Miracle-dz/UniFAR-FTUs huggingface dataset:
Object Detection Method for Grasping Robot Based on Improved YOLOv5

In the industrial field, the anthropomorphism of grasping robots is the trend of future development, however, the basic vision technology adopted by the grasping robot at this stage has problems such as inaccurate positioning and low recognition efficiency. Based on this practical problem, in order to achieve more accurate positioning and recognition of objects, an object detection method for grasping robot based on improved YOLOv5 was proposed in this paper. Firstly, the robot object detection platform was designed, and the wooden block image data set is being proposed. Secondly, the Eye-In-Hand calibration method was used to obtain the relative three-dimensional pose of the object. Then the network pruning method was used to optimize the YOLOv5 model from the two dimensions of network depth and network width. Finally, the hyper parameter optimization was carried out. The simulation results show that the improved YOLOv5 network proposed in this paper has better object detection performance. The specific performance is that the recognition precision, recall, mAP value and F1 score are 99.35%, 99.38%, 99.43% and 99.41% respectively. Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the mAP of the YOLOv5_ours model has increased by 1.12%, 1.2% and 1.27%, respectively, and the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively. The object detection experiment has verified the feasibility of the method proposed in this paper.
Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity

High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (Vcmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R2 ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for Vcmax) performed better than the trait-based method (R2 ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for Vcmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R2 ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis.
FUNGICIDAL ACTIVITY OF NANOEMULSIFIED ESSENTIAL OILS AGAINST BOTRYTIS LEAF BLIGHT OF POINSETTIA (EUPHORBIA PULCHERRIMA) IN EGYPT

otrytis blight was observed in Egypt (Nov., 2016) on Poinsettia (Euphorbia pulcherrima) that caused small light brown spots at the edge of leaves and enlarged to include the whole leaf. The isolated fungus was identified as Botrytis cinerea based on its morphological and cultural characteristics. In greenhouse, artificial inoculation of plants developed blight symptoms. The effect of ultrasonication on the particle size of lemongrass and citronella essential oil nanoemulsions was determined. The results showed that the particle sizes from sonication for 30 minutes were around 90.6 and 79.8 nm, and TEM study revealed the spherical shape. Changes in the essential oil emulsions components were observed in the nanoemulsions compared with the original as the size of the oil particles become small, the stability and main component of the emulsion were significantly improved. In vitro, the nanoemulsion at different concentrations (1000 – 8000 ppm) were capable of adversely affecting the mycelial growth of Botrytis cinerea. In greenhouse experiment, poinsettia plants were sprayed with the two essential oils (emulsion and nanoemulsion) and the fungicide carbendazim 50% WP (as additional control), after and before inoculation by the fungus. The disease severity was decreased by the treatments compared to the untreated control. The lowest disease severity, being 10.0 and 13.0% with nanoemulsion of lemongrass, before and after inoculation by the fungus, respectively. The results indicate an increase in the concentrations of active substances in essential oils due to their exposure to ultrasonication and their conversion to nanoemulsions, as the results suggest the potential effects of lemongrass essential oil nanoemulsion as a novel fungicide agent against Botrytis cinerea.
STC-Det: A Slender Target Detector Combining Shadow and Target Information in Optical Satellite Images

Object detection has made great progress. However, due to the unique imaging method of optical satellite remote sensing, the detection of slender targets is still insufficient. Specifically, the perspective of optical satellites is small, and the characteristics of slender targets are severely lost during imaging, resulting in insufficient detection task information; at the same time, the appearance of slender targets in the image is greatly affected by the satellite perspective, which is likely to cause insufficient generalization capabilities of conventional detection models. In response to these two points, we have made some improvements. First, in this paper, we introduce the shadow as auxiliary information to complement the trunk features of the target lost in imaging. Second, to reduce the impact of satellite perspective on imaging, in this paper, we use the characteristic that shadow information is not affected by satellite perspective to design STC-Det. STC-Det treats the shadow and the target as two different types of targets and uses the shadow information to assist the detection, reducing the impact of the satellite perspective on detection. Among them, in order to improve the performance of STC-Det, we propose an automatic matching method (AMM) of shadow and target and a feature fusion method (FFM). Finally, this paper proposes a new method to calculate the heatmaps of detectors, which verifies the effectiveness of the proposed network in a visual way. Experiments show that when the satellite perspective is variable, the precision of STC-Det is increased by 1.7%, and when the satellite perspective is small, the precision of STC-Det is increased by 5.2%.
Liposomal honokiol inhibits glioblastoma growth through regulating macrophage polarization

