Miracle-dz/UniFAR-FTUs huggingface dataset:
Belief Propagation and Wiring Length Optimization as Organizing Principles for Cortical Microcircuits

In this paper we explore how functional and anatomical constraints and resource optimization could be combined to obtain a canonical cortical micro-circuit and an explanation for its laminar organization. We start with the assumption that cortical regions are involved in Bayesian Belief Propagation. This imposes a set of constraints on the type of neurons and the connection patterns between neurons in that region. In addition there are anatomical constraints that a region has to adhere to. There are several different configurations of neurons consistent with both these constraints. Among all such configurations, it is reasonable to expect that Nature has chosen the configuration with the minimum wiring length. We cast the problem of finding the optimum configuration as a combinatorial optimization problem. A near-optimal solution to this problem matched anatomical and physiological data. As the result of this investigation, we propose a canonical cortical micro-circuit that will support Bayesian Belief Propagation computation and whose laminar organization is near optimal in its wiring length. We describe how the details of this circuit match many of the anatomical and physiological findings and discuss the implications of these results to experimenters and theorists.
Numerical investigation on the dependence of on-body channel characteristics on anthropomorphic variation of human body

A numerical investigation focusing on the correlation between on-body channel characteristics and the morphological variation of body is presented in the paper. The analysis has been carried out at 5.8GHz and a parallel version of Finite Difference Time Domain (FDTD) has been employed. Fifty digital phantoms are adopted in the simulation domain. Parameters explored for the concerned study are path loss and channel capacity. Results demonstrate that for models with similar dimensions around waist, a change of 50cms in height can cause a variation of 20% in path loss exponent and 35% in channel capacity per unit hertz.
MIC: An Agent Formal Environment

Ubiquitous systems can be considered as good challenges and case studies for the reliable development of open, cooperative and mobile systems in a globally uncontrolled environment. Furthermore, the complexity of these systems could constitute a real barrier to classical engineering approaches and validate the foundations of agent based computing. In this paper, we have abstracted a composable multi-agent system environment in an algebraic structure where mobile, interacting and autonomous entities evolve. We present also, a simple ubiquitous application developed according to the abstract model principles. keywords: agent formal deployment environment, agentbased software system, ubiquitous software system.
Real-time Arrow Traffic Light Recognition System for Intelligent Vehicle

Arrow traffic lights are common at intersections of current urban scenes. However, existing algorithms mainly focus on recognition methods of circle traffic light. A novel system used to detect and recognize arrow traffic lights in urban scenes is proposed in the paper. Firstly, the boards of traffic light are exactly localized by image segmentation and morphology processing, then candidates of traffic light are obtained according to threshold segmentation in YCbCr color space and judged by relative position between candidate and its board. Secondly, for confirming traffic light type, Gabor wavelet transform and 2D independent component analysis(2DICA) are used to extract traffic light candidate’s features. A nearest neighbor classifier identifies arrow direction and removes noise. Experimental results show that overall recognition rate of proposed method exceeds 91%, and system will provide real-time, robust, effective, and accurate traffic light outputs for intelligent vehicle.
ON INFORMATION THEORY , SUBMITTED OCT . 3 , 2000 1 Convergence analysis for Turbo-equalization

Convergence analysis for Turbo-equalization Aline Roumy, Alex J. Grant, Inbar Fijalkow, Paul D. Alexander, Didier Pirez Dept. of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA, Equipe de Traitement des Images et du Signal, ENSEA-University of Cergy-Pontoise, 95014 Cergy, France, Institute for Telecommunications Research, University of South Australia, Mawson Lakes, SA 5095, Australia, Southern Poro Communications Annandale, NSW 2038, Australia, THOMSON-CSF Communications, 92231 Gennevilliers Cedex, France.
A Mosaic of Eyes

Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties.
EPLRA: An encryption based proactive load rebalancing algorithm

Cloud computing is a major area in the research because of its high availability, data storage, security, services and load rebalancing etc. Load rebalancing is the distribution of data in such a way that data should be distributed uniformly. The load rebalancing includes passing on load to different sub servers or peers. Thus none of the peers get lightly or heavily loaded. Client attributes such as username, date of login, which of the file viewed can be analyze. After assigning the load, the remaining space of each sub server can be viewed. The encryption scheme is used to encrypt and decrypt the data so that authorized users can only do and view the process.
Backward Error Analysis for Numerical Integrators

