Query: Modeling and Analyzing Millimeter Wave Cellular Systems

Relevant passages:
An introduction to millimeter-wave mobile broadband systems <=SEP=> Almost all mobile communication systems today use spectrum in the range of 300 MHz-3 GHz. In this article, we reason why the wireless community should start looking at the 3-300 GHz spectrum for mobile broadband applications. We discuss propagation and device technology challenges associated with this band as well as its unique advantages for mobile communication. We introduce a millimeter-wave mobile broadband (MMB) system as a candidate next generation mobile communication system. We demonstrate the feasibility for MMB to achieve gigabit-per-second data rates at a distance up to 1 km in an urban mobile environment. A few key concepts in MMB network architecture such as the MMB base station grid, MMB interBS backhaul link, and a hybrid MMB + 4G system are described. We also discuss beamforming techniques and the frame structure of the MMB air interface.

Millimeter Wave Mobile Communications for 5 G Cellular : It Will Work ! <=SEP=> The global bandwidth shortage facing wireless carriers has motivated the exploration of the underutilized millimeter wave (mm-wave) frequency spectrum for future broadband cellular communication networks. There is, however, little knowledge about cellular mm-wave propagation in densely populated indoor and outdoor environments. Obtaining this information is vital for the design and operation of future fifth generation cellular networks that use the mm-wave spectrum. In this paper, we present the motivation for new mm-wave cellular systems, methodology, and hardware for measurements and offer a variety of measurement results that show 28 and 38 GHz frequencies can be used when employing steerable directional antennas at base stations and mobile devices. INDEX TERMS 28GHz, 38GHz, millimeter wave propagation measurements, directional antennas, channel models, 5G, cellular, mobile communications, MIMO.

Steering with eyes closed: Mm-Wave beam steering without in-band measurement <=SEP=> Millimeter-wave communication achieves multi-Gbps data rates via highly directional beamforming to overcome pathloss and provide the desired SNR. Unfortunately, establishing communication with sufficiently narrow beamwidth to obtain the necessary link budget is a high overhead procedure in which the search space scales with device mobility and the product of the sender-receiver beam resolution. In this paper, we design, implement, and experimentally evaluate Blind Beam Steering (BBS) a novel architecture and algorithm that removes in-band overhead for directional mm-Wave link establishment. Our system architecture couples mm-Wave and legacy 2.4/5 GHz bands using out-of-band direction inference to establish (overhead-free) multi-Gbps mm-Wave communication. Further, BBS evaluates direction estimates retrieved from passively overheard 2.4/5 GHz frames to assure highest mm-Wave link quality on unobstructed direct paths. By removing in-band overhead, we leverage mm-Wave's very high throughput capabilities, beam-width scalability and provide robustness to mobility. We demonstrate that BBS achieves 97.8% accuracy estimating direction between pairing nodes using at least 5 detection band antennas. Further, BBS successfully detects unobstructed direct path conditions with an accuracy of 96.5% and reduces the IEEE 802.11ad beamforming training overhead by 81%.

Joint Resource Partitioning and Offloading in Heterogeneous Cellular Networks <=SEP=> In heterogeneous cellular networks (HCNs), it is desirable to offload mobile users to small cells, which are typically significantly less congested than the macrocells. To achieve sufficient load balancing, the offloaded users often have much lower SINR than they would on the macrocell. This SINR degradation can be partially alleviated through interference avoidance, for example time or frequency resource partitioning, whereby the macrocell turns off in some fraction of such resources. Naturally, the optimal offloading strategy is tightly coupled with resource partitioning; the optimal amount of which in turn depends on how many users have been offloaded. In this paper, we propose a general and tractable framework for modeling and analyzing joint resource partitioning and offloading in a two-tier cellular network. With it, we are able to derive the downlink rate distribution over the entire network, and an optimal strategy for joint resource partitioning and offloading. We show that load balancing, by itself, is insufficient, and resource partitioning is required in conjunction with offloading to improve the rate of cell edge users in co-channel heterogeneous networks.

