Query: Question Answering in the Context of Stories Generated by Computers

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An Introduction to Least Commitment Planning <=SEP=> Recent developments have clari ed the process of generating partially ordered, partially speci ed sequences of actions whose execution will achive an agent's goal. This paper summarizes a progression of least commitment planners, starting with one that handles the simple strips representation, and ending with one that manages actions with disjunctive precondition, conditional e ects and universal quanti cation over dynamic universes. Along the way we explain how Chapman's formulation of the Modal Truth Criterion is misleading and why his NP-completeness result for reasoning about plans with conditional e ects does not apply to our planner. 1 I thank Franz Amador, Tony Barrett, Darren Cronquist, Denise Draper, Ernie Davis, Oren Etzioni, Nort Fowler, Rao Kambhampati, Craig Knoblock, Nick Kushmerick, Neal Lesh, Karen Lochbaum, Drew McDermott, Ramesh Patil, Kari Pulli, Ying Sun, Austin Tate and Mike Williamson for helpful comments, but retain sole responsibility for errors. This research was funded in part by O ce of Naval Research Grant 90-J-1904 and by National Science Foundation Grant IRI-8957302

A Computational Model of Plan-Based Narrative Conflict at the Fabula Level <=SEP=> Conflict is an essential element of interesting stories. In this paper, we operationalize a narratological definition of conflict and extend established narrative planning techniques to incorporate this definition. The conflict partial order causal link planning algorithm (CPOCL) allows narrative conflict to arise in a plan while maintaining causal soundness and character believability. We also define seven dimensions of conflict in terms of this algorithm's knowledge representation. The first three-participants, reason, and duration-are discrete values which answer the “who?” “why?” and “when?” questions, respectively. The last four-balance, directness, stakes, and resolution-are continuous values which describe important narrative properties that can be used to select conflicts based on the author's purpose. We also present the results of two empirical studies which validate our operationalizations of these narrative phenomena. Finally, we demonstrate the different kinds of stories which CPOCL can produce based on constraints on the seven dimensions.

Introduction to Algorithms <=SEP=> problems To understand the class of polynomial-time solvable proble ms, we must first have a formal notion of what a “problem” is. We define anbstract problemQ to be a binary relation on a set I of probleminstancesand a setS of problemsolutions. For example, an instance for SHORTEST-PATH is a triple consi sting of a graph and two vertices. A solution is a sequence of vertices in the g raph, with perhaps the empty sequence denoting that no path exists. The problem SHORTEST-PATH itself is the relation that associates each instance of a gra ph and two vertices with a shortest path in the graph that connects the two vertices. S ince shortest paths are not necessarily unique, a given problem instance may have mo r than one solution. This formulation of an abstract problem is more general than is required for our purposes. As we saw above, the theory of NP-completeness res tricts attention to decision problems : those having a yes/no solution. In this case, we can view an abstract decision problem as a function that maps the instan ce setI to the solution set {0, 1}. For example, a decision problem related to SHORTEST-PATH i s the problem PATH that we saw earlier. If i = 〈G,u, v,k〉 is an instance of the decision problem PATH, then PATH(i ) = 1 (yes) if a shortest path fromu to v has at mostk edges, and PATH (i ) = 0 (no) otherwise. Many abstract problems are not decision problems, but rather optimization problems , in which some value must be minimized or maximized. As we saw above, however, it is usual ly a simple matter to recast an optimization problem as a decision problem that is no harder. 1See Hopcroft and Ullman [156] or Lewis and Papadimitriou [20 4] for a thorough treatment of the Turing-machine model. 34.1 Polynomial time 973

