Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergent dynamics in an astrocyte-neuronal network coupled via nitric oxide

In the brain, both neurons and glial cells work in conjunction with each other during information processing. Stimulation of neurons can induce calcium oscillations in astrocytes which in turn can affect neuronal calcium dynamics. The ‘glissandi’ effect is one such phenomenon, associated with a decrease in infraslow fluctuations, in which synchronized calcium oscillations propagate as a wave in hundreds of astrocytes. Nitric oxide molecules released from the astrocytes contribute to synaptic functions based on the underlying astrocyte–neuron interaction network. In this study, by defining an astrocyte–neuronal (A–N) calcium unit as an integrated circuit of one neuron and one astrocyte, we developed a minimal model of neuronal stimulus-dependent and NO-mediated emergence of calcium waves in astrocytes. Incorporating inter-unit communication via NO molecules, a coupled network of 1000 such A–N calcium units is developed in which multiple stable regimes were found to emerge in astrocytes. We examined the ranges of neuronal stimulus strength and the coupling strength between A–N calcium units that give rise to such dynamical behaviors. We also report that there exists a range of coupling strength, wherein units not receiving stimulus also start showing oscillations and become synchronized. Our results support the hypothesis that glissandi-like phenomena exhibiting synchronized calcium oscillations in astrocytes help in efficient synaptic transmission by reducing the energy demand of the process.
Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks

Memory-augmented neural networks enhance a neural network with an external key-value (KV) memory whose complexity is typically dominated by the number of support vectors in the key memory. We propose a generalized KV memory that decouples its dimension from the number of support vectors by introducing a free parameter that can arbitrarily add or remove redundancy to the key memory representation. In effect, it provides an additional degree of freedom to flexibly control the tradeoff between robustness and the resources required to store and compute the generalized KV memory. This is particularly useful for realizing the key memory on in-memory computing hardware where it exploits nonideal, but extremely efficient nonvolatile memory devices for dense storage and computation. Experimental results show that adapting this parameter on demand effectively mitigates up to 44% nonidealities, at equal accuracy and number of devices, without any need for neural network retraining.
Vehicle logo recognition based on deep learning architecture in video surveillance for intelligent traffic system

Vehicle logo acquisition or recognition has been a popular study field in intelligent traffic system for the latest decade. In this paper, a vehicle logo recognition method based on CNN (Convolutional Neural Network) is introduced. In the experiment, two classification methods with different feature extraction methods were applied. However, these two classification methods were utilized the same SVM classifier in classification procedure. The experiment uses input images with height of 140 pixels and width of 100 pixels. The samples include 26 classes, which are derived from 16 different vehicle brands. The comparative experiment results indicate the CNN performs better in recognizing vehicle logos; the average accuracy rate of 26 classes achieves 99.23%, which is 8.61% higher than the other approach based on SIFT, whose accuracy rate is 90.62%.
Test Plan For Detecting Mersenne Twister Faults In BIST

— Mersenne twister can be utilized to generate test patterns for detecting LSI faults. The utilization of the Mersenne twister as a test pattern generator can improve the fault coverage. However, the Mersenne Twister may contain faults. Therefore, this paper examines whether there is a test sequence for detecting the Mersenne Twister faults. Experimental results show that the Mersenne Twister has a test sequence that reaches a 100% fault coverage for stuck-at faults
Review of, “Sensitivity of the Ross Ice Shelf to environmental and glaciological controls”, by Baldacchino et al.

The Ross Ice Shelf (RIS) is currently stable but recent observations have indicated that basal melt rates beneath the ice shelf are expected to increase. It is important to know which areas of the RIS are more sensitive to enhanced basal melting as well as other external forcings or internal material properties of the ice to understand how climate change will influence RIS mass balance. In this paper, we use Automatic Differentiation and the Ice Sheet and Sea-level System Model to quantify the sensitivity of the RIS to changes in basal friction, ice rigidity, surface mass balance, and basal melting. Using Volume 5 Above Flotation (VAF) as our quantity of interest, we find that the RIS is most sensitive to changes in basal friction and ice rigidity close to grounding lines and along shear margins of the Siple Coast Ice Streams and Transantarctic Mountains Outlet Glaciers. RIS sensitivity to surface mass balance is uniform over grounded ice, while the sensitivity to basal melting is more spatially variable. With changes in basal melting close to the grounding zones of the Siple Coast Ice Streams and Transantarctic Mountains Outlet Glaciers as well as the pinning points of the RIS including Ross Island, Roosevelt Island, Crary Ice Rise, 10 Steershead Ice Rise and the Shirase Coast Ice Rumples and the ice shelf shear margins having a larger impact on the RIS VAF. Our sensitivity maps allow areas of greatest future vulnerability to be identified.
High frequency low voltage shock free ferroelectric liquid crystal: A new electro-optical mode with electrically suppressed helix

