title: Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs

abstract: We present a 3-week user study in which we tracked the locations of 27 subjects and asked them to rate when, where, and with whom they would have been comfortable sharing their locations. The results of analysis conducted on over 7,500 h of data suggest that the user population represented by our subjects has rich location-privacy preferences, with a number of critical dimensions, including time of day, day of week, and location. We describe a methodology for quantifying the effects, in terms of accuracy and amount of information shared, of privacy-setting types with differing levels of complexity (e.g., setting types that allow users to specify location- and/or time-based rules). Using the detailed preferences we collected, we identify the best possible policy (or collection of rules granting access to one's location) for each subject and privacy-setting type. We measure the accuracy with which the resulting policies are able to capture our subjects' preferences under a variety of assumptions about the sensitivity of the information and user-burden tolerance. One practical implication of our results is that today's location-sharing applications may have failed to gain much traction due to their limited privacy settings, as they appear to be ineffective at capturing the preferences revealed by our study.


title: Use of Web 2.0 Technologies in LIS Education: Experiences at Tallinn University, Estonia

abstract: Purpose – The purpose of this paper is to describe the experiences of the Institute of Information Studies of Tallinn University in introducing ICT, including Web 2.0 technologies, in library and information science education, and to explore the role that these can play in new models of learning and teaching.Design/methodology/approach – Web 2.0 applications are reviewed in this paper and the role that these can play in new models of learning and teaching. The introduction of new technologies into library and information science (LIS) education is examined through a case study at the Tallinn University.Findings – Web 2.0 is influencing the way in which people learn, access information and communicate with one another. The Institute of Information Studies of Tallinn University has a long history in using ICT in its teaching and learning. Experiences with open and distance learning and e‐learning have transformed teaching and learning, provided new alternative delivery modes, and helped to reach new target ...


title: Challenge models to assess new therapies in chronic obstructive pulmonary disease

abstract: Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality. Current therapies confer partial benefits either by incompletely improving airflow limitation or by reducing acute exacerbations, hence new therapies are desirable. In the absence of robust early predictors of clinical efficacy, the potential success of novel therapeutic agents in COPD will not entirely be known until the drugs enter relatively large and costly clinical trials. New predictive models in humans, and new study designs are being sought to allow for confirmation of pharmacodynamic and potentially clinically meaningful effects in early development. This review focuses on human challenge models with lipopolysaccharide endotoxin, ozone, and rhinovirus, in the early clinical development phases of novel therapeutic agents for the treatment and reduction of exacerbations in COPD.


Mass detection on mammogram images : A first assessment of deep learning techniques


Advances in the data ̄ ow computational model


Intrinsic disentanglement: an invariance view for deep generative models


title: A multichannel short range bidirectional infrared link with code division multiple access (CDMA)

abstract: In this paper, we present an infrared transmission system meant for indoor applications. It is based on CDMA technology and therefore it is highly resistant to environmental interference such as fluorescent light. A very simple synchronization scheme is employed based on a slight detuning of quartz oscillators in the terminals and central station. The following issues are discussed: principle of operation, link configuration, code selection, synchronization, and range of operation. Measurement results are also presented.


title: Neutrality in evolutionary algorithms ... What do we know ?

abstract: Over the last years, the effects of neutrality have attracted the attention of many researchers in the Evolutionary Algorithms (EAs) community. A mutation from one gene to another is considered as neutral if this modification does not affect the phenotype. This article provides a general overview on the work carried out on neutrality in EAs. Using as a framework the origin of neutrality and its study in different paradigms of EAs (e.g., Genetic Algorithms, Genetic Programming), we discuss the most significant works and findings on this topic. This work points towards open issues, which we belive the community needs to address.


title: Phylogenetic relationships in Opuntia (Cactaceae, Opuntioideae) from southern South America

abstract: The patterns of relationships between species of Opuntia from southern South America are scarcely known in spite of the importance of this region as a diversification center for the Cactaceae. This paper contributes to the better understanding of the genetic and phylogenetic relationships of 15 Opuntia species from Argentina, Bolivia, Brazil, Paraguay, and Uruguay by generating new genetic data through Inter-Simple Sequence Repeat (ISSR) genotyping and the sequencing of plastid intergenic spacers trnL-trnF and psbJ-petA. The species surveyed are: O. anacantha, O. arechavaletae, O. aurantiaca, O. bonaerensis, O. colubrina, O. discolor, O. elata, O. megapotamica, O. monacantha, O. penicilligera, O. quimilo, O. salmiana, O. schickendantzii, O. sulphurea, and O. ventanensis. The genetic distance-based analysis of 110 ISSR bands, applying the Neighbor-Joining and NeighborNet algorithms, evidenced considerable intraspecific variation in O. aurantiaca, O. elata, O. discolor, and O. salmiana. The emergent clustering pattern and the species assignment to taxonomic series show a general agreement for Armatae and Aurantiacae. The phylogenetic relationships were investigated via haplotype network and maximum likelihood approaches, within a broader sampling that involves most species currently accepted for South America, and samples from throughout the American continent. Hence, 15 haplotypes are recognized for southern South American opuntias whereas eight haplotypes are established for Northern Hemisphere opuntias. Biparentally and maternally inherited genetic data yield partially consistent results, giving genetic support for morphologically defined taxonomic series.


title: Unprocessing Images for Learned Raw Denoising

abstract: Machine learning techniques work best when the data used for training resembles the data used for evaluation. This holds true for learned single-image denoising algorithms, which are applied to real raw camera sensor readings but, due to practical constraints, are often trained on synthetic image data. Though it is understood that generalizing from synthetic to real images requires careful consideration of the noise properties of camera sensors, the other aspects of an image processing pipeline (such as gain, color correction, and tone mapping) are often overlooked, despite their significant effect on how raw measurements are transformed into finished images. To address this, we present a technique to “unprocess” images by inverting each step of an image processing pipeline, thereby allowing us to synthesize realistic raw sensor measurements from commonly available Internet photos. We additionally model the relevant components of an image processing pipeline when evaluating our loss function, which allows training to be aware of all relevant photometric processing that will occur after denoising. By unprocessing and processing training data and model outputs in this way, we are able to train a simple convolutional neural network that has 14%-38% lower error rates and is 9×-18× faster than the previous state of the art on the Darmstadt Noise Dataset, and generalizes to sensors outside of that dataset as well.


title: Internet-enabled Access Control System using a Mobile Application

abstract: The need to secure property or restrict entrance in special areas by controlling access to them has always been extremely important for life safety and personal assets, as well as those of the organizations. Different mechanical or electronic door locks are available today using keypads and passwords, RFID devices, biometric data, in order to prevent unauthorized access in homes, research labs, banks, stores, etc. This paper presents a secure access control system based on a Raspberry Pi3 model B board connected to Internet via the local area network. The system monitors in real-time the front of the door using a camera and face recognition features, and notify the system administrator if anyone spends time in front of the door more than a specified limit. Registered users have allowed access and may unlock the door through a mobile application, as the identification may be done by face recognition, password, or fingerprint, subject of availability. The system administrator also has access to the camera captured frames and may unlock the door remotely per request. A web server running on the Raspberry Pi3 embedded system maintain a local database containing the registered user list and related data, as well as a log file that provide the system administrator detailed information about any detected and recorded events.


title: Design and implementation of home energy and power management and control system

abstract: Efficient use of electric energy by users is one of the critical ways for increasing energy efficiency, reducing cost of power generation and supply, and environmental concerns. As part of remedial measures to address issues of energy use, many technologies for demand reduction ranging from energy saving devices through a variety of energy management systems have emerged to help users manage and control their consumption. In this paper, we present the design and implementation of a home energy and power management system that enables users to monitor, regulate, and manage their demand response dynamically through scheduling and controlling of their appliances using a mobile application. At the crux of the power management system is an embedded microcontroller which is encoded with schemes for monitoring, managing, scheduling and controlling operations. The user interface on the mobile device directly communicates with the power management system via integrated Raspberry Pi server that uses Wi-Fi communication protocol while communication between the Raspberry Pi and the embedded microcontroller is based on Bluetooth communication. A numerical simulation of the proposed design was first developed and a prototype of the design was implemented and tested. The test results show that the system was able to effectively monitor, control and regulate the power usage in the home. The flexibility offered by the system allows the user to self-regulate the amount of power usage, which translates into cost savings with the overall effect of flattening of the demand peaks.


title: Form Design for High Accuracy Optical Character Recognition

abstract: To successfully apply character recognition technology most of the forms currently hand-keyed will need to be redesigned. This paper presents results from a comprehensive study of three versions of a redesigned tax form. Analyses show that using separately spaced character boxes provides superior machine readability over fields containing combs and adjoining character boxes. It is shown that character boxes containing two vertically stacked ovals cause writers much more difficulty. Analyses provide proof that writer idiosyncratic responses on forms are the major source of errors, and proper form design can reduce these errors.


