title: Radar signal recognition algorithm based on entropy theory

abstract: With the increasingly complex electromagnetic environment of communication, as well as the gradually increased radar signal types, how to effectively identify the types of radar signals at low SNR becomes a hot topic. A radar signal recognition algorithm based on entropy features, which describes the distribution characteristics for different types of radar signals by extracting Shannon entropy, Singular spectrum Shannon entropy and Singular spectrum index entropy features, was proposed to achieve the purpose of signal identification. Simulation results show that, the algorithm based on entropies has good anti-noise performance, and it can still describe the characteristics of signals well even at low SNR, which can achieve the purpose of identification and classification for different radar signals.


Raman Nanotechnology - The Lycurgus Cup


title: An X- and Ku-Band Wideband Recursive Receiver MMIC With Gain-Reuse

abstract: This paper presents an 8-18 GHz wideband receiver with recursive super-heterodyne topology. A multi-feedback technology is utilized in the LNA design for the input matching over the wide frequency range in X- and Ku-band. In order to save power, both the RF and IF signals share a tunable transconductance stage. The IF output of the first mixer is fed back into the tunable input stage for IF amplification in a recursive manner, which significantly enhances the gain tuning without increasing the power. The wideband receiver MMIC is implemented in a 0.13 SiGe BiCMOS technology and achieves a 6.7-7.8 dB noise figure. The receiver average gain over the frequency range is measured as 53 dB maximum gain with 20 dB continual tuning and 36 dB discrete tuning. The average output P1dB over the frequency range is measured as 10 dBm at maximum gain. The receiver dissipates only 180 mW with a 2.2 V power supply.


title: Putting an “End” to the Motor Cortex Representations of Action Words

abstract: Language processing is an example of implicit learning of multiple statistical cues that provide probabilistic information regarding word structure and use. Much of the current debate about language embodiment is devoted to how action words are represented in the brain, with motor cortex activity evoked by these words assumed to selectively reflect conceptual content and/or its simulation. We investigated whether motor cortex activity evoked by manual action words (e.g., caress) might reflect sensitivity to probabilistic orthographic-phonological cues to grammatical category embedded within individual words. We first review neuroimaging data demonstrating that nonwords evoke activity much more reliably than action words along the entire motor strip, encompassing regions proposed to be action category specific. Using fMRI, we found that disyllabic words denoting manual actions evoked increased motor cortex activity compared with non-body-part-related words (e.g., canyon), activity which overlaps that evoked by observing and executing hand movements. This result is typically interpreted in support of language embodiment. Crucially, we also found that disyllabic nonwords containing endings with probabilistic cues predictive of verb status (e.g., -eve) evoked increased activity compared with nonwords with endings predictive of noun status (e.g., -age) in the identical motor area. Thus, motor cortex responses to action words cannot be assumed to selectively reflect conceptual content and/or its simulation. Our results clearly demonstrate motor cortex activity reflects implicit processing of ortho-phonological statistical regularities that help to distinguish a word's grammatical class.


title: The raven roosting optimisation algorithm

abstract: A significant stream of literature which draws inspiration from the foraging activities of various organisms to design optimisation algorithms has emerged over the past decade. The success of these algorithms across a wide variety of application domains has spurred interest in the examination of the foraging behaviours of other organisms to develop novel and powerful, optimisation algorithms. A variety of animals, including some species of birds and bats, engage in social roosting whereby large numbers of conspecifics gather together to roost, either overnight or for longer periods. It has been claimed that these roosts can serve as information centres to spread knowledge concerning the location of food resources in the environment. In this paper we look at the social roosting and foraging behaviour of one species of bird, the common raven, and take inspiration from this to design a novel optimisation algorithm which we call the raven roosting optimisation algorithm. The utility of the algorithm is assessed on a series of benchmark problems and the results are found to be competitive. We also provide a novel taxonomy which classifies foraging-inspired optimisation algorithms based on the underlying social communication mechanism embedded in the algorithms.


title: Strategic decision making for automated driving on two-lane, one way roads using model predictive control

abstract: This paper presents an algorithm for strategic decision making regarding when lane change and overtake manoeuvres are desirable and feasible. By considering the task of driving on two-lane, one-way roads, as the selection of desired lane and velocity profile, the algorithm provides useful results in terms of velocity control as well as a decision variable corresponding to whether a lane change manoeuvre should be performed. The decision process is modelled through a mixed logical dynamical system which is solved through model predictive control using mixed integer program formulation. The performance of the proposed control system is explored through simulations of varying driving scenaria on a two-lane, one-way road, which shows the capability of the system to achieve appropriate longitudinal and lateral control strategies depending on the traffic situation.


title: Enablers and barriers to the organizational adoption of sustainable business practices

abstract: Individuals, organizations, and governments are increasingly becoming aware of the necessity of sustainability in living, organizing, performing, and managing work. In this context, “green IS” has become an established colloquial term, acknowledging that information technology, corporate information systems, and the surrounding practices are both a contributor to the sustainability challenge and a potential enabler for green and sustainable practices. To date, however, there are few reported studies on the role of information systems for the challenge, and solution, of sustainability. This paper presents results from a case study of a world-wide operating IT software solution provider that is engaged in the development and adoption of sustainable practices. Our study suggests that the adoption of sustainable practices comes along with a number of particularities. We found information technology to be a key enabler of transparency about the progress of sustainability operations. We further found personal, motivator factors as well as organizational factors such as business inclusion, strategy definition, and a dialectic top-management and bottom-up support, to play a role in enabling a company to manage their sustainability. We describe a set of conjectures forthcoming from our case analysis, and detail some implications for further research in this area.


title: General linear-optical quantum state generation scheme: Applications to maximally path-entangled states

abstract: We introduce schemes for linear-optical quantum state generation. A quantum state generator is a device that prepares a desired quantum state using product inputs from photon sources, linear-optical networks, and postselection using photon counters. We show that this device can be concisely described in terms of polynomial equations and unitary constraints. We illustrate the power of this language by applying the Grobner-basis technique along with the notion of vacuum extensions to solve the problem of how to construct a quantum state generator analytically for any desired state, and use methods of convex optimization to identify bounds to success probabilities. In particular, we disprove a conjecture concerning the preparation of the maximally path-entangled n ,0 + 0, nNOON state by providing a counterexample using these methods, and we derive a new upper bound on the resources required for NOON-state generation.


title: A Model-based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds

abstract: Detection of vehicles in crowded 3-D urban scenes is a challenging problem in many computer vision related research fields, such as robot perception, autonomous driving, self-localization, and mapping. In this paper we present a model-based approach to solve the recognition problem from 3-D range data. In particular, we aim to detect and recognize vehicles from continuously streamed LIDAR point cloud sequences of a rotating multi-beam laser scanner. The end-to-end pipeline of our framework working on the raw streams of 3-D urban laser data consists of three steps (1) producing distinct groups of points which represent different urban objects (2) extracting reliable 3-D shape descriptors specifically designed for vehicles, considering the need for fast processing speed (3) executing binary classification on the extracted descriptors in order to perform vehicle detection. The extraction of our efficient shape descriptors provides a significant speedup with and increased detection accuracy compared to a PCA based 3-D bounding box fitting method used as baseline.


title: Preparation and properties of ethylenebisformamide plasticized potato starch (EPTPS)

abstract: Abstract Ethylenebisformamide plasticized potato starch (EPTPS) was prepared by extrusion. Starch was blended with ethylenebisformamide at 25, 30, and 35% (W/W) and extruded in a single-screw extruder. The samples were extruded at 135–140–135–135 °C from feed zone to die. FT-IR expressed that the microcosmic chemical environments of the group in EPTPS compared to the native potato starch, which indicated that, the strong and stable hydrogen bond had been formed between ethylenebisformamide and starch. By Scanning Electron Microscope (SEM) native potato starch granules were proved to transfer to a continuous phase. After being stored for 1 week at RH=33%, the mechanical properties of ethylenebisformamide plasticized thermoplastic starch (EPTPS) was also studied and the elongation reached to 140% utmost with a decrease of the tensile strength. X-ray diffraction (XRD) showed that the typical B-style crystallinity in the native potato starch has been destructed. On the other hand, water resistance of EPTPS was better than glycerol plasticized potato starch (GPTPS). It decreased from 50.2% for the glycerol plasticized potato starch down to 35.3% for the ethylenebisformamide plasticized sample at RH 100%. EPTPSs proved to be having good thermal stability by thermogravimetric analysis (TGA). T g (detected by differential scanning calorimetry (DSC)) of EPTPS was higher than that GPTPS.


title: Deep Learning for Hate Speech Detection in Tweets

abstract: Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points.


title: A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization

abstract: In evolutionary multiobjective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms (EAs). In addition, it becomes increasingly important to incorporate user preferences because it will be less likely to achieve a representative subset of the Pareto-optimal solutions using a limited population size as the number of objectives increases. This paper proposes a reference vector-guided EA for many-objective optimization. The reference vectors can be used not only to decompose the original multiobjective optimization problem into a number of single-objective subproblems, but also to elucidate user preferences to target a preferred subset of the whole Pareto front (PF). In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space. An adaptation strategy is proposed to dynamically adjust the distribution of the reference vectors according to the scales of the objective functions. Our experimental results on a variety of benchmark test problems show that the proposed algorithm is highly competitive in comparison with five state-of-the-art EAs for many-objective optimization. In addition, we show that reference vectors are effective and cost-efficient for preference articulation, which is particularly desirable for many-objective optimization. Furthermore, a reference vector regeneration strategy is proposed for handling irregular PFs. Finally, the proposed algorithm is extended for solving constrained many-objective optimization problems.


