Miracle-dz/UniFAR-FTUs huggingface dataset:
Chaotic analysis of seismic time series and short term forecasting using neural networks

In this study, a chaotic analysis approach was applied to a time series composed of seismic events occurred in Greece. The dynamics of the earthquakes belong to the category of dissipative systems, which exhibit chaotic behavior. After the chaotic analysis, short term forecasting using an artificial neural network has been performed. Neural networks, under appropriate conditions, are known to be universal function approximators, thus they have been used as tools for time series forecasting. Here, a neural network is trained to make short term earthquake predictions. The network architecture is dictated by the calculated characteristics of the time series itself. Preliminary results indicate that this is a promising approach.
Development of a Real-Time Estimate of Flu Activity in the United States Using Dynamically Updated Lasso Regressions and Google Search Queries

Seasonal influenza is a significant public health threat, annually infecting three to five million and killing 250,000 to 500,000 people worldwide. In addition, epidemics such as the pH1N1 outbreak remind the world of even graver dangers if faced with influenza subtypes for which there is little to no preexisting human immunity, with the potential to cause devastating pandemics. On both fronts, improving surveillance of outbreaks before and as they occur can quicken response times and possibly save millions of lives. Google Flu Trends (GFT) was developed as a tool for large-scale, real-time surveillance of influenza-like illnesses (ILI) using Google search queries. Unfortunately, it provided inaccurate estimates during crucial influenza outbreaks, particularly during the pH1N1 outbreak, and also, even after GFT received an update to its database of search queries used in its model, during the most recent 2012-13 influenza season. The data used in GFT was also not made publicly available. However, using open-source tools, we assemble here a proxy dataset of search queries and build a dynamically updated, lasso regression model for ILI surveillance, and successfully outperform GFT on the national level, especially during crucial periods for influenza surveillance. In particular, we achieve Pearson correlation of 0.828 where the original GFT model achieves 0.290 during pH1N1, and we achieve correlation of 0.982 and average relative error of 12.1% from Sep. 2009, when GFT was updated, through Jan. 2013, while the updated GFT model achieves correlation of 0.854 and average relative error of 30.7% during the same period. Our results show that improving the underlying regression model makes substantial and long-term improvements to ILI surveillance using query-based methods, rendering the update to GFT’s database of search queries unnecessary.
Research on emission reduction decisions in multi-echelon supply chain with low-carbon policy considered

Along with the trend of low-carbon development, the government adopts a series of measures to control carbon emissions. Here, in the case with constraint of carbon cap and both firms and consumers have low carbon preference, we study the firm production decision problems in a supply chain consists of three firms The latest-firm-dominant situation is discussed by using Stackelberg game and we obtain the optimal output and emission reductions by the converse solution method. It shows that the optimal reduction decision of a firm is to make its marginal emission reduction cost slightly larger than purchase the corresponding emission rights from the carbon transaction market. The emission reduction of one side in a supply chain will motivate the others in the supply chain. With the enhancement of consumer low-carbon preference, the sales price will rise and lead to profits rise of the whole supply chain, but not all firms are profits benefit. Based on the above analysis, we extend the model to n firms aims to make some guideline sense for supply chain firms launching cooperation in areas of emission reduction.
STREPTOMYCES SPECIES COMPRISING THE BLUE-SPORE SERIES

Trejo, W. H. (Squibb Institute for Medical Research, New Brunswick, N.J.) and R. E. Bennett. Streptomyces species comprising the blue-spore series. J. Bacteriol. 85:676–690. 1963. —The objective of this study was to define and delimit the streptomycetes of the blue-spored (Viridochromogenes) series. The series, as defined in this study, includes 11 blue and blue-green species. The green-spored species were excluded on the basis of morphology as well as color. It was proposed that NRRL B-1511 be designated as the neotype strain of Streptomyces viridochromogenes (Krainsky) Waksman and Henrici, and that IMRU 3761 be designated as the neotype for Streptomyces cyaneus (Krassilnikov) Waksman. Evidence was presented to show that physiological criteria cannot be used to differentiate these organisms below the series level. The major characteristics of the Viridochromogenes series are blue to blue-green spores borne in spirals, and chromogenicity (melanin-positive). Reverse color and spore morphology provide a basis for separation below the series level.
Inversion-based control of electromechanical systems using causal graphical descriptions