Newly developed ferroelectric liquid crystal (FLC) with submicron helix pitch p 0 comprises advantages of both nematic and smectic C* phases in terms of steadiness to the mechanical stress and high contrast ratio more than 7000:1 at the operation frequency range up to 1 kHz with (cid:114) 1V/ (cid:80) m applied electric field.
Cassie Learning Dodging Skills: A Hierarchical Reinforcement Learning Based Approach

Bipedal locomotion presents a challenging set of control tasks that can be decomposed as (1) primitive and (2) task-speciﬁc high-level tasks. There exists a diverse set of data-driven and model-based controller optimization methods that enable legged robots to perform well on primitive (low-level) tasks such as walking, standing, and jumping. Learning high-level legged locomotion tasks is generally modeled as a two-layer optimization problem with a high-level and low-level control policy. We propose a similar and simple framework that uses Deep Reinforcement Learning (RL) to learn a high-level task given a ﬁxed and high-performing low-level policy. State-of-the-art architectures are developed as end-to-end frameworks where both the parameters of low-level and high-level policies are optimized jointly during training. Our method decouples the learning proce-dure of the high and low-level tasks as disjoint optimization problems, and uses a curriculum learning based approach to optimize the high-level task. We demonstrate the learning framework by teaching Cassie, a bipedal robot, to dodge a rolling ball using a jumping and standing primitive controller.
Araucaria Angustifolia: Influence of Mother Tree Sex and Provenance in Grafting Success

The development of vegetative propagation programs of Araucaria angustifolia still lacks on information about genetic factor that may influence on its success. We aimed to evaluate how mother tree sex and provenance influence on grafting success. So, we collected orthotropic branches from 26 mother trees male and female, of a clonal garden established from matrices from different provenances, and carried out an experiment using a patch grafting technique. Grafting survival, number and length of shoots did not differ between mother tree sex and provenance. The absence of significant variations between mother trees and no correlation with sex or provenance indicates that the morphological characteristics of rootstocks and shoots, and environmental conditions after grafting may have more influence on the success of grafting.
A Comparison between (ECM) and (KNN) Methods for the Multivariate skew-normal model with incomplete data

Statistical parameter estimation for multivariate data leads to waste of information, if the missing data are omitted, and in return will lead to inaccurate estimates, that is why incomplete data should be estimated using one of the statistical estimation methods to get accurate results and in return good estimates for the parameters. This paper aims to estimate the missing values for a multivariate skew normal model using the Expectation Conditional Maximization (ECM) algorithm and the K-Nearest Neighbour (KNN) method. After estimating the missing values, the model parameters will be estimated using the Maximum Likelihood Estimation (MLE) method and Newton-Raphson algorithm. A comparison between the (ECM) algorithm and the (KNN) method was conducted using the Mean Squared Error (MSE) for the model through simulation to find the best estimation method.
C22 Evolution of farming practices in the French vineyard The use of pesticides 2006-2013

The French surveys on viticulture 2006, 2010 and 2013 form part of a wide range of surveys on farming practices. One of their primary objective is to evaluate the use of pesticides. A good knowledge of their use is a major challenge for sustainable development because of their relationship with environmental and health risks.The first part of this study identifies the main indicators on the use of phytosanitary products and focuses on the treatment frequency index (TFI). Based on the Danish experience, this indicator is built with the normalized doses of pesticides actually applied by the wine producers. It can be easily expressed on a regional scale or on pesticides subdivision. In France, this indicator varies from 9.2 in Pyrénées-Orientales to 21.4 in Champagne in 2013. The second part raises the question of the climate effect on the TFI variations 2013/2010. The weather with its stimulus role in the parasite development can cause an augmentation of the use of pesticides. In 2013 degraded climatic conditions, the TFI has increased in all vineyards. The use of a fixed effect model leads us to estimate a significant climate effect in 7 vineyards out of 13. The weather variations between 2010 and 2013 contribute from 6% in Bourgogne to 31% in Val de Loire to the TFI increase.
Imiquimod as an alternative

