Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of DTPA and calcium chloride extractants for assessing extractable metal fraction in polluted soils

Abstract Metal availability in soils is often assessed by means of extraction with chemical solutions, among others the chelating agent DTPA (diethylenetriaminepentaacetic acid) and the non‐buffered salt calcium chloride (CaCl2). The same procedures are used for polluted soils that were originally created to assess the nutrient status of arable soils. We studied the influence of various parameters (type of shaker, shaking time, soil to solution ratio, and concentration of chemical extractant) and modify the DTPA and CaCl2 extraction procedures to make them suitable for the study of polluted soils. The chosen extraction ratio and extractant concentration were the followings: 8 g/20 mL of 0.1 MCaCl2 and 2 g/20 mL of 0.005 M DTPA. The optimized procedures were applied to nine soil samples affected by different sources of pollution (mine works, vehicle emissions, and various industries). Cadmium (Cd) showed the highest extractability with both extractants. Depending on the soil, copper (Cu) and zinc (Zn) (using DPTA) and Cu and manganese (Mn) (using CaCl2) were the followings in the extractable amounts. Cadmium, Cu, and Zn were highly correlated in both extractions and with total contents.
Extending Morgan and Parker's results about commuting graphs

Morgan and Parker have proved that if $G$ is a group satisfying the condition that $Z(G) = 1$, then the connected components of the commuting graph of $G$ have diameter at most $10$. Parker has proved that if in addition $G$ is solvable, then the commuting graph of $G$ is disconnected if and only if $G$ is a Frobenius group or a $2$-Frobenius group, and if the commuting graph of $G$ is connected, then its diameter is at most $8$. We prove that the hypothesis $Z (G) = 1$ in these results can be replaced with $G' \cap Z(G) = 1$. We also prove that if $G$ is solvable and $G/Z(G)$ is either a Frobenius group or a $2$-Frobenius group, then the commuting graph of $G$ is disconnected.
From Narrow-Gap and Semimagnetic Semiconductors to Spintronics and Topological Matter: A Life with Spins

The abundance of semiconductors in our smartphones, computers, fiber optic junctions, cars, light sources, photovoltaic and thermoelectric cells results from the possibilities of controlling their properties through doping, lighting, and applying various fields. This paper, a part of the volume celebrating 100 years of the Polish Physical Society, presents a biased selection of worthwhile results obtained by researchers at the Institute of Physics, Polish Academy of Sciences relevant, as seen today, to topological matter and spintronics. Comprehensive studies, combining materials development, experimental investigations, and theoretical description of narrow-gap and dilute-magnetic semiconductors have been especially significant in this context. This survey also emphasizes, in an autobiographical tone, a half of a century of the author's intellectual emotions accompanying the rise of ideas and quantitative theories, allowing identifying the physics behind ongoing and future observations.
CORRELATION OF LIQUID FRACTION IN TWO-PHASE FLOW WITH APPLICATION TO LIQUID METALS

A generalized correlation for the liquid fraction in twophase flow is presented which is proposed for use with all iluids, including liquid metals. The correlation is based on isothermal, two-phase, two-component liquid fraction data for liquid Hg--N/sub 2/ and water-air. Liquid fraction is shown to be a function of the Martinelli flow modulus and liquid/gas density and viscosity ratios. Good correspondence is indicated between the liquid fraction predicted by this correlation and the Martinelli-Nelson correlation for steam, experimental data for steam, and experimental data for Santowax R. Prediction of liquid fraction by this method is shown for Na, K, Rb, and Hg. Application of the method to boiling Hg, for a range of temperatures and exit qualities, is demonstrated for SNAP systems. (auth)
FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism

In this paper, we focus on category-level 6D pose and size estimation from a monocular RGB-D image. Previous methods suffer from inefficient category-level pose feature extraction, which leads to low accuracy and inference speed. To tackle this problem, we propose a fast shape-based network (FS-Net) with efficient category-level feature extraction for 6D pose estimation. First, we design an orientation aware autoencoder with 3D graph convolution for latent feature extraction. Thanks to the shift and scale-invariance properties of 3D graph convolution, the learned latent feature is insensitive to point shift and object size. Then, to efficiently decode category-level rotation information from the latent feature, we propose a novel decoupled rotation mechanism that employs two decoders to complementarily access the rotation information. For translation and size, we estimate them by two residuals: the difference between the mean of object points and ground truth translation, and the difference between the mean size of the category and ground truth size, respectively. Finally, to increase the generalization ability of the FS-Net, we propose an on-line box-cage based 3D deformation mechanism to augment the training data. Extensive experiments on two benchmark datasets show that the proposed method achieves state-of-the-art performance in both category- and instance-level 6D object pose estimation. Especially in category-level pose estimation, without extra synthetic data, our method outperforms existing methods by 6.3% on the NOCS-REAL dataset 1.
Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