title: Event Extraction Using Distant Supervision

abstract: Distant supervision is a successful paradigm that gathers training data for information extraction systems by automatically aligning vast databases of facts with text. Previous work has demonstrated its usefulness for the extraction of binary relations such as a person’s employer or a film’s director. Here, we extend the distant supervision approach to template-based event extraction, focusing on the extraction of passenger counts, aircraft types, and other facts concerning airplane crash events. We present a new publicly available dataset and event extraction task in the plane crash domain based on Wikipedia infoboxes and newswire text. Using this dataset, we conduct a preliminary evaluation of four distantly supervised extraction models which assign named entity mentions in text to entries in the event template. Our results indicate that joint inference over sequences of candidate entity mentions is beneficial. Furthermore, we demonstrate that the SEARN algorithm outperforms a linear-chain CRF and strong baselines with local inference.


title: Collaborative strategic board games as a site for distributed computational thinking

abstract: ABStrAct This paper examines the idea that contemporary strategic board games represent an informal, interactional context in which complex computational thinking takes place. When games are collaborative – that is, a game requires that players work in joint pursuit of a shared goal – the computational thinking is easily observed as distributed across several participants. This raises the possibility that a focus on such board games are profitable for those who wish to understand computational thinking and learning in situ. This paper intro-duces a coding scheme, applies it to the recorded discourse of three groups of game players, and provides qualitative examples of computational thinking that are observed and documented in Pandemic. The primary contributions of this work are the description of and evidence that complex computational thinking can develop spontaneously during board game play. Steinkuehler, 2006). Often, these benefits are associated with video games and other highly interactive computational media. It is largely thought that the ability to foster a sense of im-mersion is a genuine strength of video games that distinguishes them from many other learning contexts (Shelton & Wiley, 2007).Still, there are reasons to suspect that some of the generative potential of games is not re-stricted to those that take place on a computer platform. At their most base level, games are systems of rules in which players operate on representations. In a computer game, those rules are generally executed and strictly enforced by


title: Systematic topology analysis and generation using degree correlations

abstract: Researchers have proposed a variety of metrics to measure important graph properties, for instance, in social, biological, and computer networks. Values for a particular graph metric may capture a graph's resilience to failure or its routing efficiency. Knowledge of appropriate metric values may influence the engineering of future topologies, repair strategies in the face of failure, and understanding of fundamental properties of existing networks. Unfortunately, there are typically no algorithms to generate graphs matching one or more proposed metrics and there is little understanding of the relationships among individual metrics or their applicability to different settings. We present a new, systematic approach for analyzing network topologies. We first introduce the dK-series of probability distributions specifying all degree correlations within d-sized subgraphs of a given graph G. Increasing values of d capture progressively more properties of G at the cost of more complex representation of the probability distribution. Using this series, we can quantitatively measure the distance between two graphs and construct random graphs that accurately reproduce virtually all metrics proposed in the literature. The nature of the dK-series implies that it will also capture any future metrics that may be proposed. Using our approach, we construct graphs for d=0, 1, 2, 3 and demonstrate that these graphs reproduce, with increasing accuracy, important properties of measured and modeled Internet topologies. We find that the d=2 case is sufficient for most practical purposes, while d=3 essentially reconstructs the Internet AS-and router-level topologies exactly. We hope that a systematic method to analyze and synthesize topologies offers a significant improvement to the set of tools available to network topology and protocol researchers.


title: Discovering Conversational Dependencies between Messages in Dialogs

abstract: We investigate the task of inferring conversational dependencies between messages in one-on-one online chat, which has become one of the most popular forms of customer service. We propose a novel probabilistic classifier that leverages conversational, lexical and semantic information. The approach is evaluated empirically on a set of customer service chat logs from a Chinese e-commerce website. It outperforms heuristic baselines.


title: A comparative study of various current mirror configurations: Topologies and characteristics

abstract: Current mirror is a fundamental unit, widely used for current amplification, biasing and active load in various analog/mixed mode integrated circuits. Its efficient design plays a major role in deciding the overall performance of these circuits. In literature, various techniques have been developed and proposed to improve the performance parameters of a current mirror like accuracy, input resistance, output resistance and bandwidth etc. This paper is an effort to bring all these techniques on a single platform and compare their advantages and disadvantages. Here, a detailed survey of various current mirror topologies has been carried out and these topologies have been categorized on the basis of various characteristic parameters. The discussed circuits have been simulated using Mentor Graphics Eldo spice in TSMC 0.18źm CMOS, BSIM3 and Level 53 technology. In order to get a fair comparison among these techniques, simulations under similar conditions using a supply voltage of 1.8V have been performed.


abstract: This paper introduces a new method for safety analysis called HiPHOPS (Hierarchically Performed Hazard Origin and Propagation Studies). HiP-HOPS originates from a number of classical techniques such as Functional Failure Analysis, Failure Mode and Effects Analysis and Fault Tree Analysis. However, it extends, automates and integrates these techniques in order to address some of the problems currently encountered in complex safety assessments. The method enables integrated assessment of a complex system from the functional level through to the low level of component failure modes. It mechanises and simplifies a large part of the analysis, the development of fault trees, and can guarantee the consistency of results. HiP-HOPS is currently supported by a tool called the Safety Argument Manager (SAM). In this paper we introduce the method and we show how it has helped us analyse and improve the safety of a distributed brake-by-wire system for cars.


Accumulation of Human and Market Capital in the United States, 1975-2012: An Analysis by Gender


title: Variational Heteroscedastic Gaussian Process Regression

abstract: Standard Gaussian processes (GPs) model observations' noise as constant throughout input space. This is often a too restrictive assumption, but one that is needed for GP inference to be tractable. In this work we present a non-standard variational approximation that allows accurate inference in heteroscedastic GPs (i.e., under input-dependent noise conditions). Computational cost is roughly twice that of the standard GP, and also scales as O(n3). Accuracy is verified by comparing with the golden standard MCMC and its effectiveness is illustrated on several synthetic and real datasets of diverse characteristics. An application to volatility forecasting is also considered.


Thirty Years after Sharkovskiĭ's Theorem: New Perspectives: Proceedings of the Conference


title: Elliptic operator for shape analysis.

abstract: Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami operator. In this paper, we propose to adapt a classical operator from quantum mechanics to the field of shape analysis where we suggest to integrate a scalar function through a unified elliptical Hamiltonian operator. We study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. Then, we evaluate the resulting spectral basis for different applications such as mesh compression and shape matching. The suggested operator is shown to produce better functional spaces to operate with, as demonstrated by the proposed framework that outperforms existing spectral methods, for example, when applied to shape matching benchmarks.


title: OddBall: Spotting Anomalies in Weighted Graphs

abstract: Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the oddball algorithm, to find such nodes The contributions are the following: (a) we discover several new rules (power laws) in density, weights, ranks and eigenvalues that seem to govern the so-called “neighborhood sub-graphs” and we show how to use these rules for anomaly detection; (b) we carefully choose features, and design oddball, so that it is scalable and it can work un-supervised (no user-defined constants) and (c) we report experiments on many real graphs with up to 1.6 million nodes, where oddball indeed spots unusual nodes that agree with intuition.


FAST THREE-DIMENSIONAL SHAPE MEASUREMENT BY COLOR-CODED ILLUMINATION


title: General method in synthesis of pass-transistor circuits

abstract: ACtracl "- A new pass-transistor circuit synthcsls method Is presented in this paper. Several pass-transbtor logic families werc introduced recently, but systematic fiyhthds method is avnilabble tiid takes into nccaunt impact of signa! nrrungcment on circuit performance. The method is applied to pncrution of brs1c twu-input and th1.ec-Lnput logic Eatatrs in CPL, DPL and DVL, but it is gcnerni and can be expanded to synthesis of a random pass-tmirsistor circuit.


title: Weather Recognition Based on Images Captured by Vision System in Vehicle

abstract: Weather recognition is widely required in many areas, and it is also a challenging and brand-new subject. This paper proposes an approach to recognize weather based on images captured by in-vehicle vision system. We bring three groups of features, including histogram of gradient amplitude, HSV color histogram, road information, and employ an algorithm based on Real AdaBoost, making use of the category structure to achieve the task of classification. Experiments confirm superior performances on our dataset collected from images captured by vision system.


title: Early prediction of software component reliability

abstract: The ability to predict the reliability of a software system early in its development, e.g., during architectural design, can help to improve the system's quality in a cost-effective manner. Existing architecture-level reliability prediction approaches focus on system-level reliability and assume that the reliabilities of individual components are known. In general, this assumption is unreasonable, making component reliability prediction an important missing ingredient in the current literature. Early prediction of component reliability is a challenging problem because of many uncertainties associated with components under development. In this paper we address these challenges in developing a software component reliability prediction framework. We do this by exploiting architectural models and associated analysis techniques, stochastic modeling approaches, and information sources available early in the development lifecycle. We extensively evaluate our framework to illustrate its utility as an early reliability prediction approach.


