Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk identification of calibration laboratories

The article is devoted to the identification, ranking and quantitative analysis of the risks that affect the quality of calibration work in the calibration laboratory. The paper used the brainstorming technique, the Ishikawa chart, and the ranking method for research. The results are represented by the Pareto chart. The ranked list was compiled based on the statistical data of the calibration laboratory. The ranked list was used as assessment criteria of risk.
Effect of microplastics and natural microparticles on green Mussel (Perna viridis)

There are so many studies of microplastics on marine animals, but almost all of them do not compare the effect of microplastics with natural microparticles which are also can be a stressor for the animals. This can lead us to overestimate the effect of microplastics on marine animals. Therefore we conducted a study to see whether the effect of microplastics was the same as the effect of natural microparticles on green mussels (Perna viridis). This research was conducted experimentally in the laboratory for 21 days. Green mussels were exposed to PVC as microplastics and red clay as natural microparticles with 3 different concentrations (1.5 mg/l, 15 mg/l and 150 mg/l). We observed mortality, clearance rates, and BCI index. Clearance rates and mortality values were not statistically significant, while the BCI results showed that the effects of PVC and red clay at each concentration were only less than 10%. This suggests that the effect of microplastics and natural microparticles is similar, and it is necessary to include natural microparticles as a comparison in research related to microplastics.
Software fault location technology under terminal embedded component

In order to improve the software fault location capability of embedded components of heterogeneous Web server terminals, a software fault location method of embedded components of heterogeneous Web server terminals based on source code fault feature detection is proposed. The original information collection model for software fault detection of embedded components of heterogeneous Web server terminals is constructed, the big data feature quantity of software safety information of embedded components is mined. The spectrum information of software safety information is extracted by adopting a dynamic information fusion method, so as to obtain the fuzzy closeness of software faults. The big data information parameters of embedded component software faults of heterogeneous Web server terminals are adaptively adjusted, and the cross information distribution set of embedded component software faults of heterogeneous Web server terminals is determined. The disturbance of embedded component software faults of heterogeneous Web server terminals is obtained by using the method of limit mining, and the adaptive mining of software faults is realized. Simulation results show that this method is accurate in locating software faults under terminal embedded components, and the software has strong anti-attack ability.
Motion Planning for a Bounding Quadruped Robot Using iLQG Based MPC

When tackling motion control problem of the quadruped bionic robot with traditional control method, it is more and more difficult to improve the control effect due to the uncertainty caused by the factors such as model mismatch, distortion, and interference. Therefore, the demand for utilizing advanced control method to realize the motion control for legged bionic robot is growing. Among various advanced control methods, the model prediction control (MPC) has low requirements on model accuracy, and the system has good robustness and stability, which provides a good prospect for solving the balance problem of dynamic robots. In this paper model predictive control is used to realize the bounding gait motion control for a quadruped bionic robot. The MPC planner automatically generates the foot end position and contact force trajectory of the robot, so that the robot can track the desired motion state. However, as the model of the quadruped bionic robot is nonlinear, the generated MPC algorithm may be difficult to meet the real-time requirements. In order to solve this problem, this paper uses the iLQG optimization algorithm in the design of a model prediction controller and adds heuristic-based reference input. The comparison between the simulation and the classical SQP algorithm shows that the MPC based on the iLQG optimization algorithm has good convergence and tracking effect for the quadruped robot running, and has fast real-time performance.
Tetrapotassium pyrophosphates γ- and δ-K4P2O7

The structures of γ- and δ-K4P2O7 are solved by X-ray powder diffraction (conventional laboratory X-ray and synchrotron data, respectively), both in hexagonal symmetry (aγ = 5.9645(3) Å, cγ = 14.4972(8) Å, Vγ = 446.64(4) Å3 at 300 °C, Zγ = 2, space group P63/mmc; aδ = 10.211 45(7) Å, cδ = 42.6958(4) Å, Vδ = 3855.59(7) Å3 at room temperature, Zδ = 18, space group P61) with cell–supercell relations $a_\delta \approx a_\gamma \sqrt{3}$ and cδ ≈ 3 cγ. In the experimental conditions, the expected β/γ transition previously announced at 486 °C is not observed; the γ-form is stable at least up to the maximum temperature of our measurements (700 °C). In the γ-form, similar to the orthorhombic form of Na4P2O7, idealized, the pyrophosphate group is in eclipsed conformation, the K+ cations occupying three different coordinations. In the δδ-form, two of the three different [P2O7]4− groups are staggered and one eclipsed, the K+ cations occupying 12 independent sites.
An Inexact Double Primal-Dual Algorithm for Saddle Point Problems

