Miracle-dz/UniFAR-FTUs huggingface dataset:
Datawear: Self-reflection on the Go or How to Ethically Use Wearable Cameras for Research

A growing number of studies use wearable sensors, including cameras, to detect user activity patterns. When an object of academic investigation, these patterns are interpreted by researchers and conclusions are drawn about people's habits and routines. Alternatively, interpretations are provided by users themselves during extensive post-study interviews. Such approaches inevitably expose personal data collected about individuals to researchers, which can potentially change the behavior under investigation. We introduce a new approach to using wearable sensor data in research. It allows people to interpret and self-reflect on their data and submit for investigation only reflections, without sharing their raw data. In this interactivity, we present and discuss the Datawear mobile application prototype, which is designed to conduct "in the wild" studies of personal experiences.
Effects of Smoothing Functions in Cosmological Counts-in-Cells Analysis

Am ethod for a counts-in-cells analysis of the galaxy distribution is investigated with arbitrary smoothing functions for obtaining the galaxy counts. We explore the possiblity of optimizing the smoothing function, while considering a series of m-weight Epanechnikov kernels. The popular top-hat and Gaussian smoothing functions are two special cases in this series. In this paper, we mainl yc onsider the second moments of counts-in-cells as a first step. We have analytically derived the covariance matrix among different smoothing scales of cells, while taking into account possible overlaps between cells. We find that the Epanechnikov kernel of m D 1 is better tha nt op-hat and Gaussian smoothing functions in estimating cosmological parameters. As an example, we estimated expected parameter bounds that come only from an analysis of the second moments of the galaxy distributions in a survey that is similar to the Sloan Digital Sky Survey.
Project Title : Measurement of Implant Component Position and Orientation from X-ray Images

The objective for this project was to develop and test a method and software system for estimating the pose of artificial knee implant components from X-ray fluoroscopy images. The topic is of great interest to the field of orthopedics, due to the occurrence of premature failure in today's implant designs. Our approach was to use supervised iterative optimization. Specifically, a simulated annealing algorithm was used to iteratively adjust the pose of the component model to minimize the error, or discrepancy, between the predicted image of the model and the actual image. A software system was developed that allowed an operator to visualize the progress of the model fitting process, and intervene if necessary to correct any errors and guide the system to a correct solution. The resulting system was formally evaluated to determine its accuracy and repeatability. On synthetic images, we measured the mean error translation to be 0.005 mm and the mean error of rotation to be 0.0015 degree. On "in vitro" images (that is, real X-ray images of components outside the body), we measured the standard deviation to be 0.15 mm in translation and 0.17 degree in rotation. Finally, the interactive system was compared to a manual system that had been in use at RMRL. Statistical tests showed the new system to be more accurate, repeatable, and faster than the previous system. The research objectives were all accomplished. The outcome of the work was a working joint measurement software system and quantitative data to support the comparison of the new system with an existing system.
Why VLSI implementations of associative VLCNs require connection multiplexing

A discussion is presented of some of the implementation constraints imposed on VLSI architectures for emulations of very large connectionist/neural networks (VLCNs). Specifically, the authors show that multiplexing of interconnections is necessary for networks exhibiting poor locality. They show that it is more feasible to build a VLCN system with sharing or multiplexing of interconnections than to build one with dedicated wires for each connection. This is true unless the network exhibits extreme locality and all CNs are connected to others within some small-radius region. Unfortunately, association requires some global connectivity. <<ETX>>
An Approach Based on Hidden Markov Model and Gaussian Mixture Regression

We present a probabilistic approach to learning robust models of human motion through imitation. The combination of Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) allows us to extract redundancies across multiple demonstrations and build time-independent models to reproduce the dynamics of the observed movements. The approach is first compared with state-of-the-art approaches by using generated trajectories sharing similar characteristics to those of humans. Three applications on different types of robots are then presented. An experiment with the iCub humanoid robot acquiring a bimanual dancing motion is first presented to show that the system can cope with cyclic and crossing motions. An experiment with a 7 DOFs WAM robotic arm learning the motion of hitting a ball with a table tennis racket is presented to highlight the possibility to encode several movements in a single model. Finally, an experiment with a HOAP-3 humanoid robot holding a spoon and learning to feed the Robota humanoid robot is presented. It shows the capability of the system to handle several constraints simultaneously.
STATISTICAL PROPERTIES OF SEMIPARAMETRIC ESTIMATORS FOR COPULA-BASED MARKOV CHAIN VECTORS MODELS