LOW POWER HIGH SPEED I/O INTERFACES IN 0.18um CMOS


title: Sensitivity of voxel-based morphometry analysis to choice of imaging protocol at 3 T

abstract: Abstract The objective of this study was to determine which 3D T 1 -weighted acquisition protocol at 3 T is best suited to voxel-based morphometry (VBM), and to characterize the sensitivity of VBM to choice of acquisition. First, image quality of three commonly used protocols, FLASH, MP-RAGE and MDEFT, was evaluated in terms of SNR, CNR, image uniformity and point spread function. These image metrics were estimated from simulations, phantom imaging and human studies. We then performed a VBM study on nine subjects scanned twice using the three protocols to evaluate differences in grey matter (GM) density and scan–rescan variability between the protocols. These results reveal the relative bias and precision of the tissue classification obtained using the different protocols. MDEFT achieved the highest CNR between white and grey matter, and the lowest GM density variability of the three sequences. Each protocol is also characterized by a distinct regional bias in GM density due to the effect of transmission field inhomogeneity on image uniformity combined with spatially variant GM T 1 values and the sequence's T 1 contrast function. The required population sample size estimates to detect a difference in GM density in longitudinal VBM studies, i.e. based only on methodological variance, were lowest for MDEFT. Although MP-RAGE requires more subjects than FLASH, its higher cortical CNR improves the accuracy of the tissue classification results, particularly in the motor cortex. For cross-sectional VBM studies, the variance in morphology across the population is likely to be the primary source of variability in the power analysis.


title: Supervised Autonomy for Exploration and Mobile Manipulation in Rough Terrain with a Centaur-Like Robot

abstract: Planetary exploration scenarios illustrate the need for autonomous robots that are capable to operate in unknown environments without direct human interaction. At the DARPA Robotics Challenge, we demonstrated that our Centaur-like mobile manipulation robot Momaro can solve complex tasks when teleoperated. Motivated by the DLR SpaceBot Cup 2015, where robots should explore a Mars-like environment, find and transport objects, take a soil sample, and perform assembly tasks, we developed autonomous capabilities for Momaro. Our robot perceives and maps previously unknown, uneven terrain using a 3D laser scanner. Based on the generated height map, we assess drivability, plan navigation paths, and execute them using the omnidirectional drive. Using its four legs, the robot adapts to the slope of the terrain. Momaro perceives objects with cameras, estimates their pose, and manipulates them with its two arms autonomously. For specifying missions, monitoring mission progress, on-the-fly reconfiguration, and teleoperation, we developed a ground station with suitable operator interfaces. To handle network communication interruptions and latencies between robot and ground station, we implemented a robust network layer for the ROS middleware. With the developed system, our team NimbRo Explorer solved all tasks of the DLR SpaceBot Camp 2015. We also discuss the lessons learned from this demonstration.


title: Unsupervised Metaphor Paraphrasing using a Vector Space Model

abstract: We present the first fully unsupervised approach to metaphor interpretation, and a system that produces literal paraphrases for metaphorical expressions. Such a form of interpretation is directly transferable to other NLP applications that can benefit from a metaphor processing component. Our method is different from previous work in that it does not rely on any manually annotated data or lexical resources. First, our method computes candidate paraphrases according to the context in which the metaphor appears, using a vector space model. It then uses a selectional preference model to measure the degree of literalness of the paraphrases. The system identifies correct paraphrases with a precision of 0.52 at top rank, which is a promising result for a fully unsupervised approach.


title: An Efficient Hardware Architecture for H.264 Adaptive Deblocking Filter

abstract: This paper presents an efficient hardware architecture for real-time implementation of adaptive deblocking filter algorithm used in H.264 video coding standard. This hardware is designed to be used as part of a complete H.264 video coding system for portable applications. We use a novel edge filter ordering in a Macroblock to prevent the deblocking filter hardware from unnecessarily waiting for the pixels that will be filtered become available. The proposed architecture is implemented in Verilog HDL. The Verilog RTL code is verified to work at 72 MHz in a Xilinx Virtex II FPGA. The FPGA implementation can code 30 CIF frames (352x288) per second.


Design of E-shaped Microstrip Antenna And Parameters estimation using ANN – A review


title: A framework for efficient signatures, ring signatures and identity based encryption in the standard model. Cryptology ePrint Archive, Report 2010/086

abstract: In this work, we present a generic framework for constructing efficient signature schemes, ring signature schemes, and identity based encryption schemes, all in the standard model (without relying on random oracles). We start by abstracting the recent work of Hohenberger and Waters (Crypto 2009), and specifically their “prefix method”. We show a transformation taking a signature scheme with a very weak security guarantee (a notion that we call a-priori-message unforgeability under static chosen message attack) and producing a fully secure signature scheme (i.e., existentially unforgeable under adaptive chosen message attack). Our transformation uses the notion of chameleon hash functions, defined by Krawczyk and Rabin (NDSS 2000) and the “prefix method”. Constructing such weakly secure schemes seems to be significantly easier than constructing fully secure ones, and we present simple constructions based on the RSA assumption, the short integer solution (SIS) assumption, and the computational Diffie-Hellman (CDH) assumption over bilinear groups. Next, we observe that this general transformation also applies to the regime of ring signatures. Using this observation, we construct new (provably secure) ring signature schemes: one is based on the short integer solution (SIS) assumption, and the other is based on the CDH assumption over bilinear groups. As a building block for these constructions, we define a primitive that we call ring trapdoor functions. We show that ring trapdoor functions imply ring signatures under a weak definition, which enables us to apply our transformation to achieve full security. Finally, we show a connection between ring signature schemes and identity based encryption (IBE) schemes. Using this connection, and using our new constructions of ring signature schemes, we obtain two IBE schemes: The first is based on the learning with error (LWE) assumption, and is similar to the recently introduced IBE scheme of Cash-Hofheinz-Kiltz-Peikert; The second is based on the d-linear assumption over bilinear groups. ∗Weizmann Institute of Science, zvika.brakerski@weizmann.ac.il. †Microsoft Research, yael@microsoft.com.


title: Coupling Mechanism in Hybrid SIW–CPW Forward Couplers for Millimeter-Wave Substrate Integrated Circuits

abstract: Various publications address the suppression of the undesired mode coupling in laterally bounded conductor-backed coplanar waveguides (CPWs). In this paper, we present an integrated waveguide structure in which this coupling effect of different guided modes is deliberately exploited. Efficient forward coupling at arbitrary levels is achieved between a substrate integrated rectangular waveguide (SIW) and a CPW on its broad wall. Such hybrid integrated couplers combine the SIW's low-loss property with the CPW's flexibility and monolithic microwave integrated circuit compatibility. They are attractive for transitions, six-ports, antenna feeding networks, active antennas, and distributed phase shifters or amplifiers in millimeter-wave applications. The coupling mechanism is studied and a rapid design becomes feasible by use of the provided theory. Two couplers (a 3- and 0-dB version with 4.95- and 9.8-mm-long coupling sections) were designed and fabricated on alumina substrate for operation around 50 GHz. The 3-dB coupler achieves a wide relative bandwidth of 25% at 0.5-dB amplitude imbalance due to the asymmetric coupling structure, whereas the 0-dB coupler (transition) has a 7% relative bandwidth at less than -0.2-dB coupling loss. Results obtained from millimeter-wave probe measurements on realized prototypes agree well with our theory and simulations.


title: Multiobjective locomotion optimization of quadruped robot with different 2DOF configurations of actuated spine

abstract: This paper addresses the problem of finding a set of optimal foot and spine trajectories for a biologically inspired quadruped robot in order to obtain energy optimal and fast locomotion in forward direction. Based on the previous work of finding optimal foot trajectories we now extend the scope of problem by investigating the effects of an added 2DOF actuated spine on the characteristics of robot locomotion in comparison with rigid spine. To tackle this problem a multiobjective genetic optimization algorithm was used. Purpose of the optimization procedure is to give a better insight in which of the two spine configurations is more beneficial to studied bound and trot gaits and how to design a control strategy that utilizes the spine in an effective way.


title: Antisocial Behavior on the Web: Characterization and Detection

abstract: Web platforms enable unprecedented speed and ease in transmission of knowledge, and allow users to communicate and shape opinions. However, the safety, usability and reliability of these platforms are compromised by the prevalence of online antisocial behavior, for e.g. 40% of users have experienced online harassment. This is present in the form of antisocial users, such as trolls, sockpuppets and vandals, and misinformation, such as hoaxes, rumors and fraudulent reviews. This tutorial presents the state-of-the-art research spanning two aspects of antisocial behavior: characterization of their behavioral properties, and development of algorithms for identifying and predicting them. The tutorial first discusses antisocial users --- trolls, sockpuppets and vandals. We present the causes, community effects, and linguistic, social and temporal characteristics of trolls. Then we discuss the types of sockpuppets, i.e. multiple accounts of the same user, and their behavioral characteristics in Wikipedia and online discussion forums. Vandals make destructive edits on Wikipedia and we discuss the properties of vandals and vandalism edits. In each case, detection and prediction algorithms of the antisocial user are also discussed. ::: ::: The second part of the tutorial discusses about misinformation --- hoaxes, rumors and fraudulent reviews. We present the characteristics and impact of hoaxes on Wikipedia, followed by the spread and evolution of rumors on social media. Then, we discuss the algorithms to identify fake reviews and reviewers from their characteristics, and the camouflage and coordination among sophisticated fraudsters. Again, in each case, we present the detection algorithms, using textual, temporal, sentiment, network structure and rating patterns. Finally, the tutorial concludes with future research avenues.


title: CIM: Competitive edge in manufacturing

abstract: Abstract The factory of the future will be formed by a new generation of mechanical technology, computer-based information systems and electronic process controls. Innovations in information technology provide the chief impetus for new kinds of corporate-wide integrated information systems. Existing islands of automation will be integrated to form an organizational structure which will avoid the current production bottlenecks. CIM systems will significantly influence productivity and flexibility and thus will be the main determinant of profitability and competitive edge.


