Miracle-dz/UniFAR-FTUs huggingface dataset:
Accumulative sampling for noisy evolutionary multi-objective optimization

Objective evaluation is subject to noise in many real-world problems. The noise can deteriorate the performance of multi-objective evolutionary algorithms, by misleading the population to a local optimum and reducing the convergence rate. This paper proposes three novel noise handling techniques: accumulative sampling, a new ranking method, and a different selection scheme for recombination. The accumulative sampling is basically a kind of dynamic resampling, but it does not explicitly decide the number of samples. Instead, it repeatedly takes additional samples of objectives for the solutions in the archive at every generation, and updates the estimated objectives using all the accumulated samples. The new ranking method combines probabilistic Pareto rank and crowding distance into a single aggregated value to promote the diversity in the archive. Finally, the fitness function and selection method used for recombination are made different from those for the archive to accelerate the convergence rate. Experiments on various benchmark problems have shown that the algorithm adopting all these features performs better than other MOEAs in various performance metrics.
Selection of higher order subband features for ECG beat classification

Five levels of discrete wavelet transform are applied to decompose the ECG beat signal into six subband components. Higher order statistics proceeds to calculate valuable features from the three midband components. These features together with three RR interval-related features construct the primary feature set. A feature selection algorithm based on correlation coefficient and Fisher discriminality is then exploited to eliminate redundant features from the primary feature set. The feedforward backpropagation neural network (FFBNN) is employed as the classifier to justify the capacity of the method. The proposed method achieved an imposing ECG beat discrimination rate of more than 97.5%. By using the feature reduction method, the feature dimension can be readily reduced from 30 to 18 with negligible decrease in accuracy. Compared with other methods in the literature, the proposed method improves the sensitivities of most beat types, resulting in an elevated average accuracy. The results demonstrate the effectiveness and efficiency of the proposed method in ECG beat classification.
Energy-efficient antenna sharing and relaying for wireless networks

We develop energy-efficient transmission protocols for wireless networks that exploit spatial diversity created by antenna sharing: coordinated transmission and/or processing by several distributed radios. We focus on single-user transmission and examine several possibilities for the strategy employed by the assisting radio, or relay, including decoding and forwarding as well as amplifying and forwarding. In each case, we develop receivers based upon maximum-likelihood and/or maximum signal-to-noise ratio criteria, relate their structures, and compare their bit-error probability performance by means of analysis and simulations. We cast single-hop and multihop routing into our framework for comparison purposes. All of our antenna sharing protocols offer diversity gains over single-hop and multihop transmission, and our results suggest that low-complexity amplifying and forwarding is energy-efficient in spite of noise amplification at the relay.
Luminous early-type field galaxies at z~ 0.4 - II. Star formation history and space density

We present a combined photometric and spectroscopic analysis of the star formation history and space density of a sample of 485 luminous, M V - 5 log h 1), passively evolving luminosity-weighted stellar population. 21 per cent of the sample possess detectable [OII] λ3727 emission consistent with a low level (? 1 M O . yr - 1 ) of on-going star formation. Parametric and non-parametric estimates of the space density of the sample are derived. The integrated luminosity density at z ≃ 0.4, allowing only for passive luminosity evolution, is in excellent agreement with the local, ((z) =0.1), luminosity density of early-type galaxies. Overall, the sample properties are consistent with a galaxy formation scenario in which the majority of luminous field early-type galaxies formed at redshifts z > 1 and have largely evolved passively since the formation epoch.
Do Answers Help in Posing Questions

Let $T_n$ denote a complete binary tree of depth $n$. Each internal node, $v$, of $T_n$ has two children denoted by $\lf(v)$ and $\rt(v)$. Consider the following game between two Players, Paul and Carole. For each internal node, $v$, Carole chooses $X(v) \in \{ \lf(v) , \rt(v) \}$. This naturally defines a path from the root, $\lambda$, of $T_n$ to one of its leaves as follows: $$\lambda, X(\lambda), X^2(\lambda ), \ldots, X^n(\lambda). $$ Paul has to find this path by asking questions of the following form: \smallskip \centerline{$Q_v$:``Is $X(v) = \lf(v)$? ''''} \smallskip \noindent The game proceeds in $r$ rounds. In every round Paul can $k$ questions. Carole supplies the answers to these $k$ questions in parallel. We give necessary and sufficient conditions for Paul to win this game.
RRDD: Receiver-oriented robust data delivery in mobile sensor networks

