Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance evaluation of channel estimation techniques for MIMO-OFDM systems with adaptive sub-carrier allocation

Dynamic sub-carrier allocation (DSA) strategies have been shown previously to achieve significant performance benefits when applied to OFDMA systems and further benefits for MIMO-OFDMA systems. Analysis thus far has focussed on the assumption of ideal channel state information (CSI). In this paper, the impact of non-ideal CSI is investigated. Various channel estimation techniques are evaluated for application to MIMO-OFDMA systems. They are based on least squares (LS) estimation with training pilots. `Conventional' (as for MIMO-OFDM) CTP (combining training pilots) and `improved' (optimised for MIMO-OFDMA) STP (separate training pilots) versions of both frequency domain least square (FDLS) and time domain least square (TDLS) channel estimation are considered, as are the options of both space-time block coding (STBC) and spatial multiplexing (SM) as MIMO strategies. The STP-TDLS strategy is shown to significantly outperform other channel estimation options, achieving performance within 1 dB of the ideal case. Subsequently, the use of the STP-TDLS channel estimation method in conjunction with the DSA algorithm is considered in order to determine the impact of non-ideal channel knowledge on the gain achieved by DSA. The performance for the cases of ideal CSI and CSI derived via STP-TDLS channel estimation are compared and evaluated for both the STBC and SM cases. The effects of non-ideal channel estimation in both the DSA mechanism and channel equalisation separately and together are evaluated. It is shown that STP-TDLS channel estimation works better in SM (only IdB worse than ideal CSI case) than in STBC. Furthermore, it is shown that DSA is less sensitive than channel equalisation to nonideal CSI. The degradation of system performance in the realistic case of non-ideal CSI for both DSA and channel equalisation is a compound of the effects of the separate effects of non-ideal CSI error. It is shown here that in both STBC and SM cases, the effect is almost a linear addition of the two parts. Given the substantial benefits of DSA and its relative insensitivity to channel estimation errors, it is concluded that DSA remains a highly promising technique
Monitoring and control in scenario-based requirements analysis

Scenarios are an effective means for eliciting, validating and documenting requirements. At the requirements level, scenarios describe sequences of interactions between the software-to-be and agents in the environment. Interactions correspond to the occurrence of an event that is controlled by one agent and monitored by another. This paper presents a technique to analyse requirements-level scenarios for unforeseen, potentially harmful, consequences. Our aim is to perform analysis early in system development, where it is highly cost-effective. The approach recognises the importance of monitoring and control issues and extends existing work on implied scenarios accordingly. These so-called input-output implied scenarios expose problematic behaviours in scenario descriptions that cannot be detected using standard implied scenarios. Validation of these implied scenarios supports requirements elaboration. We demonstrate the relevance of input-output implied scenarios using a number of examples.
Monolithic crystals on SiPM arrays in a prototype system with depth of interaction estimation

Increased sensitivity is one of the major challenges in novel detector development. While pixelated crystals are standard, the use of continuous crystals can provide increased active volume and thus sensitivity. Accurate estimation of the interaction positions of photons in continuous crystals requires a sophisticated algorithm. Additionally, including depth of interaction (Dol) in the reconstruction process can mitigate parallax effects near the edges of field of view, therefore Dol estimation is highly desirable. In this work, the interaction position coordinates in 3D are estimated adapting an already existing analytical model to our system with no additional hardware, training data sets or Monte Carlo simulations. The algorithm is implemented in a two-detector-head system comprised of continuous crystals coupled to SiPM arrays. Acquired data is stored in list-mode where the continuous nature of the interaction position is preserved. For image reconstruction, since the crystal is continuous, pre-computation of the system matrix elements would require discretization of the detected measurements. To avoid data discretization the system matrix elements are calculated on-the-fly. Spatial information is later on retained in the reconstruction algorithm by using a Siddon-like algorithm where the ray end-points correspond to the estimated interaction positions in continuous space, introducing the interaction position estimation uncertainty in the calculation of the system matrix elements. Reconstruction using continuous data is compared to reconstruction using artificially discretized data. Results show that the spatial resolution measured from a reconstructed point source using ML-EM obtained with a continuous crystal is superior to the spatial resolution obtained with a pixelated crystal.
Lesbian relationships in correctional settings – “Let our voices be heard”

abstract In a 2006 report the Department of Correctional Services (DCS) estimated South African figures for HIV prevalence among female prisoners per total inmate population at around 8% (DCS, 2006). Against this backdrop this article reports on research in a South African prison on women who have sex with women (WSW) and their possible vulnerability to HIV infection. The study explored women's sexual activity and possible HIV transmission routes among them. Data was elicited through focus group interviews with a select sample of women in a South African prison. The data is organised into three broad categories identified as: attitudes of DCS personnel towards women inmates’ sexual practices; sexual practices among women, and knowledge and understanding of modes of HIV transmission. Empirical data for this research provides recommendations on addressing the risk of HIV transmission among incarcerated female prisoners through understanding sexual practices among them.
Counterfeit Image Detection in Face Recognition Systems using Stereo Vision and Optical Flow Methods

