Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots

Both the design and control of a robot play equally important roles in its task performance. However, while optimal control is well studied in the machine learning and robotics community, less attention is placed on finding the optimal robot design. This is mainly because co-optimizing design and control in robotics is characterized as a challenging problem, and more importantly, a comprehensive evaluation benchmark for co-optimization does not exist. In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots. In our benchmark, each robot is composed of different types of voxels (e.g., soft, rigid, actuators), resulting in a modular and expressive robot design space. Our benchmark environments span a wide range of tasks, including locomotion on various types of terrains and manipulation. Furthermore, we develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques. Evaluating the algorithms on our benchmark platform, we observe robots exhibiting increasingly complex behaviors as evolution progresses, with the best evolved designs solving many of our proposed tasks. Additionally, even though robot designs are evolved autonomously from scratch without prior knowledge, they often grow to resemble existing natural creatures while outperforming hand-designed robots. Nevertheless, all tested algorithms fail to find robots that succeed in our hardest environments. This suggests that more advanced algorithms are required to explore the high-dimensional design space and evolve increasingly intelligent robots – an area of research in which we hope Evolution Gym will accelerate progress. Our website with code, environments, documentation, and tutorials is available at http://evogym.csail. mit.edu.
Determination of Fuel Properties of Biodiesel from Sand Apple Seed Oil with Automotive Gas Oil Blend 1 ADEYEMI

The objective of this study was to determine the fuel properties of Sand Apple Ethyl Ester (SAEE) and its blends with Automotive Gas Oil (AGO). using eggshell as catalyst. Sand apple seed oil (SASO) obtained was characterized based on America Society for Testing and Material (ASTM D6751) to determine acid value, saponification, iodine content, density, kinematic viscosity, flash point, cloud point and pour point. Sand Apple fruits were processed and oil extracted using solvent extraction method. Raw eggshells were calcined at 800C for 120 min in the muffle furnace. SAEE was blended with AGO at 5 – 25 % mix. Data obtained was analyzed using ANOVA at P < 0.05 significant level. Cloud and pour points obtained for SASO are 4.68 and 3.09°C . Flash point was 103°C which fell within ASTM D93 range indicating that SASO is safe for handling and storage. Heating value was 42.61 MJ/kg, slightly lower than that of diesel oil of 44.8 MJ/kg shows that AGO has ability to produce heat of combustion than SASO. Iodine value was 80.71 g I/100g while acid value was determined to be 2.62 mgKOH/g, which was higher than that of ASTM D6751 of 0.5 mgKOH/g. Sulphur contents for AGO and SASO–AGO blends were 0.006, 0.009, 0.014, 0.016 and 0.004%, respectively. Low sulphur values indicates that hazardous sulphur dioxide emission of SAEE has reduced. This study established that all the properties obtained, except acid value, fell within the ASTM specification and could suitably be compared with those of fossil diesel. DOI: https://dx.doi. org/10.4314/jasem.v25i8. 12 Copyright: Copyright © 2021 Adeyemi et al. This is an open access article distributed under the Creative Commons Attribution License (CCL), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Dates: Received: 10 May 2021; Revised: 28 June 2021; Accepted: 01 July 2021
General Nonlinearities in SO(2)-Equivariant CNNs

Invariance under symmetry is an important problem in machine learning. Our paper looks speciﬁcally at equivariant neural networks where transformations of inputs yield homomorphic transformations of outputs. Here, steerable CNNs have emerged as the standard solution. An inherent problem of steerable representations is that general nonlinear layers break equivariance, thus restricting architectural choices. Our paper applies harmonic distortion analysis to illuminate the effect of nonlinearities on Fourier representations of SO(2). We develop a novel FFT-based algorithm for computing representations of non-linearly transformed activations while maintaining band-limitation. It yields exact equivariance for polynomial (approximations of) nonlinearities, as well as approximate solutions with tunable accuracy for general functions. We apply the approach to build a fully E(3)- equivariant network for sampled 3D surface data. In experiments with 2D and 3D data, we obtain results that compare favorably to the state-of-the-art in terms of accuracy while permitting continuous symmetry and exact equivariance.
AN M/M/1 QUEUE SUBJECT TO DIFFERENTIATED VACATION WITH PARTIAL INTERRUPTION AND CUSTOMER IMPATIENCE

