Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporally Abstract Partial Models

Humans and animals have the ability to reason and make predictions about different courses of action at many time scales. In reinforcement learning, option models (Sutton, Precup \&Singh, 1999; Precup, 2000) provide the framework for this kind of temporally abstract prediction and reasoning. Natural intelligent agents are also able to focus their attention on courses of action that are relevant or feasible in a given situation, sometimes termed affordable actions. In this paper, we define a notion of affordances for options, and develop temporally abstract partial option models, that take into account the fact that an option might be affordable only in certain situations. We analyze the trade-offs between estimation and approximation error in planning and learning when using such models, and identify some interesting special cases. Additionally, we demonstrate empirically the potential impact of partial option models on the efficiency of planning.
Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation

The efforts in compressive sensing (CS) literature can be divided into two groups: finding a measurement matrix that preserves the compressed information at its maximum level, and finding a robust reconstruction algorithm. In the traditional CS setup, the measurement matrices are selected as random matrices, and optimization-based iterative solutions are used to recover the signals. Using random matrices when handling large or multi-dimensional signals is cumbersome especially when it comes to iterative optimizations. Recent deep learning-based solutions increase reconstruction accuracy while speeding up recovery, but jointly learning the whole measurement matrix remains challenging. For this reason, state-of-the-art deep learning CS solutions such as convolutional compressive sensing network (CSNET) use block-wise CS schemes to facilitate learning. In this work, we introduce a separable multi-linear learning of the CS matrix by representing the measurement signal as the summation of the arbitrary number of tensors. As compared to block-wise CS, tensorial learning eases blocking artifacts and improves performance, especially at low measurement rates (MRs), such as $\text {MRs} < 0.1$ . The software implementation of the proposed network is publicly shared at https://github.com/mehmetyamac/GTSNET.
The young massive stellar cluster Westerlund 1 in gamma rays as seen with H.E.S.S.

Massive stellar clusters have recently been hypothesised as candidates for the acceleration of hadronic cosmic rays up to PeV energies. Previously, the H.E. S.S. Collaboration has reported about very extended γ-ray emission around Westerlund 1, a massive young stellar cluster in the Milky Way. In this contribution we present an updated analysis that employs a new analysis technique and is based on a much larger data set, allowing us to constrain better the morphology and the energy spectrum of the emission. The analysis technique used is a three-dimensional likelihood analysis, which is especially well suited for largely extended sources. The origin of the γ-ray emission will be discussed in light of multi-wavelength observations.
Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models

The capabilities of natural language models trained on large-scale data have increased immensely over the past few years. Open source libraries such as HuggingFace have made these models easily available and accessible. While prior research has identified biases in large language models, this paper considers biases contained in the most popular versions of these models when applied `out-of-the-box' for downstream tasks. We focus on generative language models as they are well-suited for extracting biases inherited from training data. Specifically, we conduct an in-depth analysis of GPT-2, which is the most downloaded text generation model on HuggingFace, with over half a million downloads per month. We assess biases related to occupational associations for different protected categories by intersecting gender with religion, sexuality, ethnicity, political affiliation, and continental name origin. Using a template-based data collection pipeline, we collect 396K sentence completions made by GPT-2 and find: (i) The machine-predicted jobs are less diverse and more stereotypical for women than for men, especially for intersections; (ii) Intersectional interactions are highly relevant for occupational associations, which we quantify by fitting 262 logistic models; (iii) For most occupations, GPT-2 reflects the skewed gender and ethnicity distribution found in US Labor Bureau data, and even pulls the societally-skewed distribution towards gender parity in cases where its predictions deviate from real labor market observations. This raises the normative question of what language models should learn - whether they should reflect or correct for existing inequalities.
Learning to Approximate a Bregman Divergence: Supplementary Material

• Background mathematical concepts required for the proofs (A1) 3 • Proof of the approximation bound (A2) 4 • Proof of the Rademacher complexity bound (A3) 5 • Proof of the metric learning generalization error bound (A4) 6 • Discussion of the regression setting, including generalization error (A5) 7 • Discussion of the case where K < n (A6) 8 • Some additional details omitted from the discussion of the algorithms (A7) 9 • Additional experimental results, including results on regression and classification (A8) 10
Easy-to-use spatial Random Forest-based downscaling-calibration method for producing high resolution and accurate precipitation data