title: Linear network coding

abstract: Consider a communication network in which certain source nodes multicast information to other nodes on the network in the multihop fashion where every node can pass on any of its received data to others. We are interested in how fast each node can receive the complete information, or equivalently, what the information rate arriving at each node is. Allowing a node to encode its received data before passing it on, the question involves optimization of the multicast mechanisms at the nodes. Among the simplest coding schemes is linear coding, which regards a block of data as a vector over a certain base field and allows a node to apply a linear transformation to a vector before passing it on. We formulate this multicast problem and prove that linear coding suffices to achieve the optimum, which is the max-flow from the source to each receiving node.


title: Statistical methodology for massive datasets and model selection

abstract: Astronomy is facing a revolution in data collection, storage, analysis, and interpretation of large datasets. The data volumes here are several orders of magnitude larger than what astronomers and statisticians are used to dealing with, and the old methods simply do not work. The National Virtual Observatory (NVO) initiative has recently emerged in recognition of this need and to federate numerous large digital sky archives, both ground based and space based, and ::: develop tools to explore and understand these vast volumes of data. ::: In this paper, we address some of the critically important statistical challenges raised by the NVO. In particular a low-storage, single-pass, sequential method for simultaneous estimation of multiple quantiles for massive datasets will be presented. Density estimation based on this procedure and a multivariate extension will also be discussed. ::: The NVO also requires statistical tools to analyze moderate size databases. Model selection is an important issue for many astrophysical databases. We present a simple likelihood based 'leave one out' method to select the best among the several possible alternatives. The performance of the method is compared to those based on Akaike Information Criterion and Bayesian Information Criterion.


title: A non-temporal texture driven approach to real-time fire detection

abstract: Here we investigate the automatic detection of fire pixel regions in conventional video (or still) imagery within realtime bounds. As an extension to prior, established approaches within this field we specifically look to extend the primary use of threshold-driven colour spectroscopy to the combined use of colour-texture feature descriptors as an input to a trained classification approach that is independent of temporal information. We show the limitations of such spectroscopy driven approaches on simple, real-world examples and propose our novel extension as a robust, real-time solution within this field by combining simple texture descriptors to illustrate maximal ∼98% fire region detection.


3 . 09 Structural Basis of Semantic Memory q


title: A low-cost semi-modular dual-stack PM BLDC motor for a PV based bore-well submersible pump

abstract: Photovoltaic (PV) powered electric water pumps are becoming popular in remote rural locations due to the non-availability of grid connectivity. A typical PV based drive scheme consists of a PV array, DC-DC converter, inverter-fed induction motor and a water pump. Induction motors are rugged in construction and offer low maintenance but their efficiency is poor at low power levels. Therefore, permanent magnet brushless DC (BLDC) motors are preferred in these applications in view of their relatively high efficiency and robust structure. But, over the years there has been a steep hike in the price of rare-earth permanent magnets like NdFeB (Neodymium Iron Boron) and SmCo (Samarium Cobalt) which make the motor un-economical. Therefore, the design of BLDC motor with low-cost ferrite permanent magnets is gaining interest. In this paper, a novel low-cost three-phase “Semi-Modular Dual-Stack” (SMDS) spoke type brushless dc motor is proposed for a PV powered deep bore-well electric submersible pump (ESP). The proposed motor has good flux concentration without compromise on the ruggedness of the rotor with additional advantage of low cogging torque. Detailed simulation studies of the proposed motor are carried in 2D and 3D-FEM environment using MagNet software to validate the claims and the results are presented.


title: Comparison between desaturation sensing and Rogowski coil current sensing for shortcircuit protection of 1.2 kV, 300 A SiC MOSFET module

abstract: Silicon Carbide (SiC) MOSFET devices have small internal inductance and small on state resistance, resulting in a huge di/dt which will cause the current to rise to a high level very fast during shortcircuit (SC), causing possible destruction of module due to excessive dissipated energy or high overshoot during the turn-off. Therefore, fast and reliable protection circuit is important to detect the SC as fast as possible to minimize the power dissipation in the components during the fault, thus preventing the device from overheating and destructing. Furthermore, soft turn-off is required to avoid high voltage spike during the turn-off of the fault current. This paper compares and evaluates the SC performance of two different protection methods on CREE 1.2 kV, 300 A SiC MOSFET module. The first protection method utilizes desaturation technique (DeSat), while the second method utilizes a Rogowski coil to sense the current through the device. Experimental results are analyzed to determine which protection method behaves better under different operating conditions.


title: A Design Technique Based on Equivalent Circuit and Coupler Theory for Broadband Linear to Circular Polarization Converters in Reflection or Transmission Mode

abstract: A new approach to designing frequency selective surface (FSS)-based linear to circular polarization (LP–CP) converters is presented. It is based on the use of FSSs which exhibit dual diagonal symmetry, and a novel four-port equivalent circuit able to describe the electrical behavior of the cells for the two linear incident polarizations at the same time. The equivalent circuit allows the use of standardized branch line coupler theory to design LP–CP converters comprising a cascade of an arbitrary number of layers, whose synthesis includes the phase and makes it possible to achieve prescribed electrical conditions systematically. A full design procedure has been developed using the new approach and several designs in both transmission and reflection modes are presented and evaluated. It has been proven that single layer reflective converters exhibit large bandwidths as the two reflected field components are in quadrature independently of the frequency. One of these devices was designed and showed an axial ratio <0.2 dB within the band from 21.5 to 28.5 GHz. The reflective LP–CP converters designed was also manufactured and tested, and the measurements were used to validate the design procedure.


Improving Self-Localization Efficiency in a Small Mobile Robot by Using a Hybrid Field of View Vision System


title: Multiple human detection in depth images

abstract: Most human detection algorithms in depth images perform well in detecting and tracking the movements of a single human object. However, their performance is rather poor when the person is occluded by other objects or when there are multiple humans present in the scene. In this paper, we propose a novel human detection technique which analyzes the edges in depth image to detect multiple people. The proposed technique detects a human head through a fast template matching algorithm and verifies it through a 3D model fitting technique. The entire human body is extracted from the image by using a simple segmentation scheme comprising a few morphological operators. Our experimental results on three large human detection datasets and the comparison with the state-of-the-art method showed an excellent performance achieving a detection rate of 94.53% with a small false alarm of 0.82%.


Features from Accelerated Segment Test ( FAST )


title: 100+ Times Faster Weighted Median Filter (WMF)

abstract: Weighted median, in the form of either solver or filter, has been employed in a wide range of computer vision solutions for its beneficial properties in sparsity representation. But it is hard to be accelerated due to the spatially varying weight and the median property. We propose a few efficient schemes to reduce computation complexity from O(r2) to O(r) where r is the kernel size. Our contribution is on a new joint-histogram representation, median tracking, and a new data structure that enables fast data access. The effectiveness of these schemes is demonstrated on optical flow estimation, stereo matching, structure-texture separation, image filtering, to name a few. The running time is largely shortened from several minutes to less than 1 second. The source code is provided in the project website.


title: Health Care Organizations as Complex Adaptive Systems

abstract: From its roots in physics, mathematics, and biology, the study of complexity science, or complex adaptive systems, has expanded into the domain of organizations and systems of organizations. Complexity science is useful for studying the evolution of complex organizations -entities with multiple, diverse, interconnected elements. Evolution of complex organizations often is accompanied by feedback effects, nonlinearity, and other conditions that add to the complexity of existing organizations and the unpredictability of the emergence of new entities. Health care organizations are an ideal setting for the application of complexity science due to the diversity of organizational forms and interactions among organizations that are evolving. Too, complexity science can benefit from attention to the world’s most complex human organizations. Organizations within and across the health care sector are increasingly interdependent. Not only are new, highly powerful and diverse organizational forms being created, but also the restructuring has occurred within very short periods of time. In this chapter, we review the basic tenets of complexity science. We identify a series of key differences between the complexity science and established theoretical approaches to studying health organizations, based on the ways in which time, space, and constructs are framed. The contrasting perspectives are demonstrated using two case examples drawn from healthcare innovation and healthcare integrated systems research. Complexity science broadens and deepens the scope of inquiry into health care organizations, expands corresponding methods of research, and increases the ability of theory to generate valid research on complex organizational forms. Formatted


title: Citation Recommendation Using Distributed Representation of Discourse Facets in Scientific Articles

abstract: Scientific articles usually follow a common pattern of discourse, and their contents can be divided into several facets, such as objective, method, and result. We examine the efficacy of using these discourse facets for citation recommendation. A method for learning multi-vector representations of scientific articles is proposed, in which each vector encodes a discourse facet present in an article. With each facet represented as a separate vector, the similarity of articles can be measured not in their entirety, but facet by facet. The proposed representation method is tested on a new citation recommendation task called context-based co-citation recommendation. This task calls for the evaluation of article similarity in terms of citation contexts, wherein facets help to abstract and generalize the diversity of contexts. The experimental results show that the facet-based representation outperforms the standard monolithic representation of articles.


