High Information Research Articles

High-dimensional quantum key agreement protocol with pairs of single qudits

In this paper, we propose a high-dimensional quantum key agreement protocol where the key is encoded by pairs of single qudits which are chosen from d-level mutually unbiased bases. A security analysis is also given to show it is secure against most of the existing attacks. This protocol is useful in practice and has a higher information transmission ability because it uses high-dimensional quantum information carries.

Modified majority logic decoding of Reed–Muller codes using factor graphs

This study investigates the decoding algorithms of binary Reed-Muller (RM) codes. The main goal of the study is to modify majority-logic decoding using sum-product or min-sum decoding algorithms at the highest-order information bits in RM codes. Numerical results show that compared with the traditional majority-logic decoding algorithm, the modified majority-logic decoding method proposed in this study can reduce the bit error rate by a large margin. This is achieved without incurring a significant increase in decoding complexity because soft-decision decoding is only used in the highest information bits in RM codes whereas majority-logic decoding is still applied at the other orders of information bits in RM codes.

ADVANCED WIRELESS NETWORKS: TO STUDY AND ANALYSIS OF 4G NETWORKS

Versatile correspondence may be creating really quickly for section for time, new advances would constantly brought on encourage those versatile clients a greater amount starting with the innovation organization. As long as innovations need aid supplanted toward new ones and necessities need aid developing for those new innovations with a chance to be formed. One such improvement is fourth era networks. Also called future era or following era networks. Those presentation of 4G need widened the degree for versatile correspondence. Presently versatile is not just a gadget utilized to talking yet all its pretty much a transportable PC that camwood serve diverse purposes. 4G offers higher information rates for consistent roaming. The versatile client camwood speak without an unsettling influence same time exchanging as much scope organize. 4G is at present death through research also consequently there need aid exactly issues that requirement will a chance to be settled so as on profit those clients starting with it fully. In this paper we talk about know era of versatile engineering also profundity ponder of 4G portable innovation and its security.

Abstract WP288: Patient, Caregiver, and Layperson Preferences of Informed Consent for tPA

Objective: Based on patient, caregiver, and layperson input, we aimed to identify themes, preferences, content, framing, and sequencing of the informed consent process for tPA in acute ischemic stroke (AIS) patients. Background: Informed consent potentially delays or results in refusal of tPA in up to 10% of eligible AIS patients. There remain critical gaps in the appropriate content, delivery mode, and optimal timing of the informed consent process for tPA. Methods: Using a mixed-methods qualitative approach through separate focus groups of stroke patients and caregivers, and laypeople with limited experience with stroke and from diverse race and ethnic backgrounds, we identified preferences in describing risks and benefits of tPA during AIS patient evaluation. All focus groups followed a structured guide designed to elicit participant feedback regarding (1) decision-making preferences, (2) existing and hypothetical decision aid content, and (3) timing of information delivery for an informed consent process for tPA. Results: Stroke patients (n=15) and their caregivers (n=7) preferred a shared decision-making model that included minimal information and emphasized rapid decision making. Laypeople (n=31) also preferred a shared decision-making model for providing informed consent, but had higher information needs than stroke patients and caregivers. The majority of all focus group participants expressed a preference for a verbal description of risk and benefits. A minority of laypeople desired detailed visual aids to facilitate information exchanged during the informed consent process. The majority of focus group participants preferred stacked bar graphs or pie charts over pictograms to convey information regarding outcomes following tPA. Conclusions: Qualitative analyses of informed consent for tPA suggest that laypeople have higher information needs than stroke patients and caregiver. Given that the majority of patients who receive tPA do not have a history of prior stroke, shared decision-making aids should consider laypeople preferences while remaining brief given the time-sensitivity of tPA therapy. Optimizing the shared decision-making process for tPA could reduce consent-related delays and refusals of tPA.

A kind of entity recognition algorithm based on Hadoop for power big data

With the coming of the era of big data, traditional entity recognition technologies have been unable to effectively finish data preprocessing due to large scale of power grid data and complex volume type features. The rising of Hadoop technologies in these years can deal with big data processings better. Therefore, this paper proposes a power big data entity recognition algorithm based on Hadoop. It applies the discretization algorithm to select higher information accuracy discrete points and put forward a discretization evaluation indicator. In the end, we finish entity recognition of the monitoring data of wind turbines on Hadoop platform.Experimental results show that the proposed algorithm performs well in terms of correctness and breakpoint number experiments and it has a good speed-up ratio. The proposed algorithm can apply to power large data entity recognition processing.