IEEE 802.11ad: directional 60 GHz communication for multi-Gigabit-per-second Wi-Fi [Invited Paper] <=SEP=> With the ratification of the IEEE 802.11ad amendment to the 802.11 standard in December 2012, a major step has been taken to bring consumer wireless communication to the millimeter wave band. However, multi-gigabit-per-second throughput and small interference footprint come at the price of adverse signal propagation characteristics, and require a fundamental rethinking of Wi-Fi communication principles. This article describes the design assumptions taken into consideration for the IEEE 802.11ad standard and the novel techniques defined to overcome the challenges of mm-Wave communication. In particular, we study the transition from omnidirectional to highly directional communication and its impact on the design of IEEE 802.11ad.

Inter-Session Modeling for Session-Based Recommendation <=SEP=> In recent years, research has been done on applying Recurrent Neural Networks (RNNs) as recommender systems. Results have been promising, especially in the session-based setting where RNNs have been shown to outperform state-of-the-art models. In many of these experiments, the RNN could potentially improve the recommendations by utilizing information about the user's past sessions, in addition to its own interactions in the current session. A problem for session-based recommendation, is how to produce accurate recommendations at the start of a session, before the system has learned much about the user's current interests. We propose a novel approach that extends an RNN recommender to be able to process the user's recent sessions, in order to improve recommendations. This is done by using a second RNN to learn from recent sessions, and predict the user's interest in the current session. By feeding this information to the original RNN, it is able to improve its recommendations. Our experiments on two different datasets show that the proposed approach can significantly improve recommendations throughout the sessions, compared to a single RNN working only on the current session. The proposed model especially improves recommendations at the start of sessions, and is therefore able to deal with the cold start problem within sessions.

Learning analytics: envisioning a research discipline and a domain of practice <=SEP=> Learning analytics are rapidly being implemented in different educational settings, often without the guidance of a research base. Vendors incorporate analytics practices, models, and algorithms from datamining, business intelligence, and the emerging "big data" fields. Researchers, in contrast, have built up a substantial base of techniques for analyzing discourse, social networks, sentiments, predictive models, and in semantic content (i.e., "intelligent" curriculum). In spite of the currently limited knowledge exchange and dialogue between researchers, vendors, and practitioners, existing learning analytics implementations indicate significant potential for generating novel insight into learning and vital educational practices. This paper presents an integrated and holistic vision for advancing learning analytics as a research discipline and a domain of practices. Potential areas of collaboration and overlap are presented with the intent of increasing the impact of analytics on teaching, learning, and the education system.

Mood and Emotions in Small Groups and Work Teams <=SEP=> Affective influences abound in groups. In this article we propose an organizing model for understanding these affective influences and their effects on group life. We begin with individuallevel affective characteristics that members bring to their groups: moods, emotions, sentiments, and emotional intelligence. These affective characteristics then combine to form a group’s affective composition. We discuss explicit and implicit processes through which this affective combination occurs by examining the research on emotional contagion, entrainment, modeling, and the manipulation of affect. We also explore how elements of the affective context, such as organizationwide emotion norms and the group’s particular emotional history, may serve to constrain or amplify group members’ emotions. The outcome, group emotion, results from the combination of the group’s affective composition and the affective context in which the group is behaving. Last, we focus on the important interaction between nonaffective factors and affective factors in group life and suggest a possible agenda for future research. q 2001 Academic Press

LoRea: A Backscatter Reader for Everyone! <=SEP=> Computational RFID (CRFID) platforms have enabled reconfigurable, battery-free applications for close to a decade. However, several factors have impeded their wide-spread adoption: low communication range, low throughput, and expensive infrastructure— CRFID readers usually cost upwards of $1000. This paper presents LoRea, a backscatter reader that achieves an order of magnitude higher range than existing CRFID readers, while costing a fraction of their price. LoRea achieves this by diverging from existing designs of CRFID readers and more specifically, how self-interference is tackled. LoRea builds on recent work that frequency-shifts backscatter transmissions away from the carrier signal to reduce selfinterference. LoRea also decouples the carrier generation from the reader, helping to further decrease self-interference. Decoupling carrier generation also enables the use of deployed infrastructure of smartphones and sensor nodes to provide the carrier signal. Together these methods reduce cost and complexity of the reader. LoRea also purposefully operates at lower bitrates than recent backscatter systems which enables high sensitivity and longer range. We evaluate LoRea experimentally and find that it achieves a communication range of up to 225 m in lineof-sight scenarios. In indoor environments, where the signal traverses several walls separating the reader and the backscatter tag, LoRea achieves a range of 30 m. These results illustrate how LoRea outperforms stateof-art backscatter systems and CRFID platforms.