Abstract High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct-electron detection. An electron probe size down to 0.5 nm in diameter is used and the sample investigated is a gold–palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.
Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation

Absolute camera pose estimation is usually addressed by sequentially solving two distinct subproblems: First a feature matching problem that seeks to establish putative 2D-3D correspondences, and then a Perspective-n-Point problem that minimizes, w.r. t. the camera pose, the sum of so-called Reprojection Errors (RE). We argue that generating putative 2D-3D correspondences 1) leads to an important loss of information that needs to be compensated as far as possible, within RE, through the choice of a robust loss and the tuning of its hyperparameters and 2) may lead to an RE that conveys erroneous data to the pose estimator. In this paper, we introduce the Neural Reprojection Error (NRE) as a substitute for RE. NRE allows to rethink the camera pose estimation problem by merging it with the feature learning problem, hence leveraging richer information than 2D-3D correspondences and eliminating the need for choosing a robust loss and its hyperparameters. Thus NRE can be used as training loss to learn image descriptors tailored for pose estimation. We also propose a coarse-to-fine optimization method able to very efficiently minimize a sum of NRE terms w.r. t. the camera pose. We experimentally demonstrate that NRE is a good substitute for RE as it significantly improves both the robustness and the accuracy of the camera pose estimate while being computationally and memory highly efficient. From a broader point of view, we believe this new way of merging deep learning and 3D geometry may be useful in other computer vision applications. Source code and model weights will be made available at hugogermain.com/nre.
Donor plasma mitochondrial DNA is associated with antibody-mediated rejection in renal allograft recipients

We previously showed that donor plasma mitochondrial DNA (dmtDNA) levels were correlated with renal allograft function. The aim of the current study was to determine whether dmtDNA levels are associated with the occurrence of antibody-mediated rejection (ABMR). This is a retrospective open cohort study comprised of 167 donors and 323 recipients enrolled from January 2015 to December 2017. We quantified the mtDNA level present in donor plasma using quantitative real-time polymerase chain reaction. The average plasma dmtDNA level in the acute rejection (AR) group was higher than that of the control group (0.156 versus 0.075, p<0.001). Multivariate logistic regression analysis showed that dmtDNA levels were also significantly associated with AR (OR=1.588, 95% CI 1.337-4.561, p<0.001). When the dmtDNA level was >0.156, the probability of AR was 62.9%. The plasma dmtDNA level in the ABMR group was significantly higher than that of the T cell-mediated rejection group (0.185 versus 0.099, p=0.032). The area under the receiver operating characteristic curve of dmtDNA for prediction of ABMR was as high as 0.910 (95% CI 0.843-0.977). We demonstrated that plasma dmtDNA was an independent risk factor for ABMR, which is valuable in organ evaluation. dmtDNA level is a possible first predictive marker for ABMR.
Neural Architecture Search based on Cartesian Genetic Programming Coding Method

Neural architecture search (NAS) is a hot topic in the field of automated machine learning and outperforms humans in designing neural architectures on quite a few machine learning tasks. Motivated by the natural representation form of neural networks by the Cartesian genetic programming (CGP), we propose an evolutionary approach of NAS based on CGP, called CGPNAS, to solve sentence classification task. To evolve the architectures under the framework of CGP, the operations such as convolution are identified as the types of function nodes of CGP, and the evolutionary operations are designed based on Evolutionary Strategy. The experimental results show that the searched architectures are comparable with the performance of human-designed architectures. We verify the ability of domain transfer of our evolved architectures. The transfer experimental results show that the accuracy deterioration is lower than 2-5%. Finally, the ablation study identifies the Attention function as the single key function node and the linear transformations along could keep the accuracy similar with the full evolved architectures, which is worthy of investigation in the future.
Reduced intensity allogeneic hematopoietic stem cell transplantation is a safe and effective treatment option in high‐risk myeloma patients – a single centre experience

Allogeneic stem cell transplantation (allo-SCT) has been exten-sively investigated as a potentially curative treatment strategy for myeloma. Early studies using myeloablative conditioning showed an unacceptable transplant-related mortality (TRM) of 53%, despite long-term remission rates (39%). 1 Recent improvements in transplant conditioning, graft-versus-host disease (GvHD) prophylaxis and treatment, and supportive care have seen allo-SCT for myeloma re-emerge as a viable option, in particular for young patients with high-risk disease, in whom the prognosis remains poor. 2 Despite this, newer therapeutic strategies have raised the bar for what allo-SCT would need to achieve to be seen as a useful option. 3 At our institution, we use a low-intensity ﬂudarabine/cy-clophosphamide conditioning regimen without pre-emptive T-cell depletion. This has been shown to be safe and effective in acute myeloid leukemia, 4 acute lymphoblastic leukaemia 5 and lymphoid malignancies 6 for patients over a wide age range (the latter report included 11 of the current cohort). For this study, we retrospectively collected data from myeloma patients who underwent allo-SCT. Patients received reduced intensity conditioning with ﬂudarabine 25 mg/ m 2 /day for 5 days and cyclophosphamide 1 g/m 2 /day for 2 days, followed by infusion of stem cells from human leukocyte antigen (HLA)-matched donors. GvHD prophylaxis consisted of short-course methotrexate (5 mg/m 2 on days + 3, + 6 and + 11), and cyclosporin (5 mg/kg on day (cid:3) 2 and 3 mg/kg per day thereafter, adjusted according to blood levels). Standard supportive therapy and antimicrobial prophylaxis were employed. Full details of the protocol are as
Stability of sputum inflammatory phenotypes in childhood asthma during stable and exacerbation phases

