Query: General transformations for GPU execution of tree traversals

Relevant passages:
A GPU implementation of inclusion-based points-to analysis <=SEP=> Graphics Processing Units (GPUs) have emerged as powerful accelerators for many regular algorithms that operate on dense arrays and matrices. In contrast, we know relatively little about using GPUs to accelerate highly irregular algorithms that operate on pointer-based data structures such as graphs. For the most part, research has focused on GPU implementations of graph analysis algorithms that do not modify the structure of the graph, such as algorithms for breadth-first search and strongly-connected components.
 In this paper, we describe a high-performance GPU implementation of an important graph algorithm used in compilers such as gcc and LLVM: Andersen-style inclusion-based points-to analysis. This algorithm is challenging to parallelize effectively on GPUs because it makes extensive modifications to the structure of the underlying graph and performs relatively little computation. In spite of this, our program, when executed on a 14 Streaming Multiprocessor GPU, achieves an average speedup of 7x compared to a sequential CPU implementation and outperforms a parallel implementation of the same algorithm running on 16 CPU cores.
 Our implementation provides general insights into how to produce high-performance GPU implementations of graph algorithms, and it highlights key differences between optimizing parallel programs for multicore CPUs and for GPUs.

Kd-Jump: a Path-Preserving Stackless Traversal for Faster Isosurface Raytracing on GPUs <=SEP=> Stackless traversal techniques are often used to circumvent memory bottlenecks by avoiding a stack and replacing return traversal with extra computation. This paper addresses whether the stackless traversal approaches are useful on newer hardware and technology (such as CUDA). To this end, we present a novel stackless approach for implicit kd-trees, which exploits the benefits of index-based node traversal, without incurring extra node visitation. This approach, which we term Kd-Jump, enables the traversal to immediately return to the next valid node, like a stack, without incurring extra node visitation (kd-restart). Also, Kd-Jump does not require global memory (stack) at all and only requires a small matrix in fast constant-memory. We report that Kd-Jump outperforms a stack by 10 to 20% and kd-restar t by 100%. We also present a Hybrid Kd-Jump, which utilizes a volume stepper for leaf testing and a run-time depth threshold to define where kd-tree traversal stops and volume-stepping occurs. By using both methods, we gain the benefits of empty space removal, fast texture-caching and realtime ability to determine the best threshold for current isosurface and view direction.

KD-tree acceleration structures for a GPU raytracer <=SEP=> Modern graphics hardware architectures excel at compute-intensive tasks such as ray-triangle intersection, making them attractive target platforms for raytracing. To date, most GPU-based raytracers have relied upon uniform grid acceleration structures. In contrast, the kd-tree has gained widespread use in CPU-based raytracers and is regarded as the best general-purpose acceleration structure. We demonstrate two kd-tree traversal algorithms suitable for GPU implementation and integrate them into a streaming raytracer. We show that for scenes with many objects at different scales, our kd-tree algorithms are up to 8 times faster than a uniform grid. In addition, we identify load balancing and input data recirculation as two fundamental sources of inefficiency when raytracing on current graphics hardware.

Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU <=SEP=> Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these kernels which makes them suitable for today's multi-core CPUs and GPUs. In the past few years there have been many studies claiming GPUs deliver substantial speedups (between 10X and 1000X) over multi-core CPUs on these kernels. To understand where such large performance difference comes from, we perform a rigorous performance analysis and find that after applying optimizations appropriate for both CPUs and GPUs the performance gap between an Nvidia GTX280 processor and the Intel Core i7-960 processor narrows to only 2.5x on average. In this paper, we discuss optimization techniques for both CPU and GPU, analyze what architecture features contributed to performance differences between the two architectures, and recommend a set of architectural features which provide significant improvement in architectural efficiency for throughput kernels.

Guaranteed-Quality Mesh Generation for Curved Surfaces <=SEP=> For several commonly-used solution techniques for partial differential equations, the first step is to divide the problem region into simply-shaped elements, creating a mesh. We present a technique for creating high-quality triangular meshes for regions on curved surfaces. This technique is an extension of previous methods we developed for regions in the plane. For both flat and curved surfaces, the resulting meshes are guaranteed to exhibit the following properties: (1) internal and external boundaries are respected, (2) element shapes are guaranteed—all elements are triangles with angles between 30 and 120 degrees (with the exception of badly shaped elements that may be required by the specified boundary), and (3) element density can be controlled, producing small elements in “interesting” areas and large elements elsewhere. An additional contribution of this paper is the development of a practical generalization of Delaunay triangulation to curved surfaces.

