Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowing What You’ve Got Once It’s Gone: Identifying Familial Norms and Values through the Lens of (Sibling) Bereavement

Following the death of a sibling, life as it was known and expected to be lived is permanently and irrevocably set on a different trajectory. Surviving siblings are left to consider all that they have lost beyond the individual who died. Using data from a qualitative study exploring experiences of sibling bereavement over the life course, this article presents a set of assumptions that people had regarding their imagined but unliveable futures. In doing so, it outlines how these ideas contribute to a currently under-developed understanding of normative expectations of the adult sibling relationship, as well as wider familial norms and values. As such, this article will demonstrate that death can actually reveal much about living relationships. It will conclude that bereavement research has much to offer the sociologies of family, relationships and personal life, as an alternative lens through which to learn more about familial norms and values.
Virial theorem analysis of the structure and stability of magnetized clouds

The tensor virial theorem is used to analyze the structure and stability of self-gravitating, magnetized spheroids surrounded by a low-density medium with pressure and magnetic field. Analytical expressions are developed for the effect of a weak field and calculate critical states when the effect of the field is arbitrarily strong, comparing the results with full magnetohydrostatic calculations. This analysis suggests that a magnetic field may prevent gravitational collapse but may also be destabilizing, depending on its degree of concentration within the cloud. 34 refs.
iMGC: Interactive Multiple Graph Clustering With Constrained Laplacian Rank

Numerous graph clustering methods have been proposed to explore aggregation structures across multiple graphs. In these methods, single-graph features are merely considered or multigraph features are simply weighted, which are insufficient for the construction of reasonable multiple graph clustering features, since the association information between pairwise graphs is ignored and the varied local correlations might influence the clustering preference. Thus, we propose an interactive multiple graph clustering model, iMGC, in this article, to achieve reasonable multiple graph clustering features, which cannot only express multiple relationships, but also preserve associations of nodes across multiple graphs. First, a unified graph matrix is constructed with the combination of structural differences quantified by graph representation learning, which is further optimized by minimizing the difference of structural characteristics between it and each single graph matrix. Thus, multiple relationships are well integrated and expressed, while the varied local correlations within different graphs are also balanced in the unified graph matrix. Then, a constrained Laplacian rank is applied on the unified graph matrix to generate the unified clustering result directly, which is able to preserve association features across multiple graphs. Furthermore, we provide a set of visualization and interaction interfaces, enabling users to intuitively optimize and evaluate the multiple graph clustering features, and interactively explore the multiple graphs. Case studies and quantitative comparisons based on real-world datasets have demonstrated the effectiveness of iMGC in the clustering performance from various perspectives and exploration of multiple graphs.
Internet of things and Big Data Analytic: A State of the Art Review

Th Connectivity plays a vital role in today's world when it comes to a device or things connection. The Internet of Things (IoT) plays a fundamental role in understanding real-world objects virtually and generating massive amounts of data from and for them. The exponential rise in data consumption and the exploding number of IoT-connected devices are only two indicators of how the expansion of Big-Data (BD) exactly parallels that of the IoT. Concerns of data collecting efficiency, data processing, analytics, and security are not trivial when it comes to managing big data in a constantly growing network. Researchers have looked at the difficulties of implementing the Internet of Things to find solutions to these issues. Despite the abundance of research on big data, analytics, and the Internet of Things, the intersection of these fields opens several prospects for developing big data and analytics in IoT settings. This paper focuses on feasible and ideal solutions for the effective integration of IoT with BD. The challenges of combining IoT and BD were also illustrated by presenting an analysis of this issue to a community of readers.
Improving multiple removal using least-squares dip filters and independent component analysis

