Miracle-dz/UniFAR-FTUs huggingface dataset:
Corrections to Chevalier-Skolnikoff ' s CoSlaboratiwe origins and gorilla data

of the original article: Spontaneous tool use and sensorimotor intelligence in Cebus were observed to determine whether tool use is discovered fortuitously and learned by trial-and-error or, rather, whether advanced sensorimotor abilities (experimentation and insight) are critical in its ontogeny and evolution. During 62 hours of observing three captive groups of Cebus monkeys, totaling 12 animals, 38 series and 66 acts of spontaneous tool use were recorded. Nine monkeys (75%) used tools; 14 different kinds of tool use were observed. Of seven captive spider monkeys observed for 21 hours, none used tools. Comparative observations of sensorimotor intelligence were made using Piaget's model. The sensorimotor basis of tool use by the Cehus was also analyzed. Cebus monkeys showed all six levels of sensorimotor intelligence, whereas the spider monkeys showed only the first four stages. Besides these correlations between tool use and advanced sensorimotor ability, 37 of the 38 tool-use series and 65 of 66 individual acts involved Stage 5 and 6 sensorimotor mechanisms (i.e., tertiary circular reactions and insight); only one series involved Stage 3 fortuitous discovery and Stage 4 coordinations. This study and a literature survey suggest that strong tool-using propensity among primates is based on advanced sensorimotor ability rather than fortuitous discovery. theory can contribute itudies dewelopment
Application of Repetitive Control Technique in Shunt Active Power Filter

Shunt active power filters (APF) have been proved as effective means to compensate distorted currents caused by nonlinear loads in power distribution systems. This paper presents an all-digital hybrid controller, called single synchronous frame hybrid (SSFH) controller concerning to special nonlinear load. The hybrid controller operates in an SSF mixing conventional PI with repetitive controller. This approach can get zero state error and improve robustness. A detailed design criterion for the SSFH controller is presented based on a frequency-response approach. The design is performed for the particular application of three-phase shunt active filter and several experimental results are also presented to show the good behavior of the closed-loop system.
Experiment Design on Control Pilot Circuit of AC Charging Coupler for Electric Vehicle

This paper presented basic functions and working princenples of control pilot circuit for charging coupler,designed a test method of matching resistance and voltage status for various measuring locations under different charging types.Confirmation of connection status for charging coupler,inspections of variation on power output capacity and execution behavior for charging stop under a normal or abnormal conditions as well as sequence of control at different charging stage were realized.Expermental results indicated that proposed test method is effective and practical for function measuring of control pilot circuit for the charging couplers.
Application of Wavelet Envelope Analysis to Rolling Bearing Diagnosis

In accordance with the defects of traditional envelope analysis to specify the resonant frequency band manually,the anthors introduce a wavelet envelope analysis method on basis of Gauss function. This method possesses a powerful property to extract the modulated frequency of resonant frequency band overall. A new scheme to select the optimum scale to monitor the condition of rolling bearings according to entropy of envelope spectrum is also proposed. Practical example shows that this method can be used to inspect accurately the defects of outer race, inner race and rolling elements in rolling bearings.
Constituency Cleavages and Congressional Parties: Measuring Homogeneity and Polarization, 1857-1913

We analyze the constituency bases of the congressional parties from 1857 through 1913 by focusing on two key concepts: party homogeneity and party polarization. With a few notable exceptions, prior efforts to assess these concepts have relied upon measures based on members' roll call votes. This is potentially problematic, as such measures are likely endogenous: They reflect the party's actual level of success as much as the party's underlying homogeneity. To address this problem, we construct measures for party homogeneity and polarization that are based on constituency characteristics, using economic-based census data and presidential voting data as proxies. We then examine how these "exogenous" measures compare to roll call–based measures. We find that changes in party unity on roll call votes track shifts in constituency characteristics fairly closely. Substantively, we find that the congressional parties went through three distinct phases during these 56 years: first, a period of extremely high overlap and low party homogeneity during the Civil War and Reconstruction, followed by a period of moderate polarization and homogeneity from the mid-1870s through the early 1890s, and concluding with a period of sharp polarization and high homogeneity, which coincided with the realignment of 1894–96. While the status of the 1894–96 elections as a critical turning point remains controversial in the historical and political science literatures, our results suggest that these elections did lead to a substantial change in the underlying characteristics of the congressional parties.
WSR #9 135-150 / 10