Data forwarding in the wireless networks typically employs a sender-oriented approach in which the next hop node is pre-selected based on neighbor or network information. This method incurs large overhead when accurate information is needed for making the optimal forwarding decision. In this paper, a receiver-oriented robust data delivery scheme (RRDD) is proposed for mobile sensor networks. In RRDD, the sender does not appoint a specific forwarder proactively, but allows its neighboring candidates to dynamically contend for the data forwarding task based on local state information. In this way, the best-suited node is elected at each hop to provide robust and efficient delivery service to data packets. Comprehensive simulations show that RRDD exhibits superior transmission performance over all of the compared schemes.
Multi-Modal Recognition Using Multiple Views

Motivation: Person tracking and recognition systems should ideally integrate information from multiple views, and work well even when people are far away. Two key issues that make this challenging are varying appearance due to changing pose, and the relatively low resolution of images taken at a distance. We wish to design a system for real-time multimodal recognition from multiple views that substantially overcomes these two problems.
Strathprints Institutional Repository

The application of microfluidic devices for DNA amplification has recently been extensively studied. Here, we review the important development of microfluidic polymerase chain reaction (PCR) devices and discuss the underlying physical principles for the optimal design and operation of the device. In particular, we focus on continuous-flow microfluidic PCR on-chip, which can be readily implemented as an integrated function of a micro-total-analysis system. To overcome sample carryover contamination and surface adsorption associated with microfluidic PCR, microdroplet technology has recently been utilized to perform PCR in droplets, which can eliminate the synthesis of short chimeric products, shorten thermal cycling time, and offers great potential for single DNA molecule and single-cell amplification. The work on chip-based PCR in droplets is highlighted.
A Molecular Mechanism for mRNA Trafficking in Neuronal Dendrites

Specific neuronal mRNAs are localized in dendrites, often concentrated in dendritic spines and spine synapses, where they are translated. The molecular mechanism of localization is mostly unknown. Here we have explored the roles of A2 response element (A2RE), a cis-acting signal for oligodendrocyte RNA trafficking, and its cognate trans-acting factor, heterogeneous nuclear ribonucleoprotein (hnRNP) A2, in neurons. Fluorescently labeled chimeric RNAs containing A2RE were microinjected into hippocampal neurons, and RNA transport followed using confocal laser scanning microscopy. These RNA molecules, but not RNA lacking the A2RE sequence, were transported in granules to the distal neurites. hnRNP A2 protein was implicated as the cognate trans-acting factor: it was colocalized with RNA in cytoplasmic granules, and RNA trafficking in neurites was compromised by A2RE mutations that abrogate hnRNP A2 binding. Coinjection of antibodies to hnRNP A2 halved the number of trafficking cells, and treatment of neurons with antisense oligonucleotides also disrupted A2RE-RNA transport. Colchicine inhibited trafficking, whereas cells treated with cytochalasin were unaffected, implicating involvement of microtubules rather than microfilaments. A2RE-like sequences are found in a subset of dendritically localized mRNAs, which, together with these results, suggests that a molecular mechanism based on this cis-acting sequence may contribute to dendritic RNA localization.
The Design of Remote Medical Monitoring System Based on Sensors and GPRS

As a new medical method, the remote medical monitoring system is being paid more and more attention to. This article proposes a kind of remote medical monitoring system which is based on the sensor and GPRS (General Packet Radio Service, GPRS). The article first analyzes the necessity of the remote medical monitoring system, and then explains the advantages of the GPRS which is used in the remote medical monitoring system, and finally gives the architecture design of the remote medical monitoring system. The remote medical monitoring system is composed of two parts, which are mobile remote medical monitoring unit and the monitoring center. The user can carry the mobile unit along, while this mobile unit can real-timely monitor the physiological data from the user with sensors. When something abnormal happens, the intelligent control system automatically connects to the monitoring center. The monitoring center is composed of the monitoring station and the information management system. The information management system is connected with the monitoring center by a local area network, and its main function is to manage information such as the electronic map, the user's medical record and so on.
Two emission line objects with z>0.2 in the optical filament apparently connecting the Seyfert galaxy NGC 7603 to its companion

We present new spectroscopic observations of an old case of anomalous redshift--NGC 7603 and its companion. The redshifts of the two galaxies which are apparently connected by a luminous filament are z=0.029 and z=0.057 respectively. We show that in the luminous filament there are two compact emission line objects with z=0.243 and z=0.391. They lie exactly on the line traced by the filament connecting the galaxies. As far as we are aware, this is the most impressive case of a system of anomalous redshifts discovered so far.
The Genetic Equidistance Phenomenon at the Proteomic Level