title: A cGANs-Based Scene Reconstruction Model Using Lidar Point Cloud

abstract: Road scene reconstruction is a fundamental and crucial module at the perception phase for autonomous vehicles, and will influence the later phase, such as object detection, motion planing and path planing. Traditionally, self-driving car uses Lidar, camera or fusion of the two kinds of sensors for sensing the environment. However, single Lidar or camera-based approaches will miss crucial information, and the fusion-based approaches often consume huge computing resources. We firstly propose a conditional Generative Adversarial Networks (cGANs)-based deep learning model that can rebuild rich semantic scene images from upsampled Lidar point clouds only. This makes it possible to remove cameras to reduce resource consumption and improve the processing rate. Simulation on the KITTI dataset also demonstrates that our model can reestablish color imagery from a single Lidar point cloud, and is effective enough for real time sensing on autonomous driving vehicles.


title: Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach

abstract: OBJECTIVE ::: In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor". ::: ::: ::: METHODS ::: This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record. ::: ::: ::: RESULTS ::: The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs. ::: ::: ::: CONCLUSION ::: Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.


title: A Citation Review on The Uniform Experimental Design

abstract: The research project" The theory,method and application of Uniform Design "was awarded the 2008 National Natural Science Award(Second class).During the last two decades,there are 2384 Chinese papers including 782 papers cited by SCI which cited or apphed the method of Uniform Design.This paper synthesizes the application areas of Uniform Design,the distribution of journals which published the articles citing Uniform Design and the variation of articles year by year.Moreover,this paper also introduces some application examples.


title: EQUILIBRIUM POINTS IN NONZERO-SUM n-PERSON

abstract: A submodular game is a finite noncooperative game in which the set of feasible joint decisions is a sublattice and the cost function of each player has properties of submodularity and antitone differences. Examples of submodular games include 1) a game version of a system with complementary products; 2) an extension of the minimum cut problem to a situation where players choose from different sets of nodes and perceive different capacities, with special cases being a game with players choosing whether or not to participate in available economic activities and a game version of the selection problem; 3) the pricing problem of competitors producing substitute products; 4) a game version of the facility location problem; and 5) a game with players determining their optimal usage of available products. A fixed point approach establishes the existence of a pure equilibrium point for certain submodular games. Two algorithms which correspond to fictitious play in dynamic games generate sequences of feasible joint decisions converging monotonically to a pure equilibrium point. Bounds show these algorithms to be very efficient when the set of feasible decisions is finite. An optimal decision for each player is an isotone function of the decisions of other players. Introduction. Consider a noncooperative n-person game with the players indicated by 1,..., n. The decision of player is an mi-vector xi. The joint decision is x =(Xl,'",x,). The set of feasible joint decisions is a subset S of E where m i=1 mi. The feasible decisions for a given player may depend upon the decisions chosen by the other players. (By assigning a very large or infinite cost to each m-vector not in S one could embed such a game into a game in which any m-vector is considered feasible, but that approach is not convenient here because it is not easy to transform subsequent assumptions about the players' cost functions into equivalent properties for such extended costs.) Let x xi (Xl, xi-1, Xi+l, Xn) be the vector of decisions of all players except player i. Let (x; yi) (xl, , xi-1, yi, Xi+l, , xn) be the vector of joint decisions for all n players where yi is the decision of player and x xi is the vector of decisions of the other n 1 players. The set of feasible decisions for player given x xi is Si(X) {yi'(X; Yi) E S}. The vectors x xi and (x; yi) and the set Si(x do not depend on xi. Define Ti {x xi Si(x) is nonempty} and Si x-x,', Si(x). As a result of a joint decision x E S, player incurs the cost fi(x) where fi(x) is a real-valued function on S for 1,..., n. A feasible joint decision x 6 S is an equilibrium point if fi(x)<-fi(x; yi) for all yi6 Si(x) and i= 1, ,n.


title: Ernest: efficient performance prediction for large-scale advanced analytics

abstract: Recent workload trends indicate rapid growth in the deployment of machine learning, genomics and scientific workloads on cloud computing infrastructure. However, efficiently running these applications on shared infrastructure is challenging and we find that choosing the right hardware configuration can significantly improve performance and cost. The key to address the above challenge is having the ability to predict performance of applications under various resource configurations so that we can automatically choose the optimal configuration. ::: ::: Our insight is that a number of jobs have predictable structure in terms of computation and communication. Thus we can build performance models based on the behavior of the job on small samples of data and then predict its performance on larger datasets and cluster sizes. To minimize the time and resources spent in building a model, we use optimal experiment design, a statistical technique that allows us to collect as few training points as required. We have built Ernest, a performance prediction framework for large scale analytics and our evaluation on Amazon EC2 using several workloads shows that our prediction error is low while having a training overhead of less than 5% for long-running jobs.


title: Constructing bibliometric networks: A comparison between full and fractional counting

abstract: The analysis of bibliometric networks, such as co-authorship, bibliographic coupling, and co-citation networks, has received a considerable amount of attention. Much less attention has been paid to the construction of these networks. We point out that different approaches can be taken to construct a bibliometric network. Normally the full counting approach is used, but we propose an alternative fractional counting approach. The basic idea of the fractional counting approach is that each action, such as co-authoring or citing a publication, should have equal weight, regardless of for instance the number of authors, citations, or references of a publication. We present two empirical analyses in which the full and fractional counting approaches yield very different results. These analyses deal with co-authorship networks of universities and bibliographic coupling networks of journals. Based on theoretical considerations and on the empirical analyses, we conclude that for many purposes the fractional counting approach is preferable over the full counting one.


title: A Scalable 64-Element 280Hz Phased-Array Transceiver with 50 dBm EIRP and 8–12 Gbps 5G Link at 300 Meters without any Calibration

abstract: This paper presents a 28 GHz 5G communication link using 64-element (8×8) phased-arrays. The design is based on quad-core (2×2) transmit/receive (TRX) beamformer chips with 6-bit phase control in SiGe BiCMOS. Sixteen of these chips are assembled on a 12-layer printed circuit board (PCB) together with a Wilkinson combiner/divider network and stacked patch antennas. The antenna can achieve an $\mathbf{S}_{11}$ bandwidth of 4 GHz (28–32 GHz) and the array has a measured saturated EIRP of 50 dBm with 5 GHz 3-dB bandwidth. The phased-array can scan to $\pm \mathbf{50}^{\circ}$ in azimuth (H-plane) and $\pm \mathbf{25}^{\circ}$ in elevation (E-plane) with low sidelobes and without any calibration. A 300 meter wireless link is demonstrated with a data rate of 8–12 Gbps over all scan angles using 16-/64-QAM waveforms. To our knowledge, this is the highest data rate at the longest link distance demonstrated for a single-beam 28 GHz 5G array.


title: Leukocytic cell sources of airway tissue kallikrein

abstract: Lung tissue kallikrein (TK) is a serine proteinase that putatively plays a role in the pathophysiology of asthma by generating kallidin and bradykinin, mediators that contribute to airway hyperresponsiveness. In previous studies we observed biphasic increases in TK activity in bronchoalveolar lavage fluid following airway allergen challenge in allergic sheep. Although glandular TK is likely a major source of the initial increase in TK, the sources of the late increases in TK that are associated with the development of airway hyperresponsiveness may be dependent on activated resident and recruited inflammatory cells including alveolar macrophages (AMs) and neutrophils (PMNs). These cells increase concomitantly with the late increases in TK activity. To test this hypothesis, we obtained AMs from bronchoalveolar lavage fluid and PMNs and monocytes (precursors of AMs) from sheep blood and determined whether these cells contained TK and whether these same cells could release TK upon activation. Using confocal microscopy, immunocytochemical techniques, and enzyme activity assays, we found that all three cell types contained and secreted TK. All three cell types demonstrated basal release of TK, which could be increased after stimulation with zymosan. In addition, PMNs also released TK in the presence of phorbol ester, suggesting multiple secretory pathways in these cells. Furthermore, we showed that human monocytes also contain and secrete TK. We conclude that in the airways, monocytes, PMNs, and AMs may contribute to increased TK activity. Knowing the sources of TK in the airways could be important in understanding the mechanisms of inflammation that contribute to the pathophysiology of asthma and may help in the development of new therapies to control the disease.


title: Microbiota abnormalities in inflammatory airway diseases - Potential for therapy.

abstract: Increasingly the development of novel therapeutic strategies is taking into consideration the contribution of the intestinal microbiota to health and disease. Dysbiosis of the microbial communities colonizing the human intestinal tract has been described for a variety of chronic diseases, such as inflammatory bowel disease, obesity and asthma. In particular, reduction of several so-called probiotic species including Lactobacilli and Bifidobacteria that are generally considered to be beneficial, as well as an outgrowth of potentially pathogenic bacteria is often reported. Thus a tempting therapeutic approach is to shape the constituents of the microbiota in an attempt to restore the microbial balance towards the growth of 'health-promoting' bacterial species. A twist to this scenario is the recent discovery that the respiratory tract also harbors a microbiota under steady-state conditions. Investigators have shown that the microbial composition of the airway flora is different between healthy lungs and those with chronic lung diseases, such as asthma, chronic obstructive pulmonary disease as well as cystic fibrosis. This is an emerging field, and thus far there is very limited data showing a direct contribution of the airway microbiota to the onset and progression of disease. However, should future studies provide such evidence, the airway microbiota might soon join the intestinal microbiota as a target for therapeutic intervention. In this review, we highlight the major advances that have been made describing the microbiota in chronic lung disease and discuss current and future approaches concerning manipulation of the microbiota for the treatment and prevention of disease.