Background Glioblastoma is a type of aggressive brain tumor-related to infiltrating microglia/macrophages. Various studies have identified antitumor properties of a bioactive plant compound named honokiol, originating from the Magnolia species. This beneficial characteristic of honokiol has been discovered in many malignant tumors. Methods We investigated the molecular mechanisms behind the anti-glioma effects of liposomal honokiol (Lip-HNK) using qRT-PCR, Western blot, co-culture, and in vivo animal experiments. Results It was discovered that the expression of M1 markers such as CD11c, inducible nitric oxide synthase (iNOS), and major histocompatibility complex (MHC) II (IA/IE subregions) induced by lipopolysaccharide (LPS)/IFN-γ was increased by Lip-HNK, and M2 markers Arg1 and CD206 induced by interleukin (IL)-4 had reduced expression, thus inhibiting tumor cell growth through co-culture experiments. After Lip-HNK treatment, a considerable increase in signal transducer and activator of transcription 1 (STAT1) activation was observed, and in contrast, STAT6 activation was suppressed. STAT1 and STAT6 are the key signaling molecules mediating M1 and M2 polarization, respectively. Furthermore, the percentage of CD11c-positive M1 macrophages was increased by Lip-HNK in G422 xenograft mice, while Lip-HNK treatment reduced the CD206-positive M2 macrophage distribution in tumor tissues. These findings are consistent with the decline in tumor volume seen in mice treated with Lip-HNK. Conclusions Lip-HNK inhibits the growth of glioblastoma by upregulating M1 macrophages and limiting M2 phenotypic macrophages.
Application of compost produced by bioconversion of coffee husk by black soldier fly larvae (Hermetia illucens) as solid fertilizer to lettuce (Lactuca sativa var. crispa) : Impact to harvested biomass and utilization of nitrogen, phosphor, and potassium

Coffee husk is main by product of coffee production and considered has low economic value. Application of this product as feedstuff of insect larvae (i.e. black soldier fly larvae (Hermetia illucens)) could potencially produce insect biomass and organic solid fertilizer which has significant improved economic value. This study aimed to observe the effect of application of manure, conventional compost of coffee husk, and compost produced by bioconversion activity of black soldier fly larvae to biomass, nitrogen, phosphor, and potassium utilization of lettuce (Lactuca sativa). The result showed lettuce cultivated with manure produced significantly highest biomass (62.513 gram) and nitrogen utilization (2.912 gram/kilogram). On the other hand, lettuce cultivated with compost produced by bioconversion had highest phosphor (0.00937 gram/kilogram) and potassium (3.18118 gram/kilogram) utilization. Based on the results, it could be concluded that compost produced by bioconversion process of coffee husk by black soldier fly larvae allowed plant to fulfill their requirement of phosphor and potassium which is considered lack in common Indonesia soil.
A comparative study of mathematical and ANFIS models to determine the effect of ultrasonic waves on the viscosity of crude oil

Abstract Increasing fuel consumption, industrial development, and population have led to energy supply problems. Hence, the recovery of oil reservoirs has been considered in recent years. One of the most effective methods in crude oil recovery is the use of ultrasonic waves. Viscosity is a significant physical property of crude oil because the crude oil with high viscosity can make serious problems in transportation pipelines. In addition to the effect of ultrasonic waves on viscosity, ultrasonic radiation leads to molecular bond vibrations in the oil and reduces paraffin deposition and clogged pores. In this study, the effect of ultrasonic waves on the viscosity of crude oil was investigated. Three transmission functions such as Inverse Square Root, Natural Log and Square Root, and ANFIS model were used to predict the viscosity of crude oil. The adequacy of the three mathematical models and the ANFIS model were proved by statistical analysis including low P-values (<0.05), high F-values and R 2 values. According to the results, the ANFIS model and Inverse Square Root transmission function showed better agreement with experimental data compared to the other two models.
Evaluation of Staling of Cooked Rice by Kinetic Analysis of Texture Changes

