Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hashing Scheme for Multi-channel Wireless Broadcast

The rapid development of wireless communication technology and battery-powered portable devices is making mobile information services increasingly popular. Since the bandwidth resource of wireless networks is scarce and the mobile devices have a limited battery capacity, any solution for information access must be devised in such a way that time and power consumption for the devices are minimized. Data broadcast is a promising technique to improve the bandwidth utilization and conserve the power consumption in a mobile computing environment. This paper proposes a hashing scheme for information access via wireless broadcast through multiple channels in which hash functions are used to index broadcast information across multiple channels. In this scheme, two different hash functions called Primary Hash Function (PHF) and Secondary Hash Function (SHF) are used, where PHF is used to determine the channel in which the desired data item is to be broadcasted and SHF is used to locate the data item within that channel. The proposed hashing scheme reduces both the access latency and tuning time and shortens the broadcast length. Moreover, Access Probabilities of data items and User Profiles that indicate the client behavior in the environment at any given time are considered in this system to construct an efficient broadcast schedule. This broadcast schedule is a non-flat data broadcast that further reduces the average access latency. Finally, Caching techniques are also implemented to further improve the access latency and tuning time.
Study of Bandwidth Consumption in P2P VoD System

Reducing server’s bandwidth pressure is one of the key problems in the designing of peer-to-peer video-on-demand system. In this paper, we analysis and model this problem via using the “pseudo sequence scheduling”, which is widely used by many peer-to-peer video-on-demand systems. We reveal the relations between the server bandwidth consumption, the cache size and the join speed of peers. This result shows its importance in the design of coordination strategy between peers in the system.
Federal Reserve Bank of New York Staff Reports the Relationship between Expected Inflation, Disagreement, and Uncertainty: Evidence from Matched Point and Density Forecasts the Relationship between Expected Inflation, Disagreement, and Uncertainty: Evidence from Matched Point and Density Forecasts

This paper presents preliminary findings and is being distributed to economists and other interested readers solely to stimulate discussion and elicit comments. The views expressed in the paper are those of the authors and are not necessarily reflective of views at the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors. Abstract This paper examines matched point and density forecasts of inflation from the Survey of Professional Forecasters to analyze the relationship between expected inflation, disagreement, and uncertainty. We extend previous studies through our data construction and estimation methodology. Specifically, we derive measures of disagreement and uncertainty by using a decomposition proposed in earlier research by Wallis and by applying the concept of entropy from information theory. We also undertake the empirical analysis within a seemingly unrelated regression framework. Our results offer mixed support for the propositions that disagreement is a useful proxy for uncertainty and that increases in expected inflation are accompanied by heightened inflation uncertainty. However, we document a robust, quantitatively and statistically significant positive association between disagreement and expected inflation. Topa for helpful comments and suggestions. Bess Rabin and Ariel Zetlin-Jones provided excellent research assistance. The views expressed in this paper are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System.
Give Everybody a Voice! The Power of Voting in a Public Goods Experiment with Externalities

We study the effect of voting when insiders’ public goods provision may affect passive outsiders. Without voting insiders’ contributions do not differ, regardless of whether outsiders are positively or negatively affected or even unaffected. Voting on the recommended contribution level enhances contributions if outsiders are unaffected and internalizes the negative externality by lowering contributions when outsiders are negatively affected. Remarkably, voting does not increase contributions when it would be most desirable, i.e. with a positive externality. Here, participants vote for high contributions, yet compliance is poor. Unfavorable payoff comparisons to the outsiders that gain a windfall profit drive contributions down.
Per-antenna-rate-control (PARC) in frequency selective fading with SIC-GRAKE receiver

In this paper we consider a generalization of the per-antenna-rate-control (PARC) MIMO scheme for the HSDPA option of WCDMA. The scheme, called PARC-N, allows the number of transmitted data substreams N to be less than the number of transmit antennas M and selects the best N antennas for transmission. We investigate PARC-N specifically in frequency selective fading environments. First, we develop a novel receiver structure that combines successive interference cancellation (SIC) with generalized-RAKE technology that achieves a good balance between performance and complexity. We then draw several interesting conclusions: (1) data throughput can be maximized by using N /spl les/ M, where N may be adaptively selected based on operating conditions; (2) data throughput is maximized by allocating all non-data signal power (pilots, voice, overhead, etc.) to as few transmit antennas as possible; and (3) the use of pilot subtraction at the receiver is much more beneficial in MIMO systems than in single-antenna or receive diversity systems.
An Attempt to Automate the Process of Source Evaluation