Sensory integration dysfunction affects efficacy of speech therapy on children with functional articulation disorders <=SEP=> BACKGROUND
Articulation disorders in young children are due to defects occurring at a certain stage in sensory and motor development. Some children with functional articulation disorders may also have sensory integration dysfunction (SID). We hypothesized that speech therapy would be less efficacious in children with SID than in those without SID. Hence, the purpose of this study was to compare the efficacy of speech therapy in two groups of children with functional articulation disorders: those without and those with SID.


METHOD
A total of 30 young children with functional articulation disorders were divided into two groups, the no-SID group (15 children) and the SID group (15 children). The number of pronunciation mistakes was evaluated before and after speech therapy.


RESULTS
There were no statistically significant differences in age, sex, sibling order, education of parents, and pretest number of mistakes in pronunciation between the two groups (P > 0.05). The mean and standard deviation in the pre- and post-test number of mistakes in pronunciation were 10.5 ± 3.2 and 3.3 ± 3.3 in the no-SID group, and 10.1 ± 2.9 and 6.9 ± 3.5 in the SID group, respectively. Results showed great changes after speech therapy treatment (F = 70.393; P < 0.001) and interaction between the pre/post speech therapy treatment and groups (F = 11.119; P = 0.002).


CONCLUSIONS
Speech therapy can improve the articulation performance of children who have functional articulation disorders whether or not they have SID, but it results in significantly greater improvement in children without SID. SID may affect the treatment efficiency of speech therapy in young children with articulation disorders.

Membrane vesicles as conveyors of immune responses <=SEP=> In multicellular organisms, communication between cells mainly involves the secretion of proteins that then bind to receptors on neighbouring cells. But another mode of intercellular communication — the release of membrane vesicles — has recently become the subject of increasing interest. Membrane vesicles are complex structures composed of a lipid bilayer that contains transmembrane proteins and encloses soluble hydrophilic components derived from the cytosol of the donor cell. These vesicles have been shown to affect the physiology of neighbouring recipient cells in various ways, from inducing intracellular signalling following binding to receptors to conferring new properties after the acquisition of new receptors, enzymes or even genetic material from the vesicles. This Review focuses on the role of membrane vesicles, in particular exosomes, in the communication between immune cells, and between tumour and immune cells.

Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion <=SEP=> Multi-view structure from motion (SfM) estimates the position and orientation of pictures in a common 3D coordinate frame. When views are treated incrementally, this external calibration can be subject to drift, contrary to global methods that distribute residual errors evenly. We propose a new global calibration approach based on the fusion of relative motions between image pairs. We improve an existing method for robustly computing global rotations. We present an efficient a contrario trifocal tensor estimation method, from which stable and precise translation directions can be extracted. We also define an efficient translation registration method that recovers accurate camera positions. These components are combined into an original SfM pipeline. Our experiments show that, on most datasets, it outperforms in accuracy other existing incremental and global pipelines. It also achieves strikingly good running times: it is about 20 times faster than the other global method we could compare to, and as fast as the best incremental method. More importantly, it features better scalability properties.

SquishedNets: Squishing SqueezeNet further for edge device scenarios via deep evolutionary synthesis <=SEP=> While deep neural networks have been shown in recent years to outperform other machine learning methods in a wide range of applications, one of the biggest challenges with enabling deep neural networks for widespread deployment on edge devices such as mobile and other consumer devices is high computational and memory requirements. Recently, there has been greater exploration into small deep neural network architectures that are more suitable for edge devices, with one of the most popular architectures being SqueezeNet, with an incredibly small model size of 4.8MB. Taking further advantage of the notion that many applications of machine learning on edge devices are often characterized by a low number of target classes, this study explores the utility of combining architectural modifications and an evolutionary synthesis strategy for synthesizing even smaller deep neural architectures based on the more recent SqueezeNet v1.1 macroarchitecture (considered state-of-the-art in efficient architectures) for applications with fewer target classes. In particular, architectural modifications are first made to SqueezeNet v1.1 to accommodate for a 10-class ImageNet-10 dataset, and then an evolutionary synthesis strategy is leveraged to synthesize more efficient deep neural networks based on this modified macroarchitecture. The resulting SquishedNets possess model sizes ranging from 2.4MB to 0.95MB (∼5.17X smaller than SqueezeNet v1.1, or 253X smaller than AlexNet). Furthermore, the SquishedNets are still able to achieve accuracies ranging from 81.2% to 77%, and able to process at speeds of 156 images/sec to as much as 256 images/sec on a Nvidia Jetson TX1 embedded chip. These preliminary results show that a combination of architectural modifications and an evolutionary synthesis strategy can be a useful tool for producing very small deep neural network architectures that are well-suited for edge device scenarios without the need for compression or quantization.