This paper proposes a preprocessing stage, using stereo images, to discriminate between a three dimensional face from a two dimensional pictures in face recognition system. In proposed system, firstly the input stereo images are analyzed using an optical flow scheme to determine if the face under analysis corresponds to a real three dimensional (3D) face or it corresponds to a 2D picture. In the latter case the image is classified as a fake and the identity is determined as negative. While, if the image under analysis is determined to be a 3D images, it is analyzed by a face recognition systems to determine if the people under analysis is whom she or he claims to be. Proposed approach provides a false acceptation rate of about 0.06%, which allows to, significantly improving the performance of face recognition system. Key-Words: Stereo vision, optical flow, Lucas-Kanade, Atomic functions, face recognition
A¢ ne Stochastic Mortality

We propose a new model for stochastic mortality. The model is based on the literature on a¢ ne term structure models. It satis es three important requirements for application in practice: analytical tractibility, clear interpretation of the factors and compatibility with nancial option pricing models. We test the model t using data on Dutch mortality rates. Furthermore, we discuss the speci cation of a market price of mortality risk and apply the model to the pricing of a Guaranteed Annuity Option and the calculation of required Economic Capital for mortality risk. Keywords: Stochastic Mortality, A¢ ne Models, Mortality Laws, Longevity Risk, Market Price of Mortality Risk, Mortality Options JEL Classi cation: G12, G13, G22, I12 SIBC Classi cation: IM10, IE43, IE50, IB10 ABN AMRO Bank, Market Risk Modeling and Product Analysis, PO Box 283, 1000 EA Amsterdam, The Netherlands University of Amsterdam, Dept. of Quantitative Economics, Roetersstraat 11, 1018 WB Amsterdam, The Netherlands, Tel. +31 (0)20 525 4125, e-mail: d.f. schrager@uva.nl
Using Support Vector Machines with Multiple Indices of Diffusion for Automated Classification of Mild Cognitive Impairment

Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy older subjects and subjects with mild cognitive impairment (MCI). Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject. DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects. Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects. Such tools may be useful for large scale drug trials in Alzheimer's disease where the early identification of subjects with MCI is critical.
Graceful anytime interruptibility for virtual agents

The demo will show two highly expressive virtual agents introducing the audience to a traditional Japanese room. The agent models and gestures have been created by a professional Japanese character designer. By adapting and extending an influential technique ("Verbs and Adverbs"), we added parameterized emotional expression and gesture interruptibility as new behaviors to increase agent believability, interactivity, and responsiveness. In the demo, users will be provided a simple interface to test the new features.
Improving Performance of Synchronous Transmission-Based Protocols Using Capture Effect over Multichannels

Synchronous transmission has been exploited recently for accelerating fundamental operations of wireless sensor networks like data dissemination, data collection, and network-wide agreement by an order of magnitude. Although these protocols (e.g., Glossy) have shown to be highly reliable for small packet sizes, the use of large packet sizes coupled with an excessive number of simultaneous transmissions can reduce reliability significantly. Our work is motivated by the observation that capture effect can improve the reliability of synchronous transmission. We experimentally study the effect of physical layer capture and identify guidelines in which it can be exploited to enhance reliability. Based on these observations, we propose Syncast, a data dissemination protocol that improves over Glossy by exploiting capture effect over multichannels. The evaluation shows that Syncast provides a robust, reliable, and scalable data dissemination of large packets with lower end-to-end delay and up to 92% reduced radio-on-time.
Digital pathology: Multiple instance learning can detect Barrett's cancer

We study diagnosis of Barrett's cancer from hematoxylin & eosin (H & E) stained histopathological biopsy images using multiple instance learning (MIL). We partition tissue cores into rectangular patches, and construct a feature vector consisting of a large set of cell-level and patch-level features for each patch. In MIL terms, we treat each tissue core as a bag (group of instances with a single group-level ground-truth label) and each patch an instance. After a benchmarking study on several MIL approaches, we find that a graph-based MIL algorithm, mi-Graph [1], gives the best performance (87% accuracy, 0.93 AUC), due to its inherent suitability to bags with spatially-correlated instances. In patch-level diagnosis, we reach 82% accuracy and 0.89 AUC using Bayesian logistic regression. We also pursue a study on feature importance, which shows that patch-level color and texture features and cell-level features all have significant contribution to prediction.
Connecting emerging relationships from news via tensor factorization

