Miracle-dz/UniFAR-FTUs huggingface dataset:
CCD system upgrading of the Kyoto3DII and integral field spectroscopic observation with the new system

The Kyoto Tridimensional Spectrograph II (Kyoto 3DII) is an optical integral field spectrograph mounted on the Subaru telescope as a PI-type instrument. Used with AO188, Kyoto 3DII provides us unique opportunities of optical Integral Field Spectroscopy (IFS) with adaptive optics (AO). While AO works better in redder wavelength regions, quantum efficiency of the previous CCD was low there with optimization for a wider wavelength coverage. To optimize Kyoto 3DII to AO observations, we have newly installed the red-sensitive Hamamatsu fully depleted CCD, which enhances the system efficiency by a factor of ~2 in the red wavelength range. Fringes are dramatically reduced, and the readout noise drops to 3:2-3:4e- about two times smaller than previous, due to refrigerator and readout system. With these improvements, we carried out engineering and scientific observations in September 2015, February and March 2016. We measured the system efficiency using a standard star, and confirmed the successful improvement of the system efficiency. We observed galactic nuclei of nearby galaxies in the Natural Guide Star (NGS) and the Laser Guide Star (LGS) modes. We found the spatial resolution of ~0.1′′ FWHM using a 9.5-magnitude NGS, and ~0.2 - 0:4′′ in LGS mode. Together with the AO resolution, improved efficiency opens a new window for Kyoto 3DII to carry out high resolution optical IFS targeting faint objects such as high-redshift galaxies as well as faint lines such as [OI] λ6300° A and absorption lines of nearby objects.
Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.
Enhancing data acquisition for calibration of optical see-through head-mounted displays

Abstract. Single point active alignment method is a widely used calibration method for optical-see-through head-mounted displays (OST-HMDs) since its appearance. It always requires high-accuracy alignment for data acquisition, and the collected data affect the calibration accuracy to a large extent. However, there are often many kinds of alignment errors occurring in the calibration process. These errors may contain random errors of manual alignment and system errors of the fixed eye-HMD model. To tackle these problems, we first leverage a random sample consensus approach to recurrently decrease the random error of the collected data sequence and use a region-induced data enhancement method to reduce the system error. We design a typical framework to enhance the data acquisition for calibration, sequentially reducing the random error and the system error. Experimental results show that the proposed method can significantly make the calibration more robust due to the elimination of sampling points with large errors. At the same time, the calibration accuracy can be increased by the proposed dynamic eye-HMD model that takes the eye movement into consideration. The improvement about calibration should be significant to promote the applications based on OST-HMDs.
Speaking Similarly: Team Personality Composition and Acoustic-Prosodic Entrainment

This study introduces the concept of acoustic-prosodic entrainment (ways people speak similarly). We review prior research on entrainment theory and methods from computational linguistics, and then apply this concept to team research by examining the relationship between team personality composition and subsequent entrainment in an exploratory case study. With 62 teams playing a cooperative board game, team average Agreeableness and team Agreeableness diversity positively, and Openness to Experience diversity negatively, preceded different kinds of entrainment. This study suggests entrainment is not a singular construct. Small group researchers could leverage technological, methodological, and conceptual advances in computational linguistics to study emergent team processes.
Structuring superconductor data with ontology: reproducing historical datasets as knowledge bases

ABSTRACT Applying historical human-readable databases to recent data-driven science is a natural concept. However, this cannot be realized by simply converting a database into a tabular format because the meaning of each table column and the relationships between columns need to be rewritten in a machine-readable format. In particular, eliminating implicit notations that can only be understood by experts in the fields covered by each database and making them machine-readable under a unified academic system is necessary when integrating data across fields with a view to solving specific social issues and social implementation. In this study, we constructed a superconducting materials ontology for SuperCon, a legacy superconductor database that was recently republished as a datasheet, based on the well-known Basic Formal Ontology (BFO) top ontology, and designed a schema for material composition, structure, properties, and processes, among others. Using this schema, we constructed and published the Resource Description Framework (RDF) for each instance in the SuperCon datasheet. We also discuss the machine-readable format of data common to materials science discovered in this process. GRAPHICAL ABSTRACT
Fabric defect detection using cartoon–texture image decomposition model and visual saliency method