Causal ordering graph and energetic macroscopic representation are graphical descriptions to model electromechanical systems using integral causality. Inversion rules have been defined in order to deduce control structure step-by-step from these graphical descriptions. These two modeling tools can be used together to develop a two-layer control of system with complex parts. A double-drive paper system is taken as an example. The deduced control yields good performances of tension regulation and velocity tracking
Demographic Fluctuations, Sustainability Factors and Intergenerational Fairness – An Assessment of Austria's New Pension System

This study discusses important elements of Austria's recently harmonized statutory pension system. In particular, the author investigates in how far the new system responds to the twofold demographic challenge of declining birth rates and increasing life expectancy and what this means in terms of fiscal sustainability and intergenerational redistribution. Austria's defined benefit system is found to have more in common with Germany's system of earnings points than with Sweden's notional account system — with the exception that the sustainability factor, the adjustment mechanism triggered by demographic changes, has been designed differently in Germany and in Austria. A critical analysis of the Austrian provisions identifies the following problems: First, the application of the sustainability factor is activated only by deviations from projections and not to demographic movements as such. Second, adjustments are not to be automatic. Third, the requirement of an “even adjustment7” is not spelled out in detail. Fourth, it is doubtful whether evenness is a desired feature in the first place since generation-specific reproductive behavior is neglected in this scenario. Overall, the basic structure of Austria's new model gets a favorable rating, as it increases the degree of intergenerational fairness, supports individual, intersectoral and international flexibility and corrects some design flaws of the old pension regime. Conversely, weaknesses are identified with regard to the transitional arrangements, the contribution side and the sustainability factor.
Rapid Exploitation and Analysis of Documents

Analysts are overwhelmed with information. They have large archives of historical data, both structured and unstructured, and continuous streams of relevant messages and documents that they need to match to current tasks, digest, and incorporate into their analysis. The purpose of the READ project is to develop technologies to make it easier to catalog, classify, and locate relevant information. We approached this task from multiple angles. First, we tackle the issue of processing large quantities of information in reasonable time. Second, we provide mechanisms that allow users to customize their queries based on latent topics exposed from corpus statistics. Third, we assist users in organizing query results, adding localized expert structure over results. Forth, we use word sense disambiguation techniques to increase the precision of matching user generated keyword lists with terms and concepts in the corpus. Fifth, we enhance co-occurrence statistics with latent topic attribution, to aid entity relationship discovery. Finally we quantitatively analyze the quality of three popular latent modeling techniques to examine under which circumstances each is useful.
ALGORITHM-BASED FAULT TOLERANCE: A PERFORMANCE PERSPECTIVE BASED ON ERROR RATE

In Algorithm-based fault tolerance (ABFT), the fault tolerance scheme is tailored to the a lgorithm performed. Most of the previous s tudies that compared various ABFT schemes considered only their error detection and correction capabilities. Some previous studies looked at the overhead in g eneral but no p revious work ‐as far as we know‐ compared d ifferent ABFT schemes considering performance a s the main metric. In this work, we c ompare the performance of two ABFT error r ecovery schemes: recomputing v s. correction, for different error r ates. We consider errors that happen during computation as well as those that happen during the e rror detection, location and correction process. The metrics we use a re success r atio and completion time. Results s how that multiple e rror correction using ABFT has worse performance than single error correction. They also show that error r ate is an essential factor in making one scheme better than another in terms of performance.
E-Government Performance Evaluation with Fuzzy Numbers

E-government is no longer just an option but a necessity for countries aiming for better governance. The e-government performance evaluation is important for the development of e-government. As conventional evaluation models for e-government performance are generally too subjective and inaccurate, this paper proposed a fuzzy TOPSIS method to evaluating the e-government performance.
Global Lake Monitoring using Group-specific Local Learning

Global lake monitoring is crucial for the effective management of water resources as well as for conducting studies that link the impact of lake dynamics on climate change. Remote sensing datasets offer an opportunity for global lake monitoring by providing discriminatory features that can help distinguish land and water bodies at a global scale and in a timely fashion. A major challenge in global lake monitoring using remote sensing datasets is the presence of a rich variety in the land and water bodies at a global scale, motivating the need for local learning algorithms that can take into account the heterogeneity in the data. We propose a novel group-specific local learning scheme that uses information about the local neighborhood of a group of test instances for estimating the relevant context for classification. By comparing the performance of the proposed scheme with baseline approaches over 180 lakes from diverse regions of the world, we are able to demonstrate that the proposed scheme provides significant improvements in the classification performance.
Parsing with Context-Free Grammars and Word Statistics