United States are likely to vary by ethnic or racial status (Williams, Lavizzo-Mourey, and Warren 1994; U.S. Department of Health and Human Services 1985), and this has been well documented for adolescents in particular (Office of Technology Assessment 1991). The health status of American Indian and Alaska Native adolescents has also been identified as poorer than that of adolescents of other racial or ethnic groups (Office of Technology Assessment 1990, 1986; Indian Health Service 1998). For American Indian and Alaska Native adolescents, social and health indicators illustrate increased risk for poor outcomes on almost all well-being measures. Overall, in American Indian and Alaska Native adolescents (ages 15 to 24), the mortality ratio is approximately three times greater (2.7:1) than for U.S. all races (Office of Technology Assessment 1986). American Indian and Alaska Native adolescents are exposed to poverty and racial discrimination, which severely limit individual, family, and community prosperity. In these environments, personal values and integrity may be discouraged or corrupted and human potential limited. In a self-administered health survey of American Indian and Alaska Native adolescents, 2% reported poor health, and another 20% reported only fair health. The level of self-perceived health status was significantly correlated with social risk factors of physical and/or sexual 135
Overexpression of miR-126 Protects Hypoxic-Reoxygenation-Exposed HUVEC Cellular Injury through Regulating LRP6 Expression

The aim of the study was to explore the clinical impact of circulatory miR-126 as a candidate for novel biomarker in patients with coronary artery disease (CAD) and its protective role against hypoxia/reoxygenation- (H/R-) exposed HUVEC cellular injury. A total of 278 subjects, which included 153 subjects with angiographically confirmed CAD, 70 unstable angina subjects, and 55 healthy individuals, along with 18-hour HR-induced HUVECs were recruited in this study. Plasma miR-126 levels were significantly downregulated in stable and unstable CAD patients as well as 18-hour HR-exposed HUVECs as compared with controls. Stable and unstable CAD subjects were significantly differentiated from healthy individuals with a predictive value of AUC 0.903 and 0.923, respectively. Moreover, peripheral circulatory miR-126 expressions in elderly (71-90 years) stable and unstable CAD patients were comparatively lower than younger (30-50 years) subjects. The caspase-3 activity, intracellular ROS concentrations, and cellular viabilities were evidently increased in 18-hour HR-exposed HUVECs than in normal cells (P < 0.001). On the contrary, mimic expressions of miR-126 prominently reduced caspase-3 activity and intracellular ROS levels and markedly enhanced HUVEC cellular viabilities (P < 0.001). LRP6 expressions were significantly elevated in HR-induced HUVECs, whereas overexpression of miR-126 remarkably decreased LRP6 expressions (P < 0.001). Plasma miR-126 could be used as a novel biomarker for early prediction of CAD subjects. Overexpression of miR-126 significantly improved HUVEC cellular viabilities by downregulation of LRP6 protein expression, suggesting a potential therapeutic target for CAD patients.
Field Determination of Phosphate in Environmental Water by Using a Hand-Powered Paper Centrifuge for Preconcentration and Digital Image Colorimetric Sensing