Bluetooth Based Home Automation and Security System Using ARM 9 <=SEP=> -Today we are living in 21 century where automation is playing important role in human life. Home automation allows us to control household appliances like light, door, fan, AC etc. It also provides home security and emergency system to be activated. Home automation not only refers to reduce human efforts but also energy saving and time efficiency. The main objective of home automation and security is to help handicapped and old aged people who will enable them to control home appliances and alert them in critical situations. This paper put forwards the design of home automation and security system using ARM7 LPC2148 board. The design is based on a standalone embedded system board ARM7 LPC2148 at home. Home appliances are connected to the ARM7 and communication is established between the ARM7 and ARM9 with Bluetooth device. The home appliances are connected to the input / output ports of the embedded system board and their status is passed to the ARM7. We would develop an authentication to the system for authorized person to access home appliances. The device with low cost and scalable to less modification to the core is much important. It presents the design and implementation of automation system that can monitor and control home appliances via ARM9 S3C2440A board.

Player Profiling with Fallout 3 <=SEP=> In previous research we concluded that a personality profile, based on the Five Factor Model, can be constructed from observations of a player’s behavior in a module that we designed for Neverwinter Nights (Lankveld et al. 2011a). In the present research, we investigate whether we can do the same thing in an actual modern commercial video game, in this case the game Fallout 3. We stored automatic observations on 36 participants who played the introductory stages of Fallout 3. We then correlated these observations with the participants’ personality profiles, expressed by values for five personality traits as measured by the standard NEO-FFI questionnaire. Our analysis shows correlations between all five personality traits and the game observations. These results validate and generalize the results from our previous research (Lankveld et al. 2011a). We may conclude that Fallout 3, and by extension other modern video games, allows players to express their personality, and can therefore be used to create person-

Future trends of electrical propulsion and implications to ship design <=SEP=> Over the last 10 years the electrical propulsion fleet grew three times faster than the world fleet. This paper provides an overlook of the main drivers supporting this impressive growth and explores supporting drivers for future growth. The paper also explores two emerging technologies in electrical propulsion and distribution and how these influence ship design: DC distribution and energy storage. with a considerable level of accuracy which led to development of Dynamic Positioning (DP). Since electric propulsion is the most suitable solution to cope with the power fluctuation and redundancy requirements from DP systems, the development of offshore segment was the biggest driver for the development of electrical propulsion, reaching more than 50% of the units in 2013. Figure 2 Distribution of electrical propulsion fleet Dredgers – Dredging equipment requires a considerable amount of power and consequently the auxiliary power tends to be quite high, very often higher than the propulsion power. Therefore, the merit of electric propulsion is to reduce the overall installed power on board. Cruise – The main driver for electrical propulsion is space saving and space optimization. By shifting into electrical propulsion it is possible to reduce the space occupied by main propulsion systems and above all to place the different equipment in a way that the spaces reserved for passengers are maximized in terms of volume and quality. Nevertheless, the redundancy levels offered by electrical propulsion and its ability to meet the regulations on this segment have also contributed significantly for the adoption of this technology. Survey vessels – These vessels are often used to tow underwater survey devices for different applications, e.g. hydrographic, seismic, etc. Therefore, the lower underwater noise offered by electrical propulsion is very attractive. In addition, the load requirements of the different operation modes can vary significantly which also favor electric propulsion. LNG carriers – When laden, LNG carriers use their own cargo as fuel which is released by the cargo containment system in a process known as boil-off. Therefore, the propulsion system is selected so that the boil-off gas can be used as fuel. Initially, most vessels were fitted with steam turbines connected to the propulsion shaft through a gearbox. However, the introduction of dual fuel engines led to the adoption of electric propulsion (Hansen et al. 2007). Icebreakers – Although traditional icebreakers use their own weight to break sheets of ice, it is quite often to have blocks of ice reaching the propellers. This causes a sudden drop of the propeller speed and consequently a sudden increase of torque which could easily stall the propulsion engine with mechanical drive. Electrical drives are able to vary the speed and the torque independently and therefore maintain the torque within the engine’s acceptable limits (Ådnanes, 2003).