Abstract. High resolution and accurate precipitation data is significantly important for numerous hydrological applications. To enhance the spatial resolution and accuracy of satellite-based precipitation products, an easy-to-use downscaling-calibration method based on spatial Random Forest (SRF) is proposed in this paper, where the spatial autocorrelation between precipitation measurements is taken into account. The proposed method consists of two main stages. Firstly, the satellite-based precipitation was downscaled by SRF with the incorporation of some high-resolution covariates including latitude, longitude, DEM, NDVI, terrain slope, aspect, relief, and land surface temperatures. Then, the downscaled precipitation was calibrated by SRF with rain gauge observations and the aforementioned high-resolution variables. The monthly Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) located in Sichuan province, China from 2015 to 2019 was processed using our method and its results were compared with those of some classical methods including geographically weighted regression (GWR), artificial neural network (ANN), random forest (RF), kriging interpolation only on gauge measurements, bilinear interpolation-based downscaling and then SRF-based calibration (Bi-SRF), and SRF-based downscaling and then geographical difference analysis (GDA)-based calibration (SRF-GDA). Results show that: (1) the proposed method outperforms the other methods as well as the original IMERG; (2) the monthly-based SRF estimation is slightly more accurate than the annual-based SRF fraction disaggregation method; (3) SRF-based downscaling and calibration preforms better than bilinear downscaling (Bi-SRF) and GDA-based calibration (SRF-GDA); (4) kriging seems more accurate than GWR and ANN in terms of quantitative accuracy measures, whereas its precipitation map cannot capture the detailed spatial precipitation patterns; and (5) among the predictors for calibration, the precipitation interpolated by kriging on the gauge measurements is the most important variable, indicating the significance for the inclusion of spatial autocorrelation information in gauge measurements.
Valuable Clues for DCNN-Based Landslide Detection from a Comparative Assessment in the Wenchuan Earthquake Area

Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside detection in the Wenchuan earthquake area, where about 200,000 secondary landslides were available. Multiple structures and layer numbers, including VGG16, VGG19, ResNet50, ResNet101, DenseNet120, DenseNet201, UNet−, UNet+, and ResUNet were investigated with different sample numbers (100, 1000, and 10,000). Results indicate that VGG models have the highest precision (about 0.9) but the lowest recall (below 0.76); ResNet models display the lowest precision (below 0.86) and a high recall (about 0.85); DenseNet models obtain moderate precision (below 0.88) and recall (about 0.8); while UNet+ also achieves moderate precision (0.8) and recall (0.84). Generally, a larger sample set can lead to better performance for VGG, ResNet, and DenseNet, and deeper layers could improve the detection results for ResNet and DenseNet. This study provides valuable clues for designing models’ type, layers, and sample set, based on tests with a large number of samples.
Common Origin of Filler Network Related Contributions to Reinforcement and Dissipation in Rubber Composites

A comparative study focusing on the visco–elastic properties of two series of carbon black filled composites with natural rubber (NR) and its blends with butadiene rubber (NR-BR) as matrices is reported. Strain sweeps at different temperatures are performed. Filler network-related contributions to reinforcement (ΔG′) are quantified by the classical Kraus equation while a modified Kraus equation is used to quantify different contributions to dissipation (ΔGD″, ΔGF″). Results indicate that the filler network is visco-elastic in nature and that it is causing a major part of the composite dissipation at small and intermediate strain amplitudes. The temperature dependence of filler network-related reinforcement and dissipation contributions is found to depend significantly on the rubber matrix composition. We propose that this is due to differences in the chemical composition of the glassy rubber bridges connecting filler particles since the filler network topology is seemingly not significantly influenced by the rubber matrix for a given filler content. The underlying physical picture explains effects in both dissipation and reinforcement. It predicts that these glassy rubber bridges will soften sequentially at temperatures much higher than the bulk Tg of the corresponding rubber. This is hypothetically due to rubber–filler interactions at interfaces resulting in an increased packing density in the glassy rubber related to the reduction of free volume. From a general perspective, this study provides deeper insights towards the molecular origin of reinforcement and dissipation in rubber composites.
Effects of Particle Size on the Dielectric, Mechanical, and Thermal Properties of Recycled Borosilicate Glass-Filled PTFE Microwave Substrates