Is There Smart Money? How Information in the Futures Market Gets Priced into the Cross-Section of Stock Returns with Delay

We document a new empirical phenomenon in which the positions of money managers (MM), who are sophisticated speculators in the commodity futures market, as disclosed by the CFTC Disaggregated Commitments of Traders (DCOT) reports, can predict the cross-section of commodity producers' stock returns in the subsequent week. We employ cross-sectional methodologies including single-sort, Jensen's alpha analysis, double-sort, and Fama-Macbeth regressions to confirm the predictability results. The results are more pronounced in firms with higher information asymmetry, proxied by analyst dispersion and historical volatility. We thus provide more empirical evidence to the literature on costly information processing which leads to market segmentation and gradual information diffusion across asset markets, as demonstrated in the lead-lag relationship.

ОЦЕНКА ИНФОРМАТИВНОСТИ И ОТБОР ПРИЗНАКОВ ПРИ ИДЕНТИФИКАЦИИ ОБЪЕКТОВ НА ЦИФРОВЫХ ИЗОБРАЖЕНИЯХ МИКРОСКОПИЧЕСКИХ ПРЕПАРАТОВ, ОКРАШЕННЫХ ПО МЕТОДУ ЦИЛЯ-НИЛЬСЕНА

Цель. Оценка информативности и осуществление отбора признаков для классификации объектов, на цифровых изображениях микроскопических препаратов, окрашенных по методу Циля-Нильсена с помощью методов Шеннона и Кульбака, а также сравнение результатов их применения.Материалы и методы. Использовались данные о 343 687 объектах, выделенных на цифровых изображениях микроскопических препаратов: 6 708 объектов – кислотоустойчивые микобактерии, 336 979 – иные объекты. Анализ объектов производился по 240 цветовым и морфометрическим признакам, оценка информативности которых осуществлялась с помощью методов Шеннона и Кульбака. Оценка качества отбора признаков осуществлялась с применением наивного Байесовского классификатора.Результаты. Наибольшую информативность по методу Шеннона имели цветовые признаки объектов и соотношения их размеров, а наибольшая доля верно классифицированных объектов (84,6%) была достигнута при включении в классификационную модель 6 признаков с наибольшей информативностью по методу Шеннона. Наибольшую информативность по методу Кульбака имели радиальные размеры объектов и их соотношения, а наибольшая доля верно классифицированных объектов (78,0%) была достигнута при включении в классификационную модель 14 признаков с наибольшей информативностью по методу Кульбака.Заключение. Методы Шеннона и Кульбака могут применяться для снижения признакового пространства при классификации объектов. Метод Шеннона позволяет в большей мере сократить количество признаков, обеспечивая при этом наибольшую долю верной классификации объектов.

Geometric multiscale community detection: Markov stability and vector partitioning

Multiscale community detection can be viewed from a dynamical perspective within the Markov Stability framework, which uses the diffusion of a Markov process on the graph to uncover intrinsic network substructures across all scales. Here we reformulate multiscale community detection as a max-sum length vector partitioning problem with respect to the set of time-dependent node vectors expressed in terms of eigenvectors of the transition matrix. This formulation provides a geometric interpretation of Markov Stability in terms of a time-dependent spectral embedding, where the Markov time acts as an inhomogeneous geometric resolution factor that zooms the components of the node vectors at different rates. Our geometric formulation encompasses both modularity and the multi-resolution Potts model, which are shown to correspond to vector partitioning in a pseudo-Euclidean space, and is also linked to spectral partitioning methods, where the number of eigenvectors used corresponds to the dimensionality of the underlying embedding vector space. Inspired by the Louvain optimisation for community detection, we then propose an algorithm based on a graph-theoretical heuristic for the vector partitioning problem. We apply the algorithm to the spectral optimisation of modularity and Markov Stability community detection. The spectral embedding based on the transition matrix eigenvectors leads to improved partitions with higher information content and higher modularity than the eigen-decomposition of the modularity matrix. We illustrate the results with random network benchmarks.