We present a language model in which the probability of a sentence is the sum of the individual parse probabilities, and these are calculated using a probabilistic context-free grammar (PCFG) plus statistics on individual words and how they fit into parses. We have used the model to improve syntactic disambiguation. After training on Wall Street Journal (WSJ) text we tested on about 200 WSJ sentence restricted to the 5400 most common words from our training. We observed a 41\ performance of our PCFG without the use of the word statistics.
Wireless Communication Networks over Tcp, Atm and Location Update and Paging Policies Enee609a Independent Study Term Report Esin Haritaoglu 1 Wireless Communication Networks over Tcp 1.1 Reliable Transmission Control Protocol (tcp)

In recent years, a variety of mobile computers equipped with wireless communication devices have become popular. These computers use applications and protocols, originally developed for wired desktop hosts to communicate over wireless channels. TCP is the most widely used transport protocol on these computers, because it is speciically developed by Arpanet for reliable data transmission over wired networks. Along with TCP, a standard link layer protocol IP (Internet Protocol) is also developed by Arpanet and TCP/IP is usually used together as a standard Internet Protocol Suite. Although TCP/IP is commonly used, both TCP and IP are independent diierent type of protocols and they can be adapted for use with other protocols (e.g UDP/IP, TCP/MAC). Since TCP is developed speciically for wired networks where the congestion is the primary cause of packet loss, it performs well and provides reliable data delivery in these networks by means of end-to-end ow control, congestion and error control mechanisms. However, the end-to-end performance of a TCP connection can suuer signiicant degradation in the presence of a wireless link because networks with wireless link and mobile hosts incur signiicant losses due to handoos and characteristics of wireless transmission environment. This wireless environment violates many of the assumptions made by TCP, causing degraded end-to-end performance. To understand how TCP performance degrades severely over wireless links, we should rst look at the end-to-end ow, congestion and error control mechanisms of TCP. At the lowest level, computer communication networks provide unreliable packet delivery. Packets can be lost when transmission errors interfere with data, when network hardware fails or when network become too heavily loaded to accommodate the load presented. Networks that route packets dynamically can deliver them out of order, deliver them after a substantial delay or deliver duplicates. At the highest level, application programs often need to send a large volumes of data from one computer to another. Using unreliable connectionless delivery system for large volume transfers become tedious and it requires programmers to build error detection and recovery into each application program. But such a solution is usually not feasible. As a consequence, one goal of network protocol research has been to nd general purpose solutions to the reliability problem. TCP emerged as a solution to this problem.
Improving the Character Ngram Model for the DSL Task with BM25 Weighting and Less Frequently Used Feature Sets

This paper describes the system developed by the Centre for English Corpus Linguistics (CECL) to discriminating similar languages, language varieties and dialects. Based on a SVM with character and POStag n-grams as features and the BM25 weighting scheme, it achieved 92.7% accuracy in the Discriminating between Similar Languages (DSL) task, ranking first among eleven systems but with a lead over the next three teams of only 0.2%. A simpler version of the system ranked second in the German Dialect Identification (GDI) task thanks to several ad hoc postprocessing steps. Complementary analyses carried out by a cross-validation procedure suggest that the BM25 weighting scheme could be competitive in this type of tasks, at least in comparison with the sublinear TF-IDF. POStag n-grams also improved the system performance.
A neural network classifier for occluded images

This paper proposes a neural network classifier which can automatically detect the occluded regions in the given image and replace that regions with estimated values. An auto-associative memory is used to detect outliers, such as pixels in the occluded regions. Certainties of each pixels are estimated by comparing the input pixels with the outputs of the auto-associative memory. The input values to the associative memory are replaced with the new values which are defined depending on the certainties. By repeating this process, we can obtain an image in which the pixel values of the occluded regions are replaced with the estimates. The proposed classifier is designed by integrating this associative memory with a simple classifier.
Fluctuations in the Radio Background from Intergalactic Synchrotron Emission