Low dielectric loss and low-cost recycled borosilicate (BRS) glass-reinforced polytetrafluoroethylene (PTFE) composites were fabricated for microwave substrate applications. The composites were prepared through a dry powder processing technique by dispersing different micron sizes (25 µm, 45 µm, 63 µm, 90 µm, and 106 µm) of the recycled BRS filler in the PTFE matrix. The effect of the filler sizes on the composites’ thermal, mechanical, and dielectric properties was studied. The dielectric properties of the composites were characterised in the frequency range of 1–12 GHz using an open-ended coaxial probe (OCP) connected to a vector network analyser (VNA). XRD patterns confirmed the phase formation of PTFE and recycled BRS glass. The scanning electron microscope also showed good filler dispersion at larger filler particle sizes. In addition, the composites’ coefficient of thermal expansion and tensile strength decreased from 12.93 MPa and 64.86 ppm/°C to 7.12 MPa and 55.77 ppm/°C when the filler size is reduced from 106 μm to 25 μm. However, moisture absorption and density of the composites increased from 0.01% and 2.17 g/cm3 to 0.04% and 2.21 g/cm3. The decrement in filler size from 106 μm to 25 μm also increased the mean dielectric constant and loss tangent of the composites from 2.07 and 0.0010 to 2.18 and 0.0011, respectively, while it reduced the mean signal transmission speed from 2.088 × 108 m/s to 2.031 × 108 m/s. The presented results showed that PTFE/recycled BRS composite exhibited comparable characteristics with commercial high-frequency laminates.
Recent Advances in Antioxidant Polymers: From Sustainable and Natural Monomers to Synthesis and Applications

Advances in technology have led to the production of sustainable antioxidants and natural monomers for food packaging and targeted drug delivery applications. Of particular importance is the synthesis of lignin polymers, and graft polymers, dopamine, and polydopamine, inulin, quercetin, limonene, and vitamins, due to their free radical scavenging ability, chemical potency, ideal functional groups for polymerization, abundance in the natural environment, ease of production, and activation of biological mechanisms such as the inhibition of the cellular activation of various signaling pathways, including NF-κB and MAPK. The radical oxygen species are responsible for oxidative damage and increased susceptibility to cancer, cardiovascular, degenerative musculoskeletal, and neurodegenerative conditions and diabetes; such biological mechanisms are inhibited by both synthetic and naturally occurring antioxidants. The orientation of macromolecules in the presence of the plasticizing agent increases the suitability of quercetin in food packaging, while the commercial viability of terpenes in the replacement of existing non-renewable polymers is reinforced by the recyclability of the precursors (thyme, cannabis, and lemon, orange, mandarin) and marginal ecological effect and antioxidant properties. Emerging antioxidant nanoparticle polymers have a broad range of applications in tumor-targeted drug delivery, food fortification, biodegradation of synthetic polymers, and antimicrobial treatment and corrosion inhibition. The aim of the review is to present state-of-the-art polymers with intrinsic antioxidant properties, including synthesis scavenging activity, potential applications, and future directions. This review is distinct from other works given that it integrates different advances in antioxidant polymer synthesis and applications such as inulin, quercetin polymers, their conjugates, antioxidant-graft-polysaccharides, and polymerization vitamins and essential oils. One of the most comprehensive reviews of antioxidant polymers was published by Cirillo and Iemma in 2012. Since then, significant progress has been made in improving the synthesis, techniques, properties, and applications. The review builds upon existing research by presenting new findings that were excluded from previous reviews.
Supervised Learning of Neural Networks for Active Queue Management in the Internet

The paper examines the AQM mechanism based on neural networks. The active queue management allows packets to be dropped from the router’s queue before the buffer is full. The aim of the work is to use machine learning to create a model that copies the behavior of the AQM PIα mechanism. We create training samples taking into account the self-similarity of network traffic. The model uses fractional Gaussian noise as a source. The quantitative analysis is based on simulation. During the tests, we analyzed the length of the queue, the number of rejected packets and waiting times in the queues. The proposed mechanism shows the usefulness of the Active Queue Management mechanism based on Neural Networks.
Effect of Glycero-(9,10-trioxolane)-trialeate on the Physicochemical Properties of Non-Woven Polylactic Acid Fiber Materials

Biocompatible glycero (9,10-trioxolane) trioleate (ozonide of oleic acid triglyceride, OTOA) was incorporated into polylactic acid (PLA) fibers by electrospinning and nonwoven PLA mats with 1%, 3% and 5% OTOA content. The morphological, mechanical, thermal and water sorption properties of electrospun PLA mats after the addition of OTOA were studied. A morphological analysis showed that the addition of OTOA increased the average fiber diameter and induced the formation of pores on the fiber surface, leading to an increase in the specific surface area for OTOA-modified PLA fibrous mats. PLA fiber mats with 3% OTOA content were characterized by a highly porous surface morphology, an increased specific surface area and high-water sorption. Differential scanning calorimetry (DSC) was used to analyze the thermal properties of the fibrous PLA mats. The glass transition temperatures of the fibers from the PLA–OTOA composites decreased as the OTOA content increased, which was attributed to the plasticizing effect of OTOA. DSC results showed that OTOA aided the PLA amorphization process, thus reducing the crystallinity of the obtained nonwoven PLA–OTOA materials. An analysis of the mechanical properties showed that the tensile strength of electrospun PLA mats was improved by the addition of OTOA. Additionally, fibrous PLA mats with 3% OTOA content showed increased elasticity compared to the pristine PLA material. The obtained porous PLA electrospun fibers with the optimal 3% OTOA content have the potential for various biomedical applications such as drug delivery and in tissue engineering.
Experimental Analysis of Kerf Taper Angle in Cutting Process of Sugar Palm Fiber Reinforced Unsaturated Polyester Composites with Laser Beam and Abrasive Water Jet Cutting Technologies

