Query: Some from Here, Some from There: Cross-Project Code Reuse in GitHub

Relevant passages:
The plastic surgery hypothesis <=SEP=> Recent work on genetic-programming-based approaches to automatic program patching have relied on the insight that the content of new code can often be assembled out of fragments of code that already exist in the code base. This insight has been dubbed the plastic surgery hypothesis; successful, well-known automatic repair tools such as GenProg rest on this hypothesis, but it has never been validated. We formalize and validate the plastic surgery hypothesis and empirically measure the extent to which raw material for changes actually already exists in projects. In this paper, we mount a large-scale study of several large Java projects, and examine a history of 15,723 commits to determine the extent to which these commits are graftable, i.e., can be reconstituted from existing code, and find an encouraging degree of graftability, surprisingly independent of commit size and type of commit. For example, we find that changes are 43% graftable from the exact version of the software being changed. With a view to investigating the difficulty of finding these grafts, we study the abundance of such grafts in three possible sources: the immediately previous version, prior history, and other projects. We also examine the contiguity or chunking of these grafts, and the degree to which grafts can be found in the same file. Our results are quite promising and suggest an optimistic future for automatic program patching methods that search for raw material in already extant code in the project being patched.

Mining StackOverflow to turn the IDE into a self-confident programming prompter <=SEP=> Developers often require knowledge beyond the one they possess, which often boils down to consulting sources of information like Application Programming Interfaces (API) documentation, forums, Q&A websites, etc. Knowing what to search for and how is non- trivial, and developers spend time and energy to formulate their problems as queries and to peruse and process the results. We propose a novel approach that, given a context in the IDE, automatically retrieves pertinent discussions from Stack Overflow, evaluates their relevance, and, if a given confidence threshold is surpassed, notifies the developer about the available help. We have implemented our approach in Prompter, an Eclipse plug-in. Prompter has been evaluated through two studies. The first was aimed at evaluating the devised ranking model, while the second was conducted to evaluate the usefulness of Prompter.

Gender and Tenure Diversity in GitHub Teams <=SEP=> Software development is usually a collaborative venture. Open Source Software (OSS) projects are no exception; indeed, by design, the OSS approach can accommodate teams that are more open, geographically distributed, and dynamic than commercial teams. This, we find, leads to OSS teams that are quite diverse. Team diversity, predominantly in offline groups, is known to correlate with team output, mostly with positive effects. How about in OSS? Using GitHub, the largest publicly available collection of OSS projects, we studied how gender and tenure diversity relate to team productivity and turnover. Using regression modeling of GitHub data and the results of a survey, we show that both gender and tenure diversity are positive and significant predictors of productivity, together explaining a sizable fraction of the data variability. These results can inform decision making on all levels, leading to better outcomes in recruiting and performance.

CCFinder: A Multilinguistic Token-Based Code Clone Detection System for Large Scale Source Code <=SEP=> ÐA code clone is a code portion in source files that is identical or similar to another. Since code clones are believed to reduce the maintainability of software, several code clone detection techniques and tools have been proposed. This paper proposes a new clone detection technique, which consists of the transformation of input source text and a token-by-token comparison. For its implementation with several useful optimization techniques, we have developed a tool, named CCFinder, which extracts code clones in C, C++, Java, COBOL, and other source files. As well, metrics for the code clones have been developed. In order to evaluate the usefulness of CCFinder and metrics, we conducted several case studies where we applied the new tool to the source code of JDK, FreeBSD, NetBSD, Linux, and many other systems. As a result, CCFinder has effectively found clones and the metrics have been able to effectively identify the characteristics of the systems. In addition, we have compared the proposed technique with other clone detection techniques. Index TermsÐCode clone, duplicated code, CASE tool, metrics, maintenance.

Comparison and evaluation of code clone detection techniques and tools: A qualitative approach <=SEP=> Over the last decade many techniques and tools for software clone detection have been proposed. In this paper, we provide a qualitative comparison and evaluation of the current state-of-the-art in clone detection techniques and tools, and organize the large amount of information into a coherent conceptual framework. We begin with background concepts, a generic clone detection process and an overall taxonomy of current techniques and tools. We then classify, compare and evaluate the techniques and tools in two different dimensions. First, we classify and compare approaches based on a number of facets, each of which has a set of (possibly overlapping) attributes. Second, we qualitatively evaluate the classified techniques and tools with respect to a taxonomy of editing scenarios designed to model the creation of Type-1, Type-2, Type-3 and Type-4 clones. Finally, we provide examples of how one might use the results of this study to choose the most appropriate clone detection tool or technique in the context of a particular set of goals and constraints. The primary contributions of this paper are: (1) a schema for classifying clone detection techniques and tools and a classification of current clone detectors based on this schema, and (2) a taxonomy of editing scenarios that produce different clone types and a qualitative evaluation of current clone detectors based on this taxonomy.