Abstract. This paper proposes a method for estimation of a class of copula-based semiparametric stationary Markov vector time series models, namely, the two-stage semiparametric pseudo maximum likelihood estimation (2SSPPMLE). These Markov vector time series models are characterized by nonparametric marginal distributions and parametric copula functions of temporal and contemporaneous dependence, while the copulas capture two classes of dependence relationships of Markov time series. We provide simple estimators of marginal distribution and two classes of copulas parameters and establish their asymptotic properties following conclusions in Chen and Fan (2006) and some easily verifiable conditions. Moreover, we obtain the estimation of conditional moment and conditional quantile functions for the bivariate Markov time series model.
Colored quantum universal enveloping algebras

We define some new algebraic structures, termed colored Hopf algebras, by combining the coalgebra structures and antipodes of a standard Hopf algebra set H, corresponding to some parameter set Q, with the transformations of an algebra isomorphism group G, herein called color group. Such transformations are labeled by some color parameters, taking values in a color set C. We show that various classes of Hopf algebras, such as almost cocommutative, coboundary, quasitriangular, and triangular ones, can be extended into corresponding colored algebraic structures, and that colored quasitriangular Hopf algebras, in particular, are characterized by the existence of a colored universal R-matrix, satisfying the colored Yang–Baxter equation. The present definitions extend those previously introduced by Ohtsuki, which correspond to some substructures in those cases where the color group is Abelian. We apply the new concepts to construct colored quantum universal enveloping algebras of both semisimple and nonsemisimp.
First detection and molecular characterization of Echinococcus equinus in a Mule in Turkey

Cystic echinococcosis is a zoonotic disease with a cosmopolital distribution. It is caused by the larval stages (metacestodes) of the parasite Echinococcus granulosus which infects different animal species. In this report, we present a case of E. granulosus infection in a mule and molecular characterization of the cyst. For this purpose parasite material was collected from the liver of a necropsied mule. DNA was isolated and PCR amplification of mitochondrial 12S rRNA as well as partial sequencing of mitochondrial cytochrome c oxidase subunit 1 (mt-CO1) genes were performed. Six unilocular cysts, filled with clear fluid were found in the liver and spleen. All cysts were found to be fertile. The 12S rRNA-PCR did not yield any band while mt-CO1-PCR yielded a 446 bp sized amplification product. Sequence corresponding to mt-CO1 gene was identical to a sequence reported for E. equinus (formerly G4) (Genbank accession number: KC953029). This is the first record of E. equinus as a cause of cystic echinococcosis in a mule in Turkey.
Study on a novel laser measurement system for float glass thickness

Float glass is a highly standardized high-tech material found in numerous applications, thickness is one of the most important parameters of float glass production. A new nontouch online float glass thickness measurement system based on the principle of twice laser refraction and reflection is present. The principle which is superior to traditional laser triangulation method can overcome shake disturbance of mechanical transmission system. By analyzing the glass processing technique and the high temperature environment, a novel sensor probe located in tin bath is designed. Also, the glass cools more than 600degC are mathematically corrected. Linear CCD is employed to scan the glass plates repetitiously to obtain thickness measuring image. An improved barycentre method is put forward to extract the centre line of thickness stripe image. The method can remove the background noise and extract the center points with a subpixel precision. By discussed in theory and experiment the measuring range is 2 to 14 mm, the resolution is 1 mum and the accuracy can within plusmn0.005 mm.
Aerial grasping of a moving target with a quadrotor UAV

For a quadrotor aircraft, we study the problem of planning a trajectory that connects two arbitrary states while allowing the UAV to grasp a moving target at some intermediate time. To this end, two classes of canonical grasping maneuvers are defined and characterized. A planning strategy relying on differential flatness is then proposed to concatenate one or more grasping maneuvers by means of spline-based subtrajectories, with the additional objective of minimizing the total transfer time. The proposed planning algorithm is not restricted to pure hovering-to-hovering motions and takes into account practical constraints, such as the finite duration of the grasping phase. The effectiveness of the proposed approach is shown by means of physically-based simulations.
QPSK Impulse Signal Transmission for Ultra Wide Band Communication Systems in Multipath Channel Environments

In this paper, three new ultra wideband (UWB) communication systems with quadrature-phase shift keying (QPSK) impulse modulation are proposed. First, direct-sequence (DS) multiple-access scheme is applied. The second proposed system is based on time-hopping (TH) multiple-access scheme. The last proposed system applies TH multiple-access scheme with QPSK impulse modulation and pulse position modulation (PPM). The conventional UWB communications as TH scheme with PPM modulation and DS scheme with binary-phase shift keying (BPSK) are used to compare. The simulation results show that all proposed UWB communication systems can provide obviously better performances compared with the conventional TH-PPM and DS-BPSK UWB communication systems. The comparisons in aspects of transmission bit rate and the number of users are also investigated.
Characterization of Gene Functional Expression Profiles of Plasmodium Falciparum

