Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tight Bound on the Maximum Interference of Random Sensors in the Highway Model

Consider n sensors whose positions are represented by n uniform, independent and identically distributed random variables assuming values in the open unit interval (0; 1). A natural way to guarantee connectivity in the resulting sensor network is to assign to each sensor as its range, the maximum of the two possible distances to its two neighbors. The interference at a given sensor is dened as the number of sensors that have this sensor within their range. In this paper we prove that the expected maximum interference of the sensors is ( p lnn).
GALAXY ZOO: THE FUNDAMENTALLY DIFFERENT CO-EVOLUTION OF SUPERMASSIVE BLACK HOLES AND THEIR EARLY- AND LATE-TYPE HOST GALAXIES

We use data from the Sloan Digital Sky Survey and visual classifications of morphology from the Galaxy Zoo project to study black hole growth in the nearby universe (z < 0.05) and to break down the active galactic nucleus (AGN) host galaxy population by color, stellar mass, and morphology. We find that the black hole growth at luminosities >1040 erg s−1 in early- and late-type galaxies is fundamentally different. AGN host galaxies as a population have a broad range of stellar masses (1010–1011 M☉), reside in the green valley of the color–mass diagram and their central black holes have median masses around 106.5 M☉. However, by comparing early- and late-type AGN host galaxies to their non-active counterparts, we find several key differences: in early-type galaxies, it is preferentially the galaxies with the least massive black holes that are growing, while in late-type galaxies, it is preferentially the most massive black holes that are growing. The duty cycle of AGNs in early-type galaxies is strongly peaked in the green valley below the low-mass end (1010 M☉) of the red sequence at stellar masses where there is a steady supply of blue cloud progenitors. The duty cycle of AGNs in late-type galaxies on the other hand peaks in massive (1011 M☉) green and red late-types which generally do not have a corresponding blue cloud population of similar mass. At high-Eddington ratios (L/LEdd>0.1), the only population with a substantial fraction of AGNs are the low-mass green valley early-type galaxies. Finally, the Milky Way likely resides in the “sweet spot” on the color–mass diagram where the AGN duty cycle of late-type galaxies is highest. We discuss the implications of these results for our understanding of the role of AGNs in the evolution of galaxies.
Performance evaluation of ethernet- based mobile fronthaul and wireless comp in split-PHY processing

We experimentally evaluate the mobile fronthaul (MFH) optical bandwidth and wireless coordinated multipoint (CoMP) transmission and reception performance in Ethernet-based MFH with our proposed splitphysical (PHY) processing (SPP). To reduce theMFHoptical bandwidth, SPP redefines the base station (BS) functional split between the baseband unit (BBU) and the remote radio head (RRH) in a centralized/cloud radio access network (C-RAN) with a common public radio interface. The functional split point of SPP is between wireless channel coding and wireless modulation within the PHY layer functions. SPP employs its own CoMP joint transmission and reception schemes to achieve high wireless CoMP performance comparable to that in a conventional C-RAN while reducing the MFH optical bandwidth. Experimental results in the uplink and downlink show that SPP reduces the MFH optical bandwidth by over 90% compared to the conventional C-RAN. At the same time, SPP achieves wireless performance with a signal-to-noise ratio penalty of less than 2 dB compared to the conventional C-RAN in the uplink and outputs wireless signals identical to the wireless signal output in the conventional C-RAN in the downlink.
A Travel Time Fusion Algorithm Based on Point and Interval Detector Data