Life and Understanding: The Origins of “Understanding” in Self-Organizing Nervous Systems


title: A Cross Entropy based Stochastic Approximation Algorithm for Reinforcement Learning with Linear Function Approximation

abstract: In this paper, we provide a new algorithm for the problem of prediction in Reinforcement Learning, \emph{i.e.}, estimating the Value Function of a Markov Reward Process (MRP) using the linear function approximation architecture, with memory and computation costs scaling quadratically in the size of the feature set. The algorithm is a multi-timescale variant of the very popular Cross Entropy (CE) method which is a model based search method to find the global optimum of a real-valued function. This is the first time a model based search method is used for the prediction problem. The application of CE to a stochastic setting is a completely unexplored domain. A proof of convergence using the ODE method is provided. The theoretical results are supplemented with experimental comparisons. The algorithm achieves good performance fairly consistently on many RL benchmark problems. This demonstrates the competitiveness of our algorithm against least squares and other state-of-the-art algorithms in terms of computational efficiency, accuracy and stability.


Safe Reinforcement Learning via Formal Methods: Toward Safe Control Through Proof and Learning


title: Artificial Morality: Top-down, Bottom-up, and Hybrid Approaches

abstract: A principal goal of the discipline of artificial morality is to design artificial agents to act as if they are moral agents. Intermediate goals of artificial morality are directed at building into AI systems sensitivity to the values, ethics, and legality of activities. The development of an effective foundation for the field of artificial morality involves exploring the technological and philosophical issues involved in making computers into explicit moral reasoners. The goal of this paper is to discuss strategies for implementing artificial morality and the differing criteria for success that are appropriate to different strategies.


title: Estimation of Medium Access Control Layer Packet Delay Distribution for IEEE 802.11

abstract: The most important standard in wireless local area networks is IEEE 802.11. This is why much of the research work for the enhancement of wireless network is usually based on the behavior of IEEE 802.11 protocol. However, some of the ways in which IEEE 802.11 medium access control layer behaves is still unreliable to guarantee quality of service. For instance, medium access control layer packet delay, jitter and packet loss rate still remain a challenge. The main objective of this research is to propose an accurate estimation of the medium access control layer packet delay distribution for IEEE 802.11. This estimation considers the differences between busy probability and collision probability. These differences are employed to achieve a more accurate estimation. Finally, the proposed model and simulation are implemented and validated - using MATLAB program for the purpose of simulation, and Maple program to undertake the calculation of the equations.


Researching the Applicability of Mathematical Approaches for Modeling Cyber Security Processes


title: Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming

abstract: In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labeled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel genetic programming-based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation and has significantly outperformed, or achieved a comparable performance to, the baseline methods.


title: Mantis os: An embedded multithreaded operating system for wireless micro sensor platforms

abstract: The MANTIS MultimodAl system for NeTworks of In-situ wireless Sensors provides a new multithreaded cross-platform embedded operating system for wireless sensor networks. As sensor networks accommodate increasingly complex tasks such as compression/aggregation and signal processing, preemptive multithreading in the MANTIS sensor OS (MOS) enables micro sensor nodes to natively interleave complex tasks with time-sensitive tasks, thereby mitigating the bounded buffer producer-consumer problem. To achieve memory efficiency, MOS is implemented in a lightweight RAM footprint that fits in less than 500 bytes of memory, including kernel, scheduler, and network stack. To achieve energy efficiency, the MOS power-efficient scheduler sleeps the microcontroller after all active threads have called the MOS sleep() function, reducing current consumption to the µA range. A key MOS design feature is flexibility in the form of cross-platform support and testing across PCs, PDAs, and different micro sensor platforms. Another key MOS design feature is support for remote management of in-situ sensors via dynamic reprogramming and remote login.


A Silent Hack Detection Based on Deep-Learning Technique


title: Consistent Algorithms for Multiclass Classification with a Reject Option

abstract: We consider the problem of $n$-class classification ($n\geq 2$), where the classifier can choose to abstain from making predictions at a given cost, say, a factor $\alpha$ of the cost of misclassification. Designing consistent algorithms for such $n$-class classification problems with a `reject option' is the main goal of this paper, thereby extending and generalizing previously known results for $n=2$. We show that the Crammer-Singer surrogate and the one vs all hinge loss, albeit with a different predictor than the standard argmax, yield consistent algorithms for this problem when $\alpha=\frac{1}{2}$. More interestingly, we design a new convex surrogate that is also consistent for this problem when $\alpha=\frac{1}{2}$ and operates on a much lower dimensional space ($\log(n)$ as opposed to $n$). We also generalize all three surrogates to be consistent for any $\alpha\in[0, \frac{1}{2}]$.


Effects of Bracing in Adolescents with Idiopathic Scoliosis


- Temporal Model for 6-DoF VideoClip Relocalization


title: Warpage Tuning Study for Multi-chip Last Fan Out Wafer Level Package

abstract: In recent years, the IoT popularity pushes the package development of 3C products into a more functional and thinner target. For high I/O density and low cost considered package, the promising Fan-out Wafer Level Packaging (FOWLP) provides a solution to match OSAT existing capability, besides, the chip last process in FOWLP can further enhance the total yield by selectable known-good dies (KGDs). However, under processing, the large portion of molding compound induces high warpage to challenge fabrication limitation. The additional leveling process is usually applied to lower the warpage that caused by the mismatch of coefficient of thermal expansion and Young's modulus from carriers, dies, and molding compound. This process results in the increase of package cost and even induce internal damages that affect device reliability. In order to avoid leveling process and improve warpage trend, in this paper, we simulated several models with different design of molding compound and dies, and then developed a multi-chip last FOWLP test vehicle by package dimension of 12x15 mm2 with 8x9 and 4x9 mm2 multiple dies, respectively. The test vehicle performed three redistribution layers (RDLs) including one fine pitch RDL of line width/line spacing 2um/2um, which is also the advantage of multi-chip last FOWLP, and also exhibited ball on trace structure for another low cost option. For the wafer warpage discussion, the results showed that tuning the thickness of molding compound can improve warpage trend, especially in the application of high modulus carrier, which improved wafer warpage within 1mm, for package warpage discussion, the thinner die can lower the warpage of package. Through well warpage controlling, the multi-chip last FOWLP package with ball on trace design was successfully presented in this paper, and also passed the package level reliability of TCB 1000 cycles, HTSL 1000 hrs, and uHAST 96 hrs, and drop test by board level reliability.


title: A Survey on Multicasting in Software-Defined Networking

abstract: Existing surveys of research into software-defined networking (SDN) make only minimal mention of multicasting. We present a survey of existing multicast routing protocols in Internet Protocol (IP) multicast, and a survey of existing and proposed multicast routing algorithms in SDN multicast. Each of these protocols/algorithms is designed to optimize a slightly different metric, under specific constraints, so we contrast the approaches based on the optimization goal. We then present a survey of existing and proposed approaches to security for IP multicast and SDN multicast. This reveals a striking lack of breadth in the research in the topic of SDN multicast security.


title: Variational Bayesian Inference Algorithms for Infinite Relational Model of Network Data

abstract: Network data show the relationship among one kind of objects, such as social networks and hyperlinks on the Web. Many statistical models have been proposed for analyzing these data. For modeling cluster structures of networks, the infinite relational model (IRM) was proposed as a Bayesian nonparametric extension of the stochastic block model. In this brief, we derive the inference algorithms for the IRM of network data based on the variational Bayesian (VB) inference methods. After showing the standard VB inference, we derive the collapsed VB (CVB) inference and its variant called the zeroth-order CVB inference. We compared the performances of the inference algorithms using six real network datasets. The CVB inference outperformed the VB inference in most of the datasets, and the differences were especially larger in dense networks.


title: Learning from massive noisy labeled data for image classification

abstract: Large-scale supervised datasets are crucial to train convolutional neural networks (CNNs) for various computer vision problems. However, obtaining a massive amount of well-labeled data is usually very expensive and time consuming. In this paper, we introduce a general framework to train CNNs with only a limited number of clean labels and millions of easily obtained noisy labels. We model the relationships between images, class labels and label noises with a probabilistic graphical model and further integrate it into an end-to-end deep learning system. To demonstrate the effectiveness of our approach, we collect a large-scale real-world clothing classification dataset with both noisy and clean labels. Experiments on this dataset indicate that our approach can better correct the noisy labels and improves the performance of trained CNNs.