In this paper, we study a saddle point problem and propose an inexact double primal-dual algorithm for solving the saddle point problem. The sequence generated by the proposed algorithm is proved to globally converge to the saddle point of the saddle point problem in the ergodic sense, and the convergence rate of the algorithm is also obtained. For the model of total variation image processing, the performance of the algorithm is demonstrated. Simulation results show that our method is more efficient in image denoising compared with existing methods.
SpiKeS: Precision Warm Spitzer Photometry of the Kepler Field

The ∼200,000 targets monitored for photometric variability during the Kepler prime mission include the best-studied group of stars in the sky, due both to the extensive time history provided by Kepler and to the substantial amount of ancillary data provided by other investigators or compiled by the Kepler team. To complement this wealth of data, we surveyed the entire Kepler field using the 3.6 and 4.5 μm bands of the Warm Spitzer Space Telescope, obtaining photometry in both bands for almost 170,000 objects. We demonstrate relative photometric precision ranging from better than ∼1.5% for the brighter stars down to slightly greater than ∼2% for the faintest stars monitored by Kepler. We describe the data collection and analysis phases of this work and identify several stars with large infrared excess, although none that is also known to be the host of an exoplanetary system. The final catalog resulting from this work will be available at the NASA Exoplanet Archive.
Autogenerating method of the polycarbonate glasses piezoelectric voltage measurement

The autogenerating method of polycarbonate piezoelectric voltage measurement is considered in the article, and the schematic diagram models of autogenerators are given. By use of simulation was determined that the most appropriate frequency range of autogenerators according to the Klapp scheme is 4÷9 MHz with subsequent separation of their beat signal. The carried out experimental studies have confirmed the adequacy of the modeling and have shown that frequency stability of ∼ 10−2 is sufficient to control the ultimate strength of monolithic polycarbonate glasses.
Design of flexible maintenance robot based on Gas Insulated Substation

Gas Insulated Substation (GIS) is a metal enclosed switchgear with SF6 gas as insulating medium, also known as gas insulated substation. Because of its compact structure, high security, easy installation and other characteristics, GIS is widely used in the assembly of all kinds of equipment except the main body of transformer. Due to the unique fully enclosed structure of GIS and the complexity of its own gas chamber, manual maintenance is often adopted at present, which is not only difficult but also dangerous. Based on the author’s understanding of flexible robot, a method of intelligent maintenance of GIS cavity with flexible robot is proposed. The flexible robot has a long and narrow structure, and can move flexibly in complex environment. It can also carry sensors and operating equipment into the GIS cavity, and use intelligent control, image recognition, machine learning and other technologies to determine the type and location of GIS faults and deal with them. Through this method, the maintenance of GIS is completed.
Decreased soil moisture due to warming drives phylogenetic diversity and community transitions in the tundra

Global warming leads to drastic changes in the diversity and structure of Arctic plant communities. Studies of functional diversity within the Arctic tundra biome have improved our understanding of plant responses to warming. However, these studies still show substantial unexplained variation in diversity responses. Complementary to functional diversity, phylogenetic diversity has been useful in climate change studies, but has so far been understudied in the Arctic. Here, we use a 25 year warming experiment to disentangle community responses in Arctic plant phylogenetic β diversity across a soil moisture gradient. We found that responses varied over the soil moisture gradient, where meadow communities with intermediate to high soil moisture had a higher magnitude of response. Warming had a negative effect on soil moisture levels in all meadow communities, however meadows with intermediate moisture levels were more sensitive. In these communities, soil moisture loss was associated with earlier snowmelt, resulting in community turnover towards a more heath-like community. This process of ‘heathification’ in the intermediate moisture meadows was driven by the expansion of ericoid and Betula shrubs. In contrast, under a more consistent water supply Salix shrub abundance increased in wet meadows. Due to its lower stature, palatability and decomposability, the increase in heath relative to meadow vegetation can have several large scale effects on the local food web as well as climate. Our study highlights the importance of the hydrological cycle as a driver of vegetation turnover in response to Arctic climate change. The observed patterns in phylogenetic β diversity were often driven by contrasting responses of species of the same functional growth form, and could thus provide important complementary information. Thus, phylogenetic diversity is an important tool in disentangling tundra response to environmental change.
Effect of Foliar Application of Sitofex, Potassium and Arginine on Vegetative Growth of “Zaghinia” Apricot Trees

