Miracle-dz/UniFAR-FTUs huggingface dataset:
Weighted energy detection method for spectrum sensing in cognitive radio

In order to improve the average performance of detecting spectrum holes for second user (SU), we propose the weighted energy detection method. In this paper, we firstly classify the detection situations into two modes. One corresponds to the situation that the primary user (PU) signal takes up the whole detection window if PU signal exists. The other corresponds to the situation that the PU signal only appears in part of the whole detection window. Then we introduce the weighted factor for each received signal sample in detection window. The weighted factors follow the tendency that the samples closer to the end of detection window are more important for judgment. The simulation results show that the weighted method works better than the conventional method and hybrid method in the second mode. The weighted method has the lowest detectable signal-to-noise ratio (SNR) in second mode. And the performance improvement in second mode leads to only a little performance loss in the first mode. The weighted method makes a performance compromise between the first mode and the second mode.
Indoor positioning system based on inertial MEMS sensors: Design and realization

Nowadays, location-based services (LBS) has become widely used in our daily life. The most famous system is global positioning system (GPS), which is limited to outdoor applications and provide poor locating accuracy. In this paper, we present a positioning systems based on inertial MEMS sensor which includes three-axis accelerometer, three-axis gyroscope and three-axis magnetometer. The system can help people get accurate positioning for indoor environments, also available for outdoors, because of its self-contained character. It is a foot wearable device with wireless network to transmit movement information to computer that can calculate the relative position and show the path walked by. The key concept of the positioning system is inertial navigation and dead reckoning technology. Since it needs twice-integration of the acceleration to get the position, the displacement will drift by time elapse. We make it only drift by distance increasing through gait phase analysis, a method called Zero-Velocity Update (ZVU). As the “stand-still phase” is the key of the system performance, we mainly focus on getting accurate gait phase detection. We used decision tree here and the experimental results showed that we got a gait phase detection accuracy of 99.96% and positioning accuracy of 97.37%.
The Scope and Focus of Negation: A Complete Annotation Framework for Italian

In this paper we present a complete framework for the annotation of negation in Italian, which accounts for both negation scope and negation focus, and also for language-specific phenomena such as negative concord. In our view, the annotation of negation complements more comprehensive Natural Language Processing tasks, such as temporal information processing and sentiment analysis. We applied the proposed framework and the guidelines built on top of it to the annotation of written texts, namely news articles and tweets, thus producing annotated data for a total of over 36,000 tokens.
Location patterns of mobile users: A large-scale tudy

The opportunities to understand human-mobility have increased significantly of late with the rapid adoption of wireless devices that report locations frequently. In this work1, we utilize one such rich data-set comprising of nationwide call data records from several million users to analyze and understand their location patterns. We define a location pattern as the set of locations visited by a user, which roughly speaking, can be considered to be the footprint of the user. Such an analysis is useful since it allows insight into aspects such as the range covered by a user, general direction and major routes of travel, characterization of geographic areas etc.,. These in turn are useful inputs for network planning, traffic planning and mobility models. We propose a systematic methodology that utilizes geometric structures like the Minimum Area Rectangle, line segmentation and clustering techniques to extract meaningful information for location patterns and apply it to our large data-set. Based on this we report on aspects such as the size and orientation of footprints, length of major routes as well as characterize and compare locales based on movement patterns. Finally, we identify some key features of location patterns that can be modeled very well with a single statistical distribution, the Double Pareto LogNormal (DPLN) distribution regardless of locale.
Pattern Discovery in Data Hypercubes

Data hypercubes are used to represent data along multiple dimensions of interest. Many data mining applications naturally lead to modeling and inference problems utilizing data hypercubes as the main data structure. The existing data mining tools in OLAP for data hypercubes tend to focus on efficient methods for computing relatively simple statistics to answer database user queries. In this extended abstract we point out several research issues in develop-we point out several research issues in developing computational and statistical methods that can discover more insightful and complicated patterns in data hypercubes. We concentrate on approaches exploring simultaneous clustering, mixture modeling and outlier detection. We also mention the potential use of the methods in several interesting applications including modeling and sub-topic extraction of multilingual documents, microarray analysis for gene expression.
A molecular mechanism of chronological aging in yeast