In this research, the effect of processing input parameters on the kerf taper angle response of three various material thicknesses of sugar palm fiber reinforced unsaturated polyester composite was investigated as an output parameter from abrasive waterjet and laser beam cutting techniques. The main purpose of the study is to obtain data that includes the optimum input parameters in cutting the composite utilizing these two unconventional techniques to avoid some defects that arise when using traditional cutting methods for cutting the composites, and then make a comparison to determine which is the most appropriate technique regarding the kerf taper angle response that is desired to be reduced. In the laser beam cutting process, traverse speed, laser power, and assist gas pressure were selected as the variable input parameters to optimize the kerf taper angle. While the water pressure, traverse speed, and stand-off-distance were the input variable parameters in the case of waterjet cutting process, with fixing of all the other input parameters in both cutting techniques. The levels of the input parameters that provide the optimal response of the kerf taper angle were determined using Taguchi’s approach, and the significance of input parameters was determined by computing the max–min variance of the average of the signal to-noise ratio (S/N) for each parameter. The contribution of each input processing parameter to the effects on kerf taper angle was determined using analysis of variation (ANOVA). Compared with the results that were extrapolated in the previous studies, both processes achieved acceptable results in terms of the response of the kerf taper angle, noting that the average values produced from the laser cutting process are much lower than those resulting from the waterjet cutting process, which gives an advantage to the laser cutting technique.
Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI

Abstract. The aim of this paper is two-fold: to provide guidance on how to best interpret TROPOMI trace gas retrievals and to highlight how TROPOMI trace gas data can be used to understand event-based impacts on air quality from regional to city-scales around the globe. For this study, we present the observed changes in the atmospheric column amounts of five trace gases (NO2, SO2, CO, HCHO and CHOCHO) detected by the Sentinel-5P TROPOMI instrument, driven by reductions of anthropogenic emissions due to COVID-19 lockdown measures in 2020. We report clear COVID-19-related decreases in NO2 concentrations on all continents. For megacities, reductions in column amounts of tropospheric NO2 range between 14 % and 63 %. For China and India supported by NO2 observations, where the primary source of anthropogenic SO2 is coal-fired power generation, we were able to detect sector-specific emission changes using the SO2 data. For HCHO and CHOCHO, we consistently observe anthropogenic changes in two-week averaged column amounts over China and India during the early phases of the lockdown periods. That these variations over such a short time scale are detectable from space, is due to the high resolution and improved sensitivity of the TROPOMI instrument. For CO, we observe a small reduction over China which is in concert with the other trace gas reductions observed during lockdown, however large, interannual differences prevent firm conclusions from being drawn. The joint analysis of COVID-19 lockdown-driven reductions in satellite observed trace gas column amounts, using the latest operational and scientific retrieval techniques for five species concomitantly is unprecedented. However, the meteorologically and seasonally driven variability of the five trace gases does not allow for drawing fully quantitative conclusions on the reduction of anthropogenic emissions based on TROPOMI observations alone. We anticipate that in future, the combined use of inverse modelling techniques with the high spatial resolution data from S5P/TROPOMI for all observed trace gases presented here, will yield a significantly improved sector-specific, space-based analysis of the impact of COVID-19 lockdown measures as compared to other existing satellite observations. Such analyses will further enhance the scientific impact and societal relevance of the TROPOMI mission.
The Solution for the Thermographic Measurement of the Temperature of a Small Object

This article describes the measuring system and the influence of selected factors on the accuracy of thermographic temperature measurement using a macrolens. This method enables thermographic measurement of the temperature of a small object with an area of square millimeters as, e.g., electronic elements. Damage to electronic components is often preceded by a rise in temperature, and an effective way to diagnose such components is the use of a thermographic camera. The ability to diagnose a device under full load makes thermography a very practical method that allows us to assess the condition of the device during operation. The accuracy of such a measurement depends on the conditions in which it is carried out. The incorrect selection of at least one parameter compensating the influence of the factor occurring during the measurement may cause the indicated value to differ from the correct value. This paper presents the basic issues linked to thermographic measurements and highlights the sources of errors. A measuring stand which enables the assessment of the influence of selected factors on the accuracy of thermographic measurement of electronic elements with the use of a macrolens is presented.