Backward error analysis has become an important tool for understanding the long time behavior of numerical integration methods. This is true in particular for the integration of Hamiltonian systems where backward error analysis can be used to show that a symplectic method will conserve energy over exponentially long periods of time. Such results are typically based on two aspects of backward error analysis: (i) It can be shown that the modified vector fields have some qualitative properties which they share with the given problem and (ii) an estimate is given for the difference between the best interpolating vector field and the numerical method. These aspects have been investigated recently, for example, by Benettin and Giorgilli in [ J. Statist. Phys., 74 (1994), pp. 1117--1143], by Hairer in [Ann. Numer. Math., 1 (1994), pp. 107--132], and by Hairer and Lubich in [ Numer. Math., 76 (1997), pp. 441--462]. In this paper we aim at providing a unifying framework and a simplification of the existing results and corresponding proofs. Our approach to backward error analysis is based on a simple recursive definition of the modified vector fields that does not require explicit Taylor series expansion of the numerical method and the corresponding flow maps as in the above-cited works. As an application we discuss the long time integration of chaotic Hamiltonian systems and the approximation of time averages along numerically computed trajectories.
Issue Mapping and Inquiry: Preparing College Students for the Challenges of the Changing Economic World

Students need to be equipped to handle the complexity of the world they are preparing for. Leadership skills need to be developed and cultivated and students need to understand the changing demands of the current workforce. At Quinnipiac University, we have fashioned a curriculum designed to help prepare students by helping them learn to frame problems using the technique of issue mapping. In this article, we provide both a description of how to help students learn this technique and also some of the rationale for why we think this is as effective as it appears to be.
LfhA, a Novel Factor H-Binding Protein of Leptospira interrogans

ABSTRACT The early phase of leptospiral infection is characterized by the presence of live organisms in the blood. Pathogenic Leptospira interrogans is resistant to the alternative pathway of complement mediated-killing, while nonpathogenic members of the genus are not. Consistent with that observation, only pathogenic leptospires bound factor H, a host fluid-phase regulator of the alternative complement pathway. Ligand affinity blot analyses revealed that pathogenic L. interrogans produces at least two factor H-binding proteins. Through screening of a lambda phage expression library, we identified one of these as the novel membrane protein LfhA. Ligand affinity assays and surface plasmon resonance analyses of recombinant LfhA revealed specific binding of both factor H and factor H-related protein 1. Serological examination of infected humans and horses demonstrated that LfhA is expressed by L. interrogans during mammalian infection. LfhA may therefore contribute to the resistance of pathogenic leptospires to complement-mediated killing during leptospiremic phases of the disease.
Plant leaf recognition by venation and shape using artificial neural networks

The number of known and unknown plant species increases as time goes by. Research on plant species can be further advanced if there is a quick and accurate system that can identify plants and hasten the classification process. This system will not only help in accelerating plant classification, but will also allow people who are not morphological experts to conduct their own studies. LeaVes is an application designed to classify different plant species based on the leaf's shape and venation. This system uses different image processing and machine learning techniques including centroid-radii, moment invariance, canny edge detection, morphological operations, image difference and artificial neural networks.
A Quasi-Parallel Encoder of Quasi-Cyclic LDPC Codes in IEEE 802.16e

We present a quasi-parallel LDPC encoder based on the quasi-cyclic characteristic of the parity check matrix of the LDPC code in IEEE 802.16e. A bidirectional recursion Arithmetic is used to improve its timing requires. The encoder is designed with simple shift registers and mod-2 adders, which is easy to be implemented in FPGA. By taking into account of some intermediate results, a low cost, high performance encoder is realized. The results show that, for the half rate LDPC code with code length 2304, the designed encoder can work at 50MHz on Xilinx's XC3S1000 FPGA, With total equivalent gates consumes of 65964 and encoding throughput of up to kb*50 Mb/s.
Index Theory with Bounded Geometry, the Uniformly Finite Â Class, and Infinite Connected Sums

We analyze the obstruction to metrics of positive scalar curvature within a given bounded distortion class of metrics. This obstruction lives in a non-Hausdorff cohomology group Poincare dual to the uniformly finite homology studied by Block and Weinberger. One of the applications is a converse to their theorem on infinite connected sums, giving a complete picture of when such manifolds have metrics of positive scalar curvature.
DEVELOPING CORE SOFTWARE REQUIREMENTS OF ENERGY MANAGEMENT SYSTEM FOR SMART CAMPUS WITH ADVANCED SOFTWARE ENGINEERING