The experiment was carried out at the Agricultural Research and Experiments Station in Al-Sayada site of the College of Agriculture / University of Kirkuk / Republic of Iraq, during the 2020 growing season, on six-year-old “Zaghinia” apricot trees to study the effect of foliar spraying with three concentrations of Sitofex growth regulator (0 and 7.5 and 15 mg L−1), two concentrations of potassium sulfate (0 and 3000 mg L−1) and three concentrations of amino acid (Arginine) (0, 250 and 500 mg L−1). The spraying was carried out a week after the full blooming stage, followed by two sprays with an interval of three weeks. The results indicated that spraying with 15 mg L−1 of Sitofex or spraying with 3000 mg L−1 of potassium sulfate led to a significant increase in all the studied characteristics (length and diameter of shoots, leaf area, total chlorophyll content and dry weight of leaves). As for spraying with amino acid “Arginine”, the foliar spray of 500 mg L−1 treatment gave a significant increase in the length and diameter of shoots, leaf area and its total chlorophyll content. Bilateral and triple interference treatments had a significant effect on most of the studied characteristics.
Computerized Maintenance Management System in IR4.0 Adaptation - A State of Implementation Review and Perspective

Computerized Maintenance Management System (CMMS) is a software package that stores computer databases of information about maintenance operations, evolving in the 1960s as the era of computational and mathematical systems began. Since then, the evolution of CMMS has continued to evolve with the ready availability of technology and requirements. While industry 4.0, refers to a new phase in the transformation of the industrial revolution (IR) focusing on digitization, automation, machine learning and real-time data. Therefore, the aim of this paper is to review the implementation of CMMS in revolution adjustment by investigating the functions of the IR4.0 in automation process and analysing the maintenance operations. The paper solely focused on the functionality of this system’s implementation attributes to the ability of the system integration to logically concept of IR4.0. This paper is also examined issues related to organizational and supplier readiness in integrating the IR4.0 concept into CMMS. In addition, by implementing CMMS based on IR4.0 enablement, it can have a significant impact on work processes, forward analysis, operations and maintenance costs.
Digital PCR Fluorescence Image Segmentation Algorithm Based on Image Processing

Digital PCR (Droplet digital Polymerase Chain Reaction) technology is currently one of the mainstream technologies for the detection and quantification of nucleic acid samples. This technology is widely used in the field of molecular biology. This technology counts the number of negative and positive reaction chambers in fluorescent images, and realizes absolute quantification of nucleic acid molecules. Due to the influence of the sampling equipment, some fluorescent images have uneven illumination, and the sampled droplet images will have bad spots and large areas of bright spots. The traditional Otsu segmentation algorithm cannot achieve the ideal segmentation effect. Aiming at the problem of under-segmentation of fluorescent images with uneven illumination using the traditional Otsu method, this paper proposes an algorithm for local segmentation based on Otsu segmentation. This algorithm realizes the segmentation of fluorescent images with uneven illumination and detects bright spots in large areas. The problems of dead pixel removal and scratch processing have been well resolved.
Towards Formal Specification for AADL with Behavior Annex in Isabelle

The analysis of safety-critical systems designed by architectural languages such as AADL (Architecture Analysis and Design Language) is a challenging research topic. In such a context, formal methods become an advocated practice in software engineering for rigorous analysis. Moreover, they are applied on specific formalisms to be analyzed on dedicated tools. This paper studies the comprehensive formal specification for AADL language, in particular supporting major components of AADL and Behavior Annex. The presentation of this specification and modeling is the aim of this paper. This work is illustrated with a ARINC653 case study. As a study case, this work develops an AADL model from an ARINC653, specify a set of critical properties of the model and perform formal modeling in in Isabelle/HOL.
Testing for Dark Matter in the Outskirts of Globular Clusters

The proper motions of stars in the outskirts of globular clusters are used to estimate cluster velocity dispersion profiles as far as possible within their tidal radii. We use individual color–magnitude diagrams to select high-probability cluster stars for 25 metal-poor globular clusters within 20 kpc of the Sun, 19 of which have substantial numbers of stars at large radii. Of the 19, 11 clusters have a falling velocity dispersion in the 3–6 half-mass radii range, 6 are flat, and 2 plausibly have a rising velocity dispersion. The profiles are all in the range expected from simulated clusters that started at high redshift in a zoom-in cosmological simulation. The 11 clusters with falling velocity dispersion profiles are consistent with no dark matter above the Galactic background. The six clusters with approximately flat velocity dispersion profiles could have local dark matter, but are ambiguous. The two clusters with rising velocity dispersion profiles are consistent with a remnant local dark matter halo, but need membership confirmation and detailed orbital modeling to further test these preliminary results.
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences

Commonsense reasoning is intuitive for humans but has been a long-term challenge for artificial intelligence (AI). Recent advancements in pretrained language models have shown promising results on several commonsense benchmark datasets. However, the reliability and comprehensiveness of these benchmarks towards assessing model's commonsense reasoning ability remains unclear. To this end, we introduce a new commonsense reasoning benchmark dataset comprising natural language true/false statements, with each sample paired with its complementary counterpart, resulting in 4k sentence pairs. We propose a pairwise accuracy metric to reliably measure an agent's ability to perform commonsense reasoning over a given situation. The dataset is crowdsourced and enhanced with an adversarial model-in-the-loop setup to incentivize challenging samples. To facilitate a systematic analysis of commonsense capabilities, we design our dataset along the dimensions of knowledge domains, reasoning scenarios and numeracy. Experimental results demonstrate that our strongest baseline (UnifiedQA-3B), after fine-tuning, achieves ~71% standard accuracy and ~51% pairwise accuracy, well below human performance (~95% for both metrics). The dataset is available at https://github.com/PlusLabNLP/Com2Sense.
Pricing Algorithmic Insurance

As machine learning algorithms start to get integrated into the decision-making process of companies and organizations, insurance products will be developed to protect their owners from risk. We introduce the concept of algorithmic insurance and present a quantitative framework to enable the pricing of the derived insurance contracts. We propose an optimization formulation to estimate the risk exposure and price for a binary classiﬁcation model. Our approach outlines how properties of the model, such as accuracy, inter-pretability and generalizability, can inﬂuence the insurance contract evaluation. To showcase a practical implementation of the proposed framework, we present a case study of medical malpractice in the context of breast cancer detection. Our analysis focuses on measuring the eﬀect of the model parameters on the expected ﬁnancial loss and identifying the aspects of algorithmic performance that predominantly aﬀect the price of the contract.
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data

The goal of Multi-task Bayesian Optimization (MBO) is to minimize the number of queries required to accurately optimize a target black-box function, given access to offline evaluations of other auxiliary functions. When offline datasets are large, the scalability of prior approaches comes at the expense of expressivity and inference quality. We propose JUMBO, an MBO algorithm that sidesteps these limitations by querying additional data based on a combination of acquisition signals derived from training two Gaussian Processes (GP): a cold-GP operating directly in the input domain and a warm-GP that operates in the feature space of a deep neural network pretrained using the offline data. Such a decomposition can dynamically control the reliability of information derived from the online and offline data and the use of pretrained neural networks permits scalability to large offline datasets. Theoretically, we derive regret bounds for JUMBO and show that it achieves no-regret under conditions analogous to GP-UCB (Srinivas et. al. 2010). Empirically, we demonstrate significant performance improvements over existing approaches on two real-world optimization problems: hyper-parameter optimization and automated circuit design.
Lower Perplexity is Not Always Human-Like

In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models. However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the general community. In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages. Specifically, we re-examine an established generalization —the lower perplexity a language model has, the more human-like the language model is— in Japanese with typologically different structures from English. Our experiments demonstrate that this established generalization exhibits a surprising lack of universality; namely, lower perplexity is not always human-like. Moreover, this discrepancy between English and Japanese is further explored from the perspective of (non-)uniform information density. Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.
A Generalizable Approach to Learning Optimizers

A core issue with learning to optimize neural networks has been the lack of generalization to real world problems. To address this, we describe a system designed from a generalization-first perspective, learning to update optimizer hyperparameters instead of model parameters directly using novel features, actions, and a reward function. This system outperforms Adam at all neural network tasks including on modalities not seen during training. We achieve 2x speedups on ImageNet, and a 2.5x speedup on a language modeling task using over 5 orders of magnitude more compute than the training tasks.
Low‐Light Shadow Imaging Using Quadrature‐Noise Detection with a Camera

This paper experimentally demonstrates an imaging technique based on quadrature noise modification after interaction with an opaque object. By using a homodyne‐like detection scheme, the detrimental effect of the camera dark noise is eliminated, making this approach particularly attractive for imaging scenarios which require weak illumination. As an example, the image of an object illuminated with a squeezed vacuum is reconstructed using a total of 800 probing photons, utilizing less than one photon per frame on average.
Testing Group Fairness via Optimal Transport Projections

We present a statistical testing framework to detect if a given machine learning classifier fails to satisfy a wide range of group fairness notions. The proposed test is a flexible, interpretable, and statistically rigorous tool for auditing whether exhibited biases are intrinsic to the algorithm or due to the randomness in the data. The statistical challenges, which may arise from multiple impact criteria that define group fairness and which are discontinuous on model parameters, are conveniently tackled by projecting the empirical measure onto the set of group-fair probability models using optimal transport. This statistic is efficiently computed using linear programming and its asymptotic distribution is explicitly obtained. The proposed framework can also be used to test for testing composite fairness hypotheses and fairness with multiple sensitive attributes. The optimal transport testing formulation improves interpretability by characterizing the minimal covariate perturbations that eliminate the bias observed in the audit.