The suppression of multiple events is a crucial task in seismic data processing, and the adaptive subtraction of the predicted multiples is recognized as one of the main challenges for the success of the surface-related multiple elimination technique. The traditional least-squares matching approach can affect the primary events because the estimated multiples tend to adapt to the primaries under the minimum energy condition. We investigate two filtering techniques for improving the multiple removal results. In the first proposed method, we combine the advantages of the least-squares and pattern dip-based subtraction methods. Doing so, we exploit the separation of primaries and multiples in the dip domain, and then we apply the least-squares adaptive subtraction in each dip band before recomposing the data to obtain the final subtraction result. As a result of the dip decomposition, the primary-multiple interferences are reduced, allowing for a more reliable least-squares filtering. In the second method, w.
Automated Test Case Generation from Input Specification in Natural Language

This paper studies the problem of automated test case generation for online coding test, i.e., given an input specification in natural language, how can we generate test cases automatically to examine the correctness of the code implemented by the testee? To tackle the problem, this paper proposes an approach that first extracts noun phrases from an input specification; then it removes irrelevant noun phrases and only retains the key phrases related to input construction; by reorganizing these key phrases, it can form an information tree and generate test cases accordingly. We have evaluated our approach with two datasets from LeetCode and ACM and achieved promising results.
Study on the Characteristics and Influencing Factors of Hydrophobicity Transfer of Superhydrophobic Coating

Insulators covered with superhydrophobic coatings have better anti-pollution flashover performance. In high-pollution areas, the superhydrophobic coating gets covered with a contamination layer. The droplets are in contact with the contamination layer instead of the superhydrophobic coating. However, there is still a lack of relevant research on the hydrophobicity of the contamination layer. It is not conducive to evaluating the functional performance of superhydrophobic insulators in high-pollution areas. Choose diatomite to simulate contamination. The effects of fouling time, environment temperature, diatomite density, and impurity (NaCl) on the hydrophobicity of the contamination layer are analyzed experimentally. The experiment results show that the diatomite layer becomes hydrophobic over time. The higher the temperature is, the faster the hydrophobicity of the contamination layer will increase, and the higher the peak value of the static contact angle will be. However, the diatomite density and the exitance of NaCl have little influence on the hydrophobicity of the diatomite layer. Furtherly, the composition of the superhydrophobic coating is found in the diatomite layer, which proves the existence of hydrophobicity transfer. According to theoretical analysis, the appearance of transfer will enhance the hydrophobicity of diatomite particles. When the angular coordinate of the liquid-protrusions-gas triple line is greater than 90°, the mechanical conditions for the diatomite particles carried away by the droplet can be satisfied. As a result, hydrophobic diatomite is attached to the droplets’ surface.
Data Analytics of Procurement Fraud Risks in Indonesia

The era of digital transformation requires management and auditors to apply data analytics. This era also increases the risk of procurement fraud. This study aims to determine the application of data analytics in handling the risk of procurement fraud in the 4.0 era. Quantitative methods are applied with data analytics techniques to 2018 provincial tender data. The results show that descriptive statistical techniques can reveal the profile of collusion risk attributes. The average provincial construction tender in 2018 indicates a risk of collusion above 50% for the high price attribute. The visualization technique produces a tender collusion risk dashboard. There are 3 provincial LPSEs detected to meet the combined risk indications of collusion. The level of likelihood of collusion risk in the 3 provinces also shows “almost certain to occur” or with a value of 5. The results of this study have implications for data analytics which are very important to be immediately applied in government procurement to manage the risk of procurement fraud.
Facts and Artifacts in Interstellar Diamond Spectra

Absorption spectra of presolar diamonds extracted from the Murchison meteorite have been measured in the extended wavelength range 0.2-500 μm in order to make available optical properties of this supposed component of interstellar carbon dust. In contrast to terrestrial natural and synthetic diamonds, spectra of the meteoritic diamonds show prominent bands in the middle-IR. In this Letter, experimental evidence is presented that the OH band at 3200 cm-1 and the CH bands in the 2800-3000 cm-1 range are not intrinsic features of the diamonds and that the band at 1100 cm-1 contains an artificial component due to the extraction procedure. In addition, in our spectra a conspicuous band at 120 cm-1 was found. If the intrinsic character of this band, which, up to now, is unidentified, is confirmed, it would offer a chance to observe interstellar diamonds, e.g., by the ISO satellite. We encourage laboratory astrophysicists and observers to study this promising possibility.
A Dynamic Maintenance Task Scheduling Method Considering Maintenance Mode