Fast and Scalable Learning of Neuro-Symbolic Representations of Biomedical Knowledge <=SEP=> In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge. Based on a recently published comprehensive biological knowledge graph (Alshahrani, 2017) that was used for demonstrating neurosymbolic representation learning, we show how to train fast (under 1 minute) log-linear neural embeddings of the entities. We utilize these representations as inputs for machine learning classifiers to enable important tasks such as biological link prediction. Classifiers are trained by concatenating learned entity embeddings to represent entity relations, and training classifiers on the concatenated embeddings to discern true relations from automatically generated negative examples. Our simple embedding methodology greatly improves on classification error compared to previously published state-of-the-art results, yielding a maximum increase of +0.28 F-measure and +0.22 ROC AUC scores for the most difficult biological link prediction problem. Finally, our embedding approach is orders of magnitude faster to train (≤ 1 minute vs. hours), much more economical in terms of embedding dimensions (d = 50 vs. d = 512), and naturally encodes the directionality of the asymmetric biological relations, that can be controlled by the order with which we concatenate the embeddings.

TensorLog: Deep Learning Meets Probabilistic DBs <=SEP=> We present an implementation of a probabilistic first-order logic called TensorLog, in which classes of logical queries are compiled into differentiable functions in a neuralnetwork infrastructure such as Tensorflow or Theano. This leads to a close integration of probabilistic logical reasoning with deep-learning infrastructure: in particular, it enables high-performance deep learning frameworks to be used for tuning the parameters of a probabilistic logic. Experimental results show that TensorLog scales to problems involving hundreds of thousands of knowledge-base triples and tens of thousands of examples.

A Unified Model for Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media <=SEP=> Named entity recognition (NER) in Chinese social media is important but difficult because of its informality and strong noise. Previous methods only focus on in-domain supervised learning which is limited by the rare annotated data. However, there are enough corpora in formal domains and massive in-domain unannotated texts which can be used to improve the task. We propose a unified model which can learn from out-of-domain corpora and in-domain unannotated texts. The unified model contains two major functions. One is for cross-domain learning and another for semi-supervised learning. Cross-domain learning function can learn out-of-domain information based on domain similarity. Semi-Supervised learning function can learn in-domain unannotated information by self-training. Both learning functions outperform existing methods for NER in Chinese social media. Finally, our unified model yields nearly 11% absolute improvement over previously published results.

Longitudinal relations between children's exposure to TV violence and their aggressive and violent behavior in young adulthood: 1977-1992. <=SEP=> Although the relation between TV-violence viewing and aggression in childhood has been clearly demonstrated, only a few studies have examined this relation from childhood to adulthood, and these studies of children growing up in the 1960s reported significant relations only for boys. The current study examines the longitudinal relations between TV-violence viewing at ages 6 to 10 and adult aggressive behavior about 15 years later for a sample growing up in the 1970s and 1980s. Follow-up archival data (N = 450) and interview data (N = 329) reveal that childhood exposure to media violence predicts young adult aggressive behavior for both males and females. Identification with aggressive TV characters and perceived realism of TV violence also predict later aggression. These relations persist even when the effects of socioeconomic status, intellectual ability, and a variety of parenting factors are controlled.

Boot Attestation: Secure Remote Reporting with Off-The-Shelf IoT Sensors <=SEP=> A major challenge in computer security is about establishing the trustworthiness of remote platforms. Remote attestation is the most common approach to this challenge. It allows a remote platform to measure and report its system state in a secure way to a third party. Unfortunately, existing attestation solutions either provide low security, as they rely on unrealistic assumptions, or are not applicable to commodity low-cost and resource-constrained devices, as they require custom secure hardware extensions that are difficult to adopt across IoT vendors. In this work, we propose a novel remote attestation scheme, named Boot Attestation, that is particularly optimized for low-cost and resource-constrained embedded devices. In Boot Attestation, software integrity measurements are immediately committed to during boot, thus relaxing the traditional requirement for secure storage and reporting. Our scheme is very light on cryptographic requirements and storage, allowing efficient implementations, even on the most low-end IoT platforms available today. We also describe extensions for more flexible management of ownership and third party (public-key) attestation that may be desired in fully Internet-enabled devices. Our scheme is supported by many existing off-the-shelf devices. To this end, we review the hardware protection capabilities for a number of popular device types and present implementation results for two such commercially available platforms.