Five polished rice varieties with different amylose contents were cooked, sealed, and stored at 4°C for 168 h. The slope of first-order plots of hardness and stickiness changed during storage; the change process was divided into two periods: the first and latter halves of the period, each with different rate constants. The rate constant of hardness increase in the first half period was greater for rice with a higher amylose content; conversely, in the latter half period, it was greater for rice with a lower amylose content. The rate constant for stickiness decrease in the latter half was greater than that in the first half, and the higher the amylose content, the larger the rate constant in both periods. By simulating a change in the stickiness-hardness ratio using kinetic parameters with texture changes, the time taken for the quality of cooked rice to deteriorate during storage was predicted.
Satisfaction with travel, ideal commuting, and accessibility to employment

This paper explores relationships between commuting times, job accessibility, and commuting satisfaction based on a large-scale survey applied in the Greater London Area (GLA), the municipality of São Paulo (MSP) and the Dutch Randstad (NLR). Potential accessibility to jobs is estimated under 3 different scenarios: reported actual commuting times (ACT), ideal commuting times (ICT), and maximum willingness to commute (MCT). In addition, binary logistic regression models, estimated using generalized linear modeling (GLM), are performed to assess the impact of these temporal preferences on the likelihood of being satisfied with commuting. As expected, ideal and maximum commuting preferences strongly impact the volume and spatial distribution of the measured accessibility to jobs. In the selected case studies, estimated ICT-based job accessibility significantly decreases total measured accessibility (60 to 100 percent), with those living in the lowest accessibility zones impacted most. Furthermore, although specific results varied between regions, the overall findings show an association between ACT and satisfaction. Likewise, commuting mode is found to be a strong predictor of travel satisfaction. Those actively traveling in all three metropolitan regions tend to be more satisfied with their commutes. Potential job accessibility is found to be only weakly associated with travel satisfaction.
Wettability Alteration and Enhanced Oil Recovery Induced by Proximal Adsorption of 
Na+
, 
Cl−
, 
Ca2+
, 
Mg2+
, and 
SO42−
 Ions on Calcite

The underlying mechanism of wettability alteration is vital for enhancement of oil recovery (EOR) by water flooding in carbonate reservoirs, such as calcite rock. Based on first-principles molecular dynamics simulations and core-flooding measurements, the authors find that proximal adsorption of ions in brine affects the wettability of calcite: Some ions primarily disturb the interfacial water structure, while others modify the effective surface charge, thus inhibiting and enhancing oil recovery respectively. This study provides needed insight into the physics of wetting at the atomic scale for EOR.
LIMEADE: A General Framework for Explanation-Based Human Tuning of Opaque Machine Learners

Research in human-centered AI has shown the beneﬁts of systems that can explain their predictions. Methods that allow humans to tune a model in response to the explanations are similarly useful. While both capabilities are well-developed for transparent learning models (e.g., linear models and GA 2 Ms), and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, no method for tuning opaque models in response to explanations has been user-tested to date. This paper introduces LIMEADE, a general framework for tuning an arbitrary machine learning model based on an explanation of the model’s prediction. We demonstrate the generality of our approach with two case studies. First, we successfully utilize LIMEADE for the human tuning of opaque image classiﬁers. Second, we apply our framework to a neural recommender system for scientiﬁc papers on a public website and report on a user study showing that our framework leads to signiﬁcantly higher perceived user control, trust, and satisfaction. Analyzing 300 user logs from our publicly-deployed website, we uncover a tradeoff between canonical greedy explanations and diverse explanations that better facilitate human tuning.
A Survey on Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

—Inﬂuenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed signiﬁcant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this ﬁeld to facilitate researchers and practitioners working on recommender systems. Speciﬁcally, based on the data usage during recommendation modeling we divide the work into collaborative ﬁltering and information-rich recommendation: 1) collaborative ﬁltering , which leverages the key source of user-item interaction data; 2) content enriched recommendation , which additionally utilizes the side information associated with users and items, like user proﬁle and item knowledge graph; and 3) temporal/sequential recommendation , which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative work for each type, we ﬁnally discuss some promising directions in this ﬁeld. We have also summarized the related papers at https://github.com/lmcRS/AWS-recommendation-papers.
Systems of dyadic cubes of complete, doubling, uniformly perfect metric spaces without detours

Systems of dyadic cubes are the basic tools of harmonic analysis and geometry, and this notion had been extended to general metric spaces. In this paper, we construct systems of dyadic cubes of complete, doubling, uniformly perfect metric spaces, such that for any two points in the metric space, there exists a chain of three cubes whose diameters are comparable to the distance of the points. We also give an application of our construction to previous research of potential analysis and geometry of metric spaces.
Additive Density-on-Scalar Regression in Bayes Hilbert Spaces with an Application to Gender Economics