WILDSENSING: Design and deployment of a sustainable sensor network for wildlife monitoring <=SEP=> The increasing adoption of wireless sensor network technology in a variety of applications, from agricultural to volcanic monitoring, has demonstrated their ability to gather data with unprecedented sensing capabilities and deliver it to a remote user. However, a key issue remains how to maintain these sensor network deployments over increasingly prolonged deployments. In this article, we present the challenges that were faced in maintaining continual operation of an automated wildlife monitoring system over a one-year period. This system analyzed the social colocation patterns of European badgers (Meles meles) residing in a dense woodland environment using a hybrid RFID-WSN approach. We describe the stages of the evolutionary development, from implementation, deployment, and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We highlight the main lessons learned: the need to factor in the maintenance costs while designing the system; to consider carefully software and hardware interactions; the importance of rapid prototyping for initial deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.

Merging thermal and visual images by a contrast pyramid <=SEP=> J. M. Valeton I nstitute for Perception TNO Kampweg 5 3769 DE Soesterberg, The Netherlands Abstract. Integration of images from different sensing modalities can produce information that cannot be obtained by viewing the sensor outputs separately and consecutively. This paper introduces a hierarchical i mage merging scheme based on a multiresolution contrast decomposition (the ratio of a low-pass pyramid). The composite images produced by this scheme preserve those details from the input images that are most relevant to visual perception. The method is tested by merging parallel registered thermal and visual images. The results show that the fused images present a more detailed representation of the depicted scene. Detection, recognition, and search tasks may therefore benefit from this new image representation.

Fast Eigenspace Approximation using Random Signals <=SEP=> We focus in this work on the estimation of the first k eigenvectors of any graph Laplacian using filtering of Gaussian random signals. We prove that we only need k such signals to be able to exactly recover as many of the smallest eigenvectors, regardless of the number of nodes in the graph. In addition, we address key issues in implementing the theoretical concepts in practice using accurate approximated methods. We also propose fast algorithms both for eigenspace approximation and for the determination of the kth smallest eigenvalue λk. The latter proves to be extremely efficient under the assumption of locally uniform distribution of the eigenvalue over the spectrum. Finally, we present experiments which show the validity of our method in practice and compare it to state-of-the-art methods for clustering and visualization both on synthetic smallscale datasets and larger real-world problems of millions of nodes. We show that our method allows a better scaling with the number of nodes than all previous methods while achieving an almost perfect reconstruction of the eigenspace formed by the first k eigenvectors. Keywords—Graph signal processing, low-rank reconstruction, partitionning, spectral graph theory, spectrum analysis, subspace approximation, visualization

LPV subspace identification of the edgewise vibrational dynamics of a wind turbine rotor <=SEP=> In this paper we apply a state-of-the-art algorithm for subspace identification of linear parameter-varying (LPV) systems to identify the coupled dynamics of the drive-train and the edgewise bending motion of the rotor blades of three-bladed wind turbines. These dynamics are varying with the rotor speed. The identification algorithm uses a factorization which makes it possible to form predictors based on past inputs, outputs, and the known rotor speed. The predictors contain the LPV equivalent of the Markov parameters. Using the predictors, ideas from Predictor Based Subspace IDentification (PBSID) were developed to estimate the state sequence from which the LPV system matrices can be constructed. The algorithm was applied not only to synthetic data generated by a computer simulation of a reference wind turbine, but also to data measured from the CART3 research wind turbine at the National Wind Technology Center of the National Renewable Energy Laboratory (NREL). This paper demonstrates that the linear time-varying behavior of the aeroelastic dynamics of the wind turbine rotor can be captured in an LPV model identified with measured input-output data.