Artificial Bee Colony Optimization for Feature Selection of Traffic Sign Recognition

This paper proposes the application of a swarm intelligence algorithm called Artificial Bee Colony (ABC) for the feature selection to feed a Random Forest (RF) classifier aiming to recognise Traffic Signs. In this paper, the authors define and assess several fitness functions for the feature selection stage. The idea is to minimise the correlation and maximise the entropy of a set of masks to be used for feature extraction results in a higher information gain and allows to reach recognition accuracies comparable with other state-of-art algorithms. The RF comprises as a committee based on decision trees, which allows handling large datasets and features with high performance, enabling a Traffic Sign Recognition (TSR) system oriented for real-time implementations. The German Traffic Sign Recognition Benchmark (GTSRB) was used for experiments, serving as a real basis for comparison of performance for the authors' proposal.

Stochastic Quantization Using Magnetic Tunnel Junction Devices: A Simulation Study

In this paper, we investigate a magnetic tunneling junction (MTJ) device as a stochastic quantizer. Thermal noise in an MTJ is one of the parameters in determining its output, 0 or 1. This thermal noise, which causes random switching in a magnet, can be successfully exploited to implement high-performance stochastic quantization on an analog input (the function relating MTJ’s output state to its analog input is defined as an MTJ quantization function). Subsequently, an optimized signal-to-noise ratio (SNR) is found to maximize the information throughput. The best output to input correlation is achieved when the input signal’s amplitude is 1.5 times the standard deviation of the MTJ’s quantization function. We also demonstrate that an MTJ-based quantizer offers the highest performance when the information to be quantized is normally distributed in the input signal. Simulations to quantize images containing information, such as light intensity and pattern, also confirmed the highest information throughput when this optimum SNR is utilized.

Variable chlorophyll fluorescence of apple skin during ripening and storage

This paper considers the method of control over apple maturation in an orchard based on the photosynthetic activity measured by the variation of induction of chlorophyll fluorescence in the apple skin. It is shown that the highest information value is inherent to the shadow side of the apple fruit, where the variation of induction of chlorophyll shows the rate of apple maturity, whereas the photosynthetic activity on the sun side is minimal due to the almost complete absence of chlorophyll. During maturation in the orchard, there are seven-day periods related to endogenous physiological processes that take place in the fruit independently of external factors. The revealed mechanism will make it possible to develop the method for determination of optimum harvest date in the orchard as well as to control fruit ripening during storage at low temperatures.

TERM WEIGHTING BASED ON POSITIVE IMPACT FACTOR QUERY FOR ARABIC FIQH DOCUMENT RANKING

Query becomes one of the most decisive factor on documents searching. A query contains several words, where one of them will become a key term. Key term is a word that has higher information and value than the others in query. It can be used in any kind of text documents, including Arabic Fiqh documents. Using key term in term weighting process could led to an improvement on result’s relevancy. In Arabic Fiqh document searching, not using the proper method in term weighting will relieve important value of key term. In this paper, we propose a new term weighting method based on Positive Impact Factor Query (PIFQ) for Arabic Fiqh documents ranking. PIFQ calculated using key term’s frequency on each category (mazhab) on Fiqh. The key term that frequently appear on a certain mazhab will get higher score on that mazhab, and vice versa. After PIFQ values are acquired, TF.IDF calculation will be done to each words. Then, PIFQ weight will be combine with the result from TF.IDF so that the new weight values for each words will be produced. Experimental result performed on a number of queries using 143 Arabic Fiqh documents show that the proposed method is better than traditional TF.IDF, with 77.9%, 83.1%, and 80.1% of precision, recall, and F-measure respectively.