The field of molecular evolution started with the alignment of a few protein sequences in the early 1960s. Among the first results found, the genetic equidistance result has turned out to be the most unexpected. It directly inspired the ad hoc universal molecular clock hypothesis that in turn inspired the neutral theory. Unfortunately, however, what is only a maximum distance phenomenon was mistakenly transformed into a mutation rate phenomenon and became known as such. Previous work studied a small set of selected proteins. We have performed proteome wide studies of 7 different sets of proteomes involving a total of 15 species. All 7 sets showed that within each set of 3 species the least complex species is approximately equidistant in average proteome wide identity to the two more complex ones. Thus, the genetic equidis-tance result is a universal phenomenon of maximum distance. There is a reality of constant albeit stepwise or discontinuous increase in complexity during evolution, the rate of which is what the original molecular clock hypothesis is really about. These results provide additional lines of evidence for the recently proposed maximum genetic diversity (MGD) hypothesis. Availability and implementation The source code repository is publicly available at https://github.com/Sephiroth1st/EquidistanceScript Contact huangshi@sklmg.edu. cn Supplementary information Supplementary data are available online.
STUDIES OF THE BEAM-BEAM INTERACTION AT CESR ⁄

The Cornell Electron Storage ring facility CESR operates with 2 GeV multi-bunch electron and positron beams in a single beam-pipe. Electrostatic separators are used to separate the two counter-rotating beams at the parasitic crossings. When the beam energy was lowered from 5 GeV in 2003,the strength of the beam-beaminteractionbecame a more important factor in beam-current limitations, resulting in extensive experimental and modeling studies of their characteristics. The CESR lattice design procedure has been modified recently to account explicitly for their dynamic consequences. We describe our modeling of the beam-beam interaction, experimental validation techniques, and investigations into compensation strategies.
The Real Effects of Investor Sentiment

Does inefficiency of financial markets have real consequences? Or does it only result in transfers of wealth from noise traders to arbitrageurs? We study firm business investment to address this question. In our model, benevolent managers of overvalued companies invest in projects with negative net present value and managers of undervalued companies forego projects with positive net present value. Empirically, we find a positive relation between investment and a number of proxies for mispricing, controlling for investment opportunities and financial slack, suggesting that overpriced (underpriced) firms tend to over invest (under invest). Consistent with the predictions of our model, we find that investment is more sensitive to mispricing for firms with higher R&D intensity (suggesting longer periods of information asymmetry) or share turnover (suggesting that the firms' shareholders are short-term investors). We document similar patterns in the cross-section of average returns. Firms with relatively high (low) investment subsequently have relatively low (high) stock returns, after controlling for investment opportunities and other characteristics linked to return predictability. These patterns are stronger for firms with higher R&D intensity or higher share turnover.
Dynamic shadow removal from front projection displays

Front-projection display environments suffer from a fundamental problem: users and other objects in the environment can easily and inadvertently block projectors, creating shadows on the displayed image. We introduce a technique that detects and corrects transient shadows in a multi-projector display. Our approach is to minimize the difference between predicted (generated) and observed (camera) images by continuous modification of the projected image values for each display device. We speculate that the general predictive monitoring framework introduced here is capable of addressing more general radiometric consistency problems. Using an automatically-derived relative position of cameras and projectors in the display environment and a straightforward color correction scheme, the system renders an expected image for each camera location. Cameras observe the displayed image, which is compared with the expected image to detect shadowed regions. These regions are transformed to the appropriate projector frames, where corresponding pixel values are increased. In display regions where more than one projector contributes to the image, shadow regions are eliminated. We demonstrate an implementation of the technique in a multiprojector system.
Colour image smoothing through a soft-switching mechanism using a graph model

In this study, the authors propose a soft-switching filter to improve the performance of recent colour image smoothing filters when processing homogeneous image regions. The authors use a recent filter mixed with the classical arithmetic mean filter (AMF). The recent method is used to process image pixels close to edges, texture and details and the AMF is only used to process homogeneous regions. To this end, the authors propose a method based on the graph theory to distinguish image details and homogeneous regions and to perform a soft switching between the two filters. Experimental results show that the proposed method provides improved results which supports the appropriateness of the graph theory-based method and suggests that the same structure can be used to improve the performance of other non-linear colour image smoothing methods.
Perspective: Semantic Data Management for the Home

Perspective is a storage system designed for the home, with the decentralization and flexibility sought by home users and a new semantic filesystem construct, the view, to simplify management. A view is a semantic description of a set of files, specified as a query on file attributes, and the ID of the device on which they are stored. By examining and modifying the views associated with a device, a user can identify and control the files stored on it. This approach allows users to reason about what is stored where in the same way (semantic naming) as they navigate their digital content. Thus, in serving as their own administrators, users do not have to deal with a second data organization scheme (hierarchical naming) to perform replica management tasks, such as specifying redundancy to increase reliability and data partitioning to address device capacity exhaustion. Experiences with Perspective deployments and user studies confirm the efficacy of view-based data management.
Emerging issues in radiogenic cataracts and cardiovascular disease