Knowledge management technologies and applications - literature review from 1995 to 2002 <=SEP=> This paper surveys knowledge management (KM) development using a literature review and classification of articles from 1995 to 2002 with keyword index in order to explore how KM technologies and applications have developed in this period. Based on the scope of 234 articles of knowledge management applications, this paper surveys and classifies KM technologies using the seven categories as: KM framework, knowledge-based systems, data mining, information and communication technology, artificial intelligence/expert systems, database technology, and modeling, together with their applications for different research and problem domains. Some discussion is presented, indicating future development for knowledge management technologies and applications as the followings: (1) KM technologies tend to develop towards expert orientation, and KM applications development is a problem-oriented domain. (2) Different social studies methodologies, such as statistical method, are suggested to implement in KM as another kind of technology. (3) Integration of qualitative and quantitative methods, and integration of KM technologies studies may broaden our horizon on this subject. (4) The ability to continually change and obtain new understanding is the power of KM technologies and will be the application of future works. q 2003 Elsevier Science Ltd. All rights reserved.

Partially Observable Reinforcement Learning for Intelligent Transportation Systems <=SEP=> Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as ‘Partially Detected Intelligent Transportation Systems’. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.

Comparison of NRZ and RZ modulations in laser intersatellite communication systems <=SEP=> Laser intersatellite communication (LIC) is a promising choice for intersatellite communication due to the high data rate that can be achieved with the advantages of light weight, smaller size and lower power consumption. In this paper, we compare the maximum transmission distance with non-return-to-zero (NRZ) and return-to-zero (RZ) modulations for two different LIC scenarios. Simulation results show that for the long-range LIC system with a saturated booster optical amplifier, the RZ modulation scheme can offer a longer transmission distance than the NRZ modulation scheme. For the short-range LIC system without an optical amplifier, the RZ modulation scheme performs almost the same as the NRZ modulation scheme.

Relative clustering validity criteria: A comparative overview <=SEP=> Many different relative clustering validity criteria exist that are very useful in practice as quantitative measures for evaluating the quality of data partitions, and new criteria have still been proposed from time to time. These criteria are endowed with particular features that may make each of them able to outperform others in specific classes of problems. In addition, they may have completely different computational requirements. Then, it is a hard task for the user to choose a specific criterion when he or she faces such a variety of possibilities. For this reason, a relevant issue within the field of clustering analysis consists of comparing the performances of existing validity criteria and, eventually, that of a new criterion to be proposed. In spite of this, the comparison paradigm traditionally adopted in the literature is subject to some conceptual limitations. The present paper describes an alternative, possibly complementary methodology for comparing clustering validity criteria and uses it to make an extensive comparison of the performances of 40 criteria over a collection of 962,928 partitions derived from five well-known clustering algorithms and 1080 different data sets of a given class of interest. A detailed review of the relative criteria under investigation is also provided that includes an original comparative asymptotic analysis of their computational complexities. This work is intended to be a complement of the classic study reported in 1985 by Milligan and Cooper as well as a thorough extension of a preliminary paper by the authors themselves.  2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3:

An overview of substrate noise reduction techniques <=SEP=> This paper provides an overview of the recent circuit level and physical level substrate noise reduction techniques. Several of these techniques are compared for their advantages and disadvantages in "system-on-chip" applications.

Big Data Analytics for Security <=SEP=> Big data is changing the landscape of security tools for network monitoring, security information and event management, and forensics; however, in the eternal arms race of attack and defense, security researchers must keep exploring novel ways to mitigate and contain sophisticated attackers.

Direct Multisearch for Multiobjective Optimization <=SEP=> In practical applications of optimization it is common to have several conflicting objective functions to optimize. Frequently, these functions are subject to noise or can be of black-box type, preventing the use of derivative-based techniques. We propose a novel multiobjective derivative-free methodology, calling it direct multisearch (DMS), which does not aggregate any of the objective functions. Our framework is inspired by the search/poll paradigm of direct-search methods of directional type and uses the concept of Pareto dominance to maintain a list of nondominated points (from which the new iterates or poll centers are chosen). The aim of our method is to generate as many points in the Pareto front as possible from the polling procedure itself, while keeping the whole framework general enough to accommodate other disseminating strategies, in particular when using the (here also) optional search step. DMS generalizes to multiobjective optimization (MOO) all direct-search methods of directional type. We prove under the common assumptions used in direct search for single optimization that at least one limit point of the sequence of iterates generated by DMS lies in (a stationary form of) the Pareto front. However, extensive computational experience has shown that our methodology has an impressive capability of generating the whole Pareto front, even without using a search step. Two by-products of this paper are (i) the development of a collection of test problems for MOO and (ii) the extension of performance and data profiles to MOO, allowing a comparison of several solvers on a large set of test problems, in terms of their efficiency and robustness to determine Pareto fronts.