Information Consumption and Strategic Voting: Evidence from Brazil

In a seminal work about strategic voting, Duverger suggested that single-ballot electoral systems generate incentives for voters to behave strategically, which ultimately might lead to two-party systems. Dual-ballot electoral systems, in turn, allow voters to behave sincerely, which might favors multi-party systems. Even though several works have found results consistent with strategic voting behavior, some findings also suggest that not all voters behave the same way. This leads to the question on who exactly are the voters behaving strategically and, more importantly, why some voters do not behave this way. This paper investigates whether voters with different access to information behave differently when choosing their candidates in Brazilian mayoral elections. Following the theoretical literature, we predict that voters with lower availability of information are more likely to behave sincerely while voters with a higher information level are more likely to behave in a strategic way. We test this prediction exploiting an exogenous variation in Brazilian electoral rules and using a unique database with information disaggregated at the polling station level, that allow us to identify votes and characteristics of small groups of voters. Our results show that strategic behaviour indeed varies greatly depending on voters access to information. We also show that both demand for information (measured by voters’ level of education) and supply of information (measured by media availability at municipalities) are self-enforcing mechanisms for strategic voting. Therefore, whether a voter behaves strategically or not depends on both her ability to gather information and the availability of such information in her municipality.

Do Individual Investors Have Information Processing Skills?

Combining a large archive of public corporate news events with retail trading records from NYSE, we examine the profitability of individual investors’ trades around public news releases. We find that individual investors benefit significantly from the news releases when trading. The relation between net trading by individual investors and future returns is four times as large on positive news days as that on no news days. Moreover, the results are more pronounced for firms with higher information uncertainty and at times of high market uncertainty. Individual investors do not anticipate news and our results are not explained by liquidity provision, higher investor attention, or changes in liquidity around news days. Overall, the evidence suggests that an important channel through which individual traders predict future stock returns is their ability to successfully process the information revealed in the news events.

Center of Volume Mass: Does Aggregate Options Market Opinion Predict Future Equity Returns?

The volume-weighted strike-spot price ratio (VWKS) across option contracts explains subsequent price discovery in the underlying. Specifically, there is a robust positive relation between VWKS and subsequent stock returns, up to a week, after controlling for other known return predictors. This link is stronger and more persistent among stocks with higher information asymmetries and arbitrage costs. Moreover, VWKS exhibits abnormal run-ups and becomes more informative about future returns ahead of value relevant news, consistent with VWKS reflecting fundamental information. Collectively, we conclude that the distribution of options volume across contracts with different strike prices contains valuable information about the underlying stock.

Constructing Long-Only Multi-Factor Strategies: Portfolio Blending versus Signal Blending

We propose a framework for comparing and contrasting the portfolio blending and signal blending approaches for constructing long-only multi-factor strategies, based on exposure-matched portfolios. According to this framework, we expect the two approaches to perform similarly at low-to-moderate levels of factor exposures, due to a high degree of overlap between the two approaches. At high levels of factor exposures, due to better diversification, we expect signal blending to outperform portfolio blending. In empirical tests, which consider value, momentum, quality, and volatility factors across global equity markets, the expectation is validated for high levels of factor exposures. For low-to-moderate levels of factor exposures, however, we find that portfolio blending generates generally higher information ratios. This finding is largely driven by the interaction effects between the considered factors. Such interaction effects are overwhelmed by the high concentration and stock-specific risk in the portfolio blend at high levels of factor exposures. Furthermore, in the context of smart beta investing, portfolio blending may be better suited to achieve additional portfolio objectives, such as transparency in performance attribution, ability to time factors, ability to better control turnover and unrewarded risks at the individual factor level, and catering to certain asset owner governance considerations. However, the analysis and results reported in this article notwithstanding, it is also our view that an assessment of the relative merits of the two approaches needs to be conducted in the context of a manager’s specific investment process and key portfolio objectives pursued by an asset owner.

An N100-P300 Spelling Brain-Computer Interface with Detection of Intentional Control

A brain-computer interface (BCI) is a tool to communicate with a computer via brain signals without the user making any physical movements, thus enabling disabled people to communicate with their environment and with others. P300-based ERP spellers are a widely used spelling visual BCI using the P300 component of event-related potential (ERP). However, they have a technical problem in that at least 2 N flashes are required to present N characters. This prevents the improvement of accuracy and restricts the typing speed. To address this issue, we propose a method that uses N100 in addition to P300. We utilize novel stimulus images to detect the user’s gazing position by using N100. By using both P300 and N100, the proposed visual BCI reduces the number of flashes and improves the accuracy of the P300 speller. We also propose using N100 to classify non-control (NC) and intentional control (IC) states. In our experiments, the detection accuracy of N100 was significantly higher than that of P300 and the proposed method exhibited a higher information transfer rate (ITR) than the P300 speller.