Maintenance scheduling has become an indispensable part of today's industrial technology development. Reasonable arrangement of maintenance tasks can greatly speed up the maintenance process, ensure the quality of maintenance, and improve efficiency. However, as an important branch of maintenance schedule, there is no tasks scheduling of maintenance that dynamically considers the impact of maintenance mode (replacement, overhaul, and minor repair). Therefore, this paper will create a mixed-integer mathematical model to dynamically schedule maintenance tasks considering maintenance mode. Different involvement of maintenance mode is included in the experiments and the experimental results proved that the proposed method can save the maintenance cost.
Learning Important Regions via Attention for Video Streaming on Cloud Robotics

Cloud robotics, i.e., controlling robots from the cloud, make it possible to perform more complex processes, make robots smaller, and coordinate multi-robots by sharing information between robots and utilizing abundant computing resources. In cloud robotics, robots need to transmit videos to the cloud in real time to recognize their surroundings. Lowering the video quality reduces the bitrate in low bandwidth environments; however, this may lead to control errors and misrecognition due to lack of detailed image features. Even with 5G, bandwidth fluctuates widely, especially in moving robots, making it difficult to upload high quality video consistently. To reduce bitrate while preserving Quality of Control (QoC), we propose a method of learning the important regions for a pretrained autonomous agent using self-attention, and transmitting the video to the agent by controlling the image quality of each region on the basis of the estimated importance. The evaluation results demonstrate that our approach can maintain QoC while reducing the bitrate to 26% by setting important regions to high quality and the rest to low quality.
Atomic layer deposition of lanthanum aluminum oxide nano-laminates for electrical applications

Lanthanum aluminum oxide thin films were grown by atomic layer deposition from a lanthanum precursor, tris(N,N′-diisopropylacetamidinato)lanthanum (La(iPrAMD)3), trimethylaluminum and water. Smooth, amorphous films having compositions La0.5Al1. 5O3 and La0.9Al1. 1O3 were deposited on HF-last silicon and characterized without postdeposition annealing. The films contained less than 1 at. % of carbon according to Rutherford backstattering spectrometry and secondary ion mass spectrometry. A thin (9.8 nm) film showed low leakage current (<5*10−8 A/cm2 at 1 V for an equivalent oxide thickness of 2.9 nm), flatband voltage of −0.1 V and low hysteresis (20 mV). Thicker films had even lower leakage currents (<10−8 A/cm2 at 2 MV/cm) but larger flatband shifts and more hysteresis. The permittivity of the films was 13 and the dielectric strength 4 MV/cm. Cross sectional high-resolution transmission electron microscopy showed a sharp interface between the film and the silicon substrate.
Decreased Enterobacteriaceae translocation due to gut microbiota remodeling mediates the alleviation of premature aging by a high‐fat diet

Aging‐associated microbial dysbiosis exacerbates various disorders and dysfunctions, and is a major contributor to morbidity and mortality in the elderly, but the underlying cause of this aging‐related syndrome is confusing. SIRT6 knockout (SIRT6 KO) mice undergo premature aging and succumb to death by 4 weeks, and are therefore useful as a premature aging research model. Here, fecal microbiota transplantation from SIRT6 KO mice into wild‐type (WT) mice phenocopies the gut dysbiosis and premature aging observed in SIRT6 KO mice. Conversely, an expanded lifespan was observed in SIRT6 KO mice when transplanted with microbiota from WT mice. Antibiotic cocktail treatment attenuated inflammation and cell senescence in KO mice, directly suggesting that gut dysbiosis contributes to the premature aging of SIRT6 KO mice. Increased Enterobacteriaceae translocation, driven by the overgrowth of Escherichia coli, is the likely mechanism for the premature aging effects of microbiome dysregulation, which could be reversed by a high‐fat diet. Our results provide a mechanism for the causal link between gut dysbiosis and aging, and support a beneficial effect of a high‐fat diet for correcting gut dysbiosis and alleviating premature aging. This study provides a rationale for the integration of microbiome‐based high‐fat diets into therapeutic interventions against aging‐associated diseases.
A Framework of Rehabilitation-assisted Robot Skill Representation, Learning, and Modulation via Manifold-Mappings and Gaussian Processes