Motivated by research on gender identity norms and the distribution of the woman’s share in a couple’s total labor income, we consider functional additive regression models for probability density functions as responses with scalar covariates. To preserve nonnegativity and integration to one under summation and scalar multiplication, we formulate the model for densities in a Bayes Hilbert space with respect to an arbitrary finite measure. This enables us to not only consider continuous densities, but also, e.g., discrete or mixed densities. Mixed densities occur in our application, as the woman’s income share is a continuous variable having discrete point masses at zero and one for single-earner couples. We discuss interpretation of effect functions in our model via odds-ratios. Estimation is based on a gradient boosting algorithm, allowing for potentially numerous flexible covariate effects. We show how to handle the challenging estimation for mixed densities within our framework using an orthogonal decomposition. Applying this approach to data from the German Socio-Economic Panel Study (SOEP) shows a more symmetric distribution in East German than in West German couples after reunification and a smaller child penalty comparing couples with and without minor children. These West-East differences become smaller, but are persistent over time.
SwiftLane: Towards Fast and Efficient Lane Detection

Recent work done on lane detection has been able to detect lanes accurately in complex scenarios, yet many fail to deliver real-time performance specifically with limited computational resources. In this work, we propose SwiftLane: a simple and light-weight, end-to-end deep learning based framework, coupled with the row-wise classification formulation for fast and efficient lane detection. This framework is supplemented with a false positive suppression algorithm and a curve fitting technique to further increase the accuracy. Our method achieves an inference speed of 411 frames per second, surpassing state-of the-art in terms of speed while achieving comparable results in terms of accuracy on the popular CULane benchmark dataset. In addition, our proposed framework together with TensorRT optimization facilitates real-time lane detection on a Nvidia Jetson AGX Xavier as an embedded system while achieving a high inference speed of 56 frames per second.
The Critique of Crowds: Using Collective Criticism to Crowdsource Subjective Preferences

Crowdsourcing encompasses everything from large collaborative projects to microtasks performed in parallel and at scale. However, understanding subjective preferences can still be difficult: a majority of problems do not have validated questionnaires and pairwise comparisons do not scale, even with access to the crowd. Furthermore, in daily life we are used to expressing opinions as critiques (e.g. it was too cold, too spicy, too big), rather than describing precise preferences or choosing between (perhaps equally bad) discrete options. Unfortunately, it is difficult to analyze such qualitative feedback, especially when we want to make quantitative decisions. Inthisarticle, we present collective criticism , a crowdsourcing approach where users provide feedback to microtasks in the form of critiques, such as “it was too easy/too challenging”. This qualitative feedback is used to perform quantitative analysis of users’ preferences and opinions. Collective criticism has several advantages over other approaches: “too much/too little”-style critiques are easy for users to provide and it allows us to build predictive models for the optimal parameterization of the variables being cri-tiqued. We present two case studies where we model: (i) aesthetic preferences in neural style transfer and (ii) hedonic experiences in the video game Tetris. These studies demonstrate the flexibility of our approach, and show that it produces robust results that are straightforward for experimenters to interpret and inline with users’ stated preferences.
GRASS: Distinguishing Planet-induced Doppler Signatures from Granulation with a Synthetic Spectra Generator

Owing to recent advances in radial-velocity instrumentation and observation techniques, the detection of Earth-mass planets around Sun-like stars may soon be primarily limited by intrinsic stellar variability. Several processes contribute to this variability, including starspots, pulsations, and granulation. Although many previous studies have focused on techniques to mitigate signals from pulsations and other types of magnetic activity, granulation noise has to date only been partially addressed by empirically motivated observation strategies and magnetohydrodynamic simulations. To address this deficit, we present the GRanulation And Spectrum Simulator (GRASS), a new tool designed to create time-series synthetic spectra with granulation-driven variability from spatially and temporally resolved observations of solar absorption lines. In this work, we present GRASS, detail its methodology, and validate its model against disk-integrated solar observations. As a first-of-its-kind empirical model for spectral variability due to granulation in a star with perfectly known center-of-mass radial-velocity behavior, GRASS is an important tool for testing new methods of disentangling granular line-shape changes from true Doppler shifts.