Towards the Next Generation of Tabletop Gaming Experiences <=SEP=> In this paper we present a novel hardware and software platform (STARS) to realize computer augmented tabletop games that unify the strengths of traditional board games and computer games. STARS game applications preserve the social situation of traditional board games and provide a tangible interface with physical playing pieces to facilitate natural interaction. The virtual game components offer exciting new opportunities for game design and provide richer gaming experiences impossible to realize with traditional media. This paper describes STARS in terms of the hardware setup and the software platform used to develop and play STARS games. The interaction design within STARS is discussed and sample games are presented with regard to their contributions to enhancing user experience. Finally, realworld experiences with the platform are reported.

Integrated feature selection and higher-order spatial feature extraction for object categorization <=SEP=> In computer vision, the bag-of-visual words image representation has been shown to yield good results. Recent work has shown that modeling the spatial relationship between visual words further improves performance. Previous work extracts higher-order spatial features exhaustively. However, these spatial features are expensive to compute. We propose a novel method that simultaneously performs feature selection and feature extraction. Higher-order spatial features are progressively extracted based on selected lower order ones, thereby avoiding exhaustive computation. The method can be based on any additive feature selection algorithm such as boosting. Experimental results show that the method is computationally much more efficient than previous approaches, without sacrificing accuracy.

A Comparison Among ARIMA, BP-NN, and MOGA-NN for Software Clone Evolution Prediction <=SEP=> Software evolution continues throughout the life cycle of the software. During the evolution of software system, it has been observed that the developers have a tendency to copy the modules completely or partially and modify them. This practice gives rise to identical or very similar code fragments called software clones. This paper examines the evolution of clone components by using advanced time series analysis. In the first phase, software clone components are extracted from the source repository of the software application by using the abstract syntax tree approach. Then, the evolution of software clone components is analyzed. In this paper, three models, Autoregressive Integrated Moving Average, back propagation neural network, and multi-objective genetic algorithm-based neural network, have been compared for the prediction of the evolution of software clone components. Evaluation is performed on the large open-source software application, ArgoUML. The ability to predict the clones helps the software developer to reduce the effort during software maintenance activities.

Automated detection of optic disc and blood vessel in retinal image using morphological, edge detection and feature extraction technique <=SEP=> Reliable, fast and efficient optic disc localization and blood-vessel detection are the primary tasks in computer analyses of retinal image. Most of the existing algorithms suffer due to inconsistent image contrast, varying individual condition, noises and computational complexity. This paper presents an algorithm to automatically detect landmark features of retinal image, such as optic disc and blood vessel. First, optic disc and blood vessel pixels are detected from blue plane of the image. Then, using OD location the vessel pixels are connected. The detection scheme utilizes basic operations like edge detection, binary thresholding and morphological operation. This method was evaluated on standard retinal image databases, such as STARE and DRIVE. Experimental results demonstrate that the high accuracy achieved by the proposed method is comparable to that reported by the most accurate methods in literature, alongside a substantial reduction of execution time. Thus the method may provide a reliable solution in automatic mass screening and diagnosis of the retinal diseases.

Linked data on the web (LDOW2008) <=SEP=> The Web is increasingly understood as a global information space consisting not just of linked documents, but also of Linked Data. More than just a vision, the resulting Web of Data has been brought into being by the maturing of the Semantic Web technology stack, and by the publication of an increasing number of datasets according to the principles of Linked Data.
 The Linked Data on the Web (LDOW2008) workshop brings together researchers and practitioners working on all aspects of Linked Data. The workshop provides a forum to present the state of the art in the field and to discuss ongoing and future research challenges. In this workshop summary we will outline the technical context in which Linked Data is situated, describe developments in the past year through initiatives such as the Linking Open Data community project, and look ahead to the workshop itself.