The molecular mechanisms that cause organismal aging are a topic of intense scrutiny and debate. Dietary restriction extends the life span of many organisms, including yeast, and efforts are underway to understand the biochemical and genetic pathways that regulate this life span extension in model organisms. Here we describe the mechanism by which dietary restriction extends yeast chronological life span, defined as the length of time stationary yeast cells remain viable in a quiescent state. We find that aging under standard culture conditions is the result of a cell-extrinsic component that is linked to the pH of the culture medium. We identify acetic acid as a cell-extrinsic mediator of cell death during chronological aging, and demonstrate that dietary restriction, growth in a non-fermentable carbon source, or transferring cells to water increases chronological life span by reducing or eliminating extracellular acetic acid. Other life span extending environmental and genetic interventions, such as growth in high osmolarity media, deletion of SCH9 or RAS2, increase cellular resistance to acetic acid. We conclude that acetic acid induced morality is the primary mechanism of chronological aging in yeast under standard conditions.
Domain specific sentiment analysis using cross-domain data

Sentiment analysis is a tool used to guide critical business and engineering decisions. Such applications demand sentiment analysis specialized in specific domain areas. In this work we investigate the effectiveness of standard machine learning techniques and feature engineering that can effectively be applied in the industry. Further we investigate the use of labeled data from multiple domain areas to improve the results in a specific domain area in a product review setting. Finally we propose a method based on dimensionality reduction and clustering to reduce the size of the problem without loss in performance.
Bone loss in the ovariectomized baboon Papio ursinus: densitometry, histomorphometry and biochemistry

To develop a non‐human primate model of systemic bone loss after ovariectomy, 24 ovariectomized (OVX) and eight control (non‐OVX) female baboons Papio ursinus were investigated over a period of 48 months using bone mineral density (BMD), iliac crest bone histomorphometry, bone turnover markers, and variables of calcium metabolism. Lumbar spine (L1–L4) BMD measured by dual energy X‐ray absorptiometry (DXA) decreased in OVX animals in the first 12 months (−7.6%) and showed a slow trend towards recovery after 24 months. Controls showed a slow increase in spinal BMD over 4 years (+9.7%). Total hip BMD decreased slowly up to 48 months in all animals (OVX −12.6%versus controls −10%); this indicated that OVX had a limited effect on total hip BMD. Forearm BMD did not change. The significant decrease in trabecular bone volume (TBV) of the iliac crest from baseline to 12 months was followed by some recovery. Microarchitectural deterioration of trabecular bone in OVX animals was demonstrated by a decline in trabecular number and an increase in trabecular spacing. These changes were also evident on sections of whole vertebrae, proximal femora and iliac crests. Changes in iliac TBV reflected spinal but not hip BMD changes in the OVX animals. Static and dynamic histomorphometric variables indicated that bone turnover was increased for 36 months following OVX. Controls showed no changes in histomorphometric variables. Bone specific alkaline phosphatase (ALPs) in OVX animals remained elevated throughout the study; osteocalcin (OC) was significantly elevated only at 6 and 12 months, and deoxypyridinoline (Pyr‐D) was elevated at 12 months but declined after 24 months. ALPs was thus more sensitive to the long‐term effects of OVX than were OC or Pyr‐D. Controls showed no changes in bone turnover markers. This study showed consistent deleterious changes in lumbar BMD, bone histomorphometry with microarchitectural deterioration together with altered biochemical markers of bone turnover in the first 12 months after OVX. Since these changes resemble those in post‐menopausal women, the non‐human primate Papio ursinus is suitable for the study of bone loss in post‐menopausal women.
The Role of Experienced Regret on Intertemporal Choice: An Experiment

Theoretical and empirical body of research have exposed the powerful role of experiencing regret in guiding choice behavior. In this paper, we examined the impact of experienced regret and rejoicing induced by feedback provided on a risk decision prior to a two-period intertemporal choice. To our knowledge, this is the first attempt to bring together experienced regret and choice over time. We used the two-component discounted utility model approach as a framework. We applied previous research findings on the effect of experienced regret on utility, and we performed an experiment to test whether experienced regret and rejoicing have an impact on the discount factor. We found that both experienced regret and rejoicing have an impact on the way people discount future: when regret is experienced the discount factor decreases, whereas when rejoicing is experienced the discount factor increases.
Integrated power clock generators for low energy logic

Low-energy (adiabatic) logic families have been proposed to reduce energy consumption of VLSI logic devices. Instead of the conventional DC power supply, these logic families require AC power supplies (power clocks) that allow energy recovery and also serve as timing clocks for the logic. In this paper, high-frequency resonant DC/AC inverters are proposed as power clock generators where all power switches and control circuitry are integrated on the same chip with low-energy logic. This results in better system efficiency and simpler power distribution. Closed-form results are derived to facilitate efficiency-optimized design of the proposed power clock generators. To illustrate system integration and energy savings, the optimized power clock is used to supply a novel clocked CMOS adiabatic logic (CAL). <<ETX>>
Implementation and Evaluation Scheme for Stereoscopic Display of Networks