In 2011, the International Commission on Radiological Protection issued a statement on tissue reactions (formerly termed non-stochastic or deterministic effects) to recommend lowering the threshold for cataracts and the occupational equivalent dose limit for the crystalline lens of the eye. Furthermore, this statement was the first to list circulatory disease (cardiovascular and cerebrovascular disease) as a health hazard of radiation exposure and to assign its threshold for the heart and brain. These changes have stimulated various discussions and may have impacts on some radiation workers, such as those in the medical sector. This paper considers emerging issues associated with cataracts and cardiovascular disease. For cataracts, topics dealt with herein include (i) the progressive nature, stochastic nature, target cells and trigger events of lens opacification, (ii) roles of lens protein denaturation, oxidative stress, calcium ions, tumor suppressors and DNA repair factors in cataractogenesis, (iii) dose rate effect, radiation weighting factor, and classification systems for cataracts, and (iv) estimation of the lens dose in clinical settings. Topics for cardiovascular disease include experimental animal models, relevant surrogate markers, latency period, target tissues, and roles of inflammation and cellular senescence. Future research needs are also discussed.
Towards High-Efficiency Cascading Outage Simulation and Analysis in Power Systems: A Sequential Importance Sampling Approach

This paper addresses how to improve the computational efficiency and estimation reliability in cascading outage analysis. We first formulate a cascading outage as a Markov chain with specific state space and transition probability by leveraging the Markov property of cascading outages. It provides a rigorous formulation that allows analytic investigation on cascading outages in the framework of standard mathematical statistics. Then we derive a sequential importance sampling (SIS) based simulation strategy for cascading outage simulation and blackout risk analysis with theoretical justification. Numerical experiments manifest that the proposed SIS strategy can significantly bring down the number of simulations and reduce the estimation variance of cascading outage analysis compared with the traditional Monte Carlo simulation strategy.
Joint low-rank representation and matrix completion under a singular value thresholding framework

Matrix completion is the process of estimating missing entries from a matrix using some prior knowledge. Typically, the prior knowledge is that the matrix is low-rank. In this paper, we present an extension of standard matrix completion that leverages prior knowledge that the matrix is low-rank and that the data samples can be efficiently represented by a fixed known dictionary. Specifically, we compute a low-rank representation of a data matrix with respect to a given dictionary using only a few observed entries. A novel modified version of the singular value thresholding (SVT) algorithm named joint low-rank representation and matrix completion SVT (J-SVT) is proposed. Experiments on simulated data show that the proposed J-SVT algorithm provides better reconstruction results compared to standard matrix completion.
A hybrid method for bilingual text sentiment classification based on deep learning

Text sentiment classification has occupied a pivotal position in sentiment analysis research, it offers important opinion mining functions. Nowadays, with explosion of information, many researchers are focusing on sentiment classification research on massive amounts of data. However, the traditional machine learning methods cannot acquire text semantic information and most research achievements are about single language, in this paper, a hybrid method which integrates the deep learning features and shallow learning features is proposed. The hybrid method can not only realize single language text sentiment classification but realize bilingual text sentiment classification as well. Models such as recurrent neural networks (RNNs) with long short term memory(LSTM), Naïve Bayes Support Vector Machine (NB-SVM), word vectors and bag-of-words are explored. Firstly, these models are studied separately in sentiment classification task. The paper then integrates the above methods as a whole to complete the task. Different combination strategies are discussed regarding the contribution of each method. The experiments show that the accuracy can reach 89% and the hybrid method performs much better than any other method individually. The proposed method achieves a performance close to the state-of-the-art methods based on the had-engineered features. What's more, the hybrid model can learn more linguistic phenomena with the growth of the accuracy of emotional tendency discrimination when more background knowledge is available.
Adaptive EEG artifact rejection for cognitive games

The separation of the informative part from an observed dataset is a significant step for dimension reduction and features extraction. In this paper, we present an approach for adaptive artifact rejection from the electroencephalogram (EEG). The main aim of our work is to increase performance of classification algorithms which have a deal with the EEG and are used in cognitive games. We provide a method to separate the EEG into informative and noised parts, select informative one and rank its dimensions. The proposed approach is based on the theoretical relation between classification accuracy, mutual information and normalized graph cut (NC) value. The presented algorithm requires a priori known class labeled EEG dataset that are utilized for a calibration phase of the brain-computer interface (BCI). Experimental results on datasets from BCI competitions show its applicability for cognitive games.
Checking that finite state concurrent programs satisfy their linear specification

We present an algorithm for checking satisfiability of a linear time temporal logic formula over a finite state concurrent program. The running time of the algorithm is exponential in the size of the formula but linear in the size of the checked program. The algorithm yields also a formal proof in case the formula is valid over the program. The algorithm has four versions that check satisfiability by unrestricted, impartial, just and fair computations of the given program.