Spectrogram Enhancement Using Multiple Window Savitzky-Golay (MWSG) Filter for Robust Bird Sound Detection <=SEP=> Bird sound detection from real-field recordings is essential for identifying bird species in bioacoustic monitoring. Variations in the recording devices, environmental conditions, and the presence of vocalizations from other animals make the bird sound detection very challenging. In order to overcome these challenges, we propose an unsupervised algorithm comprising two main stages. In the first stage, a spectrogram enhancement technique is proposed using a multiple window Savitzky-Golay MWSG filter. We show that the spectrogram estimate using MWSG filter is unbiased and has lower variance compared with its single window counterpart. It is known that bird sounds are highly structured in the time-frequency T-F plane. We exploit these cues of prominence of T-F activity in specific directions from the enhanced spectrogram, in the second stage of the proposed method, for bird sound detection. In this regard, we use a set of four moving average filters that when applied to the enhanced spectrogram, yield directional spectrograms that capture the direction specific information. We propose a thresholding scheme on the time varying energy profile computed from each of these directional spectrograms to obtain frame-level binary decisions of bird sound activity. These individual decisions are then combined to obtain the final decision. Experiments are performed with three different datasets, with varying recording and noise conditions. Frame level F-score is used as the evaluation metric for bird sound detection. We find that the proposed method, on average, achieves higher F-score $10.24\%$ relative compared to the best of the six baseline schemes considered in this work.

A fast and efficient python library for interfacing with the Biological Magnetic Resonance Data Bank <=SEP=> The Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis. To aid in the use of this public resource, we have developed a package called nmrstarlib in the popular open-source programming language Python. The nmrstarlib’s implementation is very efficient, both in design and execution. The library has facilities for reading and writing both NMR-STAR version 2.1 and 3.1 formatted files, parsing them into usable Python dictionary- and list-based data structures, making access and manipulation of the experimental data very natural within Python programs (i.e. “saveframe” and “loop” records represented as individual Python dictionary data structures). Another major advantage of this design is that data stored in original NMR-STAR can be easily converted into its equivalent JavaScript Object Notation (JSON) format, a lightweight data interchange format, facilitating data access and manipulation using Python and any other programming language that implements a JSON parser/generator (i.e., all popular programming languages). We have also developed tools to visualize assigned chemical shift values and to convert between NMR-STAR and JSONized NMR-STAR formatted files. Full API Reference Documentation, User Guide and Tutorial with code examples are also available. We have tested this new library on all current BMRB entries: 100% of all entries are parsed without any errors for both NMR-STAR version 2.1 and version 3.1 formatted files. We also compared our software to three currently available Python libraries for parsing NMR-STAR formatted files: PyStarLib, NMRPyStar, and PyNMRSTAR. The nmrstarlib package is a simple, fast, and efficient library for accessing data from the BMRB. The library provides an intuitive dictionary-based interface with which Python programs can read, edit, and write NMR-STAR formatted files and their equivalent JSONized NMR-STAR files. The nmrstarlib package can be used as a library for accessing and manipulating data stored in NMR-STAR files and as a command-line tool to convert from NMR-STAR file format into its equivalent JSON file format and vice versa, and to visualize chemical shift values. Furthermore, the nmrstarlib implementation provides a guide for effectively JSONizing other older scientific formats, improving the FAIRness of data in these formats.

Bibliometric information retrieval system (BIRS): A web search interface utilizing bibliometric research results <=SEP=> The aim of this article is to test whether the results obtained from a specific bibliographic research can be applied to a real search environment and enhance the level of utility of an information retrieval session for all levels of end users. In this respect, a Web-based Bibliometric Information Retrieval System (BIRS) has been designed and created, with facilities to assist the end users to get better understanding of their search domain, formulate and expand their search queries, and visualize the bibliographic research results. There are three specific features in the system design of the BIRS: the information visualization feature of the BIRS (cocitation maps) to guide the end users to identify the important research groups and capture the detailed information about the intellectual structure of the search domain; the multilevel browsing feature to allow the end users to go to different levels of interesting topics; and the common user interface feature to enable the end users to search all kinds of databases regardless of different searching systems, different working platforms, different database producer and supplier, such as different Web search engines, different library OPACs, or different on-line databases. A preliminary user evaluation study of BIRS revealed that users generally found it easy to form and expand their queries, and that BIRS helped them acquire useful background information about the search domain. They also pointed out aspects of information visualization, multilevel browsing, and common user interface as novel characteristics exhibited by BIRS.