An Autonomous Reliability-Aware Negotiation Strategy for Cloud Computing Environments <=SEP=> Cloud computing paradigm allows subscription-based access to computing and storages services over the Internet. Since with advances of Cloud technology, operations such as discovery, scaling, and monitoring are accomplished automatically, negotiation between Cloud service requesters and providers can be a bottleneck if it is carried out by humans. Therefore, our objective is to offer a state-of-the-art solution to automate the negotiation process in Cloud environments. In previous works in the SLA negotiation area, requesters trust whatever QoS criteria values providers offer in the process of negotiation. However, the proposed negotiation strategy for requesters in this work is capable of assessing reliability of offers received from Cloud providers. In addition, our proposed negotiation strategy for Cloud providers considers utilization of resources when it generates new offers during negotiation and concedes more on the price of less utilized resources. The experimental results show that our strategy helps Cloud providers to increase their profits when they are participating in parallel negotiation with multiple requesters.

PRET DRAM controller: Bank privatization for predictability and temporal isolation <=SEP=> Hard real-time embedded systems employ high-capacity memories such as Dynamic RAMs (DRAMs) to cope with increasing data and code sizes of modern designs. However, memory controller design has so far largely focused on improving average-case performance. As a consequence, the latency of memory accesses is unpredictable, which complicates the worst-case execution time analysis necessary for hard real-time embedded systems.
 Our work introduces a novel DRAM controller design that is predictable and that significantly reduces worst-case access latencies. Instead of viewing the DRAM device as one resource that can only be shared as a whole, our approach views it as multiple resources that can be shared between one or more clients individually. We partition the physical address space following the internal structure of the DRAM device, i.e., its ranks and banks, and interleave ac- cesses to the blocks of this partition. This eliminates contention for shared resources within the device, making accesses temporally predictable and temporally isolated. This paper describes our DRAM controller design and its integration with a precision-timed (PRET) architecture called PTARM. We present analytical bounds on the latency and throughput of the proposed controller, and confirm these via simulation.

Meta-cognitive extreme learning machine for regression problems <=SEP=> In this paper, we present an efficient fast learning algorithm for regression problems using meta-cognitive extreme learning machine(McELM). The proposed algorithm has two components, namely the cognitive component and meta-cognitive component. The cognitive component is an extreme learning machine (ELM) while the meta-cognitive component which controls the cognitive component employs a self-regulating learning mechanism to decide what to learn, when to learn and how to learn. The meta-cognitive component chooses suitable learning method based on the samples presented namely, delete sample, reserve sample and network update. The use of ELM improves the network speed and reduces computational cost. Unlike traditional ELM, the number of hidden layers is not fixed priori in McELM, instead, the network is built during the learning phase. This algorithm is evaluated on a set of benchmark regression and approximation problems and also on a real-world wind force and moment coefficient prediction problem. Performance results in this study highlight that McELM can achieve better results compared with conventional ELM, support vector regression (SVR).

ERP evidence for conceptual mappings and comparison processes during the comprehension of conventional and novel metaphors <=SEP=> Cognitive linguists suggest that understanding metaphors requires activation of conceptual mappings between the involved concepts. We tested whether mappings are indeed in use during metaphor comprehension, and what mapping means as a cognitive process with Event-Related Potentials. Participants read literal, conventional metaphorical, novel metaphorical, and anomalous target sentences preceded by primes with related or unrelated mappings. Experiment 1 used sentence-primes to activate related mappings, and Experiment 2 used simile-primes to induce comparison thinking. In the unprimed conditions of both experiments, metaphors elicited N400s more negative than the literals. In Experiment 1, related sentence-primes reduced the metaphor-literal N400 difference in conventional, but not in novel metaphors. In Experiment 2, related simile-primes reduced the metaphor-literal N400 difference in novel, but not clearly in conventional metaphors. We suggest that mapping as a process occurs in metaphors, and the ways in which it can be facilitated by comparison differ between conventional and novel metaphors.

Design for auto-tuning PID controller based on genetic algorithms <=SEP=> This paper presents a method of designing proportional-integral-derivative (PID) controller based on genetic algorithms (GA). Ziegler-Nichols tuning formula has been used for predicting the range of gain coefficients of GA-PID controller. The use of GA for optimizing the gain coefficients of PID controller has considerably improved the performance of PID controller. Simulation studies on a second-order and a third-order control system demonstrate that the proposed controller provides high performance of dynamic and static characteristics.