Management strategies based on airway inflammation phenotypes are increasingly used for adults with asthma. While sputum‐based phenotypes are relatively stable in adults with asthma, there is little such data in childhood asthma. Hence, we aimed to evaluate the stability of sputum inflammatory phenotypes in children with asthma both in the stable and during exacerbation phases.
The Be Star 66 Ophiuchi: 60 Years of Disk Evolution

We use a time-dependent hydrodynamic code and a non-LTE Monte Carlo code to model disk dissipation for the Be star 66 Ophiuchi. We compiled 63 years of observations from 1957 to 2020 to encompass the complete history of the growth and subsequent dissipation of the star’s disk. Our models are constrained by new and archival photometry, spectroscopy, and polarization observations, allowing us to model the disk dissipation event. Using Markov Chain Monte Carlo methods, we find that the properties of 66 Oph are consistent with those of a standard B2Ve star. We computed a grid of 61,568 Be star disk models to constrain the density profile of the disk before dissipation using observations of the Hα line profile and spectral energy distribution. We find at the onset of dissipation the disk has a base density of 2.5 × 10−11 g cm−3 with a radial power-law index of n = 2.6. Our models indicate that after 21 yr of disk dissipation 66 Oph’s outer disk remained present and bright in the radio. We find an isothermal disk with constant viscosity with an α = 0.4 and an outer disk radius of ∼115 stellar radii best reproduces the rate of 66 Oph’s disk dissipation. We determined the interstellar polarization in the direction of the star in the V band is p = 0.63 ± 0.02% with a polarization position angle of θ IS ≈ 857 ± 07. Using the Stokes QU diagram, we find the intrinsic polarization position angle of 66 Oph’s disk is θ int ≈ 98° ± 3°.
PatchNet - Short-range Template Matching for Efficient Video Processing

Object recognition is a fundamental problem in many video processing tasks, accurately locating seen objects at low computation cost paves the way for on-device video recognition. We propose PatchNet, an efficient convolutional neural network to match objects in adjacent video frames. It learns the patchwise correlation features instead of pixel features. PatchNet is very compact, running at just 58MFLOPs, $5\times$ simpler than MobileNetV2. We demonstrate its application on two tasks, video object detection and visual object tracking. On ImageNet VID, PatchNet reduces the flops of R-FCN ResNet-101 by 5x and EfficientDet-D0 by 3.4x with less than 1% mAP loss. On OTB2015, PatchNet reduces SiamFC and SiamRPN by 2.5x with no accuracy loss. Experiments on Jetson Nano further demonstrate 2.8x to 4.3x speed-ups associated with flops reduction. Code is open sourced at https://github.com/RalphMao/PatchNet.
A Weakly Supervised Approach for Classifying Stance in Twitter Replies

Conversations on social media (SM) are increasingly being used to investigate social issues on the web, such as online harassment and rumor spread. For such issues, a common thread of research uses adversarial reactions, e.g., replies pointing out factual inaccuracies in rumors. Though adversarial reactions are prevalent in online conversations, inferring those adverse views (or stance) from the text in replies is difficult and requires complex natural language processing (NLP) models. Moreover, conventional NLP models for stance mining need labeled data for supervised learning. Getting labeled conversations can itself be challenging as conversations can be on any topic, and topics change over time. These challenges make learning the stance a difficult NLP problem. In this research, we first create a new stance dataset comprised of three different topics by labeling both users' opinions on the topics (as in pro/con) and users' stance while replying to others' posts (as in favor/oppose). As we find limitations with supervised approaches, we propose a weakly-supervised approach to predict the stance in Twitter replies. Our novel method allows using a smaller number of hashtags to generate weak labels for Twitter replies. Compared to supervised learning, our method improves the mean F1-macro by 8\% on the hand-labeled dataset without using any hand-labeled examples in the training set. We further show the applicability of our proposed method on COVID 19 related conversations on Twitter.
The Gaia spectrophotometric standard stars survey – V. Preliminary flux tables for the calibration of Gaia DR2 and (E)DR3