Explanation-Based Learning: A Problem Solving Perspective <=SEP=> This article outlines explanation-based learning (EBL) and its role in improving problem-solving performance through experience. Unlike inductive systems, which learn by abstracting common properties from multiple examples, EBL systems explain why a particular example is an instance of a concept. The explanations are then converted into operational recognition rules. In essence, the EBL approach is analytical and knowledge-intensive, whereas inductive methods are empirical and knowledge-poor. This article focuses on extensions of the basic EBL method and their integration with the PRODIGY problem-solving system. PRODIGY'S EBL method is specifically designed to acquire search control rules that are effective in reducing total search time for complex task domains. Domain-specific search control rules are learned from successful problem-solving decisions, costly failures, and unforeseen goal interactions. The ability to specify multiple learning strategies in a declarative manner enables EBL to serve as a general technique for performance improvement. PRODIGY'S EBL method is analyzed, illustrated with several examples and performance results, and compared with other methods for integrating EBL and problem solving. 'Present address: Artificial Intelligence Research Branch, NASA Ames Research Center, Sterling Federal Systems, Mail Stop 244-17, Moffett Field CA 94035. This research was sponsored in part by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976, Amendment 20, under contract number F33615-87-C-1499, monitored by the Air Force Avionics Laboratory, in part by the Office of Naval Research under contracts N00014-84-K-0345 (N91) and N00014-86-K-0678-N123, in part by NASA under contract NCC 2-463, in part by the Army Research Institute under contract MDA903-85-C-0324, under subcontract 487650-25537 through the University of California, Irvine, and in part by small contributions from private institutions. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of DARPA, ONR, NASA, ARI, or the US Government. The first and fifth authors were supported by AT&T Bell Labs Ph.D. Scholarships. Table of

Narrative Planning: Balancing Plot and Character <=SEP=> Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors – logical and aesthetic – that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audience’s suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem – to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm – the Intent-based Partial Order Causal Link (IPOCL) planner – that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners.

ISO/IEC 27000, 27001 and 27002 for Information Security Management <=SEP=> With the increasing significance of information technology, there is an urgent need for adequate measures of information security. Systematic information security management is one of most important initiatives for IT management. At least since reports about privacy and security breaches, fraudulent accounting practices, and attacks on IT systems appeared in public, organizations have recognized their responsibilities to safeguard physical and information assets. Security standards can be used as guideline or framework to develop and maintain an adequate information security management system (ISMS). The standards ISO/IEC 27000, 27001 and 27002 are international standards that are receiving growing recognition and adoption. They are referred to as “common language of organizations around the world” for information security [1]. With ISO/IEC 27001 companies can have their ISMS certified by a third-party organization and thus show their customers evidence of their security measures.

A 0.3 pJ/access 8T data-aware SRAM utilizing column-based data encoding for ultra-low power applications <=SEP=> This paper proposes an 8T SRAMs utilizing a column-based data encoding scheme to reduce read and write power when there are similarities between consecutive data. It is useful in image processing applications where nearby pixels tend to have similar values. The proposed design has two modes of operation: normal and sequential modes. In the normal mode, it operates as a normal SRAM. In the sequential mode, bit-wise differences between consecutive data are written instead of the actual data. This leads to a much higher number of zeros in the array. Accordingly, a new data-aware bitline pre-charge scheme is proposed to minimize write power when writing a zero. A PVT-tracking reference voltage generator is also employed to compensate the read-bitline leakage for ultra-low voltage operation. On average, it offers more than 30% WBL switching power reduction. A 32Kb SRAM implemented in 65 nm CMOS process demonstrated successful operation down to 0.36 V. The total power consumption is 0.37 μW, corresponding to the maximum frequency of 0.25 MHz. Its minimum energy is 0.3 pJ/access achieved at 0.5 V.

Slotless Bearingless Disk Drive for High-Speed and High-Purity Applications <=SEP=> In this paper, a bearingless drive for high-speed applications with high purity and special chemical demands is introduced. To achieve high rotational speeds with low losses, a slotless bearingless disk drive with toroidal windings is used. We present the working principle of the bearingless drive as well as a model for calculating the achievable drive torque. An advantageous winding system for independent force and torque generation is proposed, which can be realized with standard inverter technology. Additionally, the winding inductances are examined to evaluate the dynamic properties of bearing and drive. The findings are verified with simulation results and the system performance is successfully demonstrated on an experimental prototype, which runs up to 20 000 rpm and is designed for an output power of 1 kW.

Integrating low-rank and group-sparse structures for robust multi-task learning <=SEP=> Multi-task learning (MTL) aims at improving the generalization performance by utilizing the intrinsic relationships among multiple related tasks. A key assumption in most MTL algorithms is that all tasks are related, which, however, may not be the case in many real-world applications. In this paper, we propose a robust multi-task learning (RMTL) algorithm which learns multiple tasks simultaneously as well as identifies the irrelevant (outlier) tasks. Specifically, the proposed RMTL algorithm captures the task relationships using a low-rank structure, and simultaneously identifies the outlier tasks using a group-sparse structure. The proposed RMTL algorithm is formulated as a non-smooth convex (unconstrained) optimization problem. We propose to adopt the accelerated proximal method (APM) for solving such an optimization problem. The key component in APM is the computation of the proximal operator, which can be shown to admit an analytic solution. We also theoretically analyze the effectiveness of the RMTL algorithm. In particular, we derive a key property of the optimal solution to RMTL; moreover, based on this key property, we establish a theoretical bound for characterizing the learning performance of RMTL. Our experimental results on benchmark data sets demonstrate the effectiveness and efficiency of the proposed algorithm.