Suppression by Selecting Wavelets for Feature Compression in Distributed Speech Recognition <=SEP=> Distributed speech recognition DSR splits the processing of data between a mobile device and a network server. In the front-end, features are extracted and compressed to transmit over a wireless channel to a back-end server, where the incoming stream is received and reconstructed for recognition tasks. In this paper, we propose a feature compression algorithm termed suppression by selecting wavelets SSW to achieve the two main goals of DSR: Minimizing memory and device requirements while also maintaining or even improving the recognition performance. The SSW approach first applies the discrete wavelet transform DWT to filter the incoming speech feature sequence into two temporal subsequences at the client terminal. Feature compression is achieved by keeping the low modulation frequency subsequence while discarding the high frequency counterpart. The low-frequency subsequence is then transmitted across the remote network for specific feature statistics normalization. Wavelets are favorable for resolving the temporal properties of the feature sequence, and the down-sampling process in DWT achieves data compression by reducing the amount of data at the terminal prior to transmission across the network. Once the compressed features have arrived at the server, the feature sequence can be enhanced by statistics normalization, reconstructed with inverse DWT, and compensated with a simple post filter to alleviate any over-smoothing effects from the compression stage. Results on a standard robustness task Aurora-4 and on a Mandarin Chinese news corpus showed SSW outperforms conventional noise-robustness techniques while also providing nearly a 50% compression rate during the transmission stage of DSR systems.

Additive Logistic Regression : a Statistical <=SEP=> Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classiication methodology. The performance of many classiication algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input data, and taking a weighted majority vote of the sequence of classiiers thereby produced. We show that this seemingly mysterious phenomenon can be understood in terms of well known statistical principles, namely additive modeling and maximum likelihood. For the two-class problem, boosting can be viewed as an approximation to additive modeling on the logistic scale using maximum Bernoulli likelihood as a criterion. We develop more direct approximations and show that they exhibit nearly identical results to boosting. Direct multi-class generalizations based on multinomial likelihood are derived that exhibit performance comparable to other recently proposed multi-class generalizations of boosting in most situations, and far superior in some. We suggest a minor modiication to boosting that can reduce computation, often by factors of 10 to 50. Finally, we apply these insights to produce an alternative formulation of boosting decision trees. This approach, based on best-rst truncated tree induction , often leads to better performance, and can provide interpretable descriptions of the aggregate decision rule. It is also much faster com-putationally making it more suitable to large scale data mining applications .

CzEngVallex: a Bilingual Czech-English Valency Lexicon <=SEP=> This paper introduces a new bilingual Czech-English verbal valency lexicon (called CzEngVallex) representing a relatively large empirical database. It includes 20,835 aligned valency frame pairs (i.e., verb senses which are translations of each other) and their aligned arguments. This new lexicon uses data from the Prague Czech-English Dependency Treebank and also takes advantage of the existing valency lexicons for both languages: the PDT-Vallex for Czech and the EngVallex for English. The CzEngVallex is available for browsing as well as for download in the LINDAT/CLARIN repository. The CzEngVallex is meant to be used not only by traditional linguists, lexicographers, translators but also by computational linguists both for the purposes of enriching theoretical linguistic accounts of verbal valency from a cross-linguistic perspective and for an innovative use in various NLP tasks.

Cartoon-recognition using video & audio descriptors <=SEP=> We present a new approach for classifying mpeg-2 video sequences as `cartoon' or `non-cartoon' by analyzing specific video and audio features of consecutive frames in real-time. This is part of the well-known video-genre-classification problem, where popular TV-broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7 [12]. In our method the extracted features from the visual descriptors are non-linearly combined using a multilayered perceptron and then considered together with the output of the audio-descriptor to produce a reliable recognition. The results demonstrate a high identification rate based on a large collection of 100 representative video sequences (20 cartoons and 4*20 non-cartoons) gathered from free digital TV-broadcasting.

Space and space-time modeling using process convolutions <=SEP=> A continuous spatial model can be constructed by convolving a very simple, perhaps independent, process with a kernel or point spread function. This approach for constructing a spatial process offers a number of advantages over specification through a spatial covariogram. In particular, this process convolution specification leads to compuational simplifications and easily extends beyond simple stationary models. This paper uses process convolution models to build space and space-time models that are flexible and able to accomodate large amounts of data. Data from environmental monitoring is considered.