Fabric defect detection is of great importance in the modern textile industry. In actual production, subjective factors usually affect manual detection, which is prone to problems such as false and missed detection. With the development of computer vision, a large number of fabric defect automatic detection algorithms have been proposed. Traditional algorithms rely too heavily on setting parameters manually, while deep learning algorithms have expensive training and computing costs. Given these limitations, a new fabric defect detection method based on the latest cartoon texture image decomposition model, visual saliency algorithm, and mathematical morphology, is proposed in this article. A digital image acquisition system is also designed and constructed. Therefore, the self-made dataset used in the experiment is composed of self-collected images and network public images. To further evaluate the performance of the proposed method, this study conducted fabric defect detection experiments and comparison experiments based on the self-made dataset. The results show that this method can successfully detect fabric defects, having high detection accuracy and efficiency. Combining subjective vision and objective evaluation, comparison experiments prove that this method is superior to other common fabric defect detection methods and has the highest value of accuracy and F1-score. This research provides a new method and technical support for fabric defect detection and other fields.
Auditors’ Communication with Audit Committee: Evidence from Indonesia

The aim of this study is to explore the auditors’ communication with those charged with governance (TCWG). Specifically, we examine the communication between auditors and the audit committee on the audit of financial statements in the year 2020 in the Indonesian setting. We use questionnaires to collect the data. Our samples are 72 audit committee members from various industries. We find that most of the auditors have discussed the matters that are required by the audit standards. However, we find that almost ten percent of the respondents reported that there was no kick-off meeting before the start of the audit process. The absence of a kick-off meeting may indicate that the audit plan is not communicated in a timely manner to the audit committee. We also find that face-to-face communication through a meeting is preferred to written communication most of the time.
A NOTE ON THE VARIANCE OF EX-POST FORECASTS IN

IT IS WELL KNOWN that in an econometric model, under general assumptions, the ex-post forecast error (conditional on predetermined variables) can be decomposed into the sum of two independent terms; the former is the component due to error in estimated coefficients, while the latter depends on the random error terms. The asymptotic covariance matrix of the first component can be analytically derived through intermediate computation of the covariance matrix of restricted reduced form coefficients. However, in the case of overidentified models, this may lead to a great increase in the problem's dimensions, the number of reduced form parameters being larger (often much larger) than the number of structural parameters. This intermediate step is, however, unnecessary; this note discusses a straightforward analytic derivation of the desired matrix directly from the estimated structural parameters, without any increase in the dimensions of the problem, thus facilitating the computation even for medium to large econometric models. Let
ENCAPSULATION OF VENLAFAXINE HCL IN TO POLY-E-CAPROLACTONE MICROSPHERES BY W/O/W AMD W/O/O EMULSION SOLVENT EVAPORATION TECHNIQUE AND COMPARISON FOR ITS CONTROLLED RELEASE PROPERTIES.

* Manabendra Rabha 1 and Dr. Bipul Nath 2 . 1. M. Pharm. , Department of Pharmaceutics, Girijananda Institute of Pharmaceutical Sciences, Azara, Guwahati, Assam-781017, India. 2. Girijananda Chowdhury Institute of Pharmaceutical Science, Azara, Guwahati-781017, India. Manuscript Info Abstract . Manuscript History
DTSSD: Dual-Channel Transformer-Based Network for Point-Based 3D Object Detection

In the field of 3D object detection, previous methods mainly utilize one channel feature encoding network to extract point-wise features. Despite the effectiveness, we find that only leveraging one channel encoding network is not sufficient and impedes the detection performance. To this end, we propose a dual-channel transformer-based feature encoding network, which integrates both set abstraction layer and transformer block as backbone. It enables the model to exploit fine-grained as well as long-range contextual information of objects, thus providing complementary relationship of two methods. In addition, a centroid estimation module is introduced to obtain powerful representation of the whole object. Finally, considering the significance of point density, which is crucial for detection performance, we propose a central density-aware enhancement module to equip center features with distinct density features. Experimental results on KITTI dataset show the effectiveness of our proposed method.
Capacity Development of Food Institutions in the Regency of Timor Tengah Selatan