title: Emulating Human Conversations using Convolutional Neural Network-based IR

abstract: Conversational agents ("bots") are beginning to be widely used in conversational interfaces. To design a system that is capable of emulating human-like interactions, a conversational layer that can serve as a fabric for chat-like interaction with the agent is needed. In this paper, we introduce a model that employs Information Retrieval by utilizing convolutional deep structured semantic neural network-based features in the ranker to present human-like responses in ongoing conversation with a user. In conversations, accounting for context is critical to the retrieval model; we show that our context-sensitive approach using a Convolutional Deep Structured Semantic Model (cDSSM) with character trigrams significantly outperforms several conventional baselines in terms of the relevance of responses retrieved.


title: High Throughput CABAC Entropy Coding in HEVC

abstract: Context-adaptive binary arithmetic coding (CAB-AC) is a method of entropy coding first introduced in H.264/AVC and now used in the newest standard High Efficiency Video Coding (HEVC). While it provides high coding efficiency, the data dependencies in H.264/AVC CABAC make it challenging to parallelize and thus, limit its throughput. Accordingly, during the standardization of entropy coding for HEVC, both coding efficiency and throughput were considered. This paper highlights the key techniques that were used to enable HEVC to potentially achieve higher throughput while delivering coding gains relative to H.264/AVC. These techniques include reducing context coded bins, grouping bypass bins, grouping bins with the same context, reducing context selection dependencies, reducing total bins, and reducing parsing dependencies. It also describes reductions to memory requirements that benefit both throughput and implementation costs. Proposed and adopted techniques up to draft international standard (test model HM-8.0) are discussed. In addition, analysis and simulation results are provided to quantify the throughput improvements and memory reduction compared with H.264/AVC. In HEVC, the maximum number of context-coded bins is reduced by 8×, and the context memory and line buffer are reduced by 3× and 20×, respectively. This paper illustrates that accounting for implementation cost when designing video coding algorithms can result in a design that enables higher processing speed and lowers hardware costs, while still delivering high coding efficiency.


title: A Ka-band asymmetrical stacked-FET MMIC Doherty power amplifier

abstract: We present a stacked-FET monolithic millimeter-wave (mmW) integrated circuit Doherty power amplifier (DPA). The DPA employs a novel asymmetrical stack gate bias to achieve high power and high efficiency at 6-dB power back-off (PBO). The circuit is fabricated in a 0.15-µm enhancement mode (E-mode) Gallium Arsenide (GaAs) process. Experimental results demonstrate output power at 1-dB gain compression (P 1dB ) of 28.2 dBm, peak power added efficiency (PAE) of 37% and PAE at 6-dB PBO of 27% at 28 GHz. Measured small signal gain is 15 dB while the 3-dB bandwidth covers from 25.5 to 29.5 GHz. Using digital predistortion (DPD) with a 20 MHz 64 QAM modulated signal, an adjacent channel power ratio (ACPR) of −46 dBc has been observed.


title: Tropical fungal infections.

abstract: Fungal infections are more common and diverse in the tropics but are also increasingly seen in returning travelers and migrants as international travel becomes easier. They are conventionally classified into superficial, cutaneous, subcutaneous, and systemic mycoses. This article provides an overview of superficial, cutaneous, and subcutaneous mycoses that are more prevalent and/or geographically restricted to the tropics and briefly discusses fungal infections in returning travelers. Systematic data on such infections as travel-associated diseases are currently lacking, and enhanced surveillance for fungal infections may lead to early diagnosis and an understanding of the epidemiology of the fungal infections among travelers.


title: Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing

abstract: There are rising rates of depression on college campuses. Mental health services on our campuses are working at full stretch. In response researchers have proposed using mobile sensing for continuous mental health assessment. Existing work on understanding the relationship between mobile sensing and depression, however, focuses on generic behavioral features that do not map to major depressive disorder symptoms defined in the standard mental disorders diagnostic manual (DSM-5). We propose a new approach to predicting depression using passive sensing data from students' smartphones and wearables. We propose a set of symptom features that proxy the DSM-5 defined depression symptoms specifically designed for college students. We present results from a study of 83 undergraduate students at Dartmouth College across two 9-week terms during the winter and spring terms in 2016. We identify a number of important new associations between symptom features and student self reported PHQ-8 and PHQ-4 depression scores. The study captures depression dynamics of the students at the beginning and end of term using a pre-post PHQ-8 and week by week changes using a weekly administered PHQ-4. Importantly, we show that symptom features derived from phone and wearable sensors can predict whether or not a student is depressed on a week by week basis with 81.5% recall and 69.1% precision.


title: Hilbert-Huang transform based physiological signals analysis for emotion recognition

abstract: This paper presents a feature extraction technique based on the Hilbert-Huang Transform (HHT) method for emotion recognition from physiological signals. Four kinds of physiological signals were used for analysis: electrocardiogram (ECG), electromyogram (EMG), skin conductivity (SC) and respiration changes (RSP). Each signal is decomposed into a finite set of AM-FM mono components (fission process) by the Empirical Mode Decomposition (EMD) which is the key part of the HHT. The information components of interest are then combined to create feature vectors (fusion process) for the next classification stage. In addition, classification is performed by using Support Vector Machines (SVM). The classification scores show that HHT based methods outperform traditional statistical techniques and provide a promising framework for both analysis and recognition of physiological signals in emotion recognition.


title: High performance CMOS dual supply level shifter for a 0.5V input and 1V output in standard 1.2V 65nm technology process

abstract: This paper presents the design of a highly efficient CMOS level shifter qc-level shifter. Unlike many recent level shifters, the proposed qc-level shifter does not use bootstrap capacitors to minimize active area. When implemented on a 65nm CMOS technology, under the large capacitive loading condition (2pF), qc-level shifter has a lower active area (94%), and energy-delay product (21.4%) than the reference bootstrap level shifter circuit (ts-level shifter). In comparison to a conventional shifter (c-level shifter)the corresponding reductions are 9.5% and 55%, respectively. Also qc-level shifter has very small effective input capacitance in comparison with ts-level shifter as it does not need a bootstrap capacitor connected to its input.


title: A Corpus-based Approach to Finding Happiness

abstract: What are the sources of happiness and sadness in everyday life? In this paper, we employ ‘linguistic ethnography’ to seek out where happiness lies in our everyday lives by considering a corpus of blogposts from the LiveJournal community annotated with happy and sad moods. By analyzing this corpus, we derive lists of happy and sad words and phrases annotated by their ‘happiness factor.’ Various semantic analyses performed with this wordlist reveal the happiness trajectory of a 24-day (3am and 9-10p are most happy), and a 7-day week (Wednesdays are saddest), and compare the socialness and humancenteredness of happy descriptions versus sad descriptions. We evaluate our corpus-based approach in a classification task and contrast our wordlist with emotionally-annotated wordlists produced by experimental focus groups. Having located happiness temporally and semantically within this corpus of everyday life, the paper concludes by offering a corpus-inspired livable recipe for happiness 1 .


title: A Simple Method to Determine if a Music Information Retrieval System is a “Horse”

abstract: We propose and demonstrate a simple method to explain the figure of merit (FoM) of a music information retrieval (MIR) system evaluated in a dataset, specifically, whether the FoM comes from the system using characteristics confounded with the “ground truth” of the dataset. Akin to the controlled experiments designed to test the supposed mathematical ability of the famous horse “Clever Hans,” we perform two experiments to show how three state-of-the-art MIR systems produce excellent FoM in spite of not using musical knowledge. This provides avenues for improving MIR systems, as well as their evaluation. We make available a reproducible research package so that others can apply the same method to evaluating other MIR systems.


title: Flickr Circles: Aesthetic Tendency Discovery by Multi-View Regularized Topic Modeling

abstract: Aesthetic tendency discovery is a useful and interesting application in social media. In this paper we propose to categorize large-scale Flickr users into multiple circles, each containing users with similar aesthetic interests (e.g., landscapes). We notice that: 1) an aesthetic model should be flexible as different visual features may be used to describe different image sets; 2) the numbers of photos from different Flickr users vary significantly and some users may have very few photos; and 3) visual features from each Flickr photo should be seamlessly integrated at both low-level and high-level. To meet these challenges, we propose to fuze color, textural, and semantic channel features using a multi-view learning framework, where the feature weights are adjusted automatically. Then, a regularized topic model is developed to quantify each user's aesthetic interest as a distribution in the latent space. Afterward, a graph is constructed to describe the discrepancy of aesthetic interests among users. Apparently, densely connected users are with similar aesthetic interests. Thus, an efficient dense subgraph mining algorithm is adopted to group Flickr users into multiple circles. Experiments have shown that our approach performs competitively on a million-scale image set crawled from Flickr. Besides, our method can enhance the transferal-based photo cropping [40] as reported by the user study.