The shocks produced in the intergalactic medium during large-scale structure formation accelerate a population of highly relativistic electrons that emit synchrotron radiation due to intergalactic magnetic fields. In a previous paper we have shown that these electrons cool primarily by inverse Compton scattering of the microwave background photons and can thereby produce the observed intensity and spectrum of the diffuse γ-ray background. Here we calculate the intensity and angular fluctuations of the radio synchrotron background that results from the same high-energy electrons as well as the expected angular fluctuations in the γ-ray background. On angular scales smaller than a degree, the predicted fluctuations in the microwave background temperature are of order 40 μK(ξB/0.01)(ν/10 GHz)-3, where ξB is the magnetic fraction of the postshock energy density. This foreground might have already dominated the anisotropy signal detected in existing low-frequency cosmic microwave background experiments and can be identified with confidence through multifrequency observations. Detection of the synchrotron fluctuations would allow us to determine the strength of the intergalactic magnetic field. We predict a strong correlation between high-resolution maps taken at low-frequency radio waves and at high-energy γ-rays. Young X-ray clusters may also appear as radio or γ-ray clusters. The detailed study of this correlation will become easily accessible with the future launch of the Gamma-Ray Large-Area Space Telescope.
Comprehensive tool support for iterative SOA evolution

In recent years continuously changing market situations required IT systems that are exible and highly responsive to changes of the underlying business processes. The transformation to service-oriented architecture (SOA) concepts, mainly services and loose coupling, promises to meet these demands. However, elevated complexity in management and evolution processes is required for the migration of existing systems towards SOA. Studies in this area of research have revealed a gap between in continuous and actual tool support of development teams throughout the process phases of evolution processes. Thus, in this article we introduce a method that fosters evolution by an iterative approach and illustrate how each phase of this method can be tool-supported.
Adaptive estimation of the shear force in the cantilever dynamics of the Transverse Dynamic Force Microscope

The dynamics of the cantilever in a Transverse Dynamic Force Microscope (TDFM) are presented by means of irrational transfer functions. An approximate model of the cantilever can then be obtained using a finite number of rational transfer functions. The shear force for this model can be estimated by adaptive schemes. Numerical simulations are presented to show the effectiveness of the proposed methods.
An adaptive hierarchical sensing scheme for sparse signals

In this paper, we present Adaptive Hierarchical Sensing (AHS), a novel adaptive hierarchical sensing algorithm for sparse signals. For a given but unknown signal with a sparse representation in an orthogonal basis, the sensing task is to identify its non-zero transform coefficients by performing only few measurements. A measurement is simply the inner product of the signal and a particular measurement vector. During sensing, AHS partially traverses a binary tree and performs one measurement per visited node. AHS is adaptive in the sense that after each measurement a decision is made whether the entire subtree of the current node is either further traversed or omitted depending on the measurement value. In order to acquire an N -dimensional signal that is K-sparse, AHS performs O(K log N/K) measurements. With AHS, the signal is easily reconstructed by a basis transform without the need to solve an optimization problem. When sensing full-size images, AHS can compete with a state-of-the-art compressed sensing approach in terms of reconstruction performance versus number of measurements. Additionally, we simulate the sensing of image patches by AHS and investigate the impact of the choice of the sparse coding basis as well as the impact of the tree composition.
Robust roll and yaw integrated control using 4 wheel steering based on yaw moment and lateral force observers (産業計測制御研究会 産業計測制御一般)

Considering driving safety and comfort, this paper proposed a roll and yaw integrated control method utilizing four wheel steering based on disturbance observer (hereinafter DOB) and 2-degree-of-freedom (hereinafter 2DOF) control approach. Firstly, vehicle model for controller design as well as the desired roll and yaw motion were explained; then the controller design was carried out based on the well known DOB and 2DOF control method; for the controller design, details such as controller parameters selection were explained; then simulations were performed to show the effectiveness of the controller; and then, experiments were conducted with a four wheel steering electric vehicle to verify the proposed control strategy; finally, summery was made and future research directions were presented.
A Classification Algorithm for Chinese Verb Phrases Using Support Vector Machine

Chinese verb phrases classification is to determine boundaries of verb phrases and divide them exactly, using brackets, by automatically analyzing and processing by computer after the sentences have been decollated and marked the characteristic or property of a certain word. SVM classification model is a common and powerful for classification tasks. In this paper, the SVM classification model is built by extracting static features and dynamic features of Chinese verb phrases, and an algorithm to perform Chinese verb phrases classification using support vector machine is proposed. Using 3500 sentences to train and test, experiment results show that the SVM model dramatically reduces the training time and steps. Compared with the method proposed in literature 15, classification precision rate is increased by approximately 8.0% using the algorithm in this paper, which fully illustrates that the performance of the proposed algorithm is superior classification algorithm.