Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy and Quality Evaluation for Compressive Sensing of Fetal Electrocardiogram Signals

This manuscript addresses the problem of non-invasive fetal Electrocardiogram (ECG) signal acquisition with low power/low complexity sensors. A sensor architecture using the Compressive Sensing (CS) paradigm is compared to a standard compression scheme using wavelets in terms of energy consumption vs. reconstruction quality, and, more importantly, vs. performance of fetal heart beat detection in the reconstructed signals. We show in this paper that a CS scheme based on reconstruction with an over-complete dictionary has similar reconstruction quality to one based on wavelet compression. We also consider, as a more important figure of merit, the accuracy of fetal beat detection after reconstruction as a function of the sensor power consumption. Experimental results with an actual implementation in a commercial device show that CS allows significant reduction of energy consumption in the sensor node, and that the detection performance is comparable to that obtained from original signals for compression ratios up to about 75%.
Heuristic and Special Case Algorithms for Dispersion Problems

The dispersion problem arises in selecting facilities to maximize some function of the distances between the facilities. The problem also arises in selecting nondominated solutions for multiobjective decision making. It is known to be NP-hard under two objectives: maximizing the minimum distance (MAX-MIN) between any pair of facilities and maximizing the average distance (MAX-AVG). We consider the question of obtaining near-optimal solutions. for MAX-MIN, we show that if the distances do not satisfy the triangle inequality, there is no polynomial-time relative approximation algorithm unless P = NP. When the distances satisfy the triangle inequality, we analyze an efficient heuristic and show that it provides a performance guarantee of two. We also prove that obtaining a performance guarantee of less than two is NP-hard. for MAX-AVG, we analyze an efficient heuristic and show that it provides a performance guarantee of four when the distances satisfy the triangle inequality. We also present a polynomial-ti.
Nonlinear and Non-Gaussian State-Space Modeling with Monte Carlo Techniques : A Survey and Comparative Study

Since Kitagawa (1987) and Kramer and Sorenson (1988) proposed the filter and smoother using numerical integration, nonlinear and/or non-Gaussian state estimation problems have been developed. Numerical integration becomes extremely computer-intensive in the higher dimensional cases of the state vector. Therefore, to improve the above problem, the sampling techniques such as Monte Carlo integration with importance sampling, resampling, rejection sampling, Markov chain Monte Carlo and so on are utilized, which can be easily applied to multi-dimensional cases. Thus, in the last decade, several kinds of nonlinear and non-Gaussian filters and smoothers have been proposed using various computational techniques. The objective of this paper is to introduce the nonlinear and non-Gaussian filters and smoothers which can be applied to any nonlinear and/or non-Gaussian cases. Moreover, by Monte Carlo studies, each procedure is compared by the root mean square error criterion.
A novel ML based joint TOA and AOA estimator for IR-UWB systems

A novel joint TOA and AOA estimator is proposed for impulse radio Ultra-Wideband (IR-UWB) systems, in which a uniform linear array of antennas is employed at the receiver. The proposed method consists of two steps: (1) coarse estimation of the TOA and the average power delay profile; (2) joint TOA refinement and AOA estimation by maximization of a novel log likelihood function (LLF) using the coarse estimates from the first step. The derivation of the LLF is based on an original approach in which the pulse image from the primary path is modeled as a deterministic component while the superposition of the images from the secondary paths is modeled as a Gaussian random process. In addition, a special gating mechanism is used to characterize the secondary paths, thereby leading to a previously unknown form of the LLF in step (2). According to simulation experiments based on standard UWB channel models, our approach exhibits superior performance to that of a competing scheme from the recent literature.
ACCELERATING UNIVERSES WITH SCALING DARK MATTER

Friedmann–Robertson–Walker universes with a presently large fraction of the energy density stored in an X-component with wX<-1/3, are considered. We find all the critical points of the system for constant equations of state in that range. We consider further several background quantities that can distinguish the models with different wX values. Using a simple toy model with a varying equation of state, we show that even a large variation of wX at small redshifts is very difficult to observe with dL(z) measurements up to z~1. Therefore, it will require accurate measurements in the range 1<z<2 and independent accurate knowledge of Ωm,0 (and/or ΩX,0) in order to resolve a variable wX from a constant wX.
Named entity recognition in Assamese: A hybrid approach

Most NER systems have been developed using one of two approaches: Rule-based or Machine-Learning, with their strengths and weaknesses. In this paper, we propose a hybrid NER approach which is a combination of both rule-based and ML approaches to improve the overall system performance for a resource poor language like Assamese. Our proposed hybrid approach is capable of recognizing four types of NEs: Person, Location, Organization and Miscellaneous. The empirical results obtained indicate that the hybrid approach outperforms both rule-based and ML when processed independently. The hybrid Assamese NER obtains an F-measure of 85%-90%.
Well‐posedness for the Navier Slip Thin‐Film equation in the case of partial wetting