ABSTRACT Our objective was to provide functional characterization of gene expression related to Intraerythrocytic Developmental Cycle (IDC) of Plasmodium Falciparum. We explored a hypothesis that genes with same or similar function are likely to have similar expression profiles. Analysis of 1,051 Gene Ontology (GO) terms represented by at least two genes in Plasmodium Falciparum microarray data set revealed that gene expression profiles in 550 of them are significantly (P<0.05) correlated. We represented each of the 550 significant GO terms with the functional expression profile defined as average expression profile of all genes annotated with a given GO term. Using Kmeans clustering, we clustered 199 profiles corresponding to GO molecular functions into 4 groups. This was repeated on 228 profiles corresponding to GO biological process. We quantified the clustering quality by introducing a measure of GO term similarity defined as the minimal distance between two GO terms in GO direct acyclic graph. The results based on this measure showed that the obtained clusterings are biologically relevant which supported our hypothesis that genes with similar functions have similar expressions. This indicated that functional expression profiles can provide a valuable tool for analysis and interpretation of microarray data.
Sentiment Classification of Arabic Documents: Experiments with multi-type features and ensemble algorithms

Document sentiment classification is often processed by applying machine learning techniques, in particular supervised learning which consists basically of two major steps: feature extraction and training the learning model. In the literature, most existing researches rely on n-grams as selected features, and on a simple basic classifier as learning model. In the context of our work, we try to improve document classification findings in Arabic sentiment analysis by combining different types of features such as opinion and discourse features; and by proposing an ensemble-based classifier to investigate its contribution in Arabic sentiment classification. Obtained results attained 85.06% in terms of macro-averaged Fmeasure, and showed that discourse features have moderately improved Fmeasure by approximately 3% or 4%.
"The Whole Is Greater Than the Sum of Its Parts": Optimization in Collaborative Crowdsourcing

In this work, we initiate the investigation of optimization opportunities in collaborative crowdsourcing. Many popular applications, such as collaborative document editing, sentence translation, or citizen science resort to this special form of human-based computing, where, crowd workers with appropriate skills and expertise are required to form groups to solve complex tasks. Central to any collaborative crowdsourcing process is the aspect of successful collaboration among the workers, which, for the first time, is formalized and then optimized in this work. Our formalism considers two main collaboration-related human factors, affinity and upper critical mass, appropriately adapted from organizational science and social theories. Our contributions are (a) proposing a comprehensive model for collaborative crowdsourcing optimization, (b) rigorous theoretical analyses to understand the hardness of the proposed problems, (c) an array of efficient exact and approximation algorithms with provable theoretical guarantees. Finally, we present a detailed set of experimental results stemming from two real-world collaborative crowdsourcing application us- ing Amazon Mechanical Turk, as well as conduct synthetic data analyses on scalability and qualitative aspects of our proposed algorithms. Our experimental results successfully demonstrate the efficacy of our proposed solutions.
Extreme programming in reducing the rework of requirement change

Requirement changes and the involved rework are inevitable if the requirement changes could not be foreseen. Extreme programming (XP) suggests the customer be on site with the developers. The requirement changes may be forecast by the on site customer from the business perspective and by the developers from the technology perspective. Extreme programming was adopted in the Lattice Trading System reengineering project. The user stories were weighed by a set of criteria which included the risk, cost, schedule, as well as the possibility to be changed. The stories were decomposed into features, and the features were organized into components. The components with less possibility to be changed were implemented earlier. Meanwhile, spikes were conducted when the decisions were being made for some critical tasks. In such a way, most components were completed without any change to their functional features while few components were implemented with large modification. The overall effort of rework was reduced considerably with this approach. Compared with the small cost for managing the change possibility, the method introduced in this paper is of significant worth.
Assessment of Bioflocculant Production by Bacillus sp. Gilbert, a Marine Bacterium Isolated from the Bottom Sediment of Algoa Bay

The bioflocculant-producing potentials of a marine bacteria isolated from the bottom sediment of Algoa Bay was investigated using standard methods. The 16S rDNA sequence analysis revealed 98% similarity to that of Bacillus sp. HXG-C1 and the nucleotide sequence was deposited in GenBank as Bacillus sp. Gilbert with accession number HQ537128. Bioflocculant was optimally produced when sucrose (72% flocculating activity) and ammonium chloride (91% flocculating activity) were used as sole sources of carbon and nitrogen, respectively; an initial pH 6.2 of the production medium; and Mg2+ as cation. Chemical analysis of the purified bioflocculant revealed the compound to be a polysaccharide.
Automatic Learning of Chinese English Semantic Structure Mapping

