Miracle-dz/UniFAR-FTUs huggingface dataset:
Confidence Intervals for Mean and Difference between Means of Delta-Lognormal Distributions Based on Left-Censored Data

A delta-lognormal distribution consists of zero and positive values. The positive values follow a lognormal distribution, which is an asymmetric distribution. It is well known that the logarithm of these values follows a normal distribution, which is a symmetric distribution. The delta-lognormal distribution is used in medical and environmental sciences. This study considers the challenges of constructing confidence intervals for the mean and difference between means of delta-lognormal distributions containing left-censored data and applies them to compare two daily rainfall average areas in Thailand. Three different approaches for constructing confidence intervals for the mean of the delta-lognormal distribution containing left-censored data, based on the generalized confidence interval approach, the Bayesian approach, and the parametric bootstrap approach, are developed. Moreover, four different approaches for constructing confidence intervals for the difference between means of delta-lognormal distributions containing left-censored data, based on the generalized confidence interval approach, the Bayesian approach, the parametric bootstrap approach, and the method of variance estimates recovery approach, are considered. The performance of the proposed confidence intervals is evaluated by Monte Carlo simulation. The simulation studies indicate that the Bayesian approach can be considered as an alternative to construct a credible interval for the mean of the delta-lognormal distribution containing left-censored data. Additionally, the generalized confidence interval and Bayesian approaches can be recommended as alternatives to estimate the confidence interval for the difference between means of delta-lognormal distributions containing left-censored data. All approaches are illustrated using the daily rainfall data from Chiang Mai and Lampang provinces in Thailand.
Two Methods for Priorities of Complementary Judgement Matrices ——Weighted Least-square Method and Eigenvector Method

By using the transformation formulas of reciprocal judgement matrix and complementary judgement matrix, this paper defines the concepts of transformation matrices of complementary judgement matrices. A weighted least\|square method for priorities of complementary judgement matrices is proposed and the strict theoretical proof is given. Furthermore, based on the transformation matrices, an eigenvector method for priorities of complementary judgement matrices is proposed, and a corresponding iterative algorithm and a consistency checking approach are also presented. Finally, an example is given to illustrate the methods and approach.
VUS and HUM Represented with Mann-Whitney Statistic

The area under the ROC curve (AUC), the volume under the ROC surface (VUS) and the hypervolume under the ROC manifold (HUM) are defined and interpreted with probability that measures the discriminant power of classification models. AUC, VUS and HUM are expressed with the summation and integration notations for discrete and continuous random variables, respectively. AUC for discrete two random samples is represented as the nonparametric Mann-Whitney statistic. In this work, we define conditional Mann-Whitney statistics to compare more than two discrete random samples as well as propose that VUS and HUM are represented as functions of the conditional Mann-Whitney statistics. Three and four discrete random samples with some tie values are generated. Values of VUS and HUM are obtained using the proposed statistic. The values of VUS and HUM are identical with those obtained by definition; therefore, both VUS and HUM could be represented with conditional Mann-Whitney statistics proposed in this paper.
Study on the optimization method of spinning reserve schedule in wind power integrated power system based on chance-constrained programming

This paper constructs the probability density function of the wind power forecast error and establishes a mathematical model of procuring spinning reserve basing on the chance-constrained programming in which the uncertain and stochastic factors are considered such as the load forecast error, the wind power forecast error and the fault outage of generators. The model reflects the influence of abundance lever of spinning reserve on the economy of system by setting the constraint condition. The analysis shows that under the premise of system reliability, the economy of this model is better than the traditional model, and the reliability is better when the wind power forecast error obeys the probability distribution established in the paper than the Gaussian Distribution and the model can realize the balance between the reliability and economy of system.
Geoparsing at Web-scale - Challenges and Opportunities

The increasing amount of web data being generated and stored along with geographic information is of great importance to enrich future search applications in science, news, economics, etc. In addition to location information provided by users or content providers directly, the potential to extract geographic entities from unstructured web content and linking them to geographic coordinates at scale has not been fully exploited. This paper highlights the importance of geoparsing large web archives and associated challenges. Furthermore, this paper evaluates different existing methods with regard to accuracy and scalability to showcase future directions for improving their efficiency.
Nematic liquid crystal gyroids as photonic crystals