Consider a viscous liquid droplet spreading on a surface. The classical slip condition at the liquid‐solid interface is the no‐slip condition. However, this condition yields infinite dissipation rate when the contact line moves (“no‐slip paradox”). For this reason other slip conditions such as the Navier slip condition have been proposed. We prove well‐posedness for a reduced 1‐D fluid model related to Navier slip. It turns out that the profile of the droplet cannot be described by a smooth function (not even for an initially smooth profile). However, existence and uniqueness can be proved in larger classes of spaces that allow for certain classes of singular expansions at the moving contact point. © 2011 Wiley Periodicals, Inc.
USING IP ANYCAST FOR LOAD DISTRIBUTION AND SERVER LOCATION

An anycast address is an IP address that may be bound to one or more network endpoints. Unlike multicast, a packet destined to an anycast address is forwarded to any oneof the hosts with this address. In this paper, we investigate how the IP anycast service can be used as an effective load distribution mechanism at the network layer. The anycast service naturally provides a client with the ability to locate the topologically closest server. We discuss changes required in the host protocol stack that enable applications to transparently use the anycast service. More specifically, we propose a scheme that limits the required changes to the IP layer processing and describe its implementation in the AIX TCP/IP stack. Finally, we consider an application example where anycast communication is used to distribute load among a set of mirror web sites.
Abstract—the Article Focuses on Volatility Change Point

detection using SPC (Statistical Process Control), specifically through time series control charts, and stochastic differential equations (SDEs). In the paper, recent advances in timechange point for process volatility component satisfying a stochastic differential equation (SDE) based on discrete observations and also using time series control charts will be reviewed. Theoretical part will discuss the methodology of time series control charts and SDEs driven by a Brownian motion. Research part will demonstrate the methodologies using a case study focusing on analysis of Slovak currency during the period of 2000 – 2004 from the perspective of its usefulness for generating profits for company management through time series control charts and SDEs. The aim of the paper is to demonstrate use of change point detection in time series of the Slovak crown. It also aims to highlight versatility of control charts not only in manufacturing but also in managing financial stability of cash flows.
Efficacy of high frequency switched-mode stimulation in activating Purkinje cells

This paper investigates the efficacy of high frequency switched-mode neural stimulation. Instead of using a constant stimulation amplitude, the stimulus is switched on and off repeatedly with a high frequency (up to 100kHz) duty cycled signal. By means of tissue modeling that includes the dynamic properties of both the tissue material as well as the axon membrane, it is first shown that switched-mode stimulation depolarizes the cell membrane in a similar way as classical constant amplitude stimulation. These findings are subsequently verified using in vitro experiments in which the response of a Purkinje cell is measured due to a stimulation signal in the molecular layer of the cerebellum of a mouse. For this purpose a stimulator circuit is developed that is able to produce a monophasic high frequency switched-mode stimulation signal. The results confirm the modeling by showing that switched-mode stimulation is able to induce similar responses in the Purkinje cell as classical stimulation using a constant current source. This conclusion opens up possibilities for novel stimulation designs that can improve the performance of the stimulator circuitry. Care has to be taken to avoid losses in the system due to the higher operating frequency.
Early Flame Detection in Video Sequences based on D-S Evidence Theory

A multi-feature fusion early flame detection algorithm based on D-S evidence theory is proposed. In this algorithm, first the method based on YcbCr and RGB color spaces is used for extracting the flame region of interest. Then flame classifiers based on flame flicker frequency and flame image correlation between frames are selected as two features of D-S feature fusion, the basic probability assignment functions of two features are defined. Finally, the combination discipline of D-S evidence theory is used to determine the final result of all the feature classifiers. Experiments show that the proposed detection algorithm gives 89.5% correct ﬂame rate with a 4.5% false alarm rate and the method is efficient and fast with wide application prospects in fire detection.
Joint likelihood methods for mitigating visual tracking disturbances

We describe a framework that explicitly reasons about data association and combines estimates to improve the tracking performance in many difficult visual environments. This work extends two previously reported algorithms: the probabilistic data association filter (PDAF), which handles single-target tracking tasks involving agile motions and clutter; and the joint probabilistic data association filter (JPDAF), which shares information between multiple same-modality trackers (such as homogeneous regions, textured regions, or snakes). The capabilities of these methods are improved in two steps: first, by a joint likelihood filter that allows mixed tracker modalities when tracking several objects and accommodates overlaps robustly. A second technique, the constrained joint likelihood filter, tracks complex objects as conjunctions of cues that are diverse both geometrically (e.g., parts) and qualitatively (e.g., attributes). Rigid and hinge constraints between part trackers and multiple descriptive attributes for individual parts render the whole object more distinctive, reducing the susceptibility to mistracking. The generality of our approach allows for easy application to different target types, and it is flexibly defined for straightforward incorporation of other modalities.
Self-adaptive Based Cooperative Coevolutionary Algorithm for Large-scale Numerical Optimization