Stroke survivors usually have dyskinesia, who have an urgent need for rehabilitation-assist training. To reduce the labor of rehabilitation therapists, this paper attempts to investigate an effective rehabilitation-assisted robot skill acquisition framework, which is inspired by the scheme of robot learning from demonstration (LfD). Since most of the current LfD methods were implemented with rigorous assumptions that the considering motion features are only represented on an individual manifold. Meanwhile, despite many advancements that have been achieved on time-position trajectories and position-velocity trajectories, those methods are restricted to Euclidean space and can not be applied to learn those dexterous and compliant rehabilitation-assisted robot skills such as position-orientation trajectories and force-stiffness trajectories, etc. In this paper, we propose a novel skill acquisition framework for rehabilitation-assisted robot using manifold-mappings and Gaussian processes, which allows the robot to 1) simultaneously considering the robot position, orientation, force as well as stiffness by manifold-mappings among d-dimensional Euclidean space $\mathcal{R}^{d}$, special orthogonal group $S\mathcal{O}$ (3), and Riemannian space $\mathcal{M}$, respectively, which resulting in accurate motion and compliant behavior; 2) retrieving skill representation by encap-sulating the variability of multiple high-dimensional demon-strations that with input-dependent noises; 3) implementing the via-points-based trajectory modulation by considering task constraints or environmental changes. To simplify the writing, we named the proposed framework as Multi-motion Features Fusion-based Robot Skill Learning (MF2 RoSL). To effectively evaluate the effectiveness of our proposed method, an upper limb rehabilitation training system with a collaborative Kinova robot is developed. The training exercises of our system are determined according to the Brunnstrom therapeutic approach to the management of hemiplegic patients, including the 3-DoFs movement of the shoulder joint and a 7-DoF movement of an insertion/extraction task for assessing the activities of daily living (ADL). Results indicate that our proposed MF2RoSL method allows the robot to learn rehabilitation skills from the therapist and can be rapidly adapted to new patients.
Optimization of Satellite Combination in Kinematic Positioning Mode with the Aid of Genetic Algorithm

Optimization of Satellite Combination in Kinematic Positioning Mode with the Aid of Genetic Algorithm The basis of high precision relative positioning is the use of carrier phase measurements. Data differencing techniques are one of the keys to achieving high precision positioning results as they can significantly reduce a variety of errors or biases in the observations and models. Since GPS observations are usually contaminated by many errors such as the atmospheric biases, the receiver clock bias, the satellite clock bias, and so on, it is impossible to model all systematic errors in the functional model. Although the data differencing techniques are widely used for constructing the functional model, some un-modeled systematic biases still remain in the GPS observations following such differencing. Another key to achieving high precision positioning results is to fix the initial carrier phase ambiguities to their theoretical integer values. To obtain a high percentage of successful ambiguity-fixed rates, noisy GPS satellites have to be identified and removed from the data processing step. This paper introduces a new method using genetic algorithm (GA) to optimize the best combination of GPS satellites which yields the highest number of successful ambiguity-fixed solutions in kinematic positioning mode. The results indicate that the use of GA can produce higher number of ambiguity-fixed solutions than the standard data processing technique.
Evidence of discrete interface traps on thermally grown thin silicon oxide films