Phosphate concentration in natural water has been used as a water quality indicator, as it is one of the major nutrients for aquatic plants. However, the traditional phosphomolybdenum blue (PMB) method has limited sensitivity for visual or camera-based detection, leading to underestimation of the phosphate concentration. We present an ultralow-cost, rapid field preconcentration and digital image colorimetric sensing of low-concentration phosphate method for water analysis. A novel hand-powered paper centrifuge (paperfuge) is used for sample preparation and preconcentration. This paperfuge is made of two circular paper discs and a string. Six centrifuge tubes (CTs) originally used as glue dispensing tips with a sample capacity of ∼230 μL, are loaded on the paperfuge. After sampling, phosphate in the water sample is reacted to form PMB. Then, the reacted sample is drawn into a CT using an autopipette before the CT bottom is sealed by glue. After Oasis® HLB sorbents are added through the top of the CT, the CT top is also sealed with glue. The HLB sorbents adsorb PMB and are accumulated in the CT tip through centrifugation. The CT tips are cut and analyzed with the ImageJ software. It was found that the blue color intensity of sorbents is in a linear relationship to the phosphate concentration, with a linear range of 0–5 μM (r2 = 0.9921) and limit of detection of 0.19 μM. In addition, this method has been applied to in-field water analysis. The results are in agreement with the standard PMB method.
Recognition of Ocular Artifacts in EEG Signal through a Hybrid Optimized Scheme

Brain computer interface (BCI) requires an online and real-time processing of EEG signals. Hence, the accuracy of the recording system is improved by nullifying the developed artifacts. The goal of this proposal is to develop a hybrid model for recognizing and minimizing ocular artifacts through an improved deep learning scheme. The discrete wavelet transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the features are extracted by principal component analysis (PCA) and independent component analysis (ICA) techniques. After collecting the features, an optimized deformable convolutional network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying the empirical mean curve decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular artifact reduction by the proposed method.
MSNet: A Deep Multi-scale Submanifold Network for Visual Classification

—The Symmetric Positive Deﬁnite (SPD) matrix has received wide attention as a tool for visual data representation in computer vision. Although there are many different attempts to develop effective deep architectures for data processing on the Riemannian manifold of SPD matrices, a very few solutions explicitly mine the local geometrical information in deep SPD feature representations. While CNNs have demonstrated the potential of hierarchical local pattern extraction even for SPD represented data, we argue that it is of utmost importance to ensure the preservation of local geometric information in the SPD networks. Accordingly, in this work we propose an SPD network designed with this objective in mind. In particular, we propose an architecture, referred to as MSNet, which fuses geometrical multi-scale information. We ﬁrst analyse the convolution operator commonly used for mapping the local information in Euclidean deep networks from the perspective of a higher level of abstraction afforded by the Category Theory. Based on this analysis, we postulate a submanifold selection principle to guide the design of our MSNet. In particular, we use it to design a submanifold fusion block to take advantage of the rich local geometry encoded in the network layers. The experiments involving multiple visual tasks show that our algorithm outperforms most Riemannian SOTA competitors.
A Hitchhiker's Guide to Anomaly Detection with Astronomaly

The next generation of telescopes such as the SKA and the Rubin Observatory will produce enormous data sets, requiring automated anomaly detection to enable scientific discovery. Here, we present an overview and friendly user guide to the Astronomaly framework for active anomaly detection in astronomical data. Astronomaly uses active learning to combine the raw processing power of machine learning with the intuition and experience of a human user, enabling personalised recommendations of interesting anomalies. It makes use of a Python backend to perform data processing, feature extraction and machine learning to detect anomalous objects; and a JavaScript frontend to allow interaction with the data, labelling of interesting anomalous and active learning. Astronomaly is designed to be modular, extendable and run on almost any type of astronomical data. In this paper, we detail the structure of the Astronomaly code and provide guidelines for basic usage.
Design of a uniplanar printed triple band-rejected ultra-wideband antenna using particle swarm optimisation and the firefly algorithm