Implementation of Sha-256 Algorithm in Fpga Based Processor Soundarya <=SEP=> Hash functions play a significant role in today's cryptographic applications. SHA (Secure Hash Algorithm) is famous message compress standard used in computer cryptography, it can compress a long message to become a short message abstract. In this paper, SHA256 hash algorithm has been implemented using Verilog HDL (Hardware Description Language). The SHA-256 source code is divided into three modules, namely Data path, Memory and Top module. The Verilog code is synthesized using Xilinx software tool. The test vectors have been applied to verify the correctness of the SHA-256 functionality. A comparison between the proposed SHA-256 hash function implementation with other related works shows that it achieves the introduced system can working on reuse data, minimize critical paths and reduce the memory access by using cache memory, reducing clock cycles and needs less silicon area resources. The achieved performance in the term of throughput of the proposed system/architecture is much higher than the other hardware implementations. The proposed system could be used for the implementation of integrity units, and in many other sensitive cryptographic applications, such as, digital signatures, message authentication codes and random number generators.

PESTO: Parameter EStimation TOolbox <=SEP=> Summary
PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers scalable algorithms for optimization, uncertainty and identifiability analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, for which the user can provide a deterministic objective function in MATLAB.


Availability and implementation
PESTO is a MATLAB toolbox, freely available under the BSD license. The source code, along with extensive documentation and example code, can be downloaded from https://github.com/ICB-DCM/PESTO/.


Contact
jan.hasenauer@helmholtz-muenchen.de.


Supplementary information
Supplementary data are available at Bioinformatics online.

CRM ADOPTION IN A HIGHER EDUCATION INSTITUTION <=SEP=> More and more organisations, from private to public sectors, are pursuing higher levels of customer satisfaction, loyalty and retention. With this intent, higher education institutions (HEI) have adopted CRM – Customer Relationship Management. In order to analyse some of the interesting aspects of this phenomenon n, we conducted an action research in a European Institute. The main research question we answered is “how to adopt a CRM strategy in a Higher Education Institution?” Some of the main findings of this study are (1) even though HEI’s main customer is the student, there are others stakeholders that a CRM project must consider; (2) universities can use their internal resources to implement a CRM project successfully; and (3) using Agile software methodology is an effective way to define clearer, more objective and more assertive technical requirements which result in a CRM software that meet send user’s expectations and organizational strategic goals. These findings can help other HEIs 46 Rigo, G. E., Pedron, C. D., Caldeira, M., Araújo, C. C. S., JISTEM, Brazil Vol. 13, No. 1, Jan/Abr., 2016 pp. 45-60 www.jistem.fea.usp.br planning to adopt CRM as a strategic tool to improve their relationship with the stakeholders ́ community and expand their student body.

Air filter particulate loading detection using smartphone audio and optimized ensemble classification <=SEP=> Automotive engine intake filters ensure clean air delivery to the engine, though over time these filters load with contaminants hindering free airflow. Today’s open-loop approach to air filter maintenance has drivers replace elements at predetermined service intervals, causing costly and potentially harmful overand under-replacement. The result is that many vehicles consistently operate with reduced power, increased fuel consumption, or excessive particulate-related wear which may harm the catalyst or damage machined engine surfaces. We present a method of detecting filter contaminant loading from audio data collected by a smartphone and a stand microphone. Our machine learning approach to filter supervision uses Mel-Cepstrum, Fourier and Wavelet features as input into a classification model and applies feature ranking to select the best-differentiating features. We demonstrate the robustness of our technique by showing its efficacy for two vehicle types and different microphones, finding a best result of 79.7% accuracy when classifying a filter into three loading states. Refinements to this technique will help drivers supervise their filters and aid in optimally timing their replacement. This will result in an improvement in vehicle performance, efficiency, and reliability, while reducing the cost of maintenance to vehicle owners. © 2017 Elsevier Ltd. All rights reserved.

Appropriate agile measurement: using metrics and diagnostics to deliver business value <=SEP=> Agile software development continually measures both our product and the process used to create it, to allow improvement. With increased popularity, more risk-averse groups are being drawn to agile, bringing with them modes of evaluation incompatible with agile values and principles. These outmoded metrics drive dysfunctional behaviors which threaten the integrity of an emerging agile culture. This paper collects some of the current thinking on appropriate agile metrics, and proposes simple tools for use by teams or organizations. The intention of these tools is to foster dialogue about the appropriateness of metrics in a given context, and thereby to encourage measurements more congruent with the objectives of agile teamwork

A comparison of methods for differential expression analysis of RNA-seq data <=SEP=> Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data. We conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data. Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.