The use of three-dimensional computer graphics in visualisation has become widespread for helping users to access and manage large information networks. This report describes a project that used dynamic, stereoscopic displays for network visualisation in 3-D. Input devices used to interact with a 3-D space are also examined. The project shows the feasibility of stereoscopic technology for network visualisation, and highlights some important research issues, including the selection of multi-degrees-of-freedom input devices.
Large Margin Hidden Markov Models in command recognition and speaker verification problems

Discriminative properties of different HMM structures, parameters and training algorithms are analyzed in the task of isolated words recognition (digits and robot controlling commands) and speaker verification. The ergodic, Bakis and chain HMM structures are considered, having constant or variable number of states. The classical Baum-Welch training algorithm is compared with the discriminative training, using the large margin approach. The class separation is increased by using the proper HMM structure, the variable number of HMM states and a large-margin HMM training algorithm, based on the extension of the training sequence.
Obstacles and opportunities in the functional analysis of extracellular vesicle RNA – an ISEV position paper

ABSTRACT The release of RNA-containing extracellular vesicles (EV) into the extracellular milieu has been demonstrated in a multitude of different in vitro cell systems and in a variety of body fluids. RNA-containing EV are in the limelight for their capacity to communicate genetically encoded messages to other cells, their suitability as candidate biomarkers for diseases, and their use as therapeutic agents. Although EV-RNA has attracted enormous interest from basic researchers, clinicians, and industry, we currently have limited knowledge on which mechanisms drive and regulate RNA incorporation into EV and on how RNA-encoded messages affect signalling processes in EV-targeted cells. Moreover, EV-RNA research faces various technical challenges, such as standardisation of EV isolation methods, optimisation of methodologies to isolate and characterise minute quantities of RNA found in EV, and development of approaches to demonstrate functional transfer of EV-RNA in vivo. These topics were discussed at the 2015 EV-RNA workshop of the International Society for Extracellular Vesicles. This position paper was written by the participants of the workshop not only to give an overview of the current state of knowledge in the field, but also to clarify that our incomplete knowledge – of the nature of EV(-RNA)s and of how to effectively and reliably study them – currently prohibits the implementation of gold standards in EV-RNA research. In addition, this paper creates awareness of possibilities and limitations of currently used strategies to investigate EV-RNA and calls for caution in interpretation of the obtained data.
A Mathematical Analysis of the Eeect of the Variability of Input on the Mean Value of Output for a Class of Technological Processes

variability of input on the mean value of output for a class of technological processes A.V. Pokrovskii Institute for Nonlinear Science, National University of Ireland University College, Cork, Ireland E-mail: alexei@peterhead.ucc.ie K. Cronin, Department of Food Engineering National University of Ireland University College, Cork, Ireland E-mail: kc@ucc.ie K. Abodayeh Department of Mathematics, National University of Ireland University College, Cork, Ireland E-mail: kamal.ucc.ie Abstract A class of random dynamical systems is considered. We discuss a class of problems arising from an example in food production likely to have wider applicability in the content of industrial quality control.
Electricity Expenditure Risk with Renewable Energy Support Schemes †

In deregulated electricity markets, economic support schemes for renewable energy (RES-E) technologies are typically implemented under the pretense of shielding investors from revenue risk arising from stochastic wholesale electricity prices. Two dominant RES-E support policies worldwide are feed-in tariffs (FIT) and renewable portfolio standards (RPS). Thus far, the economic and policy literature has failed to acknowledge a key benefit to retail electricity providers (and, ultimately, to consumers)—that stimulating investment in RES-E generation itself reduces wholesale price risk. Using a simple theoretical model of an electricity market, we demonstrate that greater RES-E generation should reduce the short-run variance in the wholesale electricity price, and thus in total electricity expenditures per unit (which include payments to RES-E related to the prevailing support policy). We find empirical support for this hypothesis using a panel of policy, price, and generation data for 19 countries over the period 20002011. Both FIT and RPS are found to reduce the short-run variance in total electricity expenditures per unit, due largely to a reduction in the variance in wholesale prices. Moreover, we find evidence that FIT reduces the variance in electricity expenditures per unit over the long-run, whereas RPS does not. † This research was presented at the 17 Annual CU Environmental and Resource Economics Workshop (Sept. 4-5, 2015, Vail, CO), the Association for Public Policy Analysis & Management (APPAM) 2015 Fall Research Conference (Nov. 12-14, 2015, Miami, FL), and the 85 Annual Meetings of the Southern Economic Association (Nov. 21-23, 2015, New Orleans, LA). We thank participants of those events for a number of helpful comments and observations. Additionally, we thank Wesley Burnett, Christoph Graf, Ian Lange, and Mark Lively for many useful comments and suggestions. We are indebted to Nick Johnstone for providing the international RPS and FIT data. Chris Blackburn provided essential graduate assistant work related to data cleaning, and Mishal Ahmed performed minor graduate assistant work related to data collection. Finally, we thank the Georgia Tech School of Economics for financial support. * Johnson: erik.johnson@econ.gatech. edu Oliver: matthew.oliver@econ.gatech. edu
The Effects of the Corporate Average Fuel Efficiency Standards