Higher educational institutions such as universities provide campus data and information to stakeholders such as students, faculty staff, administrators, media, and community members. They also analyze such data and information for decision making, planning and continuous improvement. They obtain necessary capabilities for collecting, providing and analyzing data and information by acquiring products and services from suppliers or by developing products and services by themselves. As in today’s social infrastructure, software plays an important role and software seems to keep growing in its scope, size and complexity in infrastructure of educational institutions. However, many organizations have not invested in the capabilities necessary to effectively manage life-cycle process for such software products and services. We argue that software engineering techniques, such as process improvement framework and software product line engineering are important in developing requirements and systems effective in continuous improvement. In this paper, we discuss a trial case to improve the energy management system in our campus, as an emerging area of institutional continuous improvement, by using software engineering techniques under the goal of increasing the awareness and involvement of members for smart energy management.
Archiving, ordering, and searching: search engines, algorithms, databases, and deep mediatization

This article argues that search engines, algorithms, and databases can be considered as a way of understanding deep mediatization. They are embedded in a variety of social and cultural practices, and as such, they change our communicative actions to be shaped by their logic of archiving, ordering, and searching. I argue that, increasingly and in particular ways, search engines, algorithms, and databases shape our everyday communicative actions as they make us think, internalize, and act along the lines of their particular modes of communication action. After having briefly reviewed recent trends in mediatization research, the argument is discussed and unfolded in between the material and social constructivist-phenomenological interpretations of mediatization. In conclusion, it is discussed how deep this form of mediatization can be taken to be.
UNDERSTANDING AND IMPROVING PERFORMANCE IN MULTIHOP WIRELESS MESH NETWORK COMMUNICATION SYSTEMS

As the demand for flexible non-homogeneous wireless communication systems increases, there remain significant challenges in achieving optimal network performance. With varying number of communicating nodes, configurations and highly fluctuating spectrum conditions, we seek to provide a decentralized method for approaching optimal system performance. We are motivated by the reduction in performance traditional cognitive wireless networks face as they increase in number of nodes and have greater variance in inter-node spacing. Therefore we apply the theoretical techniques from non-cooperative game theory combined with the capabilities found in cognitive radio devices to develop a more optimal solution which avoids needing to have a centralized spectrum and rate allocation method. In addition we develop new metrics for wireless mesh network performance based on individual traffic demand and fairness. Our improved DRWF algorithm improves network fairness by an order of magnitude and while better meeting individual node traffic demands. Finally, we discuss various implications multihop mesh structures have on wireless network performance.
New Performance Indices for 6R Robot Postures

Singular postures of 6R robots must be avoided because close to singularities an exact tracking of the planned end-effector trajectory requires great angular velocities in the rotary joints. Hence there is an interest in having a distance measure of the instantaneous configuration to the nearest singularity. This article outlines three new methods to measure the closeness to such a singularity. The presented measures are invariant with regard to Euclidean motions and similarities, they have a geometric meaning, and they can be computed in real-time.
Child Pedestrian-motor Vehicle Collisions and Walking to School in the City of Toronto: The Role of the Built Environment

Introduction: Child pedestrian-motor vehicle collisions are a major population health issue worldwide. Although there are numerous benefits of active transportation, walking to school could potentially increase collision risk. The built environment has been associated with selfreported walking to school and with child pedestrian motor-vehicle collisions. It is important to determine if there are built environment features related to more walking but which also create safe walking environments. The thesis objective was to examine the relationships between observed walking to school, child pedestrian-motor vehicle collisions and the role of the built environment in Toronto, Canada. Methods: Literature related to children walking for transportation, pedestrian-motor vehicle collisions and the built environment was systematically reviewed. Observational counts of school travel mode were conducted at 118 elementary schools in 2011 and mapped onto school attendance boundaries together with police-reported child pedestrian-motor vehicle collisions (2002-2011) and built and social environment data. The relationship between walking proportions and collision rates was examined controlling for the environment. Results: There was a mean collision rate of 7.1/10,000/year within school boundaries. The mean proportion of observed walking was 67%. Several built environment features were iii related to more walking; however, school crossing guards reduced the influence of other features on walking. Walking to school was unrelated to collision rates once built environment features were controlled for. Higher multi-family dwelling density was related to lower collision rates; whereas higher one way street and traffic calming densities, lower traffic light density, school crossing guards and lower school socioeconomic status were related to higher collision rates. Significant features were generally related to road crossing. Conclusions: This is the first large observational study examining walking to school, collision risk and the environment. Results suggest that safety is concerned with built environment features primarily related to road crossing, and not the numbers walking. The associations between school crossing guards and traffic calming with higher collision rates were unexpected. Mechanisms for mitigating road crossings for children are not well understood and controlled research designs are needed. Future policy to increase children’s active transportation should be developed from strong evidence that addresses child pedestrian
Advanced Virtual Reality Based Training to Improve Young Drivers’ Latent Hazard Anticipation Ability