Up to now studies on the fusion of travel time from various detectors have been conducted based on the variance ratio of the intermittent data mainly collected by GPS or probe vehicles. The fusion model based on the variance ratio of intermittent data is not suitable for the license plate recognition AVIs that can deal with vast amount of data. This study was carried out to develop the fusion model based on travel time acquired from the license plate recognition AVIs and the point detectors. In order to fuse travel time acquired from the point detectors and the license plate recognition AVIs, the optimized fusion model and the proportional fusion model were developed in this study. As a result of verification, the optimized fusion model showed the superior estimation performance. The optimized fusion model is the dynamic fusion ratio estimation model on real time base, which calculates fusion weights based on real time historic data and applies them to the current time period. The results of this study are expected to be used effectively for National Highway Traffic Management System to provide traffic information in the future. However, there should be further studies on the proper distance for the establishment of the AVIs and the license plate matching rate according to the lanes for AVIs to be established. Keyword: Real-time travel time estimation, Point & Interval detector, AVI (Automatic Vehicle Identification), Fusion model, Fusion ratio.
Self-report and behavioral reactions to male sex-role incongruency

Although reactions to women's sex-role behavior have been studied extensively, reactions to male's sex-role behavior have been virtually neglected. Subjects listened to a male confederate give traditional "masculine" responses to questions posed by an experimenter. Subjects then performed the instrumental response, the reinforcement for which was the opportunity to hear another male confed erate respond in an androgynous manner. As expected, the speed of the instrumental response increased with the num ber of trials. Also, as expected, self-report measures indicated that subjects rated the masculine speaker higher than the androgynous speaker in most areas. No effect for sex-role orientation of the evaluator was found. Discus­ sion focuses ofi the difference of self—report and behavioral measures, and the possible meanings behind these
Dancing my adoptive identity : An autoethnographic analysis of adoption narratives and performance of identity

Family storytelling helps individuals make sense of the world around them. Stories not only help people understand their familial relationships, but understand the development of their identity. For adoptive children, family narratives are crucial to the creation of the child’s adoptive identity and how it intersects with the many other identities the child holds. This study considers the construction of identity from the adoptee’s point of view by use of autoethnographic and performance methods. Using a multi-methodological approach, this thesis uses interactive interviews, autoethnography, and performance methods as a way to assess what narratives are told within an adoptive family and how these stories aid in the construction of the adoptive child’s identity. The following adoption themes are analyzed: openness about the adoption within the adoptive family, the stigmatization attached to the word adoption, the need for adopting a child, the contrast of being a parent figure versus being a legal parent, the significance of the child’s biologically given name versus the adoptive name, what it means to be a “good” parent (distinctively a good mother), and the adoptive parents and adoptive child’s feelings towards the biological family. By way of autoethnography, a thorough analysis of the use of hands as a metaphor to thread all of these adoption narratives together and as a pre-text to aid in the creation of a dance performance to further analyze the way these stories create an adoptive identity. A description of the performance, as well as a link to a video of the performance, is provided. After the performance, a post-text was created to assess the way embodiment helps to further understand my adoptive identity, as well as to empathize with the other individuals depicted in these stories. This thesis concludes with a discussion of the research questions, personal implications from doing an autoethnographic study, and where future research in storytelling and adoptive family communication can aid in a better understanding of adoption from the perspective of the adoptive child as well as how performance and embodiment can be used as tools for understanding identity construction and adoption. DANCING MY ADOPTIVE IDENTITY: AN AUTOETHNOGRAPHIC ANALYSIS OF ADOPTION NARRATIVES AND PERFORMANCE OF IDENTITY A Thesis Submitted in Partial Fulfillment of the Requirement for the Degree Master of Arts Montana Jean Smith University of Northern Iowa July, 2016
Survey on Nearest Neighbor Search for Spatial Database

Conventional spatial queries, nearest neighbor retrieval and range search consists only conditions on objects geometric property only. But today, many modern applications support new form of queries that aim to find objects that satisfies both spatial data and their associated text. For example instead of considering all the hotels, a nearest neighbor query would instead ask for the hotel that is closest to among those who provide services such as pool, internet at the same time. For this type of query a variant of inverted index is used that is effective for multidimensional points and comes with an R-tree which is built on every inverted list, and uses the algorithm of minimum bounding method that can answer the nearest neighbor queries with keywords in real time.
Dealing with sensor displacement in motion-based onbody activity recognition systems