A clinical and genetic study of the Say/Barber/Biesecker/Young-Simpson type of Ohdo syndrome. <=SEP=> We report a series of eight patients with the Say/Barber/Biesecker/Young-Simpson (SBBYS) type of Ohdo syndrome, which is the largest cohort described to date. We expand on the type, frequency and severity of the clinical characteristics in this condition; comment on the natural history of Ohdo syndrome and further refine previously published diagnostic criteria. Cytogenetic investigations and microarray CGH analysis undertaken in this cohort of patients failed to identify a chromosomal aetiology. It remains possible that this rare condition is heterogeneous and therefore caution must be undertaken during counselling until the underlying genetic mechanism(s) is (are) identified.

WiFi RSS fingerprinting indoor localization for mobile devices <=SEP=> While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, indoor localization with no pre-deployment effort in an indoor space, such as an office building corridor, with WiFi coverage but no a priori knowledge of the placement of the access points(APs) is implemented for mobile devices. WiFi Received Signal Strength(RSS) in the considered environment is used to build radio maps using WiFi fingerprinting approach. An offline RSS fingerprint of the environment is compared with an online RSS measurement to estimate the location of a user. Two architectures are developed based on this localization algorithm. The first one involves a client-server approach where the localization algorithm runs on the server whereas the second one is a standalone architecture and the algorithm runs on the SD card of the mobile device. Experimental results in the considered environment validate the approach for both architectures.

The Multi-User Security of Authenticated Encryption: AES-GCM in TLS 1.3 <=SEP=> We initiate the study of multi-user (mu) security of authenticated encryption (AE) schemes as a way to rigorously formulate, and answer, questions about the “randomized nonce” mechanism proposed for the use of the AE scheme GCM in TLS 1.3. We (1) Give definitions of mu ind (indistinguishability) and mu kr (key recovery) security for AE (2) Characterize the intent of nonce randomization as being improved mu security as a defense against mass surveillance (3) Cast the method as a (new) AE scheme RGCM (4) Analyze and compare the mu security of both GCM and RGCM in the model where the underlying block cipher is ideal, showing that the mu security of the latter is indeed superior in many practical contexts to that of the former, and (5) Propose an alternative AE scheme XGCM having the same efficiency as RGCM but better mu security and a more simple and modular design.

How Amazon web services uses formal methods <=SEP=> Engineers use TLA+ to prevent serious but subtle bugs from reaching production.

Vestibular papillomatosis as a normal vulvar anatomical condition. <=SEP=> At the beginning of the nineteen-eighties, vulvar vestibular papillomatosis (VVP) was thought to be a human papilloma virus (HPV) disease. Based upon these findings many clinicians have been treating this condition with laser ablation or by topical application of podophyllin or trichloroacetic acid. Currently, most authors believe that VVP should be considered an anatomical variant of the vestibular mucosa and not HPV related. We present a case of VVP in which there was no histological or molecular evidence of HPV; unnecessary treatment should be avoided.

Motion-based grasp selection: Improving traditional control strategies of myoelectric hand prosthesis <=SEP=> This paper introduces a novel prosthetic hand control architecture using inertial information for grasp prediction in order to reduce the cognitive burden of amputees. A pair of inertial measurement sensors (IMUs) are fitted on the wrist and bicep to record arm trajectory when reaching to grasp an object. Each object class can be associated with different methods for grasping and manipulation. An observation experiment was conducted to find the most common grasping methods for generic object classes: Very Small (VS), Small (S), and Medium (M). A Cup (CP) class was also examined to find differences in grasping habits for pick and place, and drinking applications. The resulting grasps were used to test the discriminatory ability of inertial motion features in the upper limb for VS, S and CP object classes. Subject experiments demonstrated an average classification success rate of 90.8%, 69.2% and 88.1% for VS, S and CP classes respectively when using a k-nearest neighbors algorithm with a Euclidean distance metric. The results suggest that inertial motion features have great potential to predict the grasp pattern during reach, and to the authors' knowledge, is the first IMU-based control strategy to utilize natural motion that is aimed at hand prosthesis control.