Cervical intraepithelial neoplasia (CIN) refers to a precancerous condition which progresses to cervical cancer, the third most prevalent cancer worldwide [1]. Human papillomavirus (HPV) is one of the leading causes of CIN and cervical cancer as proven by epidemiological and molecular pathologic evidence [2]. CIN can be histologically classified into CIN 1, CIN 2, and CIN 3 [3]. The current treatment for CIN 2 and 3 involves mostly an excision, and of the different types of excision, conization is preferred, which is reported to cause a small number of short-term complications [4, 5]. However, conization can also lead to long-term complications such as preterm birth and perinatal mortality [6]. Moreover, a recent meta-analysis has reported that the loop electrosurgical excisional procedure (LEEP) is associated with preterm birth [7]. CIN commonly occurs among women of childbearing age [8]. However, there are currently no HPV therapeutic vaccines for the management of HPV-positive patients or medical therapy for CIN. Research on less invasive and safe methods of treatment for patients with CIN 2-3 who wish to become pregnant is currently needed. There has been some research on imiquimod for the conservative treatment of CIN [9-11]. Imiquimod is a therapeutic agent for genital warts, which develop as a result of HPV 6 and 11 infections, and has been used clinically since being approved in 1997 [12]. Some studies have reported imiquimod to be effective for treating HPV-related vulvar intraepithelial neoplasia [13, 14]. Imiquimod is a topical immune response modifier that has agonist effects on toll-like 7 receptors [15]. It induces the expression of cytokines including interferon-alpha, tumor necrosis factor-alpha, and interleukin 1, 6, and 8 by dendritic cells, thereby activating natural killer cells, which attack the HPV-infected and carcinogenic cells. Aside from mediating this innate immune response, imiquimod also induces the maturation of Langerhans cells and moves them into the lymph nodes, where it gives rise to an adaptive immune response including a Th1 response. Through these mechanisms, imiquimod plays both antiviral and antitumor roles [12, 16, 17]. In the present study, the authors evaluated the therapeutic effects and complications of imiquimod directly applied by a gynecologic oncologist to the cervix of women with highgrade CIN (HG-CIN, CIN 2 and 3) who refused to undergo excision therapy.
A remark on NIST SP 800-22 serial test

This paper represents a cumulative review of the serial statistical test over the canonical values used in testing and freely generated values. Also in this paper, we study by simulation, the variation of second type error, depending on certain factors: the range of p1,the length of the bit string represented by n and the m-bit pattern.
Evaluation of a Crushing-Roller Conductance Instrument for Single-Kernel Corn Moisture Measurement

ABSTRACT An instrument that sensed de electrical resistance of corn kernels as they passed between crushing rollers was evaluated for use in measuring the moisture content of single kernels. After calibration with a set of 61 corn samples representing six lots from Illinois, Iowa and Nebraska and covering a moisture range from 12 to 30%, wet basis, measurements were taken on other corn samples for verification. The standard error of performance was 0.99% moisture. The instrument was judged suitable for detecting corn lots of mixed moisture content when moisture contents of the blended lots differ by 2% or more.
Large-Scale Temporal Aggregation Using MapReduce

Temporal aggregation is a method to compute a compact summary of an input dataset that contains time varying information. Many different application domains, such as finance and environment, record huge amounts of data from which compact summaries have to be extracted. Due to the massive growth of the amount of such data, fast query processing becomes a challenge. In particular, computing instant temporal aggregation (ITA), a special form of temporal aggregation, is an expensive and time consuming process. To tackle these problems of processing massive datasets, various techniques and infrastructures have been proposed, including the use of clusters or networks of commodity machines. One of the most popular programming models recently proposed is MapReduce, which provides a framework for automatic parallel data processing in a cluster. In this thesis, we propose three different algorithms that implement the abstract programming model MapReduce in order to compute instant temporal aggregation for a given input relation. All three approaches are based on different strategies to compute temporal aggregation, hence provide distinct characteristics and strengths. The first approach is a straightforward extension of the traditional way to compute temporal aggregation. The second algorithm processes small chunks of the input file and iteratively merges the partial results to obtain the final relation. The third approach divides the timeline into independent partitions which can be processed in parallel. Furthermore, we report the results of a detailed empirical evaluation of the algorithms using differently structured datasets. All algorithms were implemented in Java and executed on a small cluster on top of the MapReduce framework Apache Hadoop. Additionally, we performed a comparison of the three MapReduce solutions with the existing Bucket algorithm for large-scale temporal aggregation. The experiments show that our algorithms outperform the Bucket algorithm by a factor of 5 to 20.
Algorithmic Correction of MOS Gas Sensor for Ambient Temperature and Relative Humidity Fluctuations

In ambient conditions, uncontrolled fluctuations of ambient temperature and relative humidity significantly reduce the accuracy of metal oxide semiconductor-based gas sensors. The main innovation of the work is an algorithmic self-correction method that employs a response from an auxiliary temperature sensor and a multivariable polynomial regression model to minimize the effect of such environmental fluctuations. Tin oxide (SnO2) MOS gas sensors were chosen for a sample study due to their fast response times and high resolution. Using the developed correction method, the study analyzes sensor accuracy while measuring a typical pollutant gas (carbon monoxide) over a temperature range from −10 to 40°C at relative humidity intervals of 35%, 65%, and 95%. On average, the corrected sensors measured a 40 ppm CO gas sample with 11 times more accuracy when compared to uncorrected sensors placed in the same variable ambient conditions. Corroborating ANOVA tests demonstrated that the prescribed correction algorithm reduced measurement variability with statistical significance. In the presence of CO2, CH4, ethanol, LPG, or smoke interference gas samples, sensors applying the correction method and temperature-based selectivity distinguished and measured CO concentrations with a maximum error of only ±2.0 ppm. Thus, the proposed technical solution is attractive for highly variable environments and field applications where low-cost drift stabilization is necessary.
The optimization of gas pipeline transmission and end segment storage in gas network