title: Speech recognition in adverse conditions: A review

abstract: This article presents a review of the effects of adverse conditions (ACs) on the perceptual, linguistic, cognitive, and neurophysiological mechanisms underlying speech recognition. The review starts with a classification of ACs based on their origin: Degradation at the source (production of a noncanonical signal), degradation during signal transmission (interfering signal or medium-induced impoverishment of the target signal), receiver limitations (peripheral, linguistic, cognitive). This is followed by a parallel, yet orthogonal classification of ACs based on the locus of their effect: Perceptual processes, mental representations, attention, and memory functions. We then review the added value that ACs provide for theories of speech recognition, with a focus on fundamental themes in psycholinguistics: Content and format of lexical representations, time-course of lexical access, word segmentation, feed-back in speech perception and recognition, lexicalsemantic integration, interface between the speech system and general cognition, neuroanatomical organisation of speech processing. We conclude by advocating an approach to speech recognition that includes rather than neutralises complex listening environments and individual differences.


title: The growth of human scalp hair mediated by visible red light laser and LED sources in males

abstract: Background and Objectives ::: Low level laser therapy (LLLT) has been used to promote hair growth. A double-blind randomized controlled trial was undertaken to define the safety and physiologic effects of LLLT on males with androgenic alopecia. ::: ::: Methods ::: Forty-four males (18–48 yo, Fitzpatrick I–IV, Hamilton–Norwood IIa–V) were recruited. A transition zone scalp site was selected; hairs were trimmed to 3 mm height; the area was tattooed and photographed. The active group received a “TOPHAT655” unit containing 21, 5 mW lasers (655 ± 5 nm), and 30 LEDS (655 ± 20 nm), in a bicycle-helmet like apparatus. The placebo group unit appeared identical, containing incandescent red lights. Patients treated at home every other day × 16 weeks (60 treatments, 67.3 J/cm2 irradiance/25 minute treatment), with follow up and photography at 16 weeks. A masked 2.85 cm2 photographic area was evaluated by another blinded investigator. The primary endpoint was the percent increase in hair counts from baseline. ::: ::: Results ::: Forty-one patients completed the study (22 active, 19 placebo). No adverse events or side effects were reported. Baseline hair counts were 162.7 ± 95.9 (N = 22) in placebo and 142.0 ± 73.0 (N = 22) and active groups respectively (P = 0.426). Post Treatment hair counts were 162.4 ± 62.5 (N = 19) and 228.7 ± 102.8 (N = 22), respectively (P = 0.0161). A 39% percent hair increase was demonstrated (28.4 ± 46.2 placebo, N = 19; 67.2 ± 33.4, active, N = 22) (P = 0.001) Deleting one placebo group subject with a very high baseline count and a very large decrease, resulted in baseline hair counts of 151.1 ± 81.0 (N = 21) and 142.0 ± 73.0 (N = 22), respectively (P = 0.680). Post treatment hair counts were 158.2 ± 61.5 (N = 18) and 228.7 ± 102.8 (N = 22) (P = 0.011), resulting in a 35% percent increase in hair growth (32.3 ± 44.2, placebo, N = 18; 67.2 ± 33.4, active, N = 22) (P = 0.003). ::: ::: Conclusions ::: LLLT of the scalp at 655 nm significantly improved hair counts in males with androgenetic alopecia. Lasers Surg. Med. 45:487–495, 2013. © 2013 Wiley Periodicals, Inc.


title: Document Clustering Using Nonnegative Matrix Factorization

abstract: A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in other techniques such as principal component analysis for semantic feature abstraction. Existing techniques for nonnegative matrix factorization are reviewed and a new hybrid technique for nonnegative matrix factorization is proposed. Performance evaluations of the proposed method are conducted on a few benchmark text collections used in standard topic detection studies.


title: A Unified Query-based Generative Model for Question Generation and Question Answering

abstract: We propose a query-based generative model for solving both tasks of question generation (QG) and question an- swering (QA). The model follows the classic encoder- decoder framework. The encoder takes a passage and a query as input then performs query understanding by matching the query with the passage from multiple per- spectives. The decoder is an attention-based Long Short Term Memory (LSTM) model with copy and coverage mechanisms. In the QG task, a question is generated from the system given the passage and the target answer, whereas in the QA task, the answer is generated given the question and the passage. During the training stage, we leverage a policy-gradient reinforcement learning algorithm to overcome exposure bias, a major prob- lem resulted from sequence learning with cross-entropy loss. For the QG task, our experiments show higher per- formances than the state-of-the-art results. When used as additional training data, the automatically generated questions even improve the performance of a strong ex- tractive QA system. In addition, our model shows bet- ter performance than the state-of-the-art baselines of the generative QA task.


title: Utility-Preserving Differentially Private Data Releases Via Individual Ranking Microaggregation

abstract: Abstract Being able to release and exploit open data gathered in information systems is crucial for researchers, enterprises and the overall society. Yet, these data must be anonymized before release to protect the privacy of the subjects to whom the records relate. Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them or because utility is only preserved for a restricted type and/or a limited number of queries. On the contrary, k-anonymity-like data releases make no assumptions on the uses of the protected data and, thus, do not restrict the number and type of doable analyses. Recently, some authors have proposed mechanisms to offer general-purpose differentially private data releases. This paper extends such works with a specific focus on the preservation of the utility of the protected data. Our proposal builds on microaggregation-based anonymization, which is more flexible and utility-preserving than alternative anonymization methods used in the literature, in order to reduce the amount of noise needed to satisfy differential privacy. In this way, we improve the utility of differentially private data releases. Moreover, the noise reduction we achieve does not depend on the size of the data set, but just on the number of attributes to be protected, which is a more desirable behavior for large data sets. The utility benefits brought by our proposal are empirically evaluated and compared with related works for several data sets and metrics.


title: Research issues in Eco-driving

abstract: Transport is a key economic sector, supporting economic development and growth, and facilitating exchange. At the same time, motor vehicles are major emitters of gaseous and particulate pollution in urban areas. The transport industry’s quest to limit its impact on the environment and improve road safety continues to drive policy, research and development. Eco-driving is a well-established, affordable and simple behavioural change intervention, which could reduce fuel consumption up to 20%. Fully electric vehicles are predicted to be available for the mass market by 2020, however an energy efficient driving style will still be necessary for these vehicles due to a relatively poor battery performance. Furthermore Eco-driving could be applied to electric or thermal vehicles. Despite a widespread adoption of Eco-driving, its safety benefits have not been clearly established. This paper discusses research issues related to Eco-driving interventions. It covers policy, industry practice and research approaches ranging from education to in-vehicle technology. This paper demonstrates the lack of comprehensive systemic research analyzing the impacts of Eco-driving on road safety. Most of the methods used to assess the benefits of eco-driving lack scientific rigour and have methodological shortcomings. Ecological Driving Assistance Systems (EDAS) has emerged as a viable ITS intervention addressing Eco-driving but the associated Human Machine Interface is still neglected. Furthermore, there is not enough research assessing the long-term effects of Eco-driving driving.


title: ETHOS: Miniature orientation sensor for wearable human motion analysis

abstract: Inertial and magnetic sensors offers a sourceless and mobile option to obtain body posture and motion for personal sports or healthcare assistants, if sensors could be unobtrusively integrated in casual garments and accessories. We present in this paper design, implementation, and evaluation results for a novel miniature attitude and heading reference system (AHRS) named ETHOS using current off-the-shelf technologies. ETHOS has a unit size of 2.5cm3, which is substantially below most currently marketed attitude heading reference systems, while the unit contains processing resources to estimate its orientation online. Results on power consumption in relation to sampling frequency and sensor use are presented. Moreover two sensor fusion algorithms to estimate orientation: a quaternion-based Kalman-, and a complementary filter. Evaluations of orientation estimation accuracy in static and dynamic conditions revealed that complementary filtering reached sufficient accuracy while consuming 46% of a Kalman's power. The system runtime of ETHOS was found to be 10 hours at a complementary filter update rate of 128Hz. Furthermore, we found that a ETHOS prototype functioned with a sufficient accuracy in estimating human movement in real-life conditions using an arm rehabilitation robot.


title: RenderBots -- Multi Agent Systems for Direct Image Generation

abstract: The term stroke-based rendering collectively describes techniques where images are generated from elements that are usually larger than a pixel. These techniques lend themselves well for rendering artistic styles such as stippling and hatching. This paper presents a novel approach for stroke-based rendering that exploits multi-agent systems. RenderBots are individual agents each of which in general represents one stroke. They form a multi-agent system and undergo a simulation to distribute themselves in the environment. The environment consists of a source image and possibly additional G-buffers. The final image is created when the simulation is finished by having each RenderBot execute its painting function. RenderBot classes differ in their physical behavior as well as their way of painting so that different styles can be created in a very flexible way.