Power-constrained CMOS scaling limits <=SEP=> The scaling of CMOS technology has progressed rapidly for three decades, but may soon come to an end because of powerdissipation constraints. The primary problem is static power dissipation, which is caused by leakage currents arising from quantum tunneling and thermal excitations. The details of these effects, along with other scaling issues, are discussed in the context of their dependence on application. On the basis of these considerations, the limits of CMOS scaling are estimated for various application scenarios.

Rejecting Motion Outliers for Efficient Crowd Anomaly Detection <=SEP=> Crowd anomaly detection is a key research area in vision-based surveillance. Most of the crowd anomaly detection algorithms are either too slow, bulky, or power-hungry to be applicable for battery-powered surveillance cameras. In this paper, we present a new crowd anomaly detection algorithm. The proposed algorithm creates a feature for every superpixel that includes the contribution from the neighboring superpixels only if their direction of motion conforms with the dominant direction of motion in the region. We also propose using univariate Gaussian discriminant analysis with the K-means algorithm for classification. Our method provides superior accuracy over numerous deep learning-based and handcrafted feature-based approaches. We also present a low-power FPGA implementation of the proposed method. The algorithm is developed such that features are extracted over non-overlapping pixels. This allows gating inputs to numerous modules resulting in higher power efficiency. The maximum energy required per pixel is 2.43 nJ in our implementation. 126.65 Mpixels can be processed per second by the proposed implementation. The speed, power, and accuracy performance of our method make it competitive for surveillance applications, especially battery-powered surveillance cameras.

A Four-Switch Single-Stage Single-Phase Buck–Boost Inverter <=SEP=> This paper proposes a single-phase, single-stage buck–boost inverter for photovoltaic (PV) systems. The presented topology has one common terminal in input and output ports which eliminates common mode leakage current problem in the grid-connected PV applications. Although it uses four switches, its operation is bimodal and only two switches receive high-frequency pulse width modulation signals in each mode. Its principle of operation is described in detail with the help of equivalent circuits. Its dynamic model is presented, based on which a bimodal controller is designed. Experimental results, in stand-alone and grid-connected mode, obtained with a 300-W laboratory prototype are presented to validate its performance.

Vehicle Number Plates Detection and Recognition using improved Algorithms : A Review with Tanzanian Case study <=SEP=> — Invented in 1976, Number Plates Recognition (NPR) has since found wide commercial applications, making its research prospects challenging and scientifically interesting. A complete NPR system functions by vz steps, license plate; localization, sizing and orientation, normalization, character recognitions and geometric analysis. This paper is a review of NPR preliminary stages; it explains number plate localization, sizing and orientations as well as normalizations sections of the Number Plates Detection and Recognition-Tanzania Case study. MATLAB R2012b is employed in these processes. The input incorporated includes front and rear photographic images of vehicles, for proximity and simulation purposes the ample angle of image is 90 degree +-15. The captured image is converted to gray scale, binarized and edge detection algorithms are used to enhance edges. The output of this stage provides the input feature extraction, segmentation and

Featureless Visual Processing for SLAM in Changing Outdoor Environments <=SEP=> Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features.

Driver behavior recognition based on deep convolutional neural networks <=SEP=> Traffic safety is a severe problem around the world. Many road accidents are normally related with the driver's unsafe driving behavior, e.g. eating while driving. In this work, we propose a vision-based solution to recognize the driver's behavior based on convolutional neural networks. Specifically, given an image, skin-like regions are extracted by Gaussian Mixture Model, which are passed to a deep convolutional neural networks model, namely R*CNN, to generate action labels. The skin-like regions are able to provide abundant semantic information with sufficient discriminative capability. Also, R*CNN is able to select the most informative regions from candidates to facilitate the final action recognition. We tested the proposed methods on Southeast University Driving-posture Dataset and achieve mean Average Precision(mAP) of 97.76% on the dataset which prove the proposed method is effective in drivers's action recognition.