ABSTRACT In this work, we consider an M/M/1 queueing model subject to differentiatedvacations. When the server is idle, he takes a break for a specific duration(termed Type I vacation). Upon completing the vacation, he takes another short break if the server stillfinds an empty system (termed as Type IIvacation). Both types of vacation follow an exponential distribution. As TypeI vacation persists for an extended period, it is reasonable to assume that theservice continues during this vacation by an alternate server at a slower rate,unlike the Type II vacation. Also, Type II being a shorter duration vacation,we assume that the Type II vacation is interrupted when the queue size reaches a predefined threshold value. Further, the arriving customers willwait only for a fixed duration of time, and if the service is incomplete by then,they leave the system permanently. We obtain an exact analytical expressionfor the transient state probabilities using Laplace transform, generatingfunctions, and continued fraction methodology. We also derive some useful measuresof effectiveness like the time-dependent mean and variance andillustrate their variations graphically.
Methods for passive fiber chip coupling of integrated optical devices

A useful technique for high precision passive coupling of single mode optical fibres to integrated optical devices is crucial for cost effective packaging especially in multiport devices like switches and other WDM components. These devices were fabricated on two different material bases, SOI and polymers. In both cases the waveguides are based on the oversized rib waveguide concept and utilize silicon as a substrate. Two possible fabrication processes for this passive fiber chip coupling IN or ON silicon are presented and compared. The first approach involves a technology similar to flip chip fabrication using a sub- and superstrate, that allows a separate processing of V-grooves for fiber alignment and the integrated optical devices. The self aligned mounting of the chip is achieved by a V-shaped rib-groove combination created by wet chemical etching, where the rib is the exact negative of the groove so that the flip chip is put on precisely defined crystal planes rather than on sensitive edges. The second approach utilizes the same chip for waveguides and fiber alignment structures which makes it possible to define both in the same lithographic step and thereby eliminating any vertical displacement. Processing difficulties arise primarily from completely different processing requirements of fiber aligning V-grooves and integrated waveguides. The need to define patterns of the size of only several /spl mu/m in the proximity to deep grooves makes the use of an electrophoretic photoresist necessary. Both processes allow for fiber chip alignment precisions in the sub-/spl mu/m range which was also experimentally verified with coupling losses as low as 0.7 dB per end-face. The fabrication processes along with experimental and theoretical results are presented.
Prediction and Compensation of Dynamic Errors for Coordinate Measuring Machines

Coordinate measuring machines (CMMs) are already widely utilized as measuring tools in the modern manufacturing industry. Rapidly approaching now is the trend for next-generation CMMs. However, the increases in measuring velocity of CMM applications are limited by dynamic errors that occur in CMMs. In this paper, a systematic approach for modeling the dynamic errors of a touch-trigger probe CMM is developed through theoretical analysis and experimental study. An overall analysis of the dynamic errors of CMMs is conducted, with weak components of the CMM identified with a laser interferometer. The probing process, as conducted with a touch-trigger probe, is analyzed. The dynamic errors are measured, modeled, and predicted using neural networks. The results indicate that, using this mode, it is possible to compensate for the dynamic errors of CMMs.
Carlin-type gold mineralization: Insights from Re-Os pyrobitumen

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Passively mode locked operation of diode pumped Nd:YVO4 laser using either second harmonic nonlinear mirror or semiconductor saturable absorber

Results of experimental investigation of diode pumped Nd:YVO4 laser passively mode locked using either second harmonic nonlinear mirror or semiconductor saturable absorber are reported. As an active element the 5mm long Nd:YVO4 crystal end pumped by 20 W fiber coupled laser diode was used. The linear folded resonator has on the other end either saturable absorber with single 15 nm thick In 0.25 Ga0.75 As quantum well layer integrated on the top of Bragg mirror (SESAM) either nonlinear mirror (NLM) consisting of a dichroic dielectric mirror and 10 mm long type I LBO crystal critically phase matched for second harmonic generation. Using the nonlinear mirror the threshold for mode locked operation was 10 W of pump power and we obtained 4.5 W of output power with single pulse duration of 6.2 Ps for pump power of 15 W. Using the SESAM the pulse duration of 13-16 ps was achieved for pump power from 3W to 18 w for low output coupling.
Evaluation of the Biocompatibility and Endothelial Differentiation Capacity of Mesenchymal Stem Cells by Polyethylene Glycol Nanogold Composites