We present a set of heuristics that significantly increase the robustness of motion sensor-based activity recognition with respect to sensor displacement. In this paper placement refers to the position within a single body part (e.g, lower arm). We show how, within certain limits and with modest quality degradation, motion sensorbased activity recognition can be implemented in a displacement tolerant way. We first describe the physical principles that lead to our heuristic. We then evaluate them first on a set of synthetic lower arm motions which are well suited to illustrate the strengths and limits of our approach, then on an extended modes of locomotion problem (sensors on the upper leg) and finally on a set of exercises performed on various gym machines (sensors placed on the lower arm). In this example our heuristic raises the displaced recognition rate from 24% for a displaced accelerometer, which had 96% recognition when not displaced, to 82%.
Compositional diversity and geologic insights of the Aristarchus crater from Moon Mineralogy Mapper data

[1] The Moon Mineralogy Mapper (M3) acquired high spatial and spectral resolution data of the Aristarchus Plateau with 140 m/pixel in 85 spectral bands from 0.43 to 3.0 μm. The data were collected as radiance and converted to reflectance using the observational constraints and a solar spectrum scaled to the Moon-Sun distance. Summary spectral parameters for the area of mafic silicate 1 and 2 μm bands were calculated from the M3 data and used to map the distribution of key units that were then analyzed in detail with the spectral data. This analysis focuses on five key compositional units in the region. (1) The central peaks are shown to be strongly enriched in feldspar and are likely from the upper plagioclase-rich crust of the Moon. (2) The impact melt is compositionally diverse with clear signatures of feldspathic crust, olivine, and glass. (3) The crater walls and ejecta show a high degree of spatial heterogeneity and evidence for massive breccia blocks. (4) Olivine, strongly concentrated on the rim, wall, and exterior of the southeastern quadrant of the crater, is commonly associated the impact melt. (5) There are at least two types of glass deposits observed: pyroclastic glass and impact glass.
SSI/ModelUI-A Tool for the Acquisition and Annotation of Human Generated Signals

Humans are used to express their needs and goals through various channels, such as speech, mimic, posture, etc. The recognition and understanding of such behaviour is a key requirement towards a more natural human-computer interaction (HCI) and believed to be an important part of next-generation user interfaces [1]. To achieve this, we need to give the computer access to human generated signals and provide adequate models to recognize and interpret the behavioural patterns. Ideally, processing should happen in (near) real-time in order to allow an application to show immediate reactions.
Evaluating Univariate , Bivariate , and Multivariate Normality Using Graphical and Statistical Procedures

Tom Burdenski, Texas A & M University This paper reviews graphical and statistical procedures for evaluating multivariate normality by guiding the reader through univariate and bivariate procedures that are necessary, but insufficient, indications of a multivariate normal distribution. A data set utilizing three dependent variables for two groups provided by George and Mallery (1999) is used to analyze kurtosis and skewness coefficients, Q-Q plots, the Shapiro-Wilk or Kolmogorov-Smirnov statistic, and bivariate scatterplots. A procedure programmed by Thompson (1990) is used to explore multivariate normality by plotting Mahalanobis distances against derived chi-square values in a scatterplot.
Combination of curvelet threshold with bilateral filtering for image denoising

In this paper, we propose a scheme for image denoising by combining bilateral filtering in spacial domain with curvelet hard shrinkage method in transformed domain. That is, we first pre-process noisy image with curvelet hard shrinkage, then to process the obtained image with bilateral filtering. Numerical experiments illustrate the good performance in comparison to the curvelet hard shrinkage method and the bilateral filtering method by using two objective measures: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).
Improved Conspicuity and Delineation of High-Grade Primary and Metastatic Brain Tumors Using “Restriction Spectrum Imaging”: Quantitative Comparison with High B-Value DWI and ADC