Emotion regulation: affective, cognitive, and social consequences. <=SEP=> One of life's great challenges is successfully regulating emotions. Do some emotion regulation strategies have more to recommend them than others? According to Gross's (1998, Review of General Psychology, 2, 271-299) process model of emotion regulation, strategies that act early in the emotion-generative process should have a different profile of consequences than strategies that act later on. This review focuses on two commonly used strategies for down-regulating emotion. The first, reappraisal, comes early in the emotion-generative process. It consists of changing the way a situation is construed so as to decrease its emotional impact. The second, suppression, comes later in the emotion-generative process. It consists of inhibiting the outward signs of inner feelings. Experimental and individual-difference studies find reappraisal is often more effective than suppression. Reappraisal decreases emotion experience and behavioral expression, and has no impact on memory. By contrast, suppression decreases behavioral expression, but fails to decrease emotion experience, and actually impairs memory. Suppression also increases physiological responding for suppressors and their social partners. This review concludes with a consideration of five important directions for future research on emotion regulation processes.

OAuth Demystified for Mobile Application Developers <=SEP=> OAuth has become a highly influential protocol due to its swift and wide adoption in the industry. The initial objective of the protocol was specific: it serves the authorization needs for websites. What motivates our work is the realization that the protocol has been significantly re-purposed and re-targeted over the years: (1) all major identity providers, e.g., Facebook, Google and Microsoft, have re-purposed OAuth for user authentication; (2) developers have re-targeted OAuth to the mobile platforms, in addition to the traditional web platform. Therefore, we believe that it is necessary and timely to conduct an in-depth study to demystify OAuth for mobile application developers. Our work consists of two pillars: (1) an in-house study of the OAuth protocol documentation that aims to identify what might be ambiguous or unspecified for mobile developers; (2) a field-study of over 600 popular mobile applications that highlights how well developers fulfill the authentication and authorization goals in practice. The result is really worrisome: among the 149 applications that use OAuth, 89 of them (59.7%) were incorrectly implemented and thus vulnerable. In the paper, we pinpoint the key portions in each OAuth protocol flow that are security critical, but are confusing or unspecified for mobile application developers. We then show several representative cases to concretely explain how real implementations fell into these pitfalls. Our findings have been communicated to vendors of the vulnerable applications. Most vendors positively confirmed the issues, and some have applied fixes. We summarize lessons learned from the study, hoping to provoke further thoughts about clear guidelines for OAuth usage in mobile applications.

Playful public participation in urban planning: A case study for online serious games <=SEP=> The aim of this paper is to study the implementation of online games to encourage public participation in urban planning. Its theoretical foundations are based on previous work in public participatory geographical information systems (PP GISs), play and games, with a special focus on serious games. Serious games aim to support learning processes in a new, more playful way. We developed the concept of playful public participation in urban planning, including playful elements such as storytelling, walking and moving, sketching, drawing, and games. A group of students designed an online serious public participatory game entitled NextCampus. The case study used in NextCampus was taken from the real-world question of a possible move of a university campus to a new location in the city of Hamburg, Germany. The development of the serious public participatory game NextCampus resulted in a physical prototype, user interface design, and a computational model of the game. The NextCampus game was tested with the help of two groups of urban planning students and presented to three external experts who provided valuable recommendations for further development. The critical comments questioned the level of complexity involved in such games. The positive comments included recognition of the potential for joy and the play-fulness a game like NextCampus could evoke. Public participatory online applications aim to attract citizens to discuss current issues related to their environment and to improve the process of public participation in general. An integration of geographic information systems (GISs) with public participatory tools represents one of the latest innovations in this area. ways of integrating the new applications into participatory processes and considers which new functionalities and technical characteristics could offer the most benefit to users. In the past, these technologies and other map-based applications were frequently criticized as being too complex for the majority of potential users (Steinmann, Krek, & Blaschke, 2004). New forms of collaboration and technical solutions emerged during the Web 2.0 era. For example , Google Maps and Google Earth can be used by lay users and non-experts without intense training. Recent research on collabo-rative mapping also known as ''geography without geographers'' prior work in PP GIS to include much wider, distributed participation (Hardy, 2008). Despite these new forms of collaboration and innovative technologies , Moody (2007) demonstrates that the use of GIS technology to involve citizens in participatory urban planning does not seem to empower citizens. An important factor in such findings …