title: MQTT based secured home automation system

abstract: In this paper we discuss and create a MQTT based Secured home automation system, by using mentioned sensors and using Raspberry pi B+ model as the network gateway, here we have implemented MQTT Protocol for transferring & receiving sensor data and finally getting access to those sensor data, also we have implemented ACL (access control list) to provide encryption method for the data and finally monitoring those data on webpage or any network devices. R-pi has been used as a gateway or the main server in the whole system, which has various sensor connected to it via wired or wireless communication.


title: Video Description Generation using Audio and Visual Cues

abstract: The recent advances in image captioning stimulate the research in generating natural language description for visual content, which can be widely applied in many applications such as assisting blind people. Video description generation is a more complex task than image caption. Most works of video description generation focus on visual information in the video. However, audio provides rich information for describing video contents as well. In this paper, we propose to generate video descriptions in natural sentences using both audio and visual cues. We use unified deep neural networks with both convolutional and recurrent structure. Experimental results on the Microsoft Research Video Description (MSVD) corpus prove that fusing audio information greatly improves the video description performance.


title: TweetAlert: Semantic Analytics in Social Networks for Citizen Opinion Mining in the City of the Future

abstract: In this paper a highly configurable, real-time analysis system to automatically record, analyze and visualize high level aggregated information of user interventions in Twitter is described. The system is designed to provide public entities with a powerful tool to rapidly and easily understand what the citizen behavior trends are, what their opinion about city services, events, etc. is, and also may used as a primary alert system that may improve the efficiency of emergency systems. The citizen is here observed as a proactive city sensor capable of generating huge amounts of very rich, high-level and valuable data through social media platforms, which, after properly processed, summarized and annotated, allows city administrators to better understand citizen necessities. The architecture and component blocks are described and some key details of the design, implementation and scenarios of application are discussed.


title: Defense Mechanisms against Machine Learning Modeling Attacks on Strong Physical Unclonable Functions for IOT Authentication: A Review

abstract: Security component in IoT system are very crucial because the devices within the IoT system are exposed to numerous malicious attacks. Typical security components in IoT system performs authentication, authorization, message and content integrity check. Regarding authentication, it is normally performed using classical authentication scheme using crypto module. However, the utilization of the crypto module in IoT authentication is not feasible because of the distributed nature of the IoT system which complicates the message cipher and decipher process. Thus, the Physical Unclonable Function (PUF) is suggested to replace crypto module for IoT authentication because it only utilizes responses from set of challenges instead of cryptographic keys to authenticate devices. PUF can generate large number of challenge-response pairs (CRPs) which is good for authentication because the unpredictability is high. However, with the emergence of machine learning modeling, the CRPs now can be predicted through machine learning algorithms. Various defense mechanisms were proposed to counter machine learning modeling attacks (ML-MA). Although they were experimentally proven to be able to increase resiliency against ML-MA, they caused the generated responses to be instable and incurred high area overhead. Thus, there is a need to design the best defense mechanism which is not only resistant to ML-MA but also produces reliable responses and reduces area overhead. This paper presents an analysis on defense mechanisms against ML-MA on strong PUFs for IoT authentication.


title: Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue

abstract: We describe the adaptation and refinement of a graphical user interface designed to facilitate a Wizard-of-Oz (WoZ) approach to collecting human-robot dialogue data. The data collected will be used to develop a dialogue system for robot navigation. Building on an interface previously used in the development of dialogue systems for virtual agents and video playback, we add templates with open parameters which allow the wizard to quickly produce a wide variety of utterances. Our research demonstrates that this approach to data collection is viable as an intermediate step in developing a dialogue system for physical robots in remote locations from their users - a domain in which the human and robot need to regularly verify and update a shared understanding of the physical environment. We show that our WoZ interface and the fixed set of utterances and templates therein provide for a natural pace of dialogue with good coverage of the navigation domain.


title: Integrated online trajectory planning and optimization in distinctive topologies

abstract: Abstract This paper presents a novel integrated approach for efficient optimization based online trajectory planning of topologically distinctive mobile robot trajectories. Online trajectory optimization deforms an initial coarse path generated by a global planner by minimizing objectives such as path length, transition time or control effort. Kinodynamic motion properties of mobile robots and clearance from obstacles impose additional equality and inequality constraints on the trajectory optimization. Local planners account for efficiency by restricting the search space to locally optimal solutions only. However, the objective function is usually non-convex as the presence of obstacles generates multiple distinctive local optima. The proposed method maintains and simultaneously optimizes a subset of admissible candidate trajectories of distinctive topologies and thus seeking the overall best candidate among the set of alternative local solutions. Time-optimal trajectories for differential-drive and carlike robots are obtained efficiently by adopting the Timed-Elastic-Band approach for the underlying trajectory optimization problem. The investigation of various example scenarios and a comparative analysis with conventional local planners confirm the advantages of integrated exploration, maintenance and optimization of topologically distinctive trajectories.


title: f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

abstract: Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights. These models are expressive and allow efficient computation of samples and derivatives, but cannot be used for computing likelihoods or for marginalization. The generative-adversarial training method allows to train such models through the use of an auxiliary discriminative neural network. We show that the generative-adversarial approach is a special case of an existing more general variational divergence estimation approach. We show that any f-divergence can be used for training generative neural samplers. We discuss the benefits of various choices of divergence functions on training complexity and the quality of the obtained generative models.


title: Assessment of the economic impacts of porcine epidemic diarrhea virus in the United States

abstract: Porcine epidemic diarrhea virus (PEDV), which first emerged in the United States in 2013, spread throughout the U.S. hog population. Limited preemptive knowledge impeded the under- standing of PEDV introduction, spread, and prospective economic impacts in the United States. To assess these impacts, this article reviews the timeline of PEDV in the United States and the corresponding impacts. PEDV is a supply-impacting disease and is not demand inhibit- ing, as pork demand remained strong since PEDV first appeared. Pig losses reached significant levels during September 2013 through August 2014, with the major- ity of pork production impacts occurring in 2014. PEDV had differing impacts for subsectors of the pork industry. A budget model demonstrates that producers could have had pig losses and decreases in productivity proportionally smaller than price increases, resulting in net returns above what was expected before the major outbreak of PEDV. Previous literature is reviewed to identify the potential main industry beneficiaries of the PEDV outbreaks in the United States. As a result of reduced volumes of available pig and hog supplies, reductions in annual returns likely occurred for packers, processors, distributors, and retailers. In addition, pork consumers who experienced reduced-supply-induced pork-price increases were likely harmed directly by higher prices paid for pork and indirectly as prices of competing meats were also likely strengthened by PEDV. This article also identifies future considerations motivated by the appearance of PEDV in the United States, such as discussions of industry-wide efficiency and competitive advantage, the future role of PEDV vaccines, enhancement in biosecurity measures, and consumer perceptions of food safety and insecurity.


title: New upper bound for space-time block coded spatial modulation

abstract: In this paper, we present a new upper bound for the bit error probability (BEP) of the so-called Space Time Block Coded Spatial Modulation (STBC-SM) system introduced by E. Basar et al. in [1] over a quasi-static Rayleigh fading channel. Based on the Verdu's theorem [2], the concept of the spatial constellation (SC) codewords and maximum likelihood (ML) decoder in [3], the upper bound is obtained by eliminating a number of redundant pairwise error probabilities (PEPs). Our approach leads to a new upper bound, which is tighter than the union bound. Consequently, it allows us to evaluate the bit-error performance of STBC-SM systems more exactly, particularly when the signal-to-noise power ratio (SNR) is sufficiently high.


omputed-aided diagnosis ( CAD ) in the detection of breast cancer


Is There More to Blood Volume Pulse Than Heart Rate Variability , Respiratory Sinus Arrhythmia , and Cardiorespiratory Synchrony ?


title: Siliceous microfossil distribution in the surficial sediments of Lake Baikal

abstract: Examination of surficial sediments at 16 stations shows minor, but consistent differences in the numbers and kinds of siliceous microfossils deposited in different regions of Lake Baikal. There is a general north-south decreasing trend in total microfossil abundance on a weight basis. Endemic plankton diatom species are the most abundant component of assemblages at all stations. Chrysophyte cysts are present at all stations, but most forms are more abundant at northern stations. Non-endemic plankton diatom species are most abundant at southern stations. Small numbers of benthic diatoms and sponge spicules are found in all samples. Although low numbers are present in offshore sediments, the benthic diatom flora is very diverse. Principal components analysis confirms primary north-south abundance trends and suggests further differentiation by station location and depth.


title: Phase identification in smart grids

abstract: Electricity is distributed throughout the electrical power network in 3-phase voltage. This power reaches households as a single-phase voltage, generally 115vac or 240vac. This is achieved by allocating households with either phases A, B, or C of the final 3-phase power distributed to the street through a low voltage transformer. A present problem confronting the electrical power industry is identification of which particular phase a household is connected to. This information is often not tracked and the mechanisms for identifying phase require either manual intervention or costly signal injection technologies. Phase information is important as it is a foundation for the larger problem of balancing phase loads. Unbalanced phases lead to significant energy losses and sharply reduced asset lifetimes. In this paper we propose a new approach to compute household phase. Our techniques are novel as they are purely based upon a time series of electrical power measurements taken at the household and at the distributing transformer. Our methods involve the use of integer programming and solutions can be retrieved using branch and bound search algorithms implemented by MIP solvers such as CPLEX. Furthermore, as the number of measurements increase, continuous relaxations of integer programs may also be used to retrieve household phase efficiently. Simulation results using a combination of synthetic and real smart meter datasets demonstrate the performance of our techniques and the number of measurements needed to uniquely identify household phase.