Credibility of a web-based document is an important concern, when a large number of documents is available on internet for a given subject. In this paper, various criteria that affect the credibility of a document are explored. An attempt is made to automate the process of assigning a credibility score to a web-based document. Presently the prototype of the tool developed is restricted to only four criteria - type of website, date of update, sentiment analysis and a pre-defined Google page rank. Also a separate module for checking "link integrity" of a website is developed. To obtain empirical validity of the tool, a pilot study is conducted which collects credibility scoring for a set of websites by human judges. The correlation between the scores given by human judges and the scores obtained by the tool developed is low. The possible reasons for the low correlation are firstly, the tool is restricted to only four criteria, and secondly, subjects themselves had no agreement. Apparently they judged the website on different criteria, and not weighted overall. Further enhancements to the work done in this paper can be of great use to a novice user, who wishes to search a reliable web- based document on any specific topic. This can be done by including all criteria (discussed in this paper) for calculating the credibility score of a website.
Working Memory Impairments Limitations of Normal Children's in Visual Stimuli Using Event-Related Potentials

This study emphases to investigate the working memory impairments limitations of normal children's in visual stimuli assessments using Event-related Potentials (ERPs) signal. 97 children aged between 7 to 12 years old were subjected to a two-phase computer based assessment while their working memory activity was concurrently recorded using Electroencephalograph (EEG) 9200 machine at the prefrontal cortex. EEG signals were segmented and averaged into the event of stimulus to obtain the ERPs pattern. In neurophysiology research, ERP component at P300 will forms the visual responsiveness to detect the cognitive processes. P300 component is decomposed using Discrete Wavelet Transform (DWT) with Daubechies (db4) at 7th level decomposition. In conclusion, visual stimuli assessment brings up a dissimilar in P300 pattern between two-phases. The mean performance shows that, the younger children tend to working memory impaired rather than older children.
Likelihood approaches to sensory coding in auditory cortex

Likelihood methods began their evolution in the early 1920s with R A Fisher, and have developed into a rich framework for inferential statistics. This framework offers tools for the analysis of the differential geometry of the full likelihood function based on observed data. We examine likelihood functions derived from inverse Gaussian (IG) probability density models of cortical ensemble responses of single units. Specifically, we investigate the problem of sound localization from the observation of an ensemble of neural responses recorded from the primary (AI) field of the auditory cortex. The problem is framed as a probabilistic inverse problem with multiple sources of ambiguity. Observed and expected Fisher information are defined for the IG cortical ensemble likelihood functions. Receptive field functions of multiple acoustic parameters are constructed and linked to the IG density. The impact of estimating multiple acoustic parameters related to the direction of a sound is discussed, and the implications of eliminating nuisance parameters are considered. We examine the degree of acuity afforded by a small ensemble of cortical neurons for locating sounds in space, and show the predicted patterns of estimation errors, which tend to follow psychophysical performance.
Optimization of Neural Predictor for Air Pollution

This paper presents a neural network modeling applied to predict monthly ozone and carbon dioxide concentration time series. The performances of neural network are critically depending on learning parameters, on its structure and control parameters. Even when a suitable setting of parameters (weights) can be found, the ability of the resulting network to generalize the data after learning may be far from optimal. For these reasons it is well suitable and attractive to apply an optimization method using genetic algorithms. In this paper, we show how Genetic algorithms may provide a powerful tool for automating the design of neural networks. This pattern presents a better fit of non linearity concerning the air pollution data. Simulation results show the effectiveness of the proposed optimization method. The results are compared and discussed to demonstrate the better quality of the forecasting model. Index Terms—Air pollution, Neural network, Genetic Algorithm, Prediction, Time Series.
The Integrative Structure and Outcome Model of Relationship Benefits: Using Data Mining

In recent years, the concept of relationship benefits has attracted increasing attention among marketing researchers and practitioners. Despite a growing body of literature in this area, no generally accepted integrative structure model and outcome model have emerged from the marketing literature. The present paper uses data mining to investigate the structure of relationship benefits based on the theoretical foundations of relational bonds, then, establishes the integrative outcome model. It is suggested that relationship benefits and their outcome should be conceptualized as multi-dimensional, higher-order structure models. These models are tested using data from a survey among some 300 customers of the services of hairdressers. The findings of this article are directions for future research and managerial implications.
Classification of syncope through data analytics