title: A Single Shot Text Detector with Scale-adaptive Anchors

abstract: Currently, most top-performing text detection networks tend to employ fixed-size anchor boxes to guide the search for text instances. They usually rely on a large amount of anchors with different scales to discover texts in scene images, thus leading to high computational cost. In this paper, we propose an end-to-end box-based text detector with scale-adaptive anchors, which can dynamically adjust the scales of anchors according to the sizes of underlying texts by introducing an additional scale regression layer. The proposed scale-adaptive anchors allow us to use a few number of anchors to handle multi-scale texts and therefore significantly improve the computational efficiency. Moreover, compared to discrete scales used in previous methods, the learned continuous scales are more reliable, especially for small texts detection. Additionally, we propose Anchor convolution to better exploit necessary feature information by dynamically adjusting the sizes of receptive fields according to the learned scales. Extensive experiments demonstrate that the proposed detector is fast, taking only $0.28$ second per image, while outperforming most state-of-the-art methods in accuracy.


title: Gamification Design for Increasing Customer Purchase Intention in a Mobile Marketing Campaign App

abstract: Mobile apps have been developed for marketing purposes and for creating new opportunities for firms to communicate with and satisfy their target audience. However, numerous mobile apps are added daily to the Google Play and Apple App Store. This study developed a mobile application to encourage customers to participate and engage in a marketing campaign in order to increase their potential opportunity for making purchases in an internationally branded apparel store. In this study, we applied a systematic framework of Internet marketing with four strategic stages: attract, engage, retain, and monetize, for guiding and ensuring the success of the marketing campaign. Two human factor researchers were planned, and we administered a focus group interview with six computer game designers for retrieving persuasive game mechanics from existing games. Afterward, we classified the collected gamification mechanics into the four stages, according to their Internet marketing functionality. By referring to the generated gamification mechanics, we then developed a mobile application for supporting a apparel store marketing campaign. Customers’ engagement behavior and purchase results were measured. This paper discussed the implications of this study on both research and practice.


title: Relational Frame Theory and Industrial / Organizational Psychology

abstract: The current paper argues that a Relational Frame Theory account of complex human behavior including an analysis of relational frames, relational networks, rules and the concept of self can provide a potentially powerful new perspective on phenomena in the applied science of industrial/organizational (I/O) psychology. In this article, we first provide a brief description of I/O psychology itself. We then expand on the core features of RFT described earlier in this collection, including how it addresses rule-governance. Finally we illustrate, using relevant examples, the ways in which these concepts can be used to understand behavior in the I/O arena.


title: A Comparative Study of Attacks on Databases and Database Security Techniques

abstract: Security has become one of the important challenges that people are facing all over the world in every aspect of their lives likewise security in electronic world has a great significance. Present day global business environment presents numerous security threats and compliance challenges. To protect against data thefts and frauds, we require security solutions that are transparent by design. Data is most important in today’s world as it helps organizations as well as individuals to extract information and use it to make various decisions. Data are generally stored in database so that retrieving and maintaining it becomes easy and manageable. In this paper, concise review of major threats in database security, database security techniques along with their usage is presented and security policy also that should be enforced to reduce and eliminate the security threats.


title: Starvation: Early Signals, Sensors, and Sequelae*

abstract: To identify the sequences of changes in putative signals, reception of these and responses to starvation, we sampled fed and starved rats at 2- to 6-h intervals after removal of food 2 h before dark. Metabolites, hormones, hypothalamic neuropeptide expression, fat depots, and leptin expression were measured. At 2 h, insulin decreased, and FFA and corticosterone (B) increased; by 4 h, leptin and glucose levels decreased. Neuropeptide Y messenger RNA (mRNA) increased 6 h after food removal and thereafter. Adrenal and plasma B did not follow ACTH and were elevated throughout, with a nadir at the dark-light transition. Leptin correlated inversely with adrenal B. Fat stores decreased during the last 12 h. Leptin mRNA in perirenal and sc fat peaked during the dark period, resembling plasma leptin in fed rats. We conclude that 1) within the first 4 h, hormonal and metabolic signals relay starvation-induced information to the hypothalamus; 2) hypothalamic neuropeptide synthesis responds rapidly to the altered metabolic signals; 3) catabolic activity quickly predominates, reinforced by elevated B, not driven by ACTH, but possibly to a minor extent by leptin, and more by adrenal neural activity; and 4) leptin secretion decreases before leptin mRNA or fat depot weight, showing synthesis-independent regulation. (Endocrinology 140: 4015‐ 4023, 1999)


title: ISSN: 1955-611XHeterogeneous Lobbying Efficiency ∗

abstract: Firms are actively involved in the formation of policies. So far, the literature has focused on the relationship between exposure to the competition and the level of protection. The ability of lobbies to achieve a more favorable policy is then directly related to the reaction of their welfare to the policy. This monotonic relationship contradicts the idea that all lobbies do not have the same efficiency. Indeed, this efficiency cannot be uniquely driven by the exposure to competition. This paper proposes an original approach of the lobbying activity taking into account that lobbies' efficiency is heterogeneous. Just as there are some skilled and unskilled cards players. This paper highlights two types of efficiency, the passive and the active. First, according to the sensitivity of the government to the policy, two lobbies equally affected by the policy may pay different contributions to obtain the same protection level. Second, if the active efficiency is introduced, then two lobbies exhibiting the same sensitivity to the policy may obtain two different equilibrium policies.


title: C-MART: Benchmarking the Cloud

abstract: Cloud computing environments provide on-demand resource provisioning, allowing applications to elastically scale. However, application benchmarks currently being used to test cloud management systems are not designed for this purpose. This results in resource underprovisioning and quality-of-service (QoS) violations when systems tested using these benchmarks are deployed in production environments. We present C-MART, a benchmark designed to emulate a modern web application running in a cloud computing environment. It is designed using the cloud computing paradigm of elastic scalability at every application tier and utilizes modern web-based technologies such as HTML5, AJAX, jQuery, and SQLite. C-MART consists of a web application, client emulator, deployment server, and scaling API. The deployment server automatically deploys and configures the test environment in orders of magnitude less time than current benchmarks. The scaling API allows users to define and provision their own customized datacenter. The client emulator generates the web workload for the application by emulating complex and varied client behaviors, including decisions based on page content and prior history. We show that C-MART can detect problems in management systems that previous benchmarks fail to identify, such as an increase from 4.4 to 50 percent error in predicting server CPU utilization and resource underprovisioning in 22 percent of QoS measurements.


title: Grapheme-to-phoneme conversion using Long Short-Term Memory recurrent neural networks

abstract: Grapheme-to-phoneme (G2P) models are key components in speech recognition and text-to-speech systems as they describe how words are pronounced. We propose a G2P model based on a Long Short-Term Memory (LSTM) recurrent neural network (RNN). In contrast to traditional joint-sequence based G2P approaches, LSTMs have the flexibility of taking into consideration the full context of graphemes and transform the problem from a series of grapheme-to-phoneme conversions to a word-to-pronunciation conversion. Training joint-sequence based G2P require explicit grapheme-to-phoneme alignments which are not straightforward since graphemes and phonemes don't correspond one-to-one. The LSTM based approach forgoes the need for such explicit alignments. We experiment with unidirectional LSTM (ULSTM) with different kinds of output delays and deep bidirectional LSTM (DBLSTM) with a connectionist temporal classification (CTC) layer. The DBLSTM-CTC model achieves a word error rate (WER) of 25.8% on the public CMU dataset for US English. Combining the DBLSTM-CTC model with a joint n-gram model results in a WER of 21.3%, which is a 9% relative improvement compared to the previous best WER of 23.4% from a hybrid system.


title: A low sidelobe fan-beam series fed linear antenna array for IEEE 802.11ac outdoor applications

abstract: This paper proposes a double-sided printed dipole linear array antenna with high gain, low sidelobe level (SLL) for IEEE 802.11ac outdoor applications. In this work, to gain low SLL, a series fed network, which has the output powers being proportional to the Chebyshev weight distribution, has been proposed. The radiating elements used to construct the array are double-sided printed dipoles (DSPD) and each single element is equally distributed on the feed network. Furthermore, to increase the directivity of the antenna, Yagi antenna theory is applied by adding driven elements above every single element and a reflector is constructed at the back of the proposed structure. The simulated results show that the proposed array antenna can operate at 5 GHz with a bandwidth of 211 MHz (5.393 GHz–5.604 GHz). The gain and SLL acquired are 17.5 dBi and −26.0 dB at the frequency of 5.5 GHz, respectively.