One of the problems faced in efforts to increase local food production in Timor Tengah Selatan regency is that institutional performance is still low. Food institutions play an important role in facilitating and participating in efforts to increase food production. Empirical facts show that food institutions such as farmer groups, farmer groups associations, Karang Taruna, PKK groups, and instructors and companions’ capacities have not been optimal in providing support for increasing food production in the Timor Tengah Selatan Regency. This research aims to find out the capacity of rural food institutions in Timor Tengah Selatan regency, using qualitative research methods. Research informants were determined purposively which included implementing policies at Food Security Service; village head; farmer group leader; Karang Taruna leader; leader of PKK groups, instructor/companion. Based on the results of the study, it is known that there are several aspects of institutional capacity that contribute to increasing food production in the regency of Timor Tengah Selatan, namely: (1) resources, (2) skills and knowledge, and (3) politics and power. In order for food institutions to function properly, it is necessary to have the active involvement of all food stakeholders to always empower existing food institutions in rural areas in Timor Tengah Selatan regency. Keywords: institutional capacity, performance, food security, food production
Precomputing Shell Fragments for OLAP using Inverted Index Data Structure

— Efficient methods to generate data cubes for On-Line Analytical Processing or OLAP are required for query processing and data analysis. OLAP involves multidimensional analysis of data and as well as selectively extracting and viewing data from different perspectives or points of view. In OLAP, a complex query can lead to many scans of the base relational database, leading to poor performance. This research paper provides an algorithm for the data cube generation suitable for OLAP systems in a fast way. The OLAP cube structure, based on aggregation operations and capable of fast retrieval of data, is extensively explored. The inverted index data structure, which is a mapping from content to index of the said content in any indexed data storage system, is used as an efficient tool for shell fragment computation. A study of efficiency and trade-offs involved in terms of processing complexity and storage space when compared to full cube computation are also provided here.
Nonuniversal Coupling of Cage Scale Hopping and Collective Elastic Distortion as the Origin of Dynamic Fragility Diversity in Glass-Forming Polymer Liquids

We formulate a theoretical mechanism for the physical origin of the massive dynamic fragility range observed in long chain glass-forming polymer melts within the context of the force-level elastically collective nonlinear Langevin equation theory of coupled local–nonlocal activated segmental relaxation. The hypothesis involves how the cage scale barrier hopping process on the three Kuhn segment length scale is quantitatively coupled to the longer range collective elastic distortion required to sterically allow large-amplitude events to occur. The key nonuniversal aspect is proposed to be an effective microscopic jump distance, a dynamical quantity associated with the activation barrier, which is influenced by nanometer-scale conformational transition physics and monomer chemistry. By introducing a single numerical factor that breaks the universality of the jump distance in our mapping of polymers to liquids of disconnected Kuhn-sized hard spheres, one can account rather well, and simultaneously, for the v.
T-Slot Antennas-Embedded ZigBee Wireless Sensor Network System for IoT-Enabled Monitoring and Control Systems

This research proposes a 2.4 GHz T-slot antennas-embedded ZigBee wireless sensor network system, consisting of an Internet of Things (IoT) gateway board and a sensor node board, for IoT applications. Simulations were first carried out to optimize the parameters for the T-shaped slot patch antenna. The prototypes of the IoT gateway and sensor node boards were subsequently fabricated and measurements undertaken. The measured impedance matching $(|S_{11}|$ ), bandwidth, and gain of the proposed ZigBee sensor network system were −18 dB, 15.38%, and 1.722 dBi, respectively. Furthermore, the ZigBee IoT-based monitoring and control schemes based on the ZigBee wireless sensor network system were set up and experiments carried out in an enclosed area for the monitoring scheme and in an open area for the control scheme. The experimental results revealed that the proposed IoT-enabled 2.4 GHz ZigBee sensor network system with embedded T-slot patch antennas could efficiently be utilized in IoT-based monitoring and control systems. In essence, the novelty of this research lies in the integration of the IoT and ZigBee sensor network technologies to store data in a cloud server in a real-time fashion, as opposed to in the microcontroller memory which is common in conventional ZigBee systems. In addition, the data stored in the cloud server are retrievable and viewable via the Blynk application on smartphone, rendering the proposed 2.4 GHz T-slot antennas-embedded ZigBee wireless sensor network system operationally suitable for IoT-based monitoring and control systems.
“A radical point of view”: The discursive construction of the political identity of student activists