Objective: Syncope is a sudden loss of consciousness with loss of postural tone and spontaneous recovery; it is a common condition, albeit one that is challenging to accurately diagnose. Uncertainties about the triggering mechanisms and their underlying pathophysiology have led to various classifications of patients exhibiting this symptom. This study presents a new way to classify syncope types using machine learning. Method: we hypothesize that syncope types can be characterized by analyzing blood pressure and heart rate time series data obtained from the head-up tilt test procedure. By optimizing classification rates, we identify a small number of determining markers which enable data clustering. Results: We apply the proposed method to clinical data from 157 subjects; each subject was identified by an expert as being either healthy or suffering from one of three conditions: cardioinhibitory syncope, vasodepressor syncope and postural orthostatic tachycardia. Clustering confirms the three disease groups and identifies two distinct subgroups within the healthy controls. Conclusion: The proposed method provides evidence to question current syncope classifications; it also offers means to refine them. Significance: Current syncope classifications are not based on pathophysiology and have not led to significant improvements in patient care. It is expected that a more faithful classification will facilitate our understanding of the autonomic system for healthy subjects, which is essential in analyzing pathophysiology of the disease groups.
Nested linear/lattice codes for Wyner-Ziv encoding

We construct structured codes for lossy compression with uncoded side information at the decoder. These codes aim at achieving the optimal performance, given by the Wyner-Ziv (1976) rate-distortion function, for two typical settings. For binary symmetric source/side-information, we show that a pair of nested linear binary codes, each individually "good" in the classical sense, approaches the WZ-function relative to the Hamming distortion measure. For the quadratic Gaussian case we show a similar result at high SNR, using a pair of nested lattices and their associated lattice-decoding functions.
Post-Communist Economies , Vol . 14 , No . 2 ( April 2002 ) 1

Infrastructure restructuring is one of the major elements of structural reforms in transition economies because of its expected large economic and social impact. This paper aims at assessing the role of infrastructure policy on the economic development in transition countries in general and on Ukraine in particular. We test the relationship between infrastructure policy and economic growth, though this test is carried out at a very aggregate level due to data problems. According to our estimations the government can make an indirect contribution to economic development by enhancing competition and efficiency within the infrastructure industries. The paper also develops a framework to analyse and compare infrastructure policies across transition countries, and provides a detailed survey of infrastructure policies in Ukraine. We conclude that the major element of reforms should be tariff reform, carried out concurrently with commercialisation and deregulation of the sector.
An Associative Neural Network Model of Classical Conditioning

In this paper we present a new associative model of classical conditioning based on a neural network. The new model is compared with a number of other well-known models of classical conditioning. The experiments that are used to evaluate the new model are commonly used and they represent the set of tasks that a model of classical conditioning needs to address in order to be successful. The new neural network based model is composed of a number of interconnected Bayesian confidence propagating neural networks (BCPNNs). The BCPNN implements Hebbian learning. This new BCPNN based model sorts under the category of associative models. A key concept of this model is to make a closer tie between the output and the underlying neural activity. The BCPNN model does not use delaylines as many other models of conditioning. The output from the BCPNN model fit the results of classical conditioning experiments.
The ygaVP Genes of Escherichia coli Form a Tributyltin-Inducible Operon

ABSTRACT A tributyltin (TBT) luxAB transcriptional fusion in Escherichia coli revealed that a TBT-activated promoter is located upstream of two cotranscribed orphan genes, ygaV and ygaP. We demonstrate that transcription from the promoter upstream of ygaVP is constitutive in a ygaVP mutant, suggesting that YgaV is an autoregulated, TBT-inducible repressor.
Translation and Linguistic Validation of the Korean Version of the Dysfunctional Voiding Symptom Score

Although studies on pediatric dysfunctional voiding are increasing, there have not been enough efforts to validate the Dysfunctional Voiding Symptom Score (DVSS) questionnaire. Therefore, we aimed to translate and validate the DVSS into Korean. The DVSS questionnaire was validated between January and October, 2013. Two bilinguals independently translated the English version of the DVSS questionnaire into Korean, and then reconciled the forward translation of the Korean version. The original DVSS was back-translated into English, then assessed for equivalence to the original. Cognitive debriefing interviews with 5 patients to test the interpretation of the translation were made, then modified and distributed to 48 patients for re-evaluation. A statistical analysis of inter-scale correlation, and test re-test consistency was performed with the Cronbach's alpha coefficient. The changes from patient interviews were reflected in the final version. In an intra-class correlation, the Cronbach's alpha was high in all of the questions (0.97, P < 0.001). Test re-test Cronbach's alpha analysis of reproducibility was higher than 0.8 for all of the 10 questions (P < 0.001). Translation and linguistic validation of Korean version of the DVSS questionnaire was completed by a proper process, with high reliability and validity. Graphical Abstract
A genetic fuzzy expert system for stock price forecasting