The crash rate for young novice drivers is at least eight times higher than that of their experienced counterparts. Literature shows that the young novice drivers are not careless drivers but they are clueless drivers’ - clueless because of their inability to predict the risk ahead of time that might materialize on the forward roadway. Other error-feedback training programs exist that emphasize the teaching of risk awareness and perception skills to young drivers. In the current study, a Virtual reality based risk awareness and perception training program (V-RAPT) was developed on the Oculus Rift and evaluated on a driving simulator. The training program provides 360 degrees’ views of 6 high risk driving scenarios towards training the young driver to anticipate and mitigate latent hazards. Twenty-four participants in three experiment groups were trained on one of 3 training programs- VRAPT, RAPT and Control, and were evaluated on a driving simulator. Eye movements were collected throughout the experiment. The simulator evaluation drives included six near-transfer scenarios used in the training and four far-transfer scenarios not used in the training but validated previously in other similar studies. The young drivers trained on the V-RAPT were found to anticipate a significantly greater proportion (86.25%) of the potential latent hazards as compared to the RAPT trained young drivers (62.36%) and control trained drivers (30.97%). The VR-based training program is shown to be effective in improving young drivers’ ability to anticipate latent threats.
Biometric gait recognition for mobile devices using wavelet transform and support vector machines

The ever growing number of mobile devices has turned the attention to security and usability. If a mobile device is lost or stolen this can lead to loss of personal information and the possibility of identity theft. People often tend not to use passwords which leads to lack of personal security mainly due to convenience and frequent use. This paper suggests to serve both convenience and security needs at the same time. Thus we suggest to observe the user's gait characteristic. Our approach realizes user authentication by applying the discrete wavelet transform (DWT) to acceleration signals obtained from mobile devices. Gait templates were constructed of Bark-frequency cepstral coefficients (BFCC) from the wavelet coefficients and these were arranged to train a support vector machine (SVM). A cross-day scenario demonstrates that the proposed approach shows competitive recognition performance, yielding 9.82% False Match Rate (FMR) at a False Non-Match Rate (FNMR) of 10.45%.
A new method for handling data skews in relational joins on graphics processing units

We present a new method for handling data skews in the database operation, relation joins. Recently, Graphics Processing Units (GPU) has become popular among high performance computing research and is used to accelerate the execution of database operations. Various parallel algorithms are developed in the past years with emerging of multi -core processors. Coordinate Module Distribution (CMD) is originally designed for clusters, we have immigrated it onto GPU to improve the performance of relational joins. We find that using one GPU card, our method runs between hash join and sort-merge join, with hash join having the best performance. But our method runs close to hash joins and faster than sort-merge joins when processing relations with data skew.
Smartphone: The key to your connected smart home

Automation systems are gaining popularity around the world. The use of these powerful technologies for home security has been proposed and some systems have been developed. Other implementations see the user taking a central role in providing and receiving updates to the system. We propose a system making use of an Android based smartphone as the user control point. Our Android application allows for dual factor (facial and secret pin) based authentication in order to protect the privacy of the user. The system successfully implements facial recognition on the limited resources of a smartphone by making use of the Eigenfaces algorithm. The system we created was designed for home automation but makes use of technologies that allow it to be applied within any environment. This opens the possibility for more research into dual factor authentication and the architecture of our system provides a blue print for the implementation of home based automation systems. This system with minimal modifications can be applied within an industrial application.
Mapping Between English Strings and Reentrant Semantic Graphs

We investigate formalisms for capturing the relation between semantic graphs and English strings. Semantic graph corpora have spurred recent interest in graph transduction formalisms, but it is not yet clear whether such formalisms are a good fit for natural language data―in particular, for describing how semantic reentrancies correspond to English pronouns, zero pronouns, reflexives, passives, nominalizations, etc. We introduce a data set that focuses on these problems, we build grammars to capture the graph/string relation in this data, and we evaluate those grammars for conciseness and accuracy.