Knowledge graphs (KGs) have been widely used to represent relationships among entities, while KGs cannot capture new relationships between entities emerging along time. Since news often provides the latest information regarding the new entities and relationships, there is an opportunity to connect emerging relationships from news timely. However, it is a challenging task due to the source heterogeneity of structured KGs and unstructured news texts. In order to address the issue, we propose a tensor-based framework to capture the complex interactions among multiple types of relations, entities and text descriptions. We further develop an efficient Text-Aware MUlti-RElational learning method (TAMURE) that can learn the embedding representations of entities and relation types from both KGs and news, by jointly factorizing the interaction parameters. Furthermore, the complexity of TAMURE is linear in the number of parameters, which makes it suitable to large-scale KGs and news texts. Extensive experiments via TensorFlow demonstrate the effectiveness of the proposed TAMURE model compared with nine state-of-the-art methods on real-world datasets.
The Study of Language in Organizations: A Symbolic Interactionist Stance

Very few models have been developed fromresearch on language in organizations, and those thatexist are usually very general. In addition, theassumptions on which they are based are not alwaysexplicit, which means that the limits of the modelsthemselves cannot be seen. After describing thecharacteristics of research that could be done onlanguage from an objectivist and a subjectivist stance,and specifying the limits of these two fundamental views, thispaper proposes an explicit model for understandinglanguage based on a symbolic interactionist stance, withemphasis on ascribing meaning and understanding language in the context of the interactivesituation in which it occurs. The interactive situationcomprises three fundamental components: theenvironmental, cognitive, and emotional contexts. In themodel, the tongue and psycholinguistic schemata of theindividuals concerned inevitably have a restrictiveeffect on the language used, and the language contextmay help make the speaker's remarks meaningful. The assumptions underlying the choice of asymbolic interactionist stance are described, and thepaper ends with some suggested avenues for researchrevealing new topics or new ways of studying oldtopics.
Théorie des systèmes et prospective: le cas de l’économie de l’énergie

The theory of systems is presented as an operational theory that can be used to modelize the structure of technical or social systems. Simulation and optimalization techniques can be used to operate this model on a computer for analyzing different scenarios. A recent study of the economy of energy and of the particular role of hydrogen is presented as an example of such a use of the theory of systems in the studies on prospective.
Death anxiety in females with and without exposure to death and dying: differential dimensions

Exposure to traumatic events like death and dying is anxiety provoking. High anxiety can contribute to psychological breakdown of the individual. A study was conducted to examine the impact of exposure to death and dying on different dimensions of fear of death. Data was obtained from 100 females who were professionally exposed to death/dying and their 100 matched controls that were not exposed to death/ dying in the last two years, using a background questionnaire and Collett-Lester’s fear of death scale. All subjects exhibited varying degrees of death anxiety from low to high. Females who were professionally exposed to death/dying reported to have significantly higher fear of death, and fear of dying of self as compared to their counterparts who were not exposed to death/dying in last two years (t= 1.65, p < .05, t= 2.74, p < .01, respectively). While comparing the fear of death of self with fear of death of others; and fear of dying of self with fear of dying of others, the females in both the groups were more fearful about the death and dying of others as compared to self. In the group exposed to death, the fear of death of self/ fear of death of others exhibited significant differences with the scores being more for others as compared to self (t = 6.87, p < .01). The relationship of socio-demographic variables (age and religious background) with death anxiety has also been explored in this study. One dimension of death anxiety i.e. fear of death of others was found to be negatively and significantly correlated with age in group-I (professionally exposed to death/ dying) as well as in group-II (not exposed to death/dying in last 2 years). The two main religious groups in this study i.e. Hindus and Sikhs were compared on all the dimensions of death anxiety and no significant difference was observed. The implications of the findings are discussed in the paper.
A Korean Noun Semantic Hierarchy (Wordnet) Construction

Since thesaurus is used as a knowledge resource in many natural language processing systems, it is very useful and necessary for the high quality systems, especially for dealing with semantics. In this paper, we introduce a semi-automatic method for the construction of Korean noun semantic hierarchy by utilizing a monolingual MRD and an existing thesaurus.
Not What, but How One Feels: The Measurement of Affect during Exercise