A compact planar monopole antenna is proposed for ultra-wideband applications. The antenna has a microstrip line feed and band-rejected characteristics and consists of a ring patch and a partial ground plane with a defective ground structure of rectangular shape. An annular strip is etched above the radiating element and two slots, one C-shaped and one arc-shaped, are embedded in the radiating patch. The proposed antenna has been optimised using bio-inspired algorithms such as the particle swarm optimisation and the firefly algorithm, based on a new software algorithm (Antenna Optimizer). Multi-objective optimisation achieves rejection bands at 3.3-3.7 GHz for Worldwide Interoperability for Microwave Access, 5.15-5.825 GHz for the 802.11a wireless local area network system or HIPERLAN/2, and 7.25-7.745 GHz for C-band satellite communication systems. Validated results show wideband performance from 2.7 to 10.6 GHz with S 11 <; -10 dB. The antenna has compact dimensions of 28 × 30 mm 2 . The radiation pattern is comparatively stable across the operating band with a relatively stable gain except in the notched bands.
Efficient Approximations of the Fisher Matrix in Neural Networks using Kronecker Product Singular Value Decomposition

We design four novel approximations of the Fisher Information Matrix (FIM) that plays a central role in natural gradient descent methods for neural networks. The newly proposed approximations are aimed at improving Martens and Grosse’s Kronecker-factored block diagonal (KFAC) one. They rely on a direct minimization problem, the solution of which can be computed via the Kronecker product singular value decomposition technique. Experimental results on the three standard deep auto-encoder benchmarks showed that they provide more accurate approximations to the FIM. Furthermore, they outperform KFAC and state-of-the-art first-order methods in terms of optimization speed.
Online Convex Optimization Using Coordinate Descent Algorithms

This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coordinate descent type algorithms to the online case, where the objective function varies after a finite number of iterations of the algorithm. Instead of solving the problem exactly at each time step, we only apply a finite number of iterations at each time step. Commonly used notions of regret are used to measure the performance of the online algorithm. Moreover, coordinate descent algorithms with different updating rules are considered, including both deterministic and stochastic rules that are developed in the literature of classical offline optimization. A thorough regret analysis is given for each case. Finally, numerical simulations are provided to illustrate the theoretical results.
A System for Image Understanding using Sensemaking and Narrative

Sensemaking and narrative are two inherently interconnected concepts about how people understand the world around them. Sensemaking is the process by which people structure and interconnect the information they encounter in the world with the knowledge and inferences they have made in the past. Narratives are important constructs that people use sensemaking to create; ones that reflect provide a more holistic account of the world than the information within any given narrative is able to alone. Both are important to how human beings parse the world, and both would be valuable for a computational system attempting to do the same. In this paper, we discuss theories of sensemaking and narrative with respect to how people build an understanding of the world based on the information they encounter, as well as the links between the fields of sensemaking and narrative research. We highlight a specific computational task, visual storytelling, whose solutions we believe can be enhanced by employing a sensemaking and narrative component. We then describe our system for visual storytelling using sensemaking and narrative and discuss examples from its current implementation.
DESIGNING A NEURO-SLIDING MODE CONTROLLER FOR NETWORKED CONTROL SYSTEMS WITH PACKET DROPOUT

A B S T R A C T This paper addresses control design in networked control system by considering stochastic packet dropouts in the forward path of the control loop. The packet dropouts are modelled by mutually independent stochastic variables satisfying Bernoulli binary distribution. A sliding mode controller is utilized to overcome the adverse influences of stochastic packet dropouts in networked control systems. Firstly, to determine the parameters of switching function used in the sliding mode control design, an improved genetic algorithm is applied. The proposed improved genetic algorithm provides a fast convergence rate and a proper dynamic performance in comparison with conventional genetic algorithms especially in online control applications. Then, an adaptive neural sliding mode control based on radial-basis function neural network approximation is proposed to eliminate chattering phenomenon in the sliding mode control. A numerical example is given to illustrate the effectiveness of the proposed controller in networked control systems. The results show that the proposed controller provides high-performance dynamic characteristics and robustness against plant parameter variations and external disturbances. doi: 10.5829/idosi.ije. 2016.29. 04a.07
Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks

Multi-access edge computing (MEC) is viewed as an integral part of future wireless networks to support new applications with stringent service reliability and latency requirements. However, guaranteeing ultra-reliable and low-latency MEC (URLL MEC) is very challenging due to uncertainties of wireless links, limited communications and computing resources, as well as dynamic network traffic. Enabling URLL MEC man-dates taking into account the statistics of the end-to-end (E2E) latency and reliability across the wireless and edge computing systems. In this paper, a novel framework is proposed to optimize the reliability of MEC networks by considering the distribution of E2E service delay, encompassing over-the-air transmission and edge computing latency. The proposed framework builds on correlated variational autoencoders (VAEs) to estimate the full distribution of the E2E service delay. Using this result, a new optimization problem based on risk theory is formulated to maximize the network reliability by minimizing the Conditional Value at Risk (CVaR) as a risk measure of the E2E service delay. To solve this problem, a new algorithm is developed to efficiently allocate users’ processing tasks to edge computing servers across the MEC network, while considering the statistics of the E2E service delay learned by VAEs. The simulation results show that the proposed scheme outperforms several baselines that do not account for the risk analyses or statistics of the E2E service delay.
Correcting for Reporting Delays in Cyber Incidents

With an ever evolving cyber domain, delays in reporting incidents are a well-known problem in the cyber insurance industry. Addressing this problem is a requisite to obtaining the true picture of cyber incident rates and to model it appropriately. The proposed algorithm addresses this problem by creating a model of the distribution of reporting delays and using the model to correct reported incident counts to account for the expected proportion of incidents that have occurred but not yet been reported. In particular, this correction shows an increase in the number of cyber events in recent months rather than the decline suggested by reported counts.
Linear seesaw in $A^\prime_5$ modular symmetry with Leptogenesis

In this paper, we investigate the implication of modular Γ (cid:48) 5 (cid:39) A (cid:48) 5 symmetry on neutrino oscillation phenomenology in the linear seesaw framework. In order to achieve the well deﬁned mass structure for the light active neutrinos as dictated by the linear seesaw mechanism, we introduce six heavy fermion ﬁelds along with a pair of weightons to retain the holomorphic nature of the superpotential. The notable feature of modular symmetry is that, it reduces the usage of ﬂavon ﬁelds signiﬁcantly. In addition, the Yukawa couplings transform non-trivially under the ﬂavor symmetry group and expressed in terms of the Dedekind eta functions, the q expansion of which renders numerical simplicity in calculations. We demonstrate that the model framework diligently accommodates all the neutrino oscillation data. Alongside, we also investigate the eﬀect of CP asymmetry generated from the decay of lightest heavy fermions to explain the observed baryon asymmetry through the phenomenon of leptogenesis.
Almost Optimal Variance-Constrained Best Arm Identification

We design and analyze Variance-Aware-Lower and Upper Confidence Bound (VA-LUCB), a parameter-free algorithm, for identifying the best arm under the fixed-confidence setup and under a stringent constraint that the variance of the chosen arm is strictly smaller than a given threshold. An upper bound on VA-LUCB’s sample complexity is shown to be characterized by a fundamental variance-aware hardness quantity <inline-formula> <tex-math notation="LaTeX">$H_{\mathrm {VA}}$ </tex-math></inline-formula>. By proving an information-theoretic lower bound, we show that sample complexity of VA-LUCB is optimal up to a factor logarithmic in <inline-formula> <tex-math notation="LaTeX">$H_{\mathrm {VA}}$ </tex-math></inline-formula>. Extensive experiments corroborate the dependence of the sample complexity on the various terms in <inline-formula> <tex-math notation="LaTeX">$H_{\mathrm {VA}}$ </tex-math></inline-formula>. By comparing VA-LUCB’s empirical performance to a close competitor RiskAverse-UCB-BAI by David et al. (2018) our experiments suggest that VA-LUCB has the lowest sample complexity for this class of risk-constrained best arm identification problems, especially for the riskiest instances.