Cardiovascular Diseases (CVDs) such as atherosclerosis, where inflammation occurs in the blood vessel wall, are one of the major causes of death worldwide. Mesenchymal Stem Cells (MSCs)-based treatment coupled with nanoparticles is considered to be a potential and promising therapeutic strategy for vascular regeneration. Thus, angiogenesis enhanced by nanoparticles is of critical concern. In this study, Polyethylene Glycol (PEG) incorporated with 43.5 ppm of gold (Au) nanoparticles was prepared for the evaluation of biological effects through in vitro and in vivo assessments. The physicochemical properties of PEG and PEG–Au nanocomposites were first characterized by UV-Vis spectrophotometry (UV-Vis), Fourier-transform infrared spectroscopy (FTIR), and Atomic Force Microscopy (AFMs). Furthermore, the reactive oxygen species scavenger ability as well as the hydrophilic property of the nanocomposites were also investigated. Afterwards, the biocompatibility and biological functions of the PEG–Au nanocomposites were evaluated through in vitro assays. The thin coating of PEG containing 43.5 ppm of Au nanoparticles induced the least platelet and monocyte activation. Additionally, the cell behavior of MSCs on PEG–Au 43.5 ppm coating demonstrated better cell proliferation, low ROS generation, and enhancement of cell migration, as well as protein expression of the endothelialization marker CD31, which is associated with angiogenesis capacity. Furthermore, anti-inflammatory and endothelial differentiation ability were both evaluated through in vivo assessments. The evidence demonstrated that PEG–Au 43.5 ppm implantation inhibited capsule formation and facilitated the expression of CD31 in rat models. TUNEL assay also indicated that PEG–Au nanocomposites would not induce significant cell apoptosis. The above results elucidate that the surface modification of PEG–Au nanomaterials may enable them to serve as efficient tools for vascular regeneration grafts.
Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence

Capturing accurate uncertainty quantiﬁcation of the predictions from deep neural networks is important in many real-world decision-making applications. A reliable predictor is expected to be accurate when it is conﬁdent about its predictions and indicate high uncertainty when it is likely to be inaccurate. However, modern neural networks have been found to be poorly calibrated, primarily in the direction of overconﬁdence. In recent years, there is a surge of research on model calibration by leveraging implicit or explicit regularization techniques during training, which achieve well calibration performance by avoiding overconﬁdent outputs. In our study, we empirically found that despite the predictions obtained from these regularized models are better calibrated , they suffer from not being as calibratable , namely, it is harder to further calibrate these predictions with post-hoc calibration methods like temperature scaling and histogram binning. We conduct a series of empirical studies showing that overconﬁdence may not hurt ﬁnal calibration performance if post-hoc calibration is allowed, rather, the penalty of conﬁdent outputs will compress the room of potential improvement in post-hoc calibration phase. Our experimental ﬁndings point out a new direction to improve calibration of DNNs by considering main training and post-hoc calibration as a uniﬁed framework.
Research of the Evaluation on Heavy-Metal Pollution in Rice by Sewage Irrigation

This paper took the SongSan wastewater irrigation district of Liaoning Province, Anshan city as object of study. Base on the survey on the sewage irrigation situation, heavy metal concentration in rice grain and drinking water were measured through single pollution index evaluation and comprehensive pollution index evaluation (Nemerow index method). It could be served as the basis for environmental management and relevant laws and regulations. Experimental results showed that drinking water were not polluted by heavy mental. Sewage irrigation had a comparatively small impact on drinking water. According to the heavy mental determination results of grain crop seeds, it can conclude that the Cu and Pb content were above the national food quality standard. The pollution is serious, however, the content of Zn and Cd were below or close to the national food quality standard, which is in danger of exceeding the standard. Determined by comprehensive pollution index, it showed that the rice in the whole sewage irrigation is serious polluted by heavy mental. The heavy metal pollution presented the characteristics of extensive area-source pollution. Pollution intensity was strengthened and more difficult to be managed.
Research on the impact of COVID-19 on Chinese small and medium-sized enterprises: Evidence from Beijing