This paper examines the effects of the Corporate Average Fuel Efficiency standards (CAFE) on the automobile product mix, prices and fuel consumption First a discrete choice model of automobile demand and a continuous model of vehicle use are estimated using micro data from the Consumer Expenditure Survey for 1984-1990. Next, the demand side model is combined with a model of oligopoly and product differentiation on the supply side. After estimating the demand and supply parameters, the effects of the CAFE regulation are assessed through simulations and compared to the effects of alternative policy instruments such as a powerful gas guzzler tax and an increase in the gasoline tax. Our results are as follows: Vehicle use is in the short run unresponsive to fuel cost changes; vehicle purchases, however, respond to both car prices and fuel cost. These results taken together imply that (1) contrary to the CAFE opponents' claims, higher fleet fuel efficiency is not neutralized by increased driving, and (2) policies to reduce fuel consumption by shifting the composition of the car fleet towards more fuel efficient vehicles are more promising than policies that target utilization. Policies with compositional effects operate through two channels: changes in vehicle prices and in operating costs. Contrary to environmental groups' claims, our results do not indicate the existence of consumer myopia. Still, we find the gasoline tax increase necessary to achieve fuel consumption reductions equivalent to the ones currently achieved through CAFE is 780%; whether an increase of this size is politically feasible is questionable. Our results indicate that the CAFE regulation reduced fuel consumption but shifts in the classification of products as domestic vs. imports weakened the effectiveness of the standards.
Key technologies of medical monitoring system of smart home

Medical monitoring and smart home technology are combined to build a medical monitoring system of smart home, so that the general public could enjoy the close, professional and comfortable medical services. Medical monitoring technology of smart home breaks through the constraints of cable, medical testing can be carried out under the user's normal as well as avoiding the result of the users' stress, anxiety and other factors that influence the detection accuracy of the testing data, thereby reducing the possibility of misdiagnosis, relieving the pressure on the hospital and promote the development of low-cost medical care. Medical monitoring technology of smart home takes advantage of the ZigBee short-range wireless communication technology ,achieves continuous, real-time and dynamic detection of the human vital signs parameters, including electrocardiograph (ECG), oxygen saturation, blood pressure detections, builds sensor network, develops intelligent integrated software to provide users with independent, health, safety, convenience and the continuing human medical monitoring services.
Image segmentation by contextual region growing based on fuzzy classification

This paper presents a semantic image segmentation approach that combines a fuzzy region classification and a contextual region-growing. First image is over-segmented and a domain knowledge based fuzzy classification is applied on obtained regions to provide a fuzzy semantic labeling. This allows the proposed approach to operate at high level instead of using low-level features and consequently to remedy to the problem of the semantic gap. Each region is represented by its fuzzy membership degrees to the different thematic labels. The whole image is therefore represented by a regions' Membership Degrees Matrix. The segmentation is achieved on this matrix instead of the image pixels through two main phases: focusing and propagation. The focusing aims at selecting seeds regions from which information propagation will be performed. The propagation phase uses fuzzy contextual information to spread toward others regions the needed knowledge ensuring the semantic segmentation. An application of the proposed approach on Synthetic Aperture Radar (SAR) images shows promising results.
Generic aspects of axonemal beating

We study the dynamics of an elastic rod-like filament in two dimensions, driven by internally generated forces. This situation is motivated by cilia and flagella which contain an axoneme. These hair-like appendages of many cells are used for swimming and to stir surrounding fluids. Our approach characterizes the general physical mechanisms that govern the behaviour of axonemes and the properties of the bending waves generated by these structures. Starting from the dynamic equations of a filament pair in the presence of internal forces we use a perturbative approach to systematically calculate filament shapes and the tension profile. We show that periodic filament motion can be generated by a self-organization of elastic filaments and internal active elements, such as molecular motors, via a dynamic instability termed Hopf bifurcation. Close to this instability, the behaviour of the system is shown to be independent of many microscopic details of the active system and only depends on phenomenological parameters such as the bending rigidity, the external viscosity and the filament length. Using a two-state model for molecular motors as an active system, we calculate the selected oscillation frequency at the bifurcation point and show that a large frequency range is accessible by varying the axonemal length between 1 and 50 µm. We discuss the effects of the boundary conditions and externally applied forces on the axonemal wave forms and calculate the swimming velocity for the case of free boundary conditions.