were excluded. Overall, the results of our review suggested that actual evidence regarding the impact of Web 2.0 technologies on student learning is as yet fairly weak. Nevertheless, the use of Web 2.0 technologies appears to have a general positive impact on student learning. None of the studies reported a detrimental or inferior effect on learning. The positive effects are not necessarily attributed to the technologies per se but to how the technologies are used, and how one conceptualizes learning. It may be tentatively concluded that a dialogic, constructionist, or coconstructive pedagogy supported by activities such as Socratic questioning, peer review and self-reflection appeared to increase student achievement in blog-, wiki-, and 3-D immersive virtual world environments, while a transmissive pedagogy supported by review activities appeared to enhance student learning using podcast. 2012 Elsevier Ltd. All rights reserved.

Precision and Recall for Regression <=SEP=> Cost sensitive prediction is a key task in many real world applications. Most existing research in this area deals with classification problems. This paper addresses a related regression problem: the prediction of rare extreme values of a continuous variable. These values are often regarded as outliers and removed from posterior analysis. However, for many applications (e.g. in finance, meteorology, biology, etc.) these are the key values that we want to accurately predict. Any learning method obtains models by optimizing some preference criteria. In this paper we propose new evaluation criteria that are more adequate for these applications. We describe a generalization for regression of the concepts of precision and recall often used in classification. Using these new evaluation metrics we are able to focus the evaluation of predictive models on the cases that really matter for these applications. Our experiments indicate the advantages of the use of these new measures when comparing predictive models in the context of our target applications.

Review on trajectory similarity measures <=SEP=> The availability of devices that can be used to track moving objects has increased dramatically leading to a great growth in movement data from almost every application domain. Therefore, there has been an increasing interest in proposing new methodologies for indexing, classifying, clustering, querying and measuring similarity between moving objects' data. One of the main functions for a wide range of application domains is to measure the similarity between two moving objects' trajectories. In this paper, we present a comparative study between widely used trajectory similarity measures observing the advantages and disadvantages of these measures.

A Parameter-Free Spatio-Temporal Pattern Mining Model to Catalog Global Ocean Dynamics <=SEP=> As spatio-temporal data have become ubiquitous, an increasing challenge facing computer scientists is that of identifying discrete patterns in continuous spatio-temporal fields. In this paper, we introduce a parameter-free pattern mining application that is able to identify dynamic anomalies in ocean data, known as ocean eddies. Despite ocean eddy monitoring being an active field of research, we provide one of the first quantitative analyses of the performance of the most used monitoring algorithms. We present an incomplete information validation technique, that uses the performance of two methods to construct an imperfect ground truth to test the significance of patterns discovered as well as the relative performance of pattern mining algorithms. These methods, in addition to the validation schemes discussed provide researchers new directions in analyzing large unlabeled climate datasets.

Use of Network Latency Profiling and Redundancy for Cloud Server Selection <=SEP=> As servers are placed in diverse locations in networked services today, it becomes vital to direct a client's request to the best server(s) to achieve both high performance and reliability. In this distributed setting, non-negligible latency and server availability become two major concerns, especially for highly-interactive applications. Profiling latencies and sending redundant data have been investigated as solutions to these issues. The notion of a cloudlet in mobile-cloud computing is also relevant in this context, as the cloudlet can supply these solution approaches on behalf of the mobile. In this paper, we investigate the effects of profiling and redundancy on latency when a client has a choice of multiple servers to connect to, using measurements from real experiments and simulations. We devise and test different server selection and data partitioning strategies in terms of profiling and redundancy. Our key findings are summarized as follows. First, intelligent server selection algorithms help find the optimal group of servers that minimize latency with profiling. Second, we can achieve good performance with relatively simple approaches using redundancy. Our analysis of profiling and redundancy provides insight to help designers determine how many servers and which servers to select to reduce latency.

Sustainable, effective implementation of a surgical preprocedural checklist: an "attestation" format for all operating team members. <=SEP=> BACKGROUND
Adoption ofa preprocedural pause (PPP) associated with a checklist and a team briefing has been shown to improve teamwork function in operating rooms (ORs) and has resulted in improved outcomes. The format of the World Health Organization Safe Surgery Saves Lives checklist has been used as a template for a PPP. Performing a PPP, described as a "time-out," is one of the three principal components, along with a preprocedure verification process and marking the procedure site, of the Joint Commission's Universal Protocol for Preventing Wrong Site, Wrong Procedure, Wrong Person Surgery. However, if the surgeon alone leads the pause, its effectiveness may be decreased by lack of input from other operating team members.


METHODS
In this study, the PPP was assessed to measure participation and input from operating team members. On the basis of low participation levels, the pause was modified to include an attestation from each member of the team.


RESULTS
Preliminary analysis of our surgeon-led pause revealed only 54% completion of all items, which increased to 97% after the intervention. With the new format, operating team members stopped for the pause in 96% of cases, compared with 78% before the change. Operating team members introduced themselves in 94% of cases, compared with 44% before the change. Follow-up analysis showed sustained performance at 18 months after implementation.


CONCLUSIONS
A preprocedural checklist format in which each member of the operating team provides a personal attestation can improve pause compliance and may contribute to improvements in the culture of teamwork within an OR. Successful online implementation of a PPP, which includes participation by all operating team members, requires little or no additional expense and only minimal formal coaching outside working situations.

Local multidimensional scaling <=SEP=> In a visualization task, every nonlinear projection method needs to make a compromise between trustworthiness and continuity. In a trustworthy projection the visualized proximities hold in the original data as well, whereas a continuous projection visualizes all proximities of the original data. We show experimentally that one of the multidimensional scaling methods, curvilinear components analysis, is good at maximizing trustworthiness. We then extend it to focus on local proximities both in the input and output space, and to explicitly make a user-tunable parameterized compromise between trustworthiness and continuity. The new method compares favorably to alternative nonlinear projection methods.