We present the flux tables of the spectro-photometric standard stars (SPSS) used to calibrate in flux the Gaia DR2 and (E)DR3 data releases. The latest SPSS grid version contains 112 stars, whose flux tables agree to better than 1% with the CALSPEC spectra of 11 flux standards for the calibration of the Hubble Space Telescope. The synthetic magnitudes computed on the SPSS spectra also agree to better than 1% with the Landolt magnitudes of 37 stars in common. The typical spreads in both comparisons are of the order of 1%. These uncertainties already meet the initial requirements for the Gaia SPSS project, but further improvements are expected in the next SPSS versions, that will be used to calibrate futureGaia releases. We complement the SPSS flux tables with literature spectra of 60 additional stars that did not pass all the criteria to be SPSS, the Passband Validation Library (PVL). The PVL contains stars of extreme spectral types, such as bright O and B stars and late M stars and brown dwarfs, and was useful to investigate systematic effects in the previous Gaia DR2 release and to minimize them in the EDR3 one. The PVL literature spectra are recalibrated as accurately as possible onto the SPSS reference scale, so that the two sets together can be used in a variety of validation and comparison studies.
Are NLP Models really able to Solve Simple Math Word Problems?

The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy. Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often considered “solved” with the bulk of research attention moving to more complex MWPs. In this paper, we restrict our attention to English MWPs taught in grades four and lower. We provide strong evidence that the existing MWP solvers rely on shallow heuristics to achieve high performance on the benchmark datasets. To this end, we show that MWP solvers that do not have access to the question asked in the MWP can still solve a large fraction of MWPs. Similarly, models that treat MWPs as bag-of-words can also achieve surprisingly high accuracy. Further, we introduce a challenge dataset, SVAMP, created by applying carefully chosen variations over examples sampled from existing datasets. The best accuracy achieved by state-of-the-art models is substantially lower on SVAMP, thus showing that much remains to be done even for the simplest of the MWPs.
Actual versus ideal self: An examination of the impact of fashion self congruence on consumer’s fashion consciousness and status consumption tendencies

ABSTRACT With growing awareness about the self, consumers are increasingly becoming conscious of the role of fashion clothing in enhancing and communicating their self-image. They seek to portray their self-image through an outward display of fashion style that is congruent to their concept of self. Consumer’s congruity however can be formed with both actual and ideal self. Recognizing this fact, the present study investigates the comparative impact of actual and ideal-fashion self-congruity on fashion consciousness and status consumption tendencies of millennials in India. The study also probes into the mediating role of fashion consciousness between the relationship of actual/ideal fashion self-congruity and status consumption. Using a sample of 751 millennials, the study confirms that ideal fashion self-congruity is a stronger predictor of fashion consciousness and status consumption in comparison to actual fashion self-congruity. In fact, actual fashion self-congruity did not have any significant impact on the two dependent variables. Additionally, the results of the study also point towards the mediating role of fashion consciousness between the relation of ideal fashion self-congruity and status consumption. The study points towards some notable implications for marketers of branded fashion wear by signifying the importance of the ideal-self in consumer decision-making process.
Solubility of hydrogen in liquid methanol and methyl formate at 20 °C to 140 °C

The solubilities of hydrogen gas in liquid phase methanol and methyl formate were measured under pressures up to 1500 kPa and temperatures to 140 °C. The mole fraction solubilities were between 7.9 × 10-4 and 6.3 × 10-3 for H2 in methanol and between 7 × 10-4 and 6.9 × 10-3 for H2 in methyl formate. The Henry's law constants were about 620 MPa for H2 in methanol and 630 MPa for H2 in methyl formate at a temperature of 20 °C. The Henry's law constants show a decrease with increase of temperature. Empirical correlations for solubilities at various temperatures were given on the basis of the experimental data.
A static relocation strategy for electric car-sharing systems in a vehicle-to-grid framework

ABSTRACT Car-Sharing Systems (CSSs) are becoming increasingly popular in urban areas replacing car ownership. The most attractive CSSs give users the opportunity to make one-way trips. This behavior creates an unbalanced status between stations. Hence, some users could leave the system because they may not find a car/parking place available near their origin/destination. In recent years, CSSs are employing Electric Vehicles (EVs). A recent technology applied to EVs, called Vehicle-to-Grid (V2G), has allowed selling energy, transferring it from vehicle batteries to an electric grid. In this paper, we adopt Electric Cars (ECs) with V2G for one-way station-based CSSs. We suggest ECs distribution among stations at the beginning of each day, simultaneously making the most of V2G technology and satisfying CSSs customers’ requests. These distributions represent the final configurations that should be obtained through overnight vehicle relocation. The proposed models have been applied to a real-sized case achieving promising results.