Information Retrieval with Verbose Queries <=SEP=> Recently, the focus of many novel search applications shifted from short keyword queries to verbose natural language queries. Examples include question answering systems and dialogue systems, voice search on mobile devices and entity search engines like Facebook's Graph Search or Google's Knowledge Graph. However the performance of textbook information retrieval techniques for such verbose queries is not as good as that for their shorter counterparts. Thus, effective handling of verbose queries has become a critical factor for adoption of information retrieval techniques in this new breed of search applications. Over the past decade, the information retrieval community has deeply explored the problem of transforming natural language verbose queries using operations like reduction, weighting, expansion, reformulation and segmentation into more effective structural representations. However, thus far, there was not a coherent and organized tutorial on this topic. In this tutorial, we aim to put together various research pieces of the puzzle, provide a comprehensive and structured overview of various proposed methods, and also list various application scenarios where effective verbose query processing can make a significant difference.

Elementary Siphons of Petri Nets and Deadlock Control in FMS <=SEP=> For responsiveness, in the Petri nets theory framework deadlock prevention policies based elementary siphons control are often utilized to deal with deadlocks caused by the sharing of resources in flexible manufacturing system (FMS) which is developing the theory of efficient strict minimal siphons of an S3PR. Analyzer of Petri net models and their P-invariant analysis, and deadlock control are presented as tools for modelling, efficiency structure analysis, control, and investigation of the FMSs when different policies can be implemented for the deadlock prevention. We are to show an effective deadlock prevention policy of a special class of Petri nets namely elementary siphons. As well, both structural analysis and reachability graph analysis and simulation are used for analysis and control of Petri nets. This work is successfully applied Petri nets to deadlock analysis using the concept of elementary siphons, for design of supervisors of some supervisory control problems of FMS and simulation of Petri net tool with MATLAB.

Omega-3 Fatty Acids and Inflammatory Processes <=SEP=> Long chain fatty acids influence inflammation through a variety of mechanisms; many of these are mediated by, or at least associated with, changes in fatty acid composition of cell membranes. Changes in these compositions can modify membrane fluidity, cell signaling leading to altered gene expression, and the pattern of lipid mediator production. Cell involved in the inflammatory response are typically rich in the n-6 fatty acid arachidonic acid, but the contents of arachidonic acid and of the n-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) can be altered through oral administration of EPA and DHA. Eicosanoids produced from arachidonic acid have roles in inflammation. EPA also gives rise to eicosanoids and these often have differing properties from those of arachidonic acid-derived eicosanoids. EPA and DHA give rise to newly discovered resolvins which are anti-inflammatory and inflammation resolving. Increased membrane content of EPA and DHA (and decreased arachidonic acid content) results in a changed pattern of production of eicosanoids and resolvins. Changing the fatty acid composition of cells involved in the inflammatory response also affects production of peptide mediators of inflammation (adhesion molecules, cytokines etc.). Thus, the fatty acid composition of cells involved in the inflammatory response influences their function; the contents of arachidonic acid, EPA and DHA appear to be especially important. The anti-inflammatory effects of marine n-3 PUFAs suggest that they may be useful as therapeutic agents in disorders with an inflammatory component.