Compact triple C shaped microstrip patch antenna for WLAN, WiMAX & Wi-Fi application at 2.5 GHz <=SEP=> In the rapid progress of commercial communication applications, the development of compact antenna has an important role. This paper presents the analysis on the performance of single band compact triple C shaped microstrip patch antenna for WLAN, WiMAX and Wi-Fi applications with center frequency at 2.5GHz. The microstrip antenna has a planar geometry and consists of a ground plane, a substrate, a compact patch as radiator. Different specifications of the proposed antenna are measured through computer simulation in free space. The antenna has an impedance bandwidth of 2500 MHz (2487MHz to 2512 MHz). The proposed antenna provides excellent SWR of 1.04 and return loss -32.80 dB while excited by a 50 O microstrip feed line. All simulation results are performed using the CST Microwave Studio.

Unsupervised models for morpheme segmentation and morphology learning <=SEP=> 

Estimation of variable-speed-drive power consumption from harmonic content <=SEP=> Nonintrusive load monitoring can be used to identify the operating schedule of individual loads strictly from measurements of an aggregate power signal. Unfortunately, certain classes of loads present a continuously varying power demand. The power demand of these loads can be difficult to separate from an aggregate measurement. Variable-speed drives (VSDs) are industrially important variable-demand loads that are difficult to track non-intrusively. This paper proposes a VSD power estimation method based on observed correlations between fundamental and higher harmonic spectral content in current. The technique can be generalized to any load with signature correlations in harmonic content, including many power electronic and electromechanical loads. The approach presented here expands the applicability and field reliability of nonintrusive load monitoring.

"In vivo" spam filtering: a challenge problem for KDD <=SEP=> Spam, also known as Unsolicited Commercial Email (UCE), is the bane of email communication. Many data mining researchers have addressed the problem of detecting spam, generally by treating it as a static text classification problem. True in vivo spam filtering has characteristics that make it a rich and challenging domain for data mining. Indeed, real-world datasets with these characteristics are typically difficult to acquire and to share. This paper demonstrates some of these characteristics and argues that researchers should pursue in vivo spam filtering as an accessible domain for investigating them.

Exploiting Heterogeneous Human Mobility Patterns for Intelligent Bus Routing <=SEP=> Optimal planning for public transportation is one of the keys to sustainable development and better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. In this paper, we focus on the identification and optimization of flawed bus routes to improve utilization efficiency of public transportation services, according to people's real demand for public transportation. To this end, we first provide an integrated mobility pattern analysis between the location traces of taxicabs and the mobility records in bus transactions. Based on mobility patterns, we propose a localized transportation mode choice model, with which we can accurately predict the bus travel demand for different bus routing. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. We also leverage the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips.

A PID Compensator Control for the Synchronous Rectifier Buck DC-DC Converter <=SEP=> This paper aims to design a PID compensator for the control of Synchronous Rectifier (SR) Buck Converter to improve its conversion efficiency under different load conditions. Since the diode rectifier is replaced by a high frequency MOSFET switch, the SR control method itself will be sufficient under heavy load condition, to attain better normal mode performance. However, this technique does not hold well in light load condition, due to increased switching losses. A new control technique accompanied with PID compensator is introduced in the paper will enable synchronous buck converter to realize ZVS, while feeding light loads. This is also least cost and highly efficient easy technique without use of extra auxiliary switches and RLC components. This control technique also proved to be efficient under input voltage variations. Simulation is done in MATLAB Simulink for proving stabilization provided by the PID compensator for synchronous rectifier (SR) buck converter. Keywords—Synchronous Rectifier (SR), Continuous Condu-ction Mode (CCM), Discontinuous Conduction Mode (DCM), Zero Voltage Switching (ZVS), Proportional-IntegralDerivative (PID).