ABSTRACT We demonstrate nematic and cholesteric liquid crystal (LC) gyroids and show their photonic properties as photonic crystals by using numerical modelling. The LC gyroids are designed as composite optical materials, where we take one labyrinth of passages to be a solid dielectric, whereas the other (complementing) labyrinth of passages is taken to be filled by chiral or achiral nematic LC, with the intermediate gyroid surface imposing homeotropic (perpendicular) surface anchoring. The nematic inside the gyroid matrix is shown to exhibit a variety of possible orientational profiles which are characterised by complex networks of topological defects – from ordered, semi-ordered, to completely disordered. The diversity of possible nematic states is shown to lead to a rich structure of photonic bands, which can be tuned by the LC volume fraction and the cholesteric pitch, including control over full – direct and indirect – band gaps. Graphical Abstract
1-O-alkyl-glycerols from Squid Berryteuthis magister Reduce Inflammation and Modify Fatty Acid and Plasmalogen Metabolism in Asthma Associated with Obesity

Asthma associated with obesity is considered the most severe phenotype and can be challenging to manage with standard medications. Marine-derived 1-O-alkyl-glycerols (AGs), as precursors for plasmalogen synthesis, have high biological activity, making them a promising substance for pharmacology. This study aimed to investigate the effect of AGs from squid Berryteuthis magister on lung function, fatty acid and plasmalogen levels, and cytokine and adipokine production in obese patients with asthma. The investigational trial included 19 patients with mild asthma associated with obesity who received 0.4 g of AGs daily for three months in addition to their standard treatment. The effects of AGs were evaluated at one and three months of treatment. The results of the study demonstrated that intake of AGs increased the FEV1 and FEV1/VC ratios, and significantly decreased the ACQ score in 17 of the 19 patients after three months of treatment. The intake of AGs increased concentration of plasmalogen and n–3 PUFA in plasma, and modified leptin/adiponectin production by adipose tissue. The supplementation of AGs decreased the plasma levels of inflammatory cytokines (TNF-α, IL-4, and IL-17a), and oxylipins (TXB2 and LTB4), suggesting an anti-inflammatory property of AGs. In conclusion, 1-O-alkyl-glycerols could be a promising dietary supplement for improving pulmonary function and reducing inflammation in obese asthma patients, and a natural source for plasmalogen synthesis. The study highlighted that the beneficial effects of AG consumption can be observed after one month of treatment, with gradual improvement after three months of supplementation.
Research on Text Classification Model Based on Self-Attention Mechanism and Multi-Neural Network

The traditional text classification algorithms based on deep learning include RNN and CNN and their variants. The too long sequence of RNN is easy to produce gradient explosion and the gradient disappears, so the long-distance dependence of the text cannot be extracted; CNN focuses on the local features of the sentence rather than the global structure of the sentence. In response to this problem, this paper proposes a text classification model (Self-attention and Multiple Neural Networks Unit based Text Classification SMNN) that integrates the self-attention mechanism and multiple neural networks. This structure uses a word embedding model based on the self-attention mechanism that focus on the important parts of the text to generate a global representation of the text, uses CNN to extract the local semantic features of the text at multiple granularities through different convolution kernel sizes and k-max-pooling, and uses BiLSTM with skip connections and pooling layers to extract the long-distance dependencies of the text for obtaining the global representation of the text. Then it fuses the global features and local features. Lastly, it classifies the text information through the softmax classifier. The experimental results on the text data set show that the SMNN model has better text classification accuracy which proves that the SMNN model has obvious advantage and value compared with the traditional text classification model.
Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation

We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals. We work in the short-time Fourier transform (STFT) domain, where convolution is routinely approximated as linear instantaneous mixing in each frequency band. Each source STFT is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization (EM) algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms are applied to stereo audio source separation in various settings, covering blind and supervised separation, music and speech sources, synthetic instantaneous and convolutive mixtures, as well as professionally produced music recordings. Our EM method produces competitive results with respect to state-of-the-art as illustrated on two tasks from the international Signal Separation Evaluation Campaign (SiSEC 2008).
Secure positioning in wireless networks

So far, the problem of positioning in wireless networks has been studied mainly in a nonadversarial setting. In this paper, we analyze the resistance of positioning techniques to position and distance spoofing attacks. We propose a mechanism for secure positioning of wireless devices, that we call verifiable multilateration. We then show how this mechanism can be used to secure positioning in sensor networks. We analyze our system through simulations.
Compressed Sensing with Embeding Negative-positive Transformation for Image Compression-encryption Applications