Forecasting stock price time series is very important and challenging in the real world because they are affected by many highly interrelated economic, social, political and even psychological factors, and these factors interact with each other in a very complicated manner. This article presents an approach based on Genetic Fuzzy Systems (GFS) for constructing a stock price forecasting expert system. We use a GFS model with the ability of rule base extraction and data base tuning for next day stock price prediction to extract useful patterns of information with a descriptive rule induction approach. We evaluate capability of the proposed approach by applying it on stock price forecasting case study of International Business Machines Corporation (IBM), and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach is able to cope with the fluctuation of stock price values and it also yields good prediction accuracy in short term stock price forecasting.
Documents Search Using Semantics Criteria

Current Information Retrieval systems generally search documents using a keywords model, which is often not expressive enough for the user. In this paper we describe some directions for improving an Information Retrieval system by letting the user specify different semantics constraints in her query, using a language based on a simplified version of first-order logic. The user can write queries that express the association between objects and attributes, temporal constraints and negation of attributes, and also perform synonyms expansion of queries. In order to evaluate the relevance of a candidate document with respect to the query, the dependency parse tree of the document is used, as well as other linguistic resources. The system was evaluated using a set of queries and a corpus extracted from the British newspaper The Times. The results are compared against the newspaper's own search engine and they look promising, showing an important improvement in precision in the first documents returned by the query.
The neural network for view-invariant object recognition and classification

We propose a neural network of ventral visual pathway for recognition and classification of 3D real-world objects. The model consists of hierarchy of modules, representing the visual areas V1-V4 and inferotemporal cortex (IT). The architecture of the model represents the self-organized map (SOM) of functional radial-basis function (RBF) modules. We show that the proposed architecture is capable of recognizing and categorizing 3D real-world objects in a way consistent with recent fMRI findings.
Dense Cores in Dark Clouds. XIV. N2H+ (1-0) Maps of Dense Cloud Cores

We present results of an extensive mapping survey of N2H+ (1-0) in about 60 low-mass cloud cores already mapped in the NH3 (1, 1) inversion transition line. The survey has been carried out at the FCRAO antenna with an angular resolution of 54'', about 1.5 times finer than the previous ammonia observations made at the Haystack telescope. The comparison between N2H+ and NH3 maps shows strong similarities in the size and morphology of the two molecular species, indicating that they are tracing the same material, especially in starless cores. Cores with stars typically have map sizes about a factor of 2 smaller for N2H+ than for NH3, indicating the presence of denser and more centrally concentrated gas compared to starless cores. The mean aspect ratio is ~2. Significant correlations are found between NH3 and N2H+ column densities and excitation temperatures in starless cores, but not in cores with stars, suggesting a different chemical evolution of the two species. Starless cores are less massive ( ≃ 3 M☉) than cores with stars ( ≃ 9 M☉). Velocity gradients range between 0.5 and 6 km s-1 pc-1, similar to what has been found with NH3 data, and the ratio β of rotational kinetic energy to gravitational energy has magnitudes between ~10-4 and 0.07, indicating that rotation is not energetically dominant in the support of the cores. "Local" velocity gradients show significant variation in both magnitude and direction, suggesting the presence of complex motions not interpretable as simple solid-body rotation. Integrated intensity profiles of starless cores present a "central flattening" and are consistent with a spherically symmetric density law n ∝ r-α, where α = 1.2 for r < rbreak and α = 2 for r > rbreak, with rbreak ~ 0.03 pc. Cores with stars are better modeled with single density power laws with α ≥ 2, in agreement with observations of submillimeter continuum emission. Line widths change across the core, but we did not find a general trend: there are cores with significant positive as well as negative linear correlations between Δv and the impact parameter b. The deviation in line width correlates with the mean line width, suggesting that the line of sight contains ~10 coherence lengths. The corresponding value of the coherence length, ~0.01 pc, is similar to the expected cutoff wavelength for MHD waves. This similarity may account for the increased "coherence" of line widths on small scales. Despite finer angular resolution, the majority of N2H+ and NH3 maps show a similar "simple" structure, with single peaks and no elongation.
UWB Energy Detection in the Presence of Multiple Narrowband Interferers

There has been an emerging interest in non-coherent ultra wideband (UWB) communications, particularly as a technology for low-data rate UWB applications, due to its low-complexity and low-power consumption. In this paper, we evaluate the performance of the energy detector (ED) receiver in terms of bit error probability (BEP). We consider an ED receiver based on a conventional square-law detector and binary pulse position modulation (BPPM). We analyze the BEP in a multipath fading channel, both in the absence and presence of narrowband interference (NBI). We consider two cases: (1) single NBI, where the interfering node is located at a fixed distance from the receiver, and (2) multiple NBI, where the interfering nodes are scattered according to a spatial Poisson process. Our framework is simple enough to allow a tractable analysis and provide insights that can be of value in the design of practical UWB systems subject to interference.