The scalability performance of the traditional evolutionary algorithms (EAs) deteriorates rapidly as the dimensionality of the optimization problems increases. Therefore, cooperative coevolutionary (CC) framework is proposed to overcome the defect. Different from existing CC algorithms, a novel self-adaptive based cooperative coevolutionary (SaCC) algorithm is presented in this paper. The SaCC employs three algorithms which with self-adaptive mechanism as sub-algorithms. The focus of this paper is on investigating two different cooperative coevolutionary manners. In the first manner, SaCC executes its sub-algorithms in parallel during evolve process and the corresponding algorithm is termed as SaCC-M1. In the second manner, SaCC executes its sub-algorithms in serial and the corresponding algorithm is termed as SaCC-M2. 26 test functions with 1000 dimensionalities are employed to verify the validity of SaCC-M1 and SaCC-M2. Experiment results demonstrate that SaCC-M2 outperforms its sub-algorithms and SaCC-M1. Besides, the results indicate that serial manner is another simple yet efficient manner for CC algorithms to solve large-scale global optimization problems.
Shale gas development impacts on surface water quality in Pennsylvania

Concern has been raised in the scientific literature about the environmental implications of extracting natural gas from deep shale formations, and published studies suggest that shale gas development may affect local groundwater quality. The potential for surface water quality degradation has been discussed in prior work, although no empirical analysis of this issue has been published. The potential for large-scale surface water quality degradation has affected regulatory approaches to shale gas development in some US states, despite the dearth of evidence. This paper conducts a large-scale examination of the extent to which shale gas development activities affect surface water quality. Focusing on the Marcellus Shale in Pennsylvania, we estimate the effect of shale gas wells and the release of treated shale gas waste by permitted treatment facilities on observed downstream concentrations of chloride (Cl−) and total suspended solids (TSS), controlling for other factors. Results suggest that (i) the treatment of shale gas waste by treatment plants in a watershed raises downstream Cl− concentrations but not TSS concentrations, and (ii) the presence of shale gas wells in a watershed raises downstream TSS concentrations but not Cl− concentrations. These results can inform future voluntary measures taken by shale gas operators and policy approaches taken by regulators to protect surface water quality as the scale of this economically important activity increases.
Using Machine Learning to Enhance Software Tools Information Management

This paper discusses the issues involved in the application of machine learning techniques to the management of Internet-based information. We present a general architecture, and describe how this has been instantiated in several different applications. The first three of these systems provide assistance to a user sorting incoming mail, reading USENET news or identifying World-Wide Web pages of interest; the final system constructs a personalised on-line newspaper, assembled from documents gathered by a Web robot. A number of machine learning techniques have been used in the construction of these systems; some comparative results are presented.
Risk and Time Preference on Schooling:Experimental Evidence from a Low-Income Country

Educational investment involves risks and long-term commitment, and the degree of risk aversion or patience of parents could play a vital role in the schooling decision. Yet, there are few studies analyzing the impact of such preferences on educational investment. This paper utilizes a unique dataset with a large-scale field experiment of preferences and estimates the impacts of the patience and risk aversion of the parents on school attendance, delayed enrollment, and the education expenditure of their children in Uganda. Our results show that the risk aversion of the parent delays enrollment of young children, especially boys. This could be explained by parents’ security concerns for their young children. Girls of impatient parents have high attendance rates when they are young (6 – 9 years old) but have low attendance rates when they are older (10 – 13 years old). Boys aged 10 to 13 have low attendance rates if their parents have a high present bias. Finally, the patience of the parents increases the education expenditure.
Design and Production of Specifically High Affinity Reacting Peptides (SHARP®-s)

Background: A partially random target selection method was developed to design and produce affinity reagents (target) to any protein query. It is based on the recent concept of Proteomic Code (for review see Biro, 2007 (1)) which suggests that significant number of amino acids in specifically interacting proteins are coded by partially complementary codons. It means that the 1 st and 3 rd residues of codons coding many co-locating amino acids are complementary but the 2 nd may but not necessarily be complementary: like 5'-AXG-3'/3'-CXT-5' codon pair, where X is any nucleotide. Results: A mixture of 45 residue long, reverse, partially complementary oligonucleotide sequences (target pool) was syn- thesized to selected epitopes of query mRNA sequences. The 2 nd codon residues were randomized. The target oligonu- cleotide pool was inserted into vectors, expressed and the protein products were screened for affinity to the query in Bac- terial Two-Hybrid System. The best clones were used for larger-scale protein syntheses and characterization. It was possi- ble to design and produce specific high affinity reacting (Kd: ~100 nM) oligopeptide reagents to GAL4 query oligopep- tides. Conclusions: Second codon residue randomization is a promising method to design and produce affinity peptides to any protein sequences. The method has the potential to be a rapid, inexpensive, high throughput, non-immunoglobulin based alternative to recent in vivo antibody generating procedures.
Dynamic analysis of congested TCP networks