Recently, compressed sensing (CS) cryptosystem has received a lot of interest. However, this cryptosystem is vulnerable to chosen-plaintext attack (CPA) because the CS sampling process is a linearity process. To solve this problem, we propose a novel CS-based cryptosystem by using negative-positive transformation (NPT) in this paper, which embeds a NPT operation in the CS sampling process. First, the image is pre-processed by using NPT operation. Then, the cipher image is re-encrypted by using CS. Last, the compressed ciphertext is quantized into bits. Since the introduction of NPT operation destroys the linearity of sampling process, our method can resist CPA. Compared with previous CS-based cryptosystems, the proposed cryptosystem has two advantages: 1) It achieves effective privacy protection against CPA; 2) It has better compression performance.
Tools/Frameworks that Support Development Process of AI-based Software: Validations in White Literature

Context: Artificial Intelligence (AI)-based software has gained increasing interest, especially in the last decade, due to advancements in underlying technologies and demands in varying business domains. With the proliferation to develop such software, there appears a need for developing methods and supporting tools/frameworks. Purpose: In this paper, we focus on tools/frameworks to automate AI-based software development process, from a holistic view. We review the scientific studies that were empirically validated and also evaluate their proposals with respect to basic characteristics including theme, research methods, types, domains, and a number of cases in empirical validations. Method: We elicit relevant studies (with the contribution type of tool or framework) from a larger set of primary studies identified by a systematic literature review on AI-based software development process. We select 14 primary studies in this context and analyze them with respect to the purposes of the proposals. Results: We review tools/frameworks that support AI-based software development process under four headings: software system development process, the development process of fair software, model development process, and model deployment and operation processes. We observe that domains of empirical validation are diverse while the number of empirical cases applied for validation is limited. Also, only half of the primary studies provide links to their proposals as open-source, which is very important for the repeatability of the empirical validations.
Combining a mobile deep neural network and a recurrent layer for violence detection in videos

Several techniques for the automatic detection of violent scenes in videos and security footage appeared in recent years, for example with the goal of unburdening authorities from the need of analyzing hours of Closed-Circuit TeleVision (CCTV) clips. In this regard, Deep Learning-based techniques such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) emerged as effective for violence detection. Nevertheless, most of such techniques require significant computational and memory resources to run the automatic detection of violence. Thus, we propose the combination of an established CNN, MobileNetV2, designed for the use in mobile and embedded devices with a recurrent layer to extract the spatio-temporal features in the security videos. A lightweight model can run in embedded devices, in a edge computing fashion, for example to allow processing the videos near the camera recording them, to preserve privacy. Specifically, we exploit transfer learning, as we use a pre-trained version of MobileNetV2, and we propose two different models combining it with a Bidirectional Long Short-Term Memory (Bi-LSTM) and a Convolutional LSTM (ConvLSTM). The paper presents accuracy tests of the two models on the AIRTLab dataset and a comparison with more complex models developed in our previous work, in order to evaluate the drop of accuracy necessary to use a model compatible with limited resources. The network composed of MobileNetV2 and the ConvLSTM scores a 94.1% accuracy, against the 96.1% of a model based on a more complex 3D CNN.
Using Simpson's Rule to Approximate Sums of Infinite Series

Rick Kreminski (kremin@boisdarc.tamu-commerce.edu) has an undergraduate degree from MIT in biology; tired of waiting for bacteria to grow, he switched to math at the University of Maryland, College Park. An associate professor of mathematics at Texas A&M University-Commerce, he is interested in applications of geometry and topology to particle physics and general relativity and also enjoys getting his computer to. well. compute things. He helps his wife Anne, an ecologist, to model ecosystems on the computer using statistical mechanics.
Improved Histogram Equalization Algorithm

Based on the common use image enhance algorithm such as gray level transformation algorithm and histogram equalization algorithm, an improved contrast enhancement algorithm was suggested. At first, the smoothness processing was carried out in histogram; at second, the histogram was equalized; at third, the uniform piston queuing was carried out for image grey degree in whole display area; at last, the noise is eliminated through median filtering to the image. The experimental result shows that this algorithm could strengthen the detail and clarity definition of the image.
Weighting Factor Design Techniques for Predictive Control of Power Electronics and Motor Drives

The rapid growth of energy demand requires progressive energy generation. This, together with the demand for higher efficiency and flexibility, has promoted the application of power electronics in energy systems. During the past decade, model predictive control (MPC) of power electronics has witnessed significant advancements in both dynamic performance and optimal control of the multi-objective terms. Several of these terms can have equal control priorities, resulting in a symmetrical cost function; however, most objectives have different priorities and require weighting factors to resolve the asymmetry in the cost function. Currently, researchers continue to encounter challenges in the optimal design of weighting factors. Moreover, the relative performance of different techniques that either utilize or avoid the weighting factor are uncertain. Therefore, this study focuses on weighting factor design techniques in the literature as applied to wind/solar energy conversion, microgrids, grid-connected converters, and other high-performance converter-based systems. These are grouped under the heuristic, offline tuning, sequential, and online optimization methods. This study demonstrates that optimal online tuning of weighting factors and sequential MPC methods can both offer improved robustness against parameter uncertainties. In addition, the advantages and limitations of different techniques are highlighted.
An Adaptive Differential Evolution Based on Multi-subpopulation for Global Optimization

