Miracle-dz/UniFAR-FTUs huggingface dataset:
Bursty transmission and glue pouring: on wireless channels with overhead costs

Power efficiency is a capital issue in the study of mobile wireless nodes owing to constraints on their battery size and weight. In practice, especially for low-power nodes, it is often the case that the power consumed for non-transmission processes is not always negligible. In this paper, we consider the channels with a special form of overhead: a processing energy cost whenever a non-zero signal is transmitted. We show that under certain conditions, achieving the capacity of such channels requires intermittent, or `bursty', transmissions. Thus, an optimal sleeping schedule can be specified for wireless nodes to achieve the optimal power efficiency. We show that in the low SNR regime, there is a simple relation between the optimal burstiness and the overhead cost: one should use a fraction of the available degrees of freedom at an SNR level of radic2epsiv, where epsiv is the normalized overhead energy cost. We extend this result to use bursty Gaussian transmissions in multiple parallel channels with different noise levels. Our result can be intuitively interpreted as a 'glue pouring' process, generalizing the wellknown water pouring solution. We then use this approach to compute the achievable rate region of the multiple access channel with overhead cost.
Prognostic significance of p53 overexpression in endometrial cancer.

Abnormalities of p53, a tumor suppressor gene, have been considered to play an important role in tumorigenesis. Clinically, overexpression of p53 has been reported to correlate with poor prognosis in several types of tumors. In this study, we examined 221 cases of endometrioid endometrial carcinoma for overexpression of p53 using immunohistochemistry in patients with a median follow-up of 41 months. Immunohistochemical analysis was performed with monoclonal antibody pAb1801. Overexpression of p53 was detected in 47 of 221 cases (21.3%). There was a statistically significant correlation between p53 overexpression and poor prognosis (P < .0001). In the early stages of cancer (stages 1 and 2), p53 was overexpressed in 11 of 22 patients (50%) who died or had a recurrence during follow-up. In contrast, overexpression was detected in only 14.7% of the 156 disease-free patients during the same period (P < .0001). In advanced stages (stages 3 and 4), tumors from patients with recurrent disease had a higher frequency of overexpression (41.2%; 7 of 17) than those of disease-free patients (23.1% 6 of 26). However, the difference between these frequencies was not statistically significant. In multivariate analysis using the Cox proportional hazard model, p53 overexpression was an independent risk factor when compared with clinical stage, nuclear grade, and patient age. Our results indicate that p53 immunohistochemical evaluation of the most common form of endometrial cancer may be useful in identifying cases of aggressive carcinoma, especially in the early stages.
Regulation of androgen receptor and histone deacetylase 1 by Mdm2-mediated ubiquitylation

The androgen receptor (AR) is a member of the nuclear hormone receptor family of transcription factors and plays a critical role in regulating the expression of genes involved in androgen-dependent and -independent tumour formation. Regulation of the AR is achieved by alternate binding of either histone acetyltransferase (HAT)-containing co-activator proteins, or histone deacetylase 1 (HDAC1). Factors that control AR stability may also constitute an important regulatory mechanism, a notion that has been confirmed with the finding that the AR is a direct target for Mdm2-mediated ubiquitylation and proteolysis. Using chromatin immunoprecipitation (ChIP) and re-ChIP analyses, we show that Mdm2 associates with AR and HDAC1 at the active androgen-responsive PSA promoter in LNCaP prostate cancer cells. Furthermore, we demonstrate that Mdm2-mediated modification of AR and HDAC1 catalyses protein destabilization and attenuates AR sactivity, suggesting that ubiquitylation of the AR and HDAC1 may constitute an additional mechanism for regulating AR function. We also show that HDAC1 and Mdm2 function co-operatively to reduce AR-mediated transcription that is attenuated by the HAT activity of the AR co-activator Tip60, suggesting interplay between acetylation status and receptor ubiquitylation in AR regulation. In all, our data indicates a novel role for Mdm2 in regulating components of the AR transcriptosome.
Sunshine-Factor Model with Treshold GARCH for Predicting Temperature of Weather Contracts

Climate changes have sparked growing interest for the weather derivatives which are financial contracts relied on a meteorological index and allowing companies to hedge against climate risk. These contracts present the particularity of providing compensation to the buyer when the meteorological index crossed a limit agreed in advance with the seller. In order to evaluate these products and to manage at best the risks associated with their exchange, it is important to be able to accurately predict the evolution of the climate variable. Several processes have been proposed in the literature to model the behaviour of the temperature which is the basis of most of the traded weather instruments. These processes relate mainly to the univariate time series modelling which is founded on the study of the autocorrelation of the stationary variable. But we know that the behaviour of the temperature can be influenced by climatic factors such as rain, wind or sunshine. In our paper, we propose to take into account the impact of sunshine on the temperature as well as the asymmetric effect of the shocks on the volatility by estimating a structural model with a periodic threshold GARCH. We show that this model provides better out-sample forecasts for 30 and 60 days ahead than those obtained by the univariate autoregressive-conditional heteroskedasticity process.
Gaussian feedback capacity