Lidar Remote Sensing for Ecosystem Studies


Novel Compact Dual Bandstop Filter Using Radial Stub


title: (Guest Editors) DiFi: Fast 3D Distance Field Computation Using Graphics Hardware

abstract: We present an algorithm for fast computation of discretized 3D distance elds using graphics hardware. Given a set of primitives and a distance metric, our algorithm computes the distance eld for each slice of a uniform spatial grid by rasterizing the distance functions of the primitives. We compute bounds on the spatial extent of the Voronoi region of each primitive. These bounds are used to cull and clamp the distance functions rendered for each slice. Our algorithm is applicable to all geometric models and does not make any assumptions about connectivity or a manifold representation. We have used our algorithm to compute distance elds of large models composed of tens of thousands of primitives on high resolution grids. Moreover, we demonstrate its application to medial axis evaluation and proximity computations. As compared to earlier approaches, we are able to achieve an order of magnitude improvement in the running time.


title: Meaningful Clustering Of Senses Helps Boost Word Sense Disambiguation Performance

abstract: Fine-grained sense distinctions are one of the major obstacles to successful Word Sense Disambiguation. In this paper, we present a method for reducing the granularity of the WordNet sense inventory based on the mapping to a manually crafted dictionary encoding sense hierarchies, namely the Oxford Dictionary of English. We assess the quality of the mapping and the induced clustering, and evaluate the performance of coarse WSD systems in the Senseval-3 English all-words task.


title: A synthetic integral feedback controller for robust tunable regulation in bacteria

abstract: We report on the first engineered integral feedback control system in a living cell. The controller is based on the recently published antithetic integral feedback motif [1] which has been analytically shown to have favorable regulation properties. It is implemented along with test circuitry in Escherichia coli using seven genes and three small-molecule inducers. The closed-loop system is highly tunable, allowing a regulated protein of interest to be driven to a desired level and maintained there with precision. Realized using a sigma/anti-sigma protein pair, the integral controller ensures that regulation is maintained in the face of perturbations that lead to the regulated protein’s degradation, thus serving as a proof-of-concept prototype of integral feedback implementation in living cells. When suitably optimized, this integral controller may be utilized as a general-purpose robust regulator for genetic circuits with unknown or partially-known topologies and parameters.


title: Deep learning EEG response representation for brain computer interface

abstract: In this paper, the multi-scale deep convolutional neural networks are introduced to deal with the representation for imagined motor Electroencephalography (EEG) signals. We propose to learn a set of high-level feature representations through deep learning algorithm, referred to as Deep Motor Features (DeepMF), for brain computer interface (BCI) with imagined motor tasks. As the extracted DeepMF are dissimilar for different tasks and alike for the same tasks, it is convenient to separate the diverse EEG signals for imagined motor tasks apart. Our approach achieves 100% accuracy for 4 classes imagined motor EEG signals classification on Project BCI — EEG motor activity dataset. Moreover, thanks to the highly abstract features DeepMF learned, only 4.125 seconds trials of training data are needed, compared with the conventional BLDA algorithm for 8.75 seconds trials demand to achieve the same accuracy, accordingly the BCI response time and the required trials for training are almost declined by half. Experiments are provided to illustrate the effectiveness of the proposed design approach.


title: Multiband antenna for wireless applications including GSM/UMTS/LTE and 5G bands

abstract: A simple ultra thin multiband antenna is introduced. The proposed antenna with four bands covers many wireless applications. The proposed antenna has simple configuration including two open L-shaped slots to create resonat mode at GSM and GPS bands and also two rectangular slots on the radiating element for creating multi resonant modes to meet the specifications of mentioned frequency bands. According to the simple, small and planar configuration, the proposed antenna design is inexpensive and can easily be integrated with the other microwave circuit boards. The proposed antenna has the small size of 10 mm and the total size of 120 mm χ 60 mm χ 0.5 mm, promising to be used widely in compact and ultra slim handsets.


title: Visibility-consistent thin surface reconstruction using multi-scale kernels

abstract: One of the key properties of many surface reconstruction techniques is that they represent the volume in front of and behind the surface, e.g., using a variant of signed distance functions. This creates significant problems when reconstructing thin areas of an object since the backside interferes with the reconstruction of the front. We present a two-step technique that avoids this interference and thus imposes no constraints on object thickness. Our method first extracts an approximate surface crust and then iteratively refines the crust to yield the final surface mesh. To extract the crust, we use a novel observation-dependent kernel density estimation to robustly estimate the approximate surface location from the samples. Free space is similarly estimated from the samples' visibility information. In the following refinement, we determine the remaining error using a surface-based kernel interpolation that limits the samples' influence to nearby surface regions with similar orientation and iteratively move the surface towards its true location. We demonstrate our results on synthetic as well as real datasets reconstructed using multi-view stereo techniques or consumer depth sensors.


title: Imperfect Information Dynamic Stackelberg Game Based Resource Allocation Using Hidden Markov for Cloud Computing

abstract: Existing static grid resource scheduling algorithms, which are limited to minimizing the makespan, cannot meet the needs of resource scheduling required by cloud computing. Current cloud infrastructure solutions provide operational support at the level of resource infrastructure only. When hardware resources form the virtual resource pool, virtual machines are deployed for use transparently. Considering the competing characteristics of multi-tenant environments in cloud computing, this paper proposes a cloud resource allocation model based on an imperfect information Stackelberg game (CSAM-IISG) using a hidden Markov model (HMM) in a cloud computing environment. CSAM-IISG was shown to increase the profit of both the resource supplier and the applicant. Firstly, we used the HMM to predict the service provider's current bid using the historical resources based on demand. Through predicting the bid dynamically, an imperfect information Stackelberg game (IISG) was established. The IISG motivates service providers to choose the optimal bidding strategy according to the overall utility, achieving maximum profits. Based on the unit prices of different types of resources, a resource allocation model is proposed to guarantee optimal gains for the infrastructure supplier. The proposed resource allocation model can support synchronous allocation for both multi-service providers and various resources. The simulation results demonstrated that the predicted price was close to the actual transaction price, which was lower than the actual value in the game model. The proposed model was shown to increase the profits of service providers and infrastructure suppliers simultaneously.


title: DRPose3D: Depth Ranking in 3D Human Pose Estimation

abstract: In this paper, we propose a two-stage depth ranking based method (DRPose3D) to tackle the problem of 3D human pose estimation. Instead of accurate 3D positions, the depth ranking can be identified by human intuitively and learned using the deep neural network more easily by solving classification problems. Moreover, depth ranking contains rich 3D information. It prevents the 2D-to-3D pose regression in two-stage methods from being ill-posed. In our method, firstly, we design a Pairwise Ranking Convolutional Neural Network (PRCNN) to extract depth rankings of human joints from images. Secondly, a coarse-to-fine 3D Pose Network(DPNet) is proposed to estimate 3D poses from both depth rankings and 2D human joint locations. Additionally, to improve the generality of our model, we introduce a statistical method to augment depth rankings. Our approach outperforms the state-of-the-art methods in the Human3.6M benchmark for all three testing protocols, indicating that depth ranking is an essential geometric feature which can be learned to improve the 3D pose estimation.


title: Innovative Instructions and Software Model for Isolated Execution

abstract: For years the PC community has struggled to provide secure solutions on open platforms. Intel has developed innovative new technology to enable SW developers to develop and deploy secure applications on open platforms. The technology enables applications to execute with confidentiality and integrity in the native OS environment. It does this by providing ISA extensions for generating hardware enforceable containers at a granularity determined by the developer. These containers while opaque to the operating system are managed by the OS. This paper analyzes the threats and attacks to applications. It then describes the ISA extension for generating a HW based container. Finally it describes the programming model of this container.


title: Automated Body Structure Extraction from Arbitrary 3D Mesh

abstract: This paper presents an automated method for 3D character skeleton extraction that can be applied for generic 3D shapes. Our work is motivated by the skeleton-based prior work on automatic rigging focused on skeleton extraction and can automatically aligns the extracted structure to fit the 3D shape of the given 3D mesh. The body mesh can be subsequently skinned based on the extracted skeleton and thus enables rigging process. In the experiment, we apply public dataset to drive the estimated skeleton from different body shapes, as well as the real data obtained from 3D scanning systems. Satisfactory results are obtained compared to the existing approaches.


title: Balancing reducer skew in MapReduce workloads using progressive sampling

abstract: The elapsed time of a parallel job depends on the completion time of its longest running constituent. We present a static load balancing algorithm that distributes work evenly across the reducers in a MapReduce job resulting in significant elapsed time reductions. Taking a user-specified model of reducer performance, our load balancer uses a progressive objective-based cluster sampler to estimate the load associated with each reduce-key. It balances the workload using Key Chopping, to split keys with large loads into sub-keys that can be assigned to different distributive reducers, and Key Packing, to assign keys with medium loads to reducers to minimize the maximum reducer load. Keys with small loads are hashed as they have little effect on the balance. This repeats until the user specified balancing objective and confidence level are achieved. The sampler and load balancer have been implemented in the Oracle Loader for Hadoop (OLH), a commercial MapReduce application that employs Apache Hadoop to perform parallel data formatting and data movement into partitioned relational tables. We present the performance improvements we achieve in both OLH and in a MapReduce program for inverted index creation. The balancer works for arbitrary IID key distributions, the time used for sampling is small and our solution is very effective at reducing the elapsed time for the MapReduce jobs we explored.


title: Deep Regionlets for Object Detection

abstract: A key challenge in generic object detection is being to handle large variations in object scale, poses, viewpoints, especially part deformations when determining the location for specified object categories. Recent advances in deep neural networks have achieved promising results for object detection by extending the traditional detection methodologies using the convolutional neural network architectures. In this paper, we make an attempt to incorporate another traditional detection schema, Regionlet into an end-to-end trained deep learning framework, and perform ablation studies on its behavior on multiple object detection datasets. More specifically, we propose a "region selection network" and a "gating network". The region selection network serves as a guidance on where to select regions to learn the features from. Additionally, the gating network serves as a local feature selection module to select and transform feature maps to be suitable for detection task. It acts as soft Regionlet selection and pooling. The proposed network is trained end-to-end without additional efforts. Extensive experiments and analysis on the PASCAL VOC dataset and Microsoft COCO dataset show that the proposed framework achieves comparable state-of-the-art results.