title: Dynamic ransomware protection using deterministic random bit generator

abstract: Ransomware has become a very significant cyber threat. The basic idea of ransomware was presented in the form of a cryptovirus in 1995. However, it was considered as merely a conceptual topic since then for over a decade. In 2017, ransomware has become a reality, with several famous cases of ransomware having compromised important computer systems worldwide. For example, the damage caused by CryptoLocker and WannaCry is huge, as well as global. They encrypt victims' files and require user's payment to decrypt them. Because they utilize public key cryptography, the key for recovery cannot be found in the footprint of the ransomware on the victim's system. Therefore, once infected, the system cannot be recovered without paying for restoration. Various methods to deal this threat have been developed by antivirus researchers and experts in network security. However, it is believed that cryptographic defense is infeasible because recovering a victim's files is computationally as difficult as breaking a public key cryptosystem. Quite recently, various approaches to protect the crypto-API of an OS from malicious codes have been proposed. Most ransomware generate encryption keys using the random number generation service provided by the victim's OS. Thus, if a user can control all random numbers generated by the system, then he/she can recover the random numbers used by the ransomware for the encryption key. In this paper, we propose a dynamic ransomware protection method that replaces the random number generator of the OS with a user-defined generator. As the proposed method causes the virus program to generate keys based on the output from the user-defined generator, it is possible to recover an infected file system by reproducing the keys the attacker used to perform the encryption.


title: Voltage-Balancing Circuit Based on a Resonant Switched-Capacitor Converter for Multilevel Inverters

abstract: This paper proposes a new voltage-balancing circuit for the split dc voltages in a diode-clamped five-level inverter. The proposed circuit is based on a resonant switched-capacitor converter (RSCC), which consists of two half-bridge inverters, a resonant inductor, and a resonant capacitor. A new phase-shift control of the RSCC is proposed to improve voltage-balancing performance. As a result, it is possible to reduce the inductor to one tenth in volume as compared to a conventional voltage-balancing circuit based on a buck-boost converter. Experimental results are shown to verify the viability of the RSCC-based voltage-balancing circuit.


title: Using a Common Average Reference to Improve Cortical Neuron Recordings From Microelectrode Arrays

abstract: In this study, we propose and evaluate a technique known as common average referencing (CAR) to generate a more ideal reference electrode for microelectrode recordings. CAR is a computationally simple technique, and therefore amenable to both on-chip and real-time applications. CAR is commonly used in EEG, where it is necessary to identify small signal sources in very noisy recordings. To study the efficacy of common average referencing, we compared CAR to both referencing with a stainless steel bone-screw and a single microelectrode site. Data consisted of in vivo chronic recordings in anesthetized Sprague-Dawley rats drawn from prior studies, as well as previously unpublished data. By combining the data from multiple studies, we generated and analyzed one of the more comprehensive chronic neural recording datasets to date. Reference types were compared in terms of noise level, signal-to-noise ratio, and number of neurons recorded across days. Common average referencing was found to drastically outperform standard types of electrical referencing, reducing noise by >30%. As a result of the reduced noise floor, arrays referenced to a CAR yielded almost 60% more discernible neural units than traditional methods of electrical referencing. CAR should impart similar benefits to other microelectrode recording technologies—for example, chemical sensing—where similar differential recording concepts apply. In addition, we provide a mathematical justification for CAR using Gauss-Markov theorem and therefore help place the application of CAR into a theoretical context.


title: The Clinical Efficacy and Safety of Tulsi in Humans: A Systematic Review of the Literature

abstract: Tulsi, also known as holy basil, is indigenous to the Indian continent and highly revered for its medicinal uses within the Ayurvedic and Siddha medical systems. Many in vitro, animal and human studies attest to tulsi having multiple therapeutic actions including adaptogenic, antimicrobial, anti-inflammatory, cardioprotective, and immunomodulatory effects, yet to date there are no systematic reviews of human research on tulsi’s clinical efficacy and safety. We conducted a comprehensive literature review of human studies that reported on a clinical outcome after ingestion of tulsi. We searched for studies published in books, theses, conference proceedings, and electronic databases including Cochrane Library, Google Scholar, Embase, Medline, PubMed, Science Direct, and Indian Medical databases. A total of 24 studies were identified that reported therapeutic effects on metabolic disorders, cardiovascular disease, immunity, and neurocognition. All studies reported favourable clinical outcomes with no studies reporting any significant adverse events. The reviewed studies reinforce traditional uses and suggest tulsi is an effective treatment for lifestyle-related chronic diseases including diabetes, metabolic syndrome, and psychological stress. Further studies are required to explore mechanisms of action, clarify the dosage and dose form, and determine the populations most likely to benefit from tulsi’s therapeutic effects.


title: Processing Long Queries Against Short Text: Top-k Advertisement Matching in News Stream Applications

abstract: Many real applications in real-time news stream advertising call for efficient processing of long queries against short text. In such applications, dynamic news feeds are regarded as queries to match against an advertisement (ad) database for retrieving the k most relevant ads. The existing approaches to keyword retrieval cannot work well in this search scenario when queries are triggered at a very high frequency. To address the problem, we introduce new techniques to significantly improve search performance. First, we devise a two-level partitioning for tight upper bound estimation and a lazy evaluation scheme to delay full evaluation of unpromising candidates, which can bring three to four times performance boosting in a database with 7 million ads. Second, we propose a novel rank-aware block-oriented inverted index to further improve performance. In this index scheme, each entry in an inverted list is assigned a rank according to its importance in the ad. Then, we introduce a block-at-a-time search strategy based on the index scheme to support a much tighter upper bound estimation and a very early termination. We have conducted experiments with real datasets, and the results show that the rank-aware method can further improve performance by an order of magnitude.


Automatically generating reading lists


title: Design, Fabrication, and Characterization of a High-Heating-Efficiency 3-D Microheater for Catalytic Gas Sensors

abstract: In this paper, a novel 3-D microheater has been developed in order to improve the performance of catalytic gas sensors. The 3-D microheater consists of a platinum heater embedded in a concave-shaped membrane which was formed in twice wet-chemical anisotropic etching with photoresist-spray-coating-based liftoff process. Based on the 3-D microheater, a catalytic gas sensor with a paired detector and compensator was developed by sol-gel process, introducing γ-Al2O3-supported 15-wt % Pd as catalyst. Finite element method analysis results suggest that sensitivity of the catalytic gas sensor can be improved by loading more catalyst on the active area and concentrating more combustion heat inside the concave-shaped membrane. Test results indicate that a high heating efficiency has been achieved. Power per active area of the 3-D microheater is only half or less than that of current 2-D microheaters. Sensor response to methane shows that the sensitivity to 50% lower explosive limit CH4 was 12 mV/ % CH4.


title: Automatic text segmentation from complex background

abstract: In this paper, we proposed an automatic method to segment text from complex background for recognition task. First, a rule-based sampling method is proposed to get portion of the text pixels. Then, the sampled pixels are used for training Gaussian mixture models of intensity and hue components in HSI color space. Finally, the trained GMMs together with the spatial connectivity information are used for segment all of text pixels form their background. We used the word recognition rate to evaluate the segmentation result. Experiments results show that the proposed algorithm can work fully automatically and performs much better than the traditional methods.


title: Disruption of the Dorsolateral Prefrontal Cortex Facilitates the Consolidation of Procedural Skills

abstract: In explicit sequence learning tasks, an improvement in performance (skill) typically occurs after sleep-leading to the recent literature on sleep-dependent motor consolidation. Consolidation can also be facilitated during wakefulness if declarative knowledge for the sequence is reduced through a secondary cognitive task. Accordingly, declarative and procedural consolidation processes appear to mutually interact. Here we used TMS to test the hypothesis that functions in the dorsolateral prefrontal cortex (DLPFC) that support declarative memory formation indirectly reduce the formation of procedural representations. We hypothesize that disrupting the DLPFC immediately after sequence learning would degrade the retention or the consolidation of the sequence within the declarative memory system and thus facilitate consolidation within procedural memory systems, evident as wakeful off-line skill improvement. Inhibitory theta-burst TMS was applied to the left DLPFC (n = 10), to the right DLPFC (n = 10), or to an occipital cortical control site (n = 10) immediately after training on the serial reaction time task (SRTT). All groups were retested after eight daytime hours without sleep. TMS of either left or right DLPFC lead to skill improvements on the SRTT. Increase in skill was greater following right DLPFC stimulation than left DLPFC stimulation; there was no improvement in skill for the control group. Across all participants, free recall of the sequence was inversely related to the improvements in performance on the SRTT. These results support the hypothesis of interference between declarative and procedural consolidation processes and are discussed in the framework of the interactions between memory systems.