Impact of mosquito gene drive on malaria elimination in a computational model with explicit spatial and temporal dynamics. <=SEP=> The renewed effort to eliminate malaria and permanently remove its tremendous burden highlights questions of what combination of tools would be sufficient in various settings and what new tools need to be developed. Gene drive mosquitoes constitute a promising set of tools, with multiple different possible approaches including population replacement with introduced genes limiting malaria transmission, driving-Y chromosomes to collapse a mosquito population, and gene drive disrupting a fertility gene and thereby achieving population suppression or collapse. Each of these approaches has had recent success and advances under laboratory conditions, raising the urgency for understanding how each could be deployed in the real world and the potential impacts of each. New analyses are needed as existing models of gene drive primarily focus on nonseasonal or nonspatial dynamics. We use a mechanistic, spatially explicit, stochastic, individual-based mathematical model to simulate each gene drive approach in a variety of sub-Saharan African settings. Each approach exhibits a broad region of gene construct parameter space with successful elimination of malaria transmission due to the targeted vector species. The introduction of realistic seasonality in vector population dynamics facilitates gene drive success compared with nonseasonal analyses. Spatial simulations illustrate constraints on release timing, frequency, and spatial density in the most challenging settings for construct success. Within its parameter space for success, each gene drive approach provides a tool for malaria elimination unlike anything presently available. Provided potential barriers to success are surmounted, each achieves high efficacy at reducing transmission potential and lower delivery requirements in logistically challenged settings.

Accurate design of corrugated waveguide low-pass filters using exclusively closed-form expressions <=SEP=> A simple, quasi-analytical, and accurate method to design classical corrugated rectangular waveguide low-pass filters is presented in this paper. Unlike in other published techniques, the dimensions of the final filter are directly obtained in a quasi-analytical manner following exclusively closed-form expressions and, in particular, using neither electromagnetic simulation nor optimization. Thus, our method is amenable to be implemented using an off-the-shelf mathematical software tool such as Matlab. The proposed method is based on the synthesis of the heights and lengths of the final structure taking into account the shunt capacitance effect (produced by the excitation of higher-order modes at the height steps) over the magnitude and phase of the local reflection coefficients. The novel method has been validated with the design of a classical Chebyshev corrugated low-pass filter of order 31, which shows a remarkable accordance between the ideal and simulated frequency responses.

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam <=SEP=> Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires more effort to implement and execute compared to maximumlikelihood methods. In this paper, we propose new natural-gradient algorithms to reduce such efforts for Gaussian mean-field VI. Our algorithms can be implemented within the Adam optimizer by perturbing the network weights during gradient evaluations, and uncertainty estimates can be cheaply obtained by using the vector that adapts the learning rate. This requires lower memory, computation, and implementation effort than existing VI methods, while obtaining uncertainty estimates of comparable quality. Our empirical results confirm this and further suggest that the weight-perturbation in our algorithm could be useful for exploration in reinforcement learning and stochastic optimization.

Mid-term electricity price forecasting using SVM <=SEP=> In the modern electricity market, it is very significant to have a precise electricity price forecasting in medium term time horizon. There are few studies done concentrating on the medium term forecasting of the electricity price. Medium term time horizon of electricity price forecasting is very useful and of great importance. It has many applications such as future maintenance scheduling of the power plants, risk management, plan future contracts, purchasing raw materials and determine the market pricing. To forecast the electricity price, some factors are very important, such as choosing the most useful price features that influence the market price, and choosing the proper prediction model that is able to predict the price variations behavior using the historical data. The proposed SVM method and the other employed methods are evaluated using the data from the New England ISO which are published at their official website.

Autonomous Intersection Management For Semi-Autonomous Vehicles <=SEP=> Recent advances in autonomous vehicle technology will open the door to highly efficient transportation systems in the future, as demonstrated by Autonomous Intersection Management, an intersection control protocol designed for fully autonomous vehicles. We, however, anticipate there will be a long transition period during which most vehicles have some but not all capabilities of fully autonomous vehicles. This chapter introduces a new protocol called Semi-Autonomous Intersection Management, which allows vehicles with partiallyautonomous features such as adaptive cruise control to enter an intersection from different directions simultaneously. Our experiments show that this protocol can greatly decrease traffic delay when most vehicles are semi-autonomous. Our incremental deployment study reveals that traffic delay keeps decreasing as more and more vehicles employ features of autonomy.

SQL injection detection and prevention techniques <=SEP=> As the use of internet is increasing rapidly, the attacks on web applications are also increasing as well. Nowadays SQL injection attack is a major issue of web applications. It allows unrestricted access to the database. The successful execution of SQL injection leads to a loss of integrity and confidentiality. In this paper, a review of different types of SQL injection attacks, their detection and prevention techniques are presented. This paper will help the researchers decide the technique of interest for further studies.