The capacity of time-varying additive Gaussian noise channels with feedback is characterized. Toward this end, an asymptotic equipartition theorem for nonstationary Gaussian processes is proved. Then, with the aid of certain matrix inequalities, it is proved that the feedback capacity C/sub FB/ in bits per transmission and the nonfeedback capacity C satisfy C >
Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests

Broadleaf forest is a major type of Earth's land cover with the highest observable vegetation density. Retrievals of biophysical parameters, such as leaf area index (LAI), of broadleaf forests at global scale constitute a major challenge to modern remote sensing techniques in view of low sensitivity (saturation) of surface reflectances to such parameters over dense vegetation. The goal of the performed research is to demonstrate physical principles of LAI retrievals over broadleaf forests with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm and to establish a basis for algorithm refinement. To sample natural variability in biophysical parameters of broadleaf forests, we selected MODIS data subsets covering deciduous broadleaf forests of the eastern part of North America and evergreen broadleaf forests of Amazonia. The analysis of an annual course of the Terra MODIS Collection 4 LAI product over broadleaf forests indicated a low portion of best quality main radiative transfer-based algorithm retrievals and dominance of low-reliable backup algorithm retrievals during the growing season. We found that this retrieval anomaly was due to an inconsistency between simulated and MODIS surface reflectances. LAI retrievals over dense vegetation are mostly performed over a compact location in the spectral space of saturated surface reflectances, which need to be accurately modeled. New simulations were performed with the stochastic radiative transfer model, which poses high numerical accuracy at the condition of saturation. Separate sets of parameters of the LAI algorithm were generated for deciduous and evergreen broadleaf forests to account for the differences in the corresponding surface reflectance properties. The optimized algorithm closely captures physics of seasonal variations in surface reflectances and delivers a majority of LAI retrievals during a phenological cycle, consistent with field measurements. The analysis of the optimized retrievals indicates that the precision of MODIS surface reflectances, the natural variability, and mixture of species set a limit to improvements of the accuracy of LAI retrievals over broadleaf forests.
The kth Total Path Length and the Total Steiner k-Distance for Digital Search Trees

The total Steiner k-distance and the k-th total path length are the sum of the size of Steiner trees and ancestor-trees over sets of k nodes of a given tree, respectively. They are useful statistics with many applications. Consequently, they have been analyzed for many different random trees, including increasing tree, binary search tree, generalized m-ary search tree and simply generated trees. In this paper, we investigate the two parameters for digital search trees, which are fundamental data structures in computer science with wide applications. We derive the means, covariances and variances for the total Steiner k-distances and the k-th total path lengths by the ”Poisson-LaplaceMellin Method”. Moreover, results about the limiting distributions are obtained as well.
An ellipse detection method for real images

A novel ellipse detection method for real images is proposed here. The method is novel in various respects. It uses a combination of geometric and Hough transform techniques for generating an initial guess of the centers of ellipses. This initial guess is used in a novel grouping technique in which the edges within a group are ranked in the order of their strength of relationship with the group. The novel relationship score is more selective and thus more efficient than the typical histogram count used in Hough transform methods. A least squares method has been adapted into an optimization scheme that reduces the number of outliers. The elliptic hypotheses are then subjected to non-heuristic saliency criteria. The thresholds for selection of elliptic hypotheses are determined by the detected hypotheses themselves, such that the selection is image independent and free of human intervention. Since only two control parameters are needed and the method requires a few seconds in most cases, it is suitable for practical applications. The experimental results for 1200 synthetic images and 400 real images from Caltech 256 dataset clearly demonstrate the superior accuracy and robustness of the proposed method as compared to contemporary ellipse detection methods.
Stream analytics for utilities. Predicting power supply and demand in a smart grid