title: A Miniature Water Surface Jumping Robot

abstract: Many small insects such as water striders can leap from water surface. Inspired by their jumping capability, we present the design of a novel, miniature, water surface jumping robot in this paper. Jumping from water surface is more challenging than jumping from ground due to the liquid water surface. We address this problem by using carbon fiber strip to store energy, two wings to flap the water surface, a hollow body to initially support the robot, and an intermittent gear train to charge and release energy. With such a design, the final robot weighs around 12.5 g and has a maximum size of 10 cm. Experimental results show that the robot can jump up to 9.5 cm in height, which is close to the predicted results from a dynamic model developed by us to capture the hydrodynamic behaviors of the robot during the whole jumping process. The research presented in this paper offers a new design of tiny robots for mimicking the water-jumping capability of aquatic insects. The robot, if equipped with sensors, can be employed for aquatic environmental monitoring of water quality.


title: Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks

abstract: The next-generation wireless networks are evolving into very complex systems because of the very diversified service requirements, heterogeneity in applications, devices, and networks. The mobile network operators (MNOs) need to make the best use of the available resources, for example, power, spectrum, as well as infrastructures. Traditional networking approaches, i.e., reactive, centrally-managed, one-size-fits-all approaches and conventional data analysis tools that have limited capability (space and time) are not competent anymore and cannot satisfy and serve that future complex networks in terms of operation and optimization in a cost-effective way. A novel paradigm of proactive, self-aware, self- adaptive and predictive networking is much needed. The MNOs have access to large amounts of data, especially from the network and the subscribers. Systematic exploitation of the big data greatly helps in making the network smart, intelligent and facilitates cost-effective operation and optimization. In view of this, we consider a data-driven next-generation wireless network model, where the MNOs employ advanced data analytics for their networks. We discuss the data sources and strong drivers for the adoption of the data analytics and the role of machine learning, artificial intelligence in making the network intelligent in terms of being self-aware, self-adaptive, proactive and prescriptive. A set of network design and optimization schemes are presented with respect to data analytics. The paper is concluded with a discussion of challenges and benefits of adopting big data analytics and artificial intelligence in the next-generation communication system.


Using the PHREG Procedure to Analyze Competing-Risks Data


title: Progressive Growing of GANs for Improved Quality, Stability, and Variation

abstract: We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset.


Doc 2 Cube : Automated Document Allocation to Text Cube via Dimension-Aware Joint Embedding


title: Autonomous robotic system using non-destructive evaluation methods for bridge deck inspection

abstract: Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments.


title: Learning implicit brain MRI manifolds with deep learning

abstract: An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease or markers of development in populations. Having a low-dimensional manifold of an image allows for easier statistical comparisons between groups and the synthesis of group representatives. Previous studies have sought to identify the best mapping of brain MRI to a low-dimensional manifold, but have been limited by assumptions of explicit similarity measures. In this work, we use deep learning techniques to investigate implicit manifolds of normal brains and generate new, high-quality images. We explore implicit manifolds by addressing the problems of image synthesis and image denoising as important tools in manifold learning. First, we propose the unsupervised synthesis of T1-weighted brain MRI using a Generative Adversarial Network (GAN) by learning from 528 examples of 2D axial slices of brain MRI. Synthesized images were first shown to be unique by performing a cross-correlation with the training set. Real and synthesized images were then assessed in a blinded manner by two imaging experts providing an image quality score of 1-5. The quality score of the synthetic image showed substantial overlap with that of the real images. Moreover, we use an autoencoder with skip connections for image denoising, showing that the proposed method results in higher PSNR than FSL SUSAN after denoising. This work shows the power of artificial networks to synthesize realistic imaging data, which can be used to improve image processing techniques and provide a quantitative framework to structural changes in the brain.


title: The future of cryptocurrencies: Bitcoin and beyond

abstract: The digital currency has caused any number of headaches for law enforcement. Now entrepreneurs and academics are scrambling to build a better version.


title: Ruthenium interconnect resistivity and reliability at 48 nm pitch

abstract: 48 nm pitch dual damascene interconnects are patterned and filled with ruthenium. Ru interconnect has comparable high yield for line and via macros. Electrical results show minimal impact for via resistance and around 2 times higher line resistance. Resistivity and cross section area of Ru interconnects are measured by temperature coefficient of resistivity method and the area was verified by TEM. Reliability results show non-failure in electromigration and longer time dependent dielectric breakdown. Based on the data collected, Ru could be a metallization contender at linewidth of 16 nm and below.


title: Spatial Data Mining Features between General Data Mining

abstract: Data mining is usually defined as searching,analyzing and sifting through large amounts of data to find relationships, patterns, or any significant statistical correlation. Spatial Data Mining (SDM) is the process of discovering interesting, useful, non-trivial patterns information or knowledge from large spatial datasets.Extracting interesting and useful patterns from spatial datasets must be more difficult than extracting the corresponding patterns from traditional numeric or categorical data due to the complexity of spatial data types, spatial relationships, and spatial auto-correlation.Emphasized overviewed the unique features that distinguish spatial data mining from classical Data Mining, and presents major accomplishments of spatial Data Mining research. Extracting interesting patterns and rules from spatial datasets, such as remotely sensed imagery and associated ground data, can be of importance in precision agriculture, community planning,resource discovery and other areas.


title: E-shaped slot antenna backed with substrate integrated waveguide cavity for 60GHZ applications

abstract: Substrate integrated waveguide is good resolution for implementing for high frequency applications. In this paper, we represented SIW backed with cavity bowtie antenna which is intended by using Rogers RT/Duriod 5880 with dielectric constant 2.2. CST software is used to simulate the proposed antenna and observed the results like reflection coefficient, VSWR, gain and radiation patterns. This structure will provide 3.52 GHz impedance bandwidth with respect to −10 dB reference line, resonant frequency is 60GHz and preserves 51.14% impedance bandwidth in between 57–64 GHz.


title: Automated Training of Deep Convolutional Neural Networks for Cell Segmentation

abstract: Deep Convolutional Neural Networks (DCNN) have recently emerged as superior for many image segmentation tasks. The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples. Here we show that DCNNs trained on ground truth created automatically using fluorescently labeled cells, perform similar to manual annotations.


title: Swarm intelligence based routing protocol for wireless sensor networks : Survey and future directions

abstract: Swarm intelligence is a relatively novel field. It addresses the study of the collective behaviors of systems made by many components that coordinate using decentralized controls and self-organization. A large part of the research in swarm intelligence has focused on the reverse engineering and the adaptation of collective behaviors observed in natural systems with the aim of designing effective algorithms for distributed optimization. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. These are key properties in the context of network routing, and in particular of routing in wireless sensor networks. Therefore, in the last decade, a number of routing protocols for wireless sensor networks have been developed according to the principles of swarm intelligence, and, in particular, taking inspiration from the foraging behaviors of ant and bee colonies. In this paper, we provide an extensive survey of these protocols. We discuss the general principles of swarm intelligence and of its application to routing. We also introduce a novel taxonomy for routing protocols in wireless sensor networks and use it to classify the surveyed protocols. We conclude the paper with a critical analysis of the status of the field, pointing out a number of fundamental issues related to the (mis) use of scientific methodology and evaluation procedures, and we identify some future research directions.


title: The Survival Filter: Joint Survival Analysis with a Latent Time Series

abstract: Survival analysis is a core task in applied statistics, which models time-to-failure or time-to-event data. In the clinical domain, for example, meaningful events are defined as the onset of different diseases for a given patient. Survival analysis is limited, however, for analyzing modern electronic health records. Patients often have a wide range of diseases, and there are complex interactions among the relative risks of different events. To this end, we develop the survival filter model, a time-series model for joint survival analysis that models multiple patients and multiple diseases. We develop a scalable variational inference algorithm and apply our method to a large data set of longitudinal patient records. The survival filter gives good predictive performance when compared to two baselines and identifies clinically meaningful patterns of disease interaction.


title: Scalable mental health analysis in the clinical whitespace via natural language processing

abstract: Our increasingly digital life provides a wealth of data about our behavior, beliefs, mood, and well-being. This data provides some insight into the lives of patients outside the healthcare setting, and in aggregate can be insightful for the person's mental health and emotional crisis. Here, we introduce this community to some of the recent advancement in using natural language processing and machine learning to provide insight into mental health of both individuals and populations. We advocate using these linguistic signals as a supplement to those that are collected in the health care system, filling in some of the so-called “whitespace” between visits.


title: A Smart Home is No Castle: Privacy Vulnerabilities of Encrypted IoT Traffic

abstract: The increasing popularity of specialized Internet-connected devices and appliances, dubbed the Internet-of-Things (IoT), promises both new conveniences and new privacy concerns. Unlike traditional web browsers, many IoT devices have always-on sensors that constantly monitor fine-grained details of users' physical environments and influence the devices' network communications. Passive network observers, such as Internet service providers, could potentially analyze IoT network traffic to infer sensitive details about users. Here, we examine four IoT smart home devices (a Sense sleep monitor, a Nest Cam Indoor security camera, a WeMo switch, and an Amazon Echo) and find that their network traffic rates can reveal potentially sensitive user interactions even when the traffic is encrypted. These results indicate that a technological solution is needed to protect IoT device owner privacy, and that IoT-specific concerns must be considered in the ongoing policy debate around ISP data collection and usage.