title: The effects of team training on team outcomes: A meta-analysis

abstract: A meta-analysis was conducted to determine relationships between team training and team effectiveness. Results from the 21 studies provided evidence that training is positively related to team effectiveness and effectiveness in five outcome categories: affective, cognitive, subjective task-based skill, objective task-based skill, and teamwork skill.


title: Analysis and Controlled Synthesis of Inhomogeneous Textures

abstract: Many interesting real-world textures are inhomogeneous and/or anisotropic. An inhomogeneous texture is one where various visual properties exhibit significant changes across the texture's spatial domain. Examples include perceptible changes in surface color, lighting, local texture pattern and/or its apparent scale, and weathering effects, which may vary abruptly, or in a continuous fashion. An anisotropic texture is one where the local patterns exhibit a preferred orientation, which also may vary across the spatial domain. While many example-based texture synthesis methods can be highly effective when synthesizing uniform stationary isotropic textures, synthesizing highly non-uniform textures, or ones with spatially varying orientation, is a considerably more challenging task, which so far has remained underexplored. In this paper, we propose a new method for automatic analysis and controlled synthesis of such textures. Given an input texture exemplar, our method generates a source guidance map comprising: i a scalar progression channel that attempts to capture the low frequency spatial changes in color, lighting, and local pattern combined, and ii a direction field that captures the local dominant orientation of the texture. Having augmented the texture exemplar with this guidance map, users can exercise better control over the synthesized result by providing easily specified target guidance maps, which are used to constrain the synthesis process.


title: A case of early sports specialization in an adolescent athlete.

abstract: Early sport specialization (ESS) refers to intense year round training in a specific sport with the exclusion of other sports at a young age. This approach to training is heavily debated and there are claims both in support and against ESS. ESS is considered to be more common in the modern day youth athlete and could be a source of overuse injuries and burnout. This case describes a 16 year old elite level baseball pitcher who engaged in high volume, intense training at a young age which lead to several significant throwing related injuries. The case highlights the historical context of ESS, the potential risk and benefits as well as the evidence for its effectiveness. It is important for health care professionals to be informed on the topic of ESS in order to educate athletes, parents, coaches and organizations of the potential risks and benefits.


EXERGY-BASED EVALUATION OF RESOURCE RECOVERY FROM MUNICIPAL SOLID WASTE


title: Zika virus impairs growth in human neurospheres and brain organoids

abstract: Since the emergence of Zika virus (ZIKV), reports of microcephaly have increased considerably in Brazil; however, causality between the viral epidemic and malformations in fetal brains needs further confirmation. We examined the effects of ZIKV infection in human neural stem cells growing as neurospheres and brain organoids. Using immunocytochemistry and electron microscopy, we showed that ZIKV targets human brain cells, reducing their viability and growth as neurospheres and brain organoids. These results suggest that ZIKV abrogates neurogenesis during human brain development.


title: Application of Support Vector Machines on Signal Strength Based Localization in Wireless Networks.

abstract: Received Signal Strength (RSS) based positioning systems are potential candidates to enable location aware computing spaces due to their economic viability. Fundamental requirement of such localization systems is to estimate location from RSS at a particular location. In this paper we present a location system based on Support Vector Machines (SVM). Characterization of different kernel functions is presented with respect to location estimation problem and accuracy of SVM based positioning is compared with other approaches. Results show that support vector machines obtain competitive accuracy in shortest model training time.


title: gHull: A GPU algorithm for 3D convex hull

abstract: A novel algorithm is presented to compute the convex hull of a point set in ℝ3 using the graphics processing unit (GPU). By exploiting the relationship between the Voronoi diagram and the convex hull, the algorithm derives the approximation of the convex hull from the former. The other extreme vertices of the convex hull are then found by using a two-round checking in the digital and the continuous space successively. The algorithm does not need explicit locking or any other concurrency control mechanism, thus it can maximize the parallelism available on the modern GPU. The implementation using the CUDA programming model on NVIDIA GPUs is exact and efficient. The experiments show that it is up to an order of magnitude faster than other sequential convex hull implementations running on the CPU for inputs of millions of points. The works demonstrate that the GPU can be used to solve nontrivial computational geometry problems with significant performance benefit.


Evolving Artificial Neural Networks


title: The YouTube-8M Kaggle Competition: Challenges and Methods

abstract: We took part in the YouTube-8M Video Understanding Challenge hosted on Kaggle, and achieved the 10th place within less than one month's time. In this paper, we present an extensive analysis and solution to the underlying machine-learning problem based on frame-level data, where major challenges are identified and corresponding preliminary methods are proposed. It's noteworthy that, with merely the proposed strategies and uniformly-averaging multi-crop ensemble was it sufficient for us to reach our ranking. We also report the methods we believe to be promising but didn't have enough time to train to convergence. We hope this paper could serve, to some extent, as a review and guideline of the YouTube-8M multi-label video classification benchmark, inspiring future attempts and research.


title: Using molecular dynamics to probe the structural basis for enhanced stability in thermal stable cytochromes P450.

abstract: High temperature molecular dynamics (MD) has been used to assess if MD can be employed as a useful tool for probing the structural basis for enhanced stability in thermal stable cytochromes P450. CYP119, the most thermal stable P450 known, unfolds more slowly during 500K MD simulations compared to P450s that melt at lower temperatures, P450cam and P450cin. A comparison of the 500K MD trajectories shows that the Cys ligand loop, a critically important structural feature just under the heme, in both P450cin and P450cam completely unfolds while this region is quite stable in CYP119. In CYP119 this region is stabilized by tight nonpolar interactions involving Tyr26 and Leu308. The corresponding residues in P450cam are Gly and Thr, respectively. The in silico generated Y26A/L308A CYP119 double mutant is substantially less stable than wild type CYP119 and the Cys ligand loop unfolds similar to P450cam. The MD thus has identified a potential “hot spot” important for stability. As an experimental test of the MD results, the Y26A/L308A double mutant was prepared and thermal melting curves show that the double mutant exhibits a melting temperature (Tm) 16°C lower than wild type CYP119. Control mutations that were predicted by MD not to destabilize the protein were also generated and the experimental melting temperature was not significantly different from the wild type enzyme. Therefore, high temperature MD is a useful tool in predicting the structural underpinnings of thermal stability in P450s.


title: Integrating DTN and MANET routing

abstract: Mobile Ad-hoc Network (MANET) routing protocols aim at establishing end-to-end paths between communicating nodes and thus support end-to-end semantics of existing transports and applications. In contrast, DTN-based communication schemes imply asynchronous communication (and thus often require new applications) but achieve better reachability, particularly in sparsely populated environments. In this paper, we suggest a hybrid scheme that combines AODV and DTN-based routing and allows keeping the AODV advantage of maintaining end-to-end semantics whenever possible while, at the same time, also offering DTN-based communication options whenever available---leaving the choice to the application. We present our protocol and system design, particularly including the interaction of AODV and DTN, demonstrate achievable performance gains based upon measurements, and report on initial experiments with our implementation in an emulation environment.


title: Realistic Adversarial Examples in 3D Meshes

abstract: Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications and achieved increasing success. However, recent studies show that such machine learning models appear to be vulnerable against adversarial examples. So far adversarial examples have been heavily explored for 2D images, while few works have conducted to understand vulnerabilities of 3D objects which exist in real world, where 3D objects are projected to 2D domains by photo taking for different learning (recognition) tasks. In this paper, we consider adversarial behaviors in practical scenarios by manipulating the shape and texture of a given 3D mesh representation of an object. Our goal is to project the optimized "adversarial meshes" to 2D with a photorealistic renderer, and still able to mislead different machine learning models. Extensive experiments show that by generating unnoticeable 3D adversarial perturbation on shape or texture for a 3D mesh, the corresponding projected 2D instance can either lead classifiers to misclassify the victim object as an arbitrary malicious target, or hide any target object within the scene from object detectors. We conduct human studies to show that our optimized adversarial 3D perturbation is highly unnoticeable for human vision systems. In addition to the subtle perturbation for a given 3D mesh, we also propose to synthesize a realistic 3D mesh and put in a scene mimicking similar rendering conditions and therefore attack different machine learning models. In-depth analysis of transferability among various 3D renderers and vulnerable regions of meshes are provided to help better understand adversarial behaviors in real-world.


title: Crowd Density Estimation Based on a Modified Multicolumn Convolutional Neural Network

abstract: Crowd management has been a topic of concern for many years because accidents frequently occur in situations with a high crowd density. With only a finite amount of space available during shows, protests, or other special occasions, a high crowd density can present a clear danger for those in the area. Considering these challenges, we employed and modified a three-tier multicolumn convolutional neural network (MCNN) system architecture to precisely estimate crowd density. We distinguished three regions from the near to far field to produce a crowd density map. Based on the MCNN system architecture, we detected changes in the size of a crowd according to a distance measure and examined additional features that can be incorporated to demonstrate their effects on crowd density maps. Examining these features using the Shanghaitech dataset demonstrated that compared with the native MCNN, the accuracy of estimating crowd counting by using our proposed method increased by 22.97% and 18.64% in terms of mean absolute error (MAE) and mean square error (MSE), respectively. A performance comparison with other state-of-the-art methods was also made. From this, we can infer that the proposed system is compatible with the other listed methods and is worthy of further investigation.