This paper proposed a multiple-subpopulation based on differential evolution with an adaptive parameter control and population reduction, called EMPADE, for global optimization. In EMPADE, an adaptive parameter control method based on population diversity and fitness value is proposed to set the parameters F. Further, a mutation strategy named DE/current-pbad/1 with archives is designed. In addition, a population size reduction strategy is devised to adaptively eliminate poor performance Individuals. The proposed algorithm has been tested by solving CEC2014 benchmark problems. The experimental results show that the EMPADE algorithm strategy is feasible and effective, and has greater competitiveness compared with other related algorithms.
Metal Character Detection Based on Improved Deformable Detr

To address the problem of inefficient and inaccurate inspection of workpiece characters In this paper, a fusion YOLOv5, we make datasets of the metal workpieces and propose character recognition methods based on deep learning. Firstly, Adding the attention mechanism (CBAM) to the backbone module of the Deformable DETR model to increase the initial picture feature extraction ability. By combining the benefits of Smooth-L1 loss and GIoU loss, the intercepted characters are passed through the ResNext50 model,thus further enhancing the recognition of characters and improving the overall recognition accuracy. The identification accuracy of the average metal workpiece based on the improved model can reach 84.5%, and the identification time of a single object is about 0.5s, which can be actually used in production.
Uncertainty-aware Unsupervised Video Hashing

Learning to hash has become popular for video retrieval due to its fast speed and low storage consumption. Previous efforts formulate video hashing as training a binary auto-encoder, for which noncontinuous latent representations are optimized by the biased straight-through (ST) back-propagation heuristic. We propose to formulate video hashing as learning a discrete variational auto-encoder with the factorized Bernoulli latent distribution, termed as Bernoulli variational auto-encoder (BerVAE). The corresponding evidence lower bound (ELBO) in our BerVAE implementation leads to closed-form gradient expression, which can be applied to achieve principled training along with some other unbiased gradient estimators. BerVAE enables uncertainty-aware video hashing by predicting the probability distribution of video hash code-words, thus providing reliable uncertainty quantification. Experiments on both simulated and real-world large-scale video data demonstrate that our BerVAE trained with unbiased gradient estimators can achieve the state-of-the-art retrieval performance. Furthermore, we show that quantified uncertainty is highly correlated to video retrieval performance, which can be leveraged to further improve the retrieval accuracy. Our code is available at
Design of Precision Configurable Multiply Accumulate Unit for Neural Network Accelerator

With the increasing size of models in Convolutional Neural Networks (CNNs) recently, the demand for memory size, bandwidth and computational resources has gradually become a central issue. Quantification has a pivotal role in dramatically reducing the computation of CNN models and bandwidth. However, quantization technology is difficult to improve the throughput and power efficiency of accurately fixed accelerators. Different applications have different requirements for accelerators in all aspects, and accurately fixing accelerators lacks the flexibility to meet these requirements. In this paper, a precision configurable processing unit (PE) is proposed, which not only simplifies the computing unit and the external complex configurable logic, but also introduces the concept of approximate calculation, while ensuring a certain precision of CNN. For the first time, approximate computation is introduced in a configurable computational unit, which allows the architecture to further reduce power consumption based on bit-level flexibility and to accommodate parameters from different quantization methods of the network. The design of this paper is implemented in SMIC 40nm process library. Compared with Bit Fusion [1], this method achieves the lowest accuracy of 98.49% in Lenet, ensuring that the area and power consumption are reduced by 53.2% and 19.8% respectively.
The Shape of Words - topological structure in natural language data

This paper presents a novel method, based on the ideas from algebraic topology, for the analysis of raw natural language text. The paper introduces the notion of a word manifold - a simplicial complex, whose topology encodes grammatical structure expressed by the corpus. Results of experiments with a variety of natural and synthetic languages are presented, showing that the homotopy type of the word manifold is influenced by linguistic structure. The analysis includes a new approach to the Voynich Manuscript - an unsolved puzzle in corpus linguistics. In contrast to existing topological data analysis approaches, we do not rely on the apparatus of persistent homology. Instead, we develop a method of generating topological structure directly from strings of words.