COVID-19 leads small and medium-sized enterprises (SMEs) to survive very hard. The development difficulties of SMEs lead to weak employment and GDP growth in various countries. In the process of COVID-19’s continuous spread, what is the major reason for the difficulties of SMEs? This paper hopes to answer this question by studying SMEs in Beijing. On this basis, this paper uses structural equation model (SEM) to study the relatively fast recovery of SMEs in Beijing, China, to explore the factors affecting SMEs in the pandemic. After detailed desk research and interviews with relevant entrepreneurs, this paper collects 234 valid questionnaires from SMEs in various industries in Beijing with the help of Federation of Industry and Commerce and Chamber of Commerce in Beijing. Then the data is analyzed with the SEM, which shows the relationship between cash flow from financing activities, markets, employees, costs, government policies and the impact of the pandemic. Finally, an impact model of the pandemic on SMEs is established. The result of the model indicates that the direct effect of the pandemic on the market is the most prominent, and government policies can significantly reduce the negative impact of the pandemic on SMEs indirectly. Based on this, this paper puts forward some policy suggestions, such as the targeted issuance of consumption vouchers and the reduction of administrative barriers. This will enable megacities in various countries to improve policy support for SMEs and promote the recovery and development of SMEs.
UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning

Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely randomized trees, k-nearest neighbor, logistic regression, partial least squares, random forest, and support vector machine) with seven different feature encodings (amino acid composition, amphiphilic pseudo-amino acid composition, dipeptide composition, composition-transition-distribution, and pseudo-amino acid composition) to develop the final meta-predictor. Extensive experimental results demonstrated that UMPred-FRL was effective and achieved more accurate performance on the benchmark dataset compared to its baseline models, and consistently outperformed the existing method on the independent test dataset. Finally, to aid in the high-throughput identification of umami peptides, the UMPred-FRL web server was established and made freely available online. It is expected that UMPred-FRL will be a powerful tool for the cost-effective large-scale screening of candidate peptides with potential umami sensory properties.
Security Providing Leadership: A Job Resource to Prevent Employees’ Burnout

Leadership styles in work contexts play a role in employees’ well-being, contributing to better health or, on the contrary, being a source of stress. In this study we propose that security providing leadership may be considered as a resource to prevent employees’ job burnout. First, we examine the relationship between employees’ perception of their leader’s degree of security in providing leadership and the employees’ degree of job-related burnout. Second, the underlying processes by which leaders as security providers exert their influence on burnout are analyzed with a focus on the mediating role of two variables: an organizational climate oriented to psychological safety and organizational dehumanization. A total of 655 Spanish employees (53.7% women) completed a paper-and-pencil self-report questionnaire. To recruit participants, we employed an exponential non-discriminative snowball sampling. Results, using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test hypotheses, show that security providing leadership was related negatively to burnout. Furthermore, psychological safety climate and organizational dehumanization mediated the relationship between security providing leadership and burnout. These findings support the attachment approach to leadership and open new avenues for creating better organizational environments. Security-providing leaders, by supporting employees and treating them in a personalized way, can enhance the psychological safety climate and prevent organizational dehumanization and consequent job burnout.
Impact of sleep on the microbiome of oral biofilms

Dysbiosis of the oral microbiome is associated with diseases such as periodontitis and dental caries. Because the bacterial counts in saliva increase markedly during sleep, it is broadly accepted that the mouth should be cleaned before sleep to help prevent these diseases. However, this practice does not consider oral biofilms, including the dental biofilm. This study aimed to investigate sleep-related changes in the microbiome of oral biofilms by using 16S rRNA gene sequence analysis. Two experimental schedules—post-sleep and pre-sleep biofilm collection—were applied to 10 healthy subjects. Subjects had their teeth and oral mucosa professionally cleaned 7 days and 24 h before sample collection. Samples were collected from several locations in the oral cavity: the buccal mucosa, hard palate, tongue dorsum, gingival mucosa, tooth surface, and saliva. Prevotella and Corynebacterium had higher relative abundance on awakening than before sleep in all locations of the oral cavity, whereas fluctuations in Rothia levels differed depending on location. The microbiome in different locations in the oral cavity is affected by sleep, and changes in the microbiome composition depend on characteristics of the surfaces on which oral biofilms form.