A very-low density (<1010 cm−2) residual discrete energy-level interface trap at 228 meV from the Si midgap is detected on crystalline silicon covered by thermally grown oxide. Three signatures pointing to discreteness are delineated in the experimental data of the gate voltage modulation of the dc steady-state electron-hole recombination rate. Using the Shockley-Read–Hall recombination kinetics, the energy level is shown to have a linewidth less than 20 meV. From the present and previous data, the dopant impurity origin of this residual interface trap species is speculated rather than the amphoteric silicon dangling bonds.
Preliminary DOE Analysis of the Polishing Process of AISI P20 Mould Steel by Ultrasonic Erosion, Using Alumina and Diamond Hard Particles

The present work examines the surface finishing of AISI P20 tool steel, average hardness of 31.5 HRc, in the polishing process by ultrasonic erosion by loose abrasive particles. The study was performed in laboratory equipment, using various alumina and diamond hard particle sizes, ultrasonic frequencies and times. P20 steel is widely employed in the fabrication of polymer injection mould cavities due to its good machinability, homogeneous microstructure, hardness, corrosion and abrasion wear resistance. Mould cavity polishing processes represents the second longest manufacturing process time required to achieve the mould surface finish for good quality polymer parts. This time-consuming and largely artisanal polishing process contributes to high mould costs. Thus, several studies have been carried out in the literature to automate the mould polishing process, aiming to reducing surface roughness and polishing time. Present investigation was carried out in a surface cleaning equipment by the ultrasonic technique with commercial paste of abrasive hard particles used in mould polishing. Three factors were investigated: abrasive particle size, ultrasonic frequency and polishing time. The investigated alumina particle sizes were 0.05, 0.3, and 1.0 micron and the diamond particle size was 1.5 micron, which are the common sizes available in commercial paste for polishing metallography samples. The studied frequencies of the ultrasonic polishing process were 28 and 40 kHz, and the times of polishing process were 15, 30, and 45 minutes. Analysis of variance (ANOVA) of factor effects and two types of DOE analysis were employed to obtain the roughness Ra outcomes by empirical response surface modelling: 3x3 partial factorial design and 4x2 factorial design were used to investigate the surface roughness. The response surface modelling results indicate that the minimum surface roughness Ra of 0.21 micron obtained by ultrasonic polishing correspond to the frequency of 40 kHz, 20 minutes of polishing and alumina particle size of 0.7 microns.
Simultaneous Gesture Classification and Speed Control for Myoelectric Prosthetic Hand Using Joint-Loss Neural Network

Gesture classification and motion speed regression are always two major issues in myoelectrical prosthetic hand research. However, there is little research considering these two issues in conjunction. Some shared EMG feature information in these two processing tasks is promising to improve the performance of prosthetic hand control. In this study, a joint-loss (JL) neural network architecture is proposed to implement gesture classification and motion speed regression problems in parallel by sharing the hidden neural units in the training process and optimizing the joint loss function. We evaluated the proposed control system through motion experiments performed on six participants. The experiment result shows that the classification and regression models can successfully reproduce smooth movement based on EMG measurement with high accuracy. Furthermore, the possibility of clinical application is demonstrated through the online movement of the real prosthetic hand.
A Data Deduplication Scheme Based on DBSCAN With Tolerable Clustering Deviation

To protect data privacy, users prefer to store encrypted data in cloud servers. Cloud servers reduce the cost of storage and network bandwidth by eliminating duplicate copies. To address the potential internal data leakage problem, the concept of clustering deviation is proposed for the first time. We improve the DBSCAN algorithm to tolerate clustering deviation. A data deduplication scheme is built upon the new algorithm, which considers users as clustering samples. Instead of immediately re-clustering new users, a certain deviation is tolerated to assign the users to the existing classes. We determine the popularity of the data according to user clustering results and apply different encryption schemes to protect the security of unpopular data more effectively. The performance of the algorithm is analyzed and compared with other methods through experiments, and the results verify the feasibility and efficiency of the proposed deduplication scheme.