Information and Liquidity in the Modern OTC Marketplace

OTC Markets Group organizes over 10,000 securities that trade over-the-counter into one of three tiered marketplaces (OTCQX, OTCQB, and OTC Pink) based on the quality and quantity of information a firm discloses. The tier designation and listing status of the securities in this study provide a unique setting to examine the effects of information disclosure on prices and market quality. We examine trading within the tiers and find that firms in higher information tiers are more liquid than firms trading in lower information tiers. Approximately 70% of the OTC securities are penny stocks trading under five dollars. We find that these securities with lottery-like features tend to generate significantly more investor interest than their non-penny OTC counterparts. Our analysis adds to the small stream of literature studying the modernized OTC marketplace and provides a more complete picture of trading in this space.

The Development of Mean-Variance-Efficient Portfolios in Japan and the U.S.

Stock selection models have been, and can be, effectively employed in Japan to deliver excess returns. In 1992, in the initial year of this Journal’s publication, Guerard and Takano (1992) reported mean-variance efficient portfolios for the Japanese and U.S. equity markets, and show that the use of a regression-weighted composite model of earnings, book value, cash flow, sales, and their relative variables outperformed their respective equity benchmarks by approximately 400 basis points annually. Markowitz and Xu (1994) tested the composite model strategy and found that its excess returns were statistically significant from a variety of models tested, and the composite model strategy was not the result of data mining. Guerard (2006) updated the Guerard and Takano stock selection models are updated through 2003 using a similar Japanese-only database. The sophisticated regression model continued to produce the highest information coefficients in Japan and the US. Guerard included a consensus analyst derived forecast, revisions, and breadth variable, and show its contribution to stock selection. Several Japanese-only analyst forecasting model results are similar to US-only results. The use of the Global Compustat database allowed the construction of global variables that produce similar stock selection models. The effectiveness of these quantitative models had not lessened during the 1992-2003 period. In this update to celebrate 25 years of the Journal of Investing, the author uses a commercially available global database, FactSet, for the 2002-June 2016 time period to address stock selection composite models, mean-variance efficient portfolios in Japan and the U.S., and reports three results: (1) the original stock selection continues to be effective in Japan and the U.S. in 2002-6/2016; (2) the mean-variance efficient portfolios outperformed in Japan and the U.S. in 2002-6/2016; and (3) the Guerard and Takano stock selection model is not the result of data mining. The author reports three additional results: (1) the weighted latent root regression Guerard and Takano can be enhanced by using the Tukey optimal influence function; (2) the mean-variance efficient portfolios failed in Japan and the U.S. in 1996-2000 with the PCAP and Global Compustat databases and 2006-2007, 2012-2013, and YTD 2016 with the FactSet databases, but work in the vast majority of the years; and (3) the Guerard and Takano portfolios outperformed in 14 of 17 years in backtest, 82.4%, of the yeas whereas the Japan model portfolios outperformed in 9 of 14.5 years, or 66.7%, of the years, post-publication and the U.S. model outperformed in 9.5 of 13.5 years, or 70%, post-publication, respectively!

Public Information, Private Information, and Financial Analysts’ Forecasts*

Abstract This paper investigates the joint impact of public and private information on analysts’ forecasting behaviour. Information disclosure determines the quality and quantity of public information. The geographical distance between listed firms’ headquarters and brokerage houses determines the difficulty and costs of analysts obtaining private information, which affect the quality and quantity of private information. Firstly, this paper finds results consistent with prior literature that the higher the disclosure level of the firm and the shorter the geographical distance between firms and brokerage headquarters, the more frequently analysts make earnings forecasts and the lower the forecast errors. Furthermore, this paper examines the joint effects of information disclosure and geographical distance on analysts’ forecasts and finds that information disclosure quality imposes a stronger impact on analysts’ forecast frequency and forecast error than distance. Moreover, the effect of geographical distance on the frequency and errors of analysts’ forecasts is manifested only in the case of higher information disclosure quality. These results imply that both public and private information affect analysts’ forecasts but that their effects are asymmetric. Public information has a significantly stronger impact on analysts’ forecasts than private information, and the effect of private information depends on a better public information environment. This paper has significant value to public policy regulators, market participants, stakeholders, and academics in China and can help to increase their understanding of the impact of public and private information on analysts’ forecasts.