title: Articles FF The Fast-Forward Planning System

abstract: Fast-forward (FF) was the most successful automatic planner in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS '00) planning systems competition. Like the well-known hsp system, FF relies on forward search in the state space, guided by a heuristic that estimates goal distances by ignoring delete lists. It differs from HSP in a number of important details. This article describes the algorithmic techniques used in FF in comparison to hsp and evaluates their benefits in terms of run-time and solution-length behavior.


title: Mammography Assessment using Multi-Scale Deep Classifiers

abstract: Applying deep learning methods to mammography assessment has remained a challenging topic. Dense noise with sparse expressions, mega-pixel raw data resolution, lack of diverse examples have all been factors affecting performance. The lack of pixel-level ground truths have especially limited segmentation methods in pushing beyond approximately bounding regions. We propose a classification approach grounded in high performance tissue assessment as an alternative to all-in-one localization and assessment models that is also capable of pinpointing the causal pixels. First, the objective of the mammography assessment task is formalized in the context of local tissue classifiers. Then, the accuracy of a convolutional neural net is evaluated on classifying patches of tissue with suspicious findings at varying scales, where highest obtained AUC is above $0.9$. The local evaluations of one such expert tissue classifier is used to augment the results of a heatmap regression model and additionally recover the exact causal regions at high resolution as a saliency image suitable for clinical settings.


title: Neural Document Summarization by Jointly Learning to Score and Select Sentences

abstract: Sentence scoring and sentence selection are two main steps in extractive document summarization systems. However, previous works treat them as two separated subtasks. In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences. It first reads the document sentences with a hierarchical encoder to obtain the representation of sentences. Then it builds the output summary by extracting sentences one by one. Different from previous methods, our approach integrates the selection strategy into the scoring model, which directly predicts the relative importance given previously selected sentences. Experiments on the CNN/Daily Mail dataset show that the proposed framework significantly outperforms the state-of-the-art extractive summarization models.


title: A unified foundation for business analytics

abstract: Synthesizing prior research, this paper designs a relatively comprehensive and holistic characterization of business analytics - one that serves as a foundation on which researchers, practitioners, and educators can base their studies of business analytics. As such, it serves as an initial ontology for business analytics as a field of study. The foundation has three main parts dealing with the whence and whither of business analytics: identification of dimensions along which business analytics possibilities can be examined, derivation of a six-class taxonomy that covers business analytics perspectives in the literature, and design of an inclusive framework for the field of business analytics. In addition to unifying the literature, a major contribution of the designed framework is that it can stimulate thinking about the nature, roles, and future of business analytics initiatives. We show how this is done by deducing a host of unresolved issues for consideration by researchers, practitioners, and educators. We find that business analytics involves issues quite aside from data management, number crunching, technology use, systematic reasoning, and so forth. Holistic foundation of business analytics introduced, as an ontology for the fieldCharacterization business analytics as evidence-based problem recognition and solvingAdvance a framework of business analytics for a cohesive unifying view of the fieldStimulates thinking about nature, role and future of business analytics initiativesDeduces unresolved issues for considerations by researchers/educators/practitioners


title: Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks

abstract: We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for multi-scale contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour strength but also orientation provides more accurate results. We perform extensive experiments for low-level applications on BSDS, PASCAL Context, PASCAL Segmentation, and NYUD to evaluate boundary detection performance, showing that COB provides state-of-the-art contours and region hierarchies in all datasets. We also evaluate COB on high-level tasks when coupled with multiple pipelines for object proposals, semantic contours, semantic segmentation, and object detection on MS-COCO, SBD, and PASCAL; showing that COB also improves the results for all tasks.


title: A series fed planar microstrip patch array antenna with 1D beam steering for 5G spectrum massive MIMO applications

abstract: A 1 dimensional (D) beam steering planar microstrip patch array antenna with through element technology at 28 GHz is designed. The frequency spectrum chosen is one of the Federal Communications Commission (FCC) released 5G bands − 27.5 GHz to 28.35 GHz. The proposed antenna shows good isolation between the antenna ports of the array, flexible radiation patterns and beam steering capabilities. Thus, it can be employed for the multiuser massive multiple input multiple output (MU-MIMO) applications by exploiting the features of digital beamforming technique.


title: How Internet-of-Things (IoT) making the university campuses smart? QA higher education (QAHE) perspective

abstract: This paper proposes a model to develop a university smart campus enabled through Internet-of-Thins (IoT) technology. The infusion of IoT in education embraces the internet-based communication between controllers, sensors and the physical objects. This landscape of modern technology has changed the universities education on a massive scale. Various objects are integrated with sensors, wearable technologies are collecting data with augmented reality and intra-communication is taking place through cloud computing. This IoT paradigm defines the diverse range of parameters for universities identifying opportunities to make them smart urging new interaction between objects and people. This research focuses to make smart rooms, smart parking as well as delivering smart education to students.


title: Performance of reliable transport protocol over IEEE 802.11 wireless LAN: analysis and enhancement

abstract: IEEE 802.11 medium access control (MAC) is proposed to support asynchronous and time bounded delivery of radio data packets in infrastructure and ad hoc networks. The basis of the IEEE 802.11 WLAN MAC protocol is a distributed coordination function (DCF), which is a carrier sense multiple access with collision avoidance (CSMA/CA) with a binary slotted exponential back-off scheme. Since IEEE 802.11 MAC has its own characteristics that are different from other wireless MAC protocols, the performance of reliable transport protocol over 802.11 needs further study. This paper proposes a scheme named DCF+, which is compatible with DCF, to enhance the performance of reliable transport protocol over WLAN. To analyze the performance of DCF and DCF+, this paper also introduces an analytical model to compute the saturated throughput of WLAN. Compared with other models, this model is shown to be able to predict the behavior of 802.11 more accurately. Moreover, DCF+ is able to improve the performance of TCP over WLAN, which is verified by modeling and elaborate simulation results.


title: A Software Engineering Perspective on SDN Programmability

abstract: Software-defined networking (SDN) has received a great deal of attention from both academia and industry in recent years. Studies on SDN have brought a number of interesting technical discussions on network architecture design, along with scientific contributions. Researchers, network operators, and vendors are trying to establish new standards and provide guidelines for proper implementation and deployment of such novel approach. It is clear that many of these research efforts have been made in the southbound of the SDN architecture, while the northbound interface still needs improvements. By focusing in the SDN northbound, this paper surveys the body of knowledge and discusses the challenges for developing SDN software. We investigate the existing solutions and identify trends and challenges on programming for SDN environments. We also discuss future developments on techniques, specifications, and methodologies for programmable networks, with the orthogonal view from the software engineering discipline.


title: Multilevel inverters: A literature survey on topologies and control strategies

abstract: Multilevel inverters have been attracting in favor of academia as well as industry in the recent decade for high-power and medium-voltage energy control. In addition, they can synthesize switched waveforms with lower levels of harmonic distortion than an equivalently rated two-level converter. The multilevel concept is used to decrease the harmonic distortion in the output waveform without decreasing the inverter power output. This paper presents the most important topologies like diode-clamped inverter (neutral- point clamped), capacitor-clamped (flying capacitor), and cascaded multilevel with separate dc sources. This paper also presents the most relevant modulation methods developed for this family of converters: multilevel sinusoidal pulse width modulation, multilevel selective harmonic elimination, and space-vector modulation. Authors strongly believe that this survey article will be very much useful to the researchers for finding out the relevant references in the field of topologies and modulation strategies of multilevel inverter.


title: A hierarchical three-way interconnect architecture for hexagonal processors

abstract: The problem of interconnect architecture arises when an array of processors needs to be integrated on one chip. With the deep sub-micron technology, devices become cheap while wires are expensive. On the other hand, high performance systems require the shortest communication routes among the processors. Non-blocking hierarchical interconnect architectures have been found to be a feasible solution. First, they can be expanded recursively and so can be applied in large-scale arrays. Second, if well designed, they have the best trade-off between the cost of wire resources and the communication performance. In this paper, a new type of non-blocking hierarchical three-way interconnect architecture, Y tree architecture, is put forward. We find that the arrays of hexagonal cells also have the property of hierarchical expansion, and we put an algorithm to build up a Y tree. We compare the Y architecture with an X hierarchical non-blocking architecture.


title: A Content-Addressable DNA Database with Learned Sequence Encodings

abstract: We present strand and codeword design schemes for a DNA database capable of approximate similarity search over a multidimensional dataset of content-rich media. Our strand designs address cross-talk in associative DNA databases, and we demonstrate a novel method for learning DNA sequence encodings from data, applying it to a dataset of tens of thousands of images. We test our design in the wetlab using one hundred target images and ten query images, and show that our database is capable of performing similarity-based enrichment: on average, visually similar images account for 30% of the sequencing reads for each query, despite making up only 10% of the database.