The age of gossip: spatial mean field regime <=SEP=> Disseminating a piece of information, or updates for a piece of information, has been shown to benefit greatly from simple randomized procedures, sometimes referred to as gossiping, or epidemic algorithms. Similarly, in a network where mobile nodes occasionally receive updated content from a base station, gossiping using opportunistic contacts allows for recent updates to be efficiently maintained, for a large number of nodes. In this case, however, gossiping depends on node mobility. For this reason, we introduce a new gossip model, with mobile nodes moving between different classes that can represent locations or states, which determine gossiping behavior of the nodes. Here we prove that, when the number of mobile nodes becomes large, the age of the latest updates received by mobile nodes approaches a deterministic mean-field regime. More precisely, we show that the occupancy measure of the process constructed, with the ages defined above, converges to a deterministic limit that can be entirely characterized by differential equations. This major simplification allows us to characterize how mobility, source inputs and gossiping influence the age distribution for low and high ages. It also leads to a scalable numerical evaluation of the performance of mobile update systems, which we validate (using a trace of 500 taxicabs) and use to propose infrastructure deployment.

An Ethernet-Based Fronthaul Implementation with MAC/PHY Split LTE Processing <=SEP=> A testbed implementation for an Ethernet fronthaul transporting signals arising from a long-term evolution (LTE) functional subdivision ("split") at the media-access control (MAC)/physical layer (PHY) interface is presented. Based on open LTE base station software, the testbed demonstrates significant data rate reductions compared to current fronthaul implementations that rely on In- phase and Quadrature radio sample transportation and data rates that scale with cell load. All generated traffic flows are clearly distinguishable using appropriate packet headers. A selection of test cases and their corresponding results are presented to demonstrate the operation of the fronthaul and the performance of individual flows in terms of data rates and overheads.

Patient factors influencing dermal filler complications: prevention, assessment, and treatment <=SEP=> While rare, complications do occur with the esthetic use of dermal fillers. Careful attention to patient factors and technique can do much to avoid these complications, and a well-informed practitioner can mitigate problems when they do occur. Since cosmetic surgery is usually an elective process, requested by the patient, clinical trials are complex to organize and run. For this reason, an international group of practicing physicians in the field of esthetics came together to share knowledge and to try and produce some informed guidance for their colleagues, considering the literature and also pooling their own extensive clinical experience. This manuscript aims to summarize the crucial aspects of patient selection, including absolute contraindications as well as situations that warrant caution, and also covers important considerations for the pre- and posttreatment periods as well as during the procedure itself. Guidance is given on both immediate and long-term management of adverse reactions. The majority of complications are related to accepting patients inappropriate for treatment or issues of sterility, placement, volume, and injection technique. It is clear that esthetic practitioners need an in-depth knowledge of all aspects of treatment with dermal fillers to achieve optimal outcomes for their patients.

Stabilizing function of the diaphragm: dynamic MRI and synchronized spirometric assessment. <=SEP=> The aim was to describe diaphragmatic behavior during postural limb activities and examine the ventilatory and stabilizing functions of the diaphragm. Thirty healthy subjects were examined in the supine position using a dynamic MRI system assessed simultaneously with specialized spirometric readings. The diaphragmatic excursions (DEs) were measured at three diaphragmatic points in the sagittal plane; the diaphragm positions (DPs) as related to a reference horizontal baseline were determined. Measurements were taken during tidal breathing (TB) and isometric flexion of upper or lower extremities against external resistance together with TB. Mean DE in both upper and lower postural limb activities was greater compared with the TB condition (P < 0.05), with the effect greater for lower limb activities. Inspiratory DPs in the upper and lower extremity activities were lower compared with TB alone (P < 0.01). Expiratory DP was lower only for lower extremity activities (P < 0.01). DP was most affected at the apex of the crescent and crural (posterior) portion of the diaphragm. DEs correlated strongly with tidal volume (Vt) in all conditions. Changes in DEs relative to the initial value were minimal for upper and lower extremities but were related to lower values of Vt (P < 0.03). Significant involvement of the diaphragm in the limb postural activities was found. Resulting DEs and DPs differed from the TB conditions, especially in lower extremity activities. The differences between the percent changes of DEs vs. Vt found for lower extremity activities were confirmed by both ventilatory and postural diaphragm recruitment in response to postural demands.