In today's networked society, getting knowledge from the data becomes in a key task for many support decisions making processes. Such kind of decisions could imply serious consequences in business and life and as more information is available about the problem, more accurate will be the decision. A largely untested hypothesis of modern society is that it is important to record data as it may contain valuable information. However, we are living in the information age and it has gone into how this quantity of data might be analyzed. The number of data sources increases, which makes harder and, in some cases, unfeasible the approach of storing every piece of data. Actually, the amount of available storage capacity could be not enough in order to record all the digital info generated in the world and this could become one the main triggers for a paradigm shift. Another aspect is that it is necessary to react instantly to meaningful changes in data and detect complex patterns over time. Data stream paradigm was born as an answer to the continuous data problem that cannot be stored and analyzed in efficient manner. Data Streams can handle bigger data sizes than memory, and can extend to challenging real-time applications not previously tackled by e.g. time series analysis, traditional, databases, machine learning and data mining techniques. The assumption is that processing examples can be processed on the fly, that is, as soon as they arrive within a high speed stream, and then, must be discarded in order to make room for subsequent data (e.g. examples in a learning scenario). In this article we will discuss the application of data stream techniques in the Smart Grid use case. The new smart grids will require real time reaction and make distribution decision very fast in order to cover unexpected demand peaks and renewable generation peaks.
lant virus identification based on neural networks with volutionary preprocessing

In this work, genetic algorithms and multilayer neural networks are applied to plant virus identification. The initial data set is derived via a well known prototype method, which uses specially designed biosensors to monitor the virus reactions. Several techniques have been introduced for preprocessing the plant virus waves. They include segmentation along the time axis for fast response, nonlinear normalization to emphasize significant information, averaging samples of the plant virus waves to suppress noise effects, reduction in the number of samples to realize a more compact network, etc. Given the features of the acquired virus time-series signals of the problem under study, an evolutionary method is proposed in order to produce meta-data from the original time-series initial information, reduce the dimensionality enetic algorithms (GAs) rtificial Neural Networks (ANNs)
PRoportional Fair Scheduling of Uplink Single-Carrier FDMA Systems

We apply novel utility-based scheduling schemes to uplink single carrier frequency division multiple access (SC-FDMA) systems. Two utility functions are used for managing two dimensional resources (time and frequency): user data rate for maximizing system capacity and logarithmic user data rate for proportional fairness. To develop utility-based scheduling algorithms, we revise channel-dependent scheduling (CDS) schemes derived in our previous work (J. Lim et al.). The results show that proportional fair scheduling with logarithmic user data rate can improve the rate-sum capacity up to 100% for localized FDMA and 30% for interleaved FDMA, with the capacity gains equally shared among all users
Suitability of Naïve Bayesian Methods for Paragraph Level Text Classification in the Kannada Language using Dimensionality Reduction Technique

The amount of data present online is growing very rapidly, hence a need for organizing and categorizing data has become an obvious need. The Information Retrieval (IR) techniques act as an aid in assisting users in obtaining relevant information. IR in the Indian context is very relevant as there are several blogs, news publications in Indian languages present online. This work looks at the suitability of Naive Bayesian methods for paragraph level text classification in the Kannada language. The Naive Bayesian methods are the most primitive algorithms for Text Categorization tasks. We apply dimensionality reduction technique using Minimum term frequency, stop word identification and elimination methods for achieving the task. It is evident that Naive Bayesian Multinomial model outperforms simple Naive Bayesian approach in paragraph classification tasks .
A Comparative Study of Newly Developed HPLC-DAD and UHPLC-UV Assays for the Determination of Posaconazole in Bulk Powder and Suspension Dosage Form

Objective. To develop and compare HPLC-DAD and UHPLC-UV assays for the quantitation of posaconazole in bulk powder and suspension dosage form. Methods. Posaconazole linearity range was 5–50 μg/mL for both assays. For HPLC-DAD assay, samples were injected through Zorbax SB-C18 (4.6 × 250 mm, 5 μm) column. The gradient elution composed of the mobile phase acetonitrile: 15 mM potassium dihydrogen orthophosphate (30 : 70 to 80 : 20, linear over 7 minutes) pumped at 1.5 mL/min. For UHPLC-UV assay, samples were injected through Kinetex-C18 (2.1 × 50 mm, 1.3 μm) column. The mobile phase composed of acetonitrile: 15 mM potassium dihydrogen orthophosphate (45 : 55) pumped isocratically at 0.4 mL/min. Detection wavelength was 262 nm in both methods. Results. The run time was 11 and 3 minutes for HPLC-DAD and UHPLC-UV assays, respectively. Both assays were linear (r 2 > 0.999) with CV% and % error of the mean <3%. Limits of detection and quantitation were 0.82 and 2.73 μg/mL for HPLC-DAD and 1.04 and 3.16 μg/mL for UHPLC-UV, respectively. The methods quantitated PSZ in suspension dosage form with no observable interferences. Conclusions. Both assays were proven sensitive and selective according to ICH guidelines. UHPLC-UV assay exhibited some economic and chromatographic separation superiority.
Negative binomial and multinomial states: Probability distributions and coherent states