The objective of this paper is to study the small-signal stability of congested TCP networks. The starting point for this analysis is a fluid model describing the interaction of TCP-controlled traffic with congested routers. Assuming integral action in the active queue management scheme, we compute the network's equilibrium point about which we determine the network's small-signal dynamics. Specializing to networks comprised of m links and m+1 sources, we study closed-loop stability, study network scaling, and evaluate robustness to variations in network parameters.
Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets

Causal structure-discovery techniques usually assume that all causes of more than one variable are observed. This is the so-called causal sufficiency assumption. In practice, it is untestable, and often violated. In this paper, we present an efficient causal structure-learning algorithm, suited for causally insufficient data. Similar to algorithms such as IC* and FCI, the proposed approach drops the causal sufficiency assumption and learns a structure that indicates (potential) latent causes for pairs of observed variables. Assuming a constant local density of the data-generating graph, our algorithm makes a quadratic number of conditional-independence tests w.r. t. the number of variables. We show with experiments that our algorithm is comparable to the state-of-the-art FCI algorithm in accuracy, while being several orders of magnitude faster on large problems. We conclude that MBCS* makes a new range of causally insufficient problems computationally tractable.
Exploring instructional leadership in Iran: A mixed methods study of high- and low-performing principals

The purpose of this study is to understand and describe patterns of principal instructional leadership practice in Iranian primary schools and to then assess whether there are differences between the high- and low-rated principals. A mixed methods design was used to collect both qualitative and quantitative data from principals and teachers within Iranian primary schools. The quantitative data were collected and analyzed from 535 teachers and 70 principals across the 70 primary schools. Next, we used ratings on the Principal Instructional Management Rating Scale to identify four of the highest and lowest performing principals. We then conducted open-ended interviews with teachers and principals in these schools. The key findings showed that, despite working in a highly centralized context, the highly ranked principals in the sample were also differentiated from their lower ranked peers on specific instructional leadership practices. They appeared to give more emphasis to their role in developing the quality of teaching and learning and monitoring student progress. In this setting, ensuring a collegial and collaborative environment for teachers is commonly articulated by the successful principals in our study as an important aspect of instructional leadership.
Genetic Risk Prediction for Normal-Karyotype Acute Myeloid Leukemia Using Whole-Exome Sequencing

Normal-karyotype acute myeloid leukemia (NK-AML) is a highly malignant and cytogenetically heterogeneous hematologic cancer. We searched for somatic mutations from 10 pairs of tumor and normal cells by using a highly efficient and reliable analysis workflow for whole-exome sequencing data and performed association tests between the NK-AML and somatic mutations. We identified 21 nonsynonymous single nucleotide variants (SNVs) located in a coding region of 18 genes. Among them, the SNVs of three leukemia-related genes (MUC4, CNTNAP2, and GNAS) reported in previous studies were replicated in this study. We conducted stepwise genetic risk score (GRS) models composed of the NK-AML susceptible variants and evaluated the prediction accuracy of each GRS model by computing the area under the receiver operating characteristic curve (AUC). The GRS model that was composed of five SNVs (rs75156964, rs56213454, rs6604516, rs10888338, and rs2443878) showed 100% prediction accuracy, and the combined effect of the three reported genes was validated in the current study (AUC, 0.98; 95% confidence interval, 0.92 to 1.00). Further study with large sample sizes is warranted to validate the combined effect of these somatic point mutations, and the discovery of novel markers may provide an opportunity to develop novel diagnostic and therapeutic targets for NK-AML.
A packaged miniature antenna for wireless networking

A novel miniature antenna structure (approaching the smallest fundamental size limits) having numerous qualities such as low cost, efficient circuit (broad bandwidth) and radiation characteristics (suitable for wide angular coverage) has been developed. Owing to its very small size and radiation performance, it is compatible for packaging constraints and can be easily integrated in a big variety of portable devices. In addition, the dual band behavior of the antenna makes it an excellent candidate to be used in communication equipment in which different radio modules are integrated. Experimental prototypes operating at different frequencies were built and measured. Comparison between measured data and simulated results are provided for both impedance behavior and radiation characteristics.