BACKGROUND AND PURPOSE: Restriction spectrum imaging is a sensitive DWI technique for probing separable water diffusion compartments in tissues. Here, we evaluate RSI-CMs derived from the spherically-restricted water compartment for improved tumor conspicuity and delineation from nontumor tissue and reduced sensitivity to edema compared with high-b-value DWI and ADC. MATERIALS AND METHODS: RSI was performed in 10 presurgical patients: 4 with glioblastoma, 3 with primary CNS lymphoma, and 3 with metastatic brain tumors. Multidirectional DWI data were collected at b = 500, 1500, and 4000 s/mm2. Quantification of tumor conspicuity, edema conspicuity, and relative sensitivity to edema for RSI-CMs; DWI at b = 4000 (DWI-4000); and ADC were compared in manually drawn VOIs. Receiver operating characteristic curves were used to evaluate the sensitivity and specificity of each method for delineating tumor from normal-appearing WM. RESULTS: Significant TC was seen with both RSI-CMs and DWI-4000, but not ADC. Significant EC was seen with ADC, but not RSI-CMs or DWI-4000. Significantly greater TC was seen with RSI-CMs compared with DWI-4000. Significantly reduced RSE was seen with RSI-CMs compared with both DWI-4000 and ADC. Greater sensitivity and specificity for delineating tumor from normal-appearing WM were seen with RSI-CMs (AUC = 0.91) compared with both DWI-4000 (AUC = 0.77) and ADC (AUC = 0.66). CONCLUSIONS: RSI-CMs offer improved conspicuity and delineation of high-grade primary and metastatic brain tumors and reduced sensitivity to edema compared with high-b-value DWI and ADC.
Constraint-based test data generation in the presence of stack-directed pointers

Constraint-Based Test data generation (CBT) exploits constraint satisfaction techniques to generate test data able to kill a given mutant or to reach a selected branch in a program. When pointer variables are present in the program, aliasing problems may arise and may lead to the failure of current CBT approaches. In our work, we propose an overall CBT method that exploits the results of an intraprocedural points-to analysis and provides two specific constraint combinators for automatically generating test data able to reach a selected branch. Our approach correctly handles multi-levels stack-directed pointers that are mainly used in real-time control systems. The method has been fully implemented in the test data generation tool INKA and first experiences in applying it to a variety of existing programs tend to show the interest of the approach.
Laplacian sift in visual search

With the explosive growth of video capture capable mobile handsets and online visual data repositories, the popular query-by-capture applications call for a compact visual descriptor with minimum descriptor length. How to preserve the visual descriptor information while minimizing the bit rate for representing the descriptors, is a focus of the ongoing MPEG7 CDVS (Compact Descriptor for Visual Search) standardization effort. In this work, we present a SIFT descriptor dimension reduction scheme based on Laplacian Graph Embedding, which computes a linear embedding that preserves the topological relationship among visual descriptors. Simulation results demonstrate the effectiveness of the proposed solution at low bit rates.
The many faces of attention deficit hyperactivity disorder: Unruly behaviour in secondary school and diagnostic solutions

In society today, there is a tendency towards over-diagnosing. This tendency in society at large seems to reflect the normalization of a diagnostic culture. Some researchers have claimed that this normalization could be defined as the ‘medicalization of childhood’. There would seem to be a need for a sociocultural analysis of this phenomenon in schools – and it is in relation to this background that the present study hopes to generate new knowledge for the research field. This article explores how professionals in student welfare teams relate to and use neuropsychiatric diagnoses. The study draws on interviews with key officials working in student welfare teams in three urban secondary schools in southern Sweden. The main aim has been to investigate how the school officials talk about diagnoses such as attention deficit hyperactivity disorder and autism spectrum disorder, and what kind of explanatory value is awarded to these diagnoses. The results indicate that the professional culture in schools plays a key role in how diagnoses are integrated or not integrated into the schools’ work with students’ behavioural problems. The results also show that the narratives about diagnoses were framed by ideas related to the students’ social-class background and gender.
A general method for the point of regard estimation in 3D space