Following the relationship between probability distribution and coherent states, for example the well known Poisson distribution and the ordinary coherent states and relatively less known one of the binomial distribution and the su(2) coherent states, we propose interpretation of su(1,1) and su(r,1) coherent states in terms of probability theory. They will be called the negative binomial (multinomial) states which correspond to the negative binomial (multinomial) distribution, the non-compact counterpart of the well known binomial (multinomial) distribution. Explicit forms of the negative binomial (multinomial) states are given in terms of various boson representations which are naturally related to the probability theory interpretation. Here, we show fruitful interplay of probability theory, group theory, and quantum theory.
Coevolutionary learning of swarm behaviors without metrics

We propose a coevolutionary approach for learning the behavior of animals, or agents, in collective groups. The approach requires a replica that resembles the animal under investigation in terms of appearance and behavioral capabilities. It is able to identify the rules that govern the animals in an autonomous manner. A population of candidate models, to be executed on the replica, compete against a population of classifiers. The replica is mixed into the group of animals and all individuals are observed. The fitness of the classifiers depends solely on their ability to discriminate between the replica and the animals based on their motion over time. Conversely, the fitness of the models depends solely on their ability to 'trick' the classifiers into categorizing them as an animal. Our approach is metric-free in that it autonomously learns how to judge the resemblance of the models to the animals. It is shown in computer simulation that the system successfully learns the collective behaviors of aggregation and of object clustering. A quantitative analysis reveals that the evolved rules approximate those of the animals with a good precision.
Reversible Watermarking Algorithm Based on Embedding Pixel Dependence

Many works have demonstrated that the performance of the prediction error (PE) based reversible watermarking algorithm depends on the precision of the prediction value. However, most algorithms compute the prediction value by exploiting only the correlation between the embedding pixel and its neighboring pixels, thus limiting the watermarking performance of these algorithms. In this work, we obtain twelve prediction candidates from neighboring pixels of the embedding pixel based on the modeling assumption -- it is easy to find out that an embedding pixel value is identical or similar to one of its neighboring pixel values in an image. The final prediction value can then be selected from these prediction candidates by using the prediction value and the original pixel value. A more accurate final prediction value can then be obtained. Certainly, the original pixel value is also recovered exactly during the decoding process, in which the final prediction value is obtained according to the twelve prediction candidates, the prediction value and the watermarked pixel value. Experimental results indicate that the variance of the prediction error histogram obtained by the proposed method is about 44.2% less than that proposed by Sachnev et al. Moreover, the mean peak signal-tonoise ratios (PSNRs) are about 1.47 dB and 1.1 dB greater than those of proposed by Sachnev as the watermark capacities are 0-0.04 bits per pixel (bpp) and 0.04-0.5 bpp, respectively. The proposed algorithm performs better than existing reversible watermarking approaches.
JOMS: A Java Message Service Provider for Disconnected MANETs

A disconnected mobile ad hoc network (or D-MANET) is a wireless network, which because of the sparse distribution of mobile hosts appears at best as a partially or intermittently connected network. Designing and implementing distributed applications capable of running in such a challenged environment is not a trivial task. Middleware systems such as Java Message Service (JMS) have made application development easy and cost-effective in traditional wired networks. It can be expected that middleware systems designed specifically for D-MANETs bring similar benefits. In this paper, we introduce JOMS (Java Opportunistic Message Service), a JMS provider for D-MANETs with which pre-existing and new JMS-based applications can be deployed simply in D-MANETs.
Feature Extraction for Advanced Control of Flexible Forming Processes

Flexible forming processes are characterized by the implementation of shape-independent tools. However, varying properties of semi-finished goods in conjunction with the lack of a forming tool technology also lead to an insufficient reproducibility of the products. In order to overcome this problem a closed-loop control system for the on-line-capable process control was worked out for laser forming of extrusion parts. A preprocessor models the forming process by means of several forming stages. Areas which are relevant for the control loop are determined in the set geometry of the forming stages. They are digitized by means of a 3D sensor during the controlled process. The determination of geometrical deviations is based on the feature extraction in these areas. The use of form features speeds up the geometrical data processing thus allowing an online process control. Furthermore, a manufacture adhering closely to specifications without computing complete surface geometries is possible. 1 Flexible forming with the laser beam
SIMPLE - a multi-agent system for simultaneous and related auctions

This work describes a multi-agent architecture and strategy for trade in simultaneous and related auctions. The proposed SIMPLE Agency combines an integer programming model, machine learning techniques and knowledge engineering strategies to build its decision making process. Some well known problems in trading were identified in order to design the architecture. Each SIMPLE agent is concerned with one of the trading subproblems. This makes it possible to apply different computational techniques to solve each subproblem separately and then combine their solutions. The Trading Agent Competition (TAC) is used to illustrate and test our approach. SIMPLE shows high performance on competitive scenarios using the TAC server environment.