Distributed Optimization Using the Primal-Dual Method of Multipliers <=SEP=> In this paper, we propose the primal-dual method of multipliers (PDMM) for distributed optimization over a graph. In particular, we optimize a sum of convex functions defined over a graph, where every edge in the graph carries a linear equality constraint. In designing the new algorithm, an augmented primal-dual Lagrangian function is constructed which smoothly captures the graph topology. It is shown that a saddle point of the constructed function provides an optimal solution of the original problem. Further under both the synchronous and asynchronous updating schemes, PDMM has the convergence rate of <inline-formula><tex-math notation="LaTeX">$O(1/K)$</tex-math></inline-formula> (where <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> denotes the iteration index) for general closed, proper, and convex functions. Other properties of PDMM such as convergence speeds versus different parameter-settings and resilience to transmission failure are also investigated through the experiments of distributed averaging.

Lateral prefrontal cortex and self-control in intertemporal choice <=SEP=> Disruption of function of left, but not right, lateral prefrontal cortex (LPFC) with low-frequency repetitive transcranial magnetic stimulation (rTMS) increased choices of immediate rewards over larger delayed rewards. rTMS did not change choices involving only delayed rewards or valuation judgments of immediate and delayed rewards, providing causal evidence for a neural lateral-prefrontal cortex–based self-control mechanism in intertemporal choice.

The use of pit and fissure sealants. <=SEP=> This paper reviews key issues of sealant use and methodology and poses recommendations to inform the discussion toward a consensus statement by participants. A comprehensive review of sealant literature, including policy recommendations from previous conferences that reviewed best practices for sealant use, was completed. Building on the review paper by Simonsen and on previous policy statements by dental and public health groups, this paper discusses key questions about sealant use in light of contemporary caries data and cost-benefit analyses. In addition, newest material advancements are reviewed to establish the next step in sealant improvement for young patients.

Physician Burnout and Well-Being: A Systematic Review and Framework for Action. <=SEP=> BACKGROUND
Physician burnout in the United States has reached epidemic proportions and is rising rapidly, although burnout in other occupations is stable. Its negative impact is far reaching and includes harm to the burned-out physician, as well as patients, coworkers, family members, close friends, and healthcare organizations.


OBJECTIVE
The purpose of this review is to provide an accurate, current summary of what is known about physician burnout and to develop a framework to reverse its current negative impact, decrease its prevalence, and implement effective organizational and personal interventions.


DATA SOURCES
I completed a comprehensive MEDLINE search of the medical literature from January 1, 2000, through December 28, 2016, related to medical student and physician burnout, stress, depression, suicide ideation, suicide, resiliency, wellness, and well-being. In addition, I selectively reviewed secondary articles, books addressing the relevant issues, and oral presentations at national professional meetings since 2013.


STUDY SELECTION
Healthcare organizations within the United States were studied.


RESULTS
The literature review is presented in 5 sections covering the basics of defining and measuring burnout; its impact, incidence, and causes; and interventions and remediation strategies.


CONCLUSIONS
All US medical students, physicians in training, and practicing physicians are at significant risk of burnout. Its prevalence now exceeds 50%. Burnout is the unintended net result of multiple, highly disruptive changes in society at large, the medical profession, and the healthcare system. Both individual and organizational strategies have been only partially successful in mitigating burnout and in developing resiliency and well-being among physicians. Two highly effective strategies are aligning personal and organizational values and enabling physicians to devote 20% of their work activities to the part of their medical practice that is especially meaningful to them. More research is needed.

Performance Analysis of Alamouti Code MIMO-OFDM Systems for Error Control and IQ Impairments <=SEP=> Bit Error Rate (BER) performance is the most important metric of any digital communication system. MIMO-OFDM systems designed with Alamouti Coding technique, using Error Control Coding schemes has been investigated under this study to achieve BER enhancement and scrutinized for Additive White Gaussian Noise (AWGN) Channel with Inphase and Quadrature-phase (IQ) impairments. This paper details out the model developed using NI LabVIEW and discusses simulation results comparing the error control coding RS, RS-CC and Convolution encoding with 4-PSK and 4-QAM. General Terms Bit Error Rate, Space Time Coding (STC).

Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks <=SEP=> Alzheimer’s disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer’s disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer’s disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer’s disease diagnosis in clinical research. Detection of Alzheimer’s disease is exacting due to the similarity in Alzheimer’s disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer’s disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer’s disease and obtains superior performance for early-stage diagnosis. We conducted ample experiments to demonstrate that our proposed model outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.