A novel approach to 3D gaze estimation for wearable multi-camera devices is proposed and its effectiveness is demonstrated both theoretically and empirically. The proposed approach, firmly grounded on the geometry of the multiple views, introduces a calibration procedure that is efficient, accurate, highly innovative but also practical and easy. Thus, it can run online with little intervention from the user. The overall gaze estimation model is general, as no particular complex model of the human eye is assumed in this work. This is made possible by a novel approach, that can be sketched as follows: each eye is imaged by a camera; two conics are fitted to the imaged pupils and a calibration sequence, consisting in the subject gazing a known 3D point, while moving his/her head, provides information to 1) estimate the optical axis in 3D world; 2) compute the geometry of the multi-camera system; 3) estimate the Point of Regard in 3D world. The resultant model is being used effectively to study visual attention by means of gaze estimation experiments, involving people performing natural tasks in wide-field, unstructured scenarios.
Emergence of bipedal walking through body/environment interactions

In biological regulatory systems, all computations result from spatial and temporal combination of simple and homogeneous computational media. This computational scheme realize the adaptability to unpredictable environmental changes, which is one of the most salient features of biological regulations. To investigate the learning process behind this computational scheme, we propose a learning method that embodies the features of biological systems, termed tacit learning. We have constructed a controller based on the notion of tacit learning and applied it to the control of the 36DOF humanoid robot to create the bipedal walking adapted to the environment. Experiments on walking showed a remarkably high adaptation capability of tacit learning in terms of gait generations, power consumption and robustness.
Properties of a Word-Valued Source with a Non-prefix-free Word Set

Recently, a word-valued source has been proposed as a new class of information source models. A word-valued source is regarded as a source with a probability distribution over a word set. Although a word-valued source is a nonstationary source in general, it has been proved that an entropy rate of the source exists and the Asymptotic Equipartition Property (AEP) holds when the word set of the source is prefix-free. However, when the word set is not prefix-free (non-prefix-free), only an upper bound on the entropy density rate for an i.i. d. word-valued source has been derived so far. In this paper, we newly derive a lower bound on the entropy density rate for an i.i. d. word-valued source with a finite non-prefix-free word set. Then some numerical examples are given in order to investigate the behavior of the bounds.
Learning the inverse kinetics of an octopus-like manipulator in three-dimensional space

This work addresses the inverse kinematics problem of a bioinspired octopus-like manipulator moving in three-dimensional space. The bioinspired manipulator has a conical soft structure that confers the ability of twirling around objects as a real octopus arm does. Despite the simple design, the soft conical shape manipulator driven by cables is described by nonlinear differential equations, which are difficult to solve analytically. Since exact solutions of the equations are not available, the Jacobian matrix cannot be calculated analytically and the classical iterative methods cannot be used. To overcome the intrinsic problems of methods based on the Jacobian matrix, this paper proposes a neural network learning the inverse kinematics of a soft octopus-like manipulator driven by cables. After the learning phase, a feed-forward neural network is able to represent the relation between manipulator tip positions and forces applied to the cables. Experimental results show that a desired tip position can be achieved in a short time, since heavy computations are avoided, with a degree of accuracy of 8% relative average error with respect to the total arm length.
Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images

In this paper, we propose a general framework of adaptive local thresholding based on a verification-based multithreshold probing scheme. Object hypotheses are generated by binarization using hypothetic thresholds and accepted/rejected by a verification procedure. The application-dependent verification procedure can be designed to fully utilize all relevant informations about the objects of interest. In this sense, our approach is regarded as knowledge-guided adaptive thresholding, in contrast to most algorithms known from the literature. We apply our general framework to detect vessels in retinal images. An experimental evaluation demonstrates superior performance over global thresholding and a vessel detection method recently reported in the literature. Due to its simplicity and general nature, our novel approach is expected to be applicable to a variety of other applications.