200V Super Junction MOSFET Fabricated by High Aspect Ratio Trench Filling <=SEP=> In this paper, a new filling process using an anisotropic epitaxial growth was proposed as the fabrication method of a super junction (SJ) MOSFET. The anisotropic growth controlled with silicon (Si) and chlorine (Cl) source gases was applied to filling of the high-aspect-ratio trenches without voids. Using the process we succeeded in filling trenches with aspect ratio of 18, which is the highest aspect ratio works previously reported, and we fabricated a 200V SJ-MOSFET with a 2.7mum pitch trench gate structure. Consequently, the MOSFET showed electrical characteristics of the lowest on-resistance of 1.5 m-ohm-cm 2 and a breakdown voltage of 225V

A survey on IoT communication and computation frameworks: An industrial perspective <=SEP=> This paper surveys fog computing and embedded systems platforms as the building blocks of Internet of Things (IoT). Many concepts around IoT architectures, with various examples, are also discussed. This paper reviews a high-level conceptual layered architecture for IoT from a computational perspective. The architecture incorporates fog computing to address several issues associated with cloud computing; however, it is never a binary decision between fog and cloud. Many of the world's physical objects are being embedded with sensors and actuators, tied by communication infrastructures, and managed by computational algorithms. IoT sensor networks and embedded systems connecting smart objects are revolutionizing how we approach our daily lives, health care, energy, and transportation. Such computational needs are addressed with an array of various models and frameworks. In an attempt to consolidate the use of these models, this paper reviews the state-of-the-art research in IoT, cloud computing, and fog computing.

The Obstetric Consequences of Female Genital Mutilation/Cutting: A Systematic Review and Meta-Analysis <=SEP=> Various forms of female genital mutilation/cutting (FGM/C) have been performed for millennia and continue to be prevalent in parts of Africa. Although the health consequences following FGM/C have been broadly investigated, divergent study results have called into question whether FGM/C is associated with obstetric consequences. To clarify the present state of empirical research, we conducted a systematic review of the scientific literature and quantitative meta-analyses of the obstetric consequences of FGM/C. We included 44 primary studies, of which 28 were comparative, involving almost 3 million participants. The methodological study quality was generally low, but several studies reported the same outcome and were sufficiently similar to warrant pooling of effect sizes in meta-analyses. The meta-analyses results showed that prolonged labor, obstetric lacerations, instrumental delivery, obstetric hemorrhage, and difficult delivery are markedly associated with FGM/C, indicating that FGM/C is a factor in their occurrence and significantly increases the risk of delivery complications. There was no significant difference in risk with respect to cesarean section and episiotomy. These results can make up the background documentation for health promotion and health care decisions that inform work to reduce the prevalence of FGM/C and improve the quality of services related to the consequences of FGM/C.

A fully integrated 77GHz FMCW radar system in 65nm CMOS <=SEP=> Millimeter-wave anti-collision radars have been widely investigated in advanced CMOS technologies recently. This paper presents a fully integrated 77GHz FMCW radar system in 65nm CMOS. The FMCW radar transmits a continuous wave which is triangularly modulated in frequency and receives the wave reflected from objects. As can be illustrated in Fig. 11.2.1, for a moving target, the received frequency would be shifted (i.e. Doppler shift), resulting in two different offset frequencies f + and f− for the falling and rising ramps. Denoting the modulation range and period as B and Tm, respectively, we can derive the distance R and the relative velocity VR as listed in Fig. 11.2.1, where fc represents the center frequency and c the speed of light.

An exploration of crowdsourcing citation screening for systematic reviews <=SEP=> Systematic reviews are increasingly used to inform health care decisions, but are expensive to produce. We explore the use of crowdsourcing (distributing tasks to untrained workers via the web) to reduce the cost of screening citations. We used Amazon Mechanical Turk as our platform and 4 previously conducted systematic reviews as examples. For each citation, workers answered 4 or 5 questions that were equivalent to the eligibility criteria. We aggregated responses from multiple workers into an overall decision to include or exclude the citation using 1 of 9 algorithms and compared the performance of these algorithms to the corresponding decisions of trained experts. The most inclusive algorithm (designating a citation as relevant if any worker did) identified 95% to 99% of the citations that were ultimately included in the reviews while excluding 68% to 82% of irrelevant citations. Other algorithms increased the fraction of irrelevant articles excluded at some cost to the inclusion of relevant studies. Crowdworkers completed screening in 4 to 17 days, costing $460 to $2220, a cost reduction of up to 88% compared to trained experts. Crowdsourcing may represent a useful approach to reducing the cost of identifying literature for systematic reviews.