This paper describes a mathematical model for natural gas pipeline network which can help the sales company achieve maximum economic profits. In this paper, we analyze the pipeline network and consider many factors, including the end segment of pipeline for storage. Then we choose the feasible direction algorithm for constrained nonlinear programming to solve the model. At the end of this paper, we give an example to support our model's effectiveness and summarize the steps about our model. We hope that the new natural gas pipeline network model can give a reference to the development of China's natural gas industry.
Intrusion Detection using Rule Learning based Classifiers

The tremendous improvements of technologies including various smart devices have made the use of computers easy for gathering and sharing information using the Internet. The various private and government organizations store valuable data over the network. The security of computer networks has become a crucial problem due to the importance and sensitivity of the information communicated. To defend against various cyber attacks and computer viruses, lots of computer security techniques such as data encryption, Virtual Private Network, proxies, avoiding programming errors, firewalls, antivirus have failed to protect networks and systems from increasingly sophisticated attacks and malwares. Intrusion Detection Systems (IDSs) are a major line of defense for protecting network resources from illegal penetrations. In this work, rule learning based techniques have been proposed for building an intrusion detection model using four classifiers, namely, Decision Table, Decision Table/Naïve Bayes (DTNB), JRip, and Ripple Down Rule Learner (RIDOR). Further, to improve the performance of the model, relevant features have been extracted from the dataset through preprocessing by applying three bio-inspired search techniques such as Ant search, Genetic search, and Particle Swarm Optimization search and two informed search techniques such as Best first search and Greedy stepwise search . The performance of the model has been evaluated on the NSL-KDD intrusion dataset in terms of accuracy, precision, detection rate or recall, false alarm rate and F-value.
A New Detached Binary USNO-B1.0 1421-0485411 with the Secondary Component having Large Albedo

USNO-B1.0 1421-0485411 is an eclipsing binary system newly discovered in the Yunnan-Hong Kong wide-field photometric survey. Its orbital period is 1.295 days. Based on the V- and R-band photometric data collected at Kunming 1.0 m telescope and spectroscopic data observed at Lijiang 2.4 m telescope, we used the Wilson-Devinney program to determine the physical parameters of the binary system. The results show that the mass and radius are 2.21 M ⊙ and 1.70 R ⊙ for the A0V primary component, and 2.11 M ⊙ and 1.77 R ⊙ for the A4–5V secondary one. The observed light curves show higher shoulders around the secondary eclipse, which can originate from hot spots, circumstellar materials, abnormal albedo of the secondary component, etc. Through analyzing and modeling, a quite large albedo (∼1.89) of the secondary component was considered to be the most possible reason for this phenomenon. The position of the two components on the H-R diagram implies that the binary is in main-sequence stage.
The Search for a Counterpart to NuSTAR J053449+2126.0

(ATel #15171) have recently reported the discovery of an X-ray source, NuSTAR J053449+2126.0, during a calibration observation which took place on 2020 April 25. We scan the Zwicky Transient Facility alerts and archival photometry to determine the nature of the source. Palomar Gattini-IR is searched as well. We identify no obvious counterpart candidate. Follow-up X-ray and optical studies are needed to determine the true counterpart.
DERMATOPHYTOSIS IN CATS AND DOGS: MOLECULAR IDENTIFICATION AND EPIDEMIOLOGY

Dermatophyte species are strongly related group of filamentous pathogenic fungi. They are keratinophilic and keratinolytic, and responsible for superficial infection designated as dermatophytosis or ringworm. The most commonly used diagnostic methods, even including fungal culture, have some negative properties. This situation encourages the searching and development of new diagnostic and identification methods. Among the most optimal and promising are methods of molecular biology based on the PCR technique. There are studies to develop the molecular methods for identification / detection of dermatophytosis from clinical samples. However, mostly are not adapted to veterinary samples. Those that are specific for samples from animals still have limitations due to the type of specimens or pathogen species of the pathogen, sensitivity and accuracy vary from species to species-carrier. In face of globalization and evolutionary changes, it is also important to pay attention to another aspect epidemiology. Epidemiological data should be one of the bases in the conduct of therapeutic and diagnostic procedures, especially when it concerns pathogenic fungi, which are common everywhere. Epidemiological data should be periodically updated and reflect a current situation. The main goal of this work was to optimize the selected protocol for identifying dermatophytes from clinical specimens of dogs and cats, as well as updating epidemiological data on dogs and cats dermatophytosis.