Three experiments are presented that evaluate the feeling scale (FS) as a measure of affect during exercise. In Experiment 1,. subjects were instructed to check adjectives on the MAACL-R that they would associate with either a "good" or a "bad" feeling during exercise. As predicted, discriminant function analysis indicated that the good/bad dimension of the FS appears to represent a core of emotional expression. In Experiment 2, subjects rated how they felt during exercise at a rate of perceived exertion (RPE) of 11, 15, and 19. There was considerable heterogeneity in FS for each given RPE. Moreover, RPEs and FS ratings were only moderately correlated, r= - . 56, suggesting that phenomenologically the two constructs are not isomorphic. Experiment 3 involved three 4-min bouts of exercise at 30, 60, and 90% V02max. Assessed were pre- and post-exercise affect as. well as RPEs, responses to the FS, Ve, RR, and VO2. Results revealed that RPE and the FS were moderately related, but only at easy and hard workload.
Toward Automatic Data Distribution for Migrating Computations

Program parallelization requires mapping computation and data to processing elements. Navigational programming (NavP), based on the principle of migrating computations, offers a different approach than the conventional solutions that use a SPMD model. This paper focuses on data distribution for NavP. We introduce the navigational trace graph (NTG), a mathematical structure that captures the alignment and distribution preferences of a sequential program. Graph partitioning is applied to NTGs to obtain data distribution solutions. The major advantage is that our methodology can focus exclusively on reducing communication overhead first and later determine the actual computation partition and parallelization, because NavP computations migrate freely across partitions. This is in stark contrast to SPMD, where the data partitioning imposes hard constraints on the threads because they are stationary. We present experimental results to demonstrate the effectiveness of our approach.
Enabling fast, dynamic network processing with clickOS

Middleboxes are both crucial to today's networks and ubiquitous, but embed knowledge of today's protocols and applications to the detriment of those of tomorrow, making the network harder to evolve. SDNs seek to make it easier to extend the network with new functionality, but most of the research effort has focused on the network's control plane, that is, how packets are switched are routed through a SDN. Given the pervasiveness and importance of middleboxes, we believe that a fully programmable network should also be able to dynamically instantiate and quickly move middlebox functionality. In this paper we shift focus towards making the data plane more programmable by introducing ClickOS, a tiny, Xen-based virtual machine that can run a wide range of middleboxes. ClickOS is small (5MB when running), can be instantiated in very small times (roughly 30 milliseconds) and can fill up a 10Gb pipe while concurrently running 128 vms on a low-cost commodity server.
Fast random wave generation in numerical tanks

Generating and absorbing random waves in numerical models is a challenging problem, in particular when meaningful wave statistics should be generated to meet design sea state requirements. The methodology presented herein allows for the generation of random wave fields (free surface elevation and velocities) to be reconstructed in time and in space by using window processing from a reference time series.
Antecedents of corporate volunteerism

Antecedents of Corporate Volunteerism. (December 2008) Jaime Blaine Henning, B.S., Missouri State University; M.S., Missouri State University Co-Chairs of Advisory Committee: Dr. Mindy E. Bergman Dr. Stephanie C. Payne Millions of individuals donate their time to volunteer work each year. Additionally, expectations of socially responsible practices on the part of organizations and the positive relationship between the social and financial performance of organizations are leading to a growing interest in corporate social responsibility among management and researchers. One method to achieving greater corporate social responsibility is corporate-sponsored volunteering. Many benefits have been associated with corporate volunteerism. However, an understanding of why these outcomes occur is lacking. Also lacking is an understanding of why individuals engage in corporate volunteerism. Although studies have identified several demographic variables associated with volunteering in general, few studies have examined variables beyond simple demographics, and fewer still have examined antecedents of corporate volunteering. In the current study, the theory of planned behavior, functional motives for volunteering and citizenship performance, and perceived locus of causality are used to integrate contextual, attitudinal, situational, and motivational variables in order to present and empirically test a framework to help explain why individuals participate in corporate
Climate change and edaphic conditions: predicting future growth of a critical boreal tree species - Jack Pine (Pinus banksiana)

ii Acknowledgements . iii List of Tables and Figures.v Introduction . 1 Methods.3 Study area and site selection . 3 Field and laboratory methods . 4 Climate data and analysis . 6 Results . 7 Effects of diameter and previous years’ growth on radial growth . 8 Effects of climate variables on radial growth . 8 Growth simulations . 8 Discussion . 9 Tables and Figures . 14 Literature Cited . 21 Supplemental Information . 27
An Improved Semi-Supervised Support Vector Machine Based Translation Algorithm for BCI Systems

In this study, we propose an improved semi-supervised support vector machine (SVM) based translation algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process and enhancing the adaptability of BCI systems. In this algorithm, we apply a semi-supervised SVM, which builds a SVM classifier based on small amounts of labeled data and large amounts of unlabeled data, to translating the features extracted from the electrical recordings of brain into control signals. For reducing the time to train the semi-supervised SVM, we improve it by introducing a batch-mode incremental training method, which also can be used to enhance the adaptability of online BCI systems. The off-line data analysis results demonstrated the effectiveness of our algorithm