We present twin results on Chinese semantic parsing, with application to English-Chinese cross- lingual verb frame acquisition. First, we describe two new state-of-the-art Chinese shallow semantic parsers leading to an F-score of 82.01 on simultaneous frame and argument boundary identification and labeling. Subsequently, we propose a model that applies the separate Chinese and English semantic parsers to learn cross-lingual semantic verb frame argument mappings with 89.3% accuracy. The only training data needed by this cross-lingual learning model is a pair of non-parallel monolingual Propbanks, plus an unannotated parallel corpus. We also present the first reported controlled comparison of maximum entropy and SVM approaches to shallow semantic parsing, using the Chinese data.
A Dykstra-like algorithm for two monotone operators

Dykstra’s algorithm employs the projectors onto two closed convex sets in a Hilbert space to construct iteratively the projector onto their intersection. In this paper, we use a duality argument to devise an extension of this algorithm for constructing the resolvent of the sum of two maximal monotone operators from the individual resolvents. This result is sharpened to obtain the construction of the proximity operator of the sum of two proper lower semicontinuous convex functions.
The Influence of High School Academics on Freshman College Mathematics and Science Courses at SUNY Oswego

THE INFLUENCE OF HIOH SCHOOL ACADEMICS ON FRESHMAN COLLEGE MATHEMATICS AND SCIENCE COURSES AT SUNY OSWEGO This study examined the relationship between 2011 freshman college mathematics and science grades and freshman students' high school academics and demographic data, exploring the factors that contribute to the success offrrst-year STEM majoring freshman students at State University of New York at Oswego. The variables were Gender, Race, SES, School Size, Parent with College Education, High School Grade Point Average (HSGPA), Transfer Credit, SAT Composite Score, and New York State Regents Exam results, based on data from 237 freshman students entering college immediately following high school. The findings show HSGP A as a significant predictor of success in freshman College Mathematics and Sciences, Transfer Credit as a significant predictor in College Mathematics and College Chemistry, SES as a significant predictor in College Biology and College Chemistry, Parent with College Education as a significant predictor in College Biology and New York State Chemistry Regents Exam as a significant predictor in College Chemistry. Based on these findings, guidance counselors, science educators, and education institutions can develop a framework to determine which measurements are meaningful and advise students to focus on excellent performance in the Chemistry Regents Exams, take more college courses during high school, and maintain a high grade point average. INFLUENCE OF HIGH SCHOOL ACADEMICS ON FRESHMAN COLLEGE COURSES 111
Fundamental performance limitations in tracking and sinusoidal signals

This paper attempts to give a thorough treatment of the performance limitation of a linear time invariant multivariable system in tracking a reference signal which is a linear combination of a step signal and several sinusoids with different frequencies. The tracking performance is measured by an integral square error between the output of the plant and the reference signal. Our purpose is to find the fundamental limitation for the attainable tracking performance, under any control structure and parameters, in terms of the characteristics and structural parameters of the given plant, as well as those of the reference signal under consideration. It is shown that this fundamental limitation depends on the interaction between the reference signal and the nonminimum phase zeros of the plant and their frequency-dependent directional information. The main results of this paper are based on the assumption that the controller accesses the full information of the reference. However, when the full information of the reference is unavailable, we also obtain a simple performance limit, for an important special case, which clearly shows the extra cost one has to pay due to the information restriction.
Robust lane localization using multiple cues on the road

This paper presents a method for estimating the current lane number in which the vehicle is traveling. An important component of visual automobile safety systems rely on knowledge of the lane number to know how far the vehicle is from the exit lane. The method uses taking the Inverse Perspective Map of an image to get a top view of the road and then detects multiple lanes using Hough transform line detection. Then the algorithm classifies the lines as solid or broken and also detects if the lane is branching or split using connected component analysis. This knowledge of the line type and whether the lane is splitting is the basis to identify if the vehicle is in the exit lane.
A 1–20-GHz All-Digital InP HBT Optical Wavelength Synthesis IC

An integrated circuit (IC) for heterodyne optical phase locking in a 1-20-GHz offset range is hereby reported. The IC, implemented in a 500-nm InP HBT process, contains an emitter coupled logic digital single-sideband mixer to provide phase locking at a ± 20-GHz offset frequency, and a wideband phase-frequency detector designed to provide loop acquisition up to ±40-GHz initial frequency offset. The all-digital IC design has phase-frequency detection gain independent of IC process parameters or optical signal levels, and provides a wide offset locking range. A 100-ps delay decreases the overall loop delay, making wideband loop filter design possible. In addition, a medium-scale high-frequency logic design methodology is presented and fully discussed.
Overview of Multi-Channel MAC Protocols in Wireless Networks

Multi-channel MAC protocols have recently been proposed to improve the throughput of wireless networks. In this paper, many multi-channel MAC protocols for wireless networks are studied. The classification standards for multi-channel MAC protocols are firstly described, and then the key mechanisms of the existing representative MAC protocols are analyzed. At last, comparison of the characteristics of these protocols is given.