Recently there has been renewed interest in the intersection of identity and epistemics in social interaction, yet epistemics has still rarely been analyzed in political identity construction. This paper combines research on identity from a sociocultural linguistic perspective with epistemics using Conversation Analysis. The focus here is on understanding how a small group of student activists construct their shared political identities through epistemic stances towards their academic majors and career goals. Through a discourse analytic study of conversational data among these activists, I demonstrate the validity of the relationality principle of identity in accounting for how identities are constructed as related to one another. Furthermore, I examine the relational process of authentication in epistemic stances to legitimate claims to knowledge regarding political and academic identities, as well as alignment of stances in building group solidarity and shared political identity.
Role of Vitamin C and Citrus Fruits on Urinary Tract Infections in Owerri, Imo State of Nigeria

Complementary and alternative medicine has been recognized as an effective approach for the treatment of urinary tract infections by antibiotic-resistant bacteria. This experimental descriptive study assessed the role of Vitamin C and Citrus fruits on Urinary Tract Infections in Owerri. A total of 384 participants comprising men and women residing in Owerri, ranging from 18 to 70 years were selected using random sampling technique. The data were collected using self-structured interview questionnaire as well as the experimental results, and were analyzed using simple percentages and Odds Ratio. Out of 384 participants, 126(32.8%) of them had urinary tract infections, 26.3% males were infected, while, 39.2% of the females had infections. Of the 107 participants who were consuming Vitamin C and Citrus fruits, 92.5% (OR=3.8) of them had no bacterial infection growth; out of 90 participants of those on Vitamin C alone, 77.8%(OR=2.4) had no infection, as 60.0%(OR=1.8) of those on fruits alone had no infection, while, those without Vitamin C and Citrus fruits had the highest growth infection rate of 65.5%. The result revealed sharp differences in the rate of prevention of growths of infection among the four groups of the participants. The data analysis, showed that there was statistical significance (p=0.05) in the rate of prevention of urinary tract infection growth rates among the four groups. It is pertinent, to create more awareness among the population in the locality and indeed in the general populace on the importance of Vitamin C and Citrus fruits.
Proximal alternating linearized method for nonconvex and nonsmooth problems

We introduce a proximal alternating linearized minimization (PALM) algorithm for solving a broad class of nonconvex and nonsmooth minimization problems. Building on the powerful Kurdyka–Łojasiewicz property, we derive a self-contained convergence analysis framework and establish that each bounded sequence generated by PALM globally converges to a critical point. Our approach allows to analyze various classes of nonconvex-nonsmooth problems and related nonconvex proximal forward–backward algorithms with semi-algebraic problem’s data, the later property being shared by many functions arising in a wide variety of fundamental applications. A by-product of our framework also shows that our results are new even in the convex setting. As an illustration of the results, we derive a new and simple globally convergent algorithm for solving the sparse nonnegative matrix factorization problem.
Pointing Bias in “Spatial Matched Filter” Beamforming at a Tri-Axial Velocity-Sensor due to Non-Perpendicular Axes

This paper investigates how non-perpendicularity in a tri-axial velocity sensor would affect the tri-axial velocity-sensor#39s azimuth-elevation beam-pattern in terms of the beam#39s pointing direction and directivity. The vertical axis was adopted as the reference axis for the analysis and a rotation matrix developed to represent the non-perpendicularity in Euclidean space. The beampattern of this deformed tri-axial velocity sensor is then analytically studied. It was found that the non-perpendicularity does not affect the overall shape of the beampattern, but only introduces a pointing offset. Also, the non-perpendicularity imperfectionreduces the directivity of the imperfect triaxial velocity sensor relative to a perfect case. This finding developed in closed the pointing bias for the described deformity hence serves for non-iterative post data acquisition correction.