Miracle-dz/UniFAR-FTUs huggingface dataset:
Compositional and structural variations in the ternary system Li – Al – Si

Abstract The Li–Al–Si ternary system has been investigated and four ternary phases characterized using both powder and single crystal X-ray diffraction data. LiAlSi and Li7Al3Si4 crystallize in the F4̅3m, cubic system, Li18Al2Si6 in tetragonal I41/amd and Li15Al3Si6 in hexagonal P63/m space group. The structure of LiAlSi (a = 5.94 Å), previously reported from powder data, has been confirmed. The new compound Li7Al3Si4 has been identified and, due to a higher lithium content, its cell parameter is slightly larger (a = 6.115 Å). The tetragonal cell (a = 6.179(1) Å, c = 12.199(4) Å) of Li18Al2Si6 may be considered as a (1 × 1 × 2) supercell of the cubic one. Structural variations are strongly correlated to compositional fluctuations and atomic substitutions, in every case the SI–Al network displays some covalent character. In the Li15Al3Si6 hexagonal cell, bonding is more covalent leading to a heterographite-like SI–Al network.
Polymerization of 3‐methyl‐1‐butene promoted by metallocene catalysts

3-Methyl-1-butene was polymerized in the presence of a number of homogeneous metallocene catalysts (co-catalyst methylalumoxane). Contrary to literature reports, it was found that even the simplest C2-symmetric metallocenes promote the isotactic polymerization of this monomer with reasonable productivities. Quite surprisingly, a prevailingly isotactic polymer was also obtained in the presence of Cs-symmetric metallocenes, which are instead syndiotactic-specific in propene polymerization.
LAND CONVERSION AND THE LEVEL COMMUNITY SOCIAL COHESION IN SUB-DISTRICT

This research aims to find out land conversion that occurs and analyze relationship between the level social cohesion and collective action community after function of the land. The type research used is quantitative research, because takes samples from one population and uses questionnaire basic data collection tool. The number respondents ware 74 people consisting 68 men and 6 women who were captured by convenience, incidental, and purposively sampling techniques taken from those who had sold land and came from various RWs in Empoang subdisrict. Data analysis using SPSS for Windows 20 application. Test hypothesis using Rank Spearman statistical test. The results study found land conversion occurs because land is less productive and also form demographic adaptation and multi-form conversion. Spearman rank test resultsare obtained sig (2-tailed) value 0.013 which is smaller than alpha 0.05. This explains there is positive relationship between the levelof social cohesion and level community involvement collective action, although the correlation coefficient is only 0.286 which explains the relationship between variables is weak. If the level of variable social cohesion increases, level of community involvement in collective action also increases even though the level increase is low due to the typerelationship between the weak variables.
Authorship ethics: an overview of research on the state of practice

Authorship ethics is a central topic of discussion in research ethics fora. There are various guidelines for authorship (i.e., naming and order). It is not easy to decide the authorship in the presence of varying authorship guidelines. This paper gives an overview of research on authorship practices and issues. It presents a review of 16 empirical research papers published between 2014 - 2020. The objective is to learn how various research disciplines handle authorship. What are the authorship practices in various research disciplines, and what are the issues associated with these practices?
Synthesize-It-Classifier: Learning a Generative Classifier through Recurrent Self-analysis

We show the generative capability of an image classifier network by synthesizing high-resolution, photo-realistic, and diverse images at scale. The overall methodology, called Synthesize-It-Classifier (STIC), does not require an explicit generator network to estimate the density of the data distribution and sample images from that, but instead uses the classifier’s knowledge of the boundary to perform gradient ascent w.r. t. class logits and then synthesizes images using the Gram Matrix Metropolis Adjusted Langevin Algorithm (GRMALA) by drawing on a blank canvas. During training, the classifier iteratively uses these synthesized images as fake samples and re-estimates the class boundary in a recurrent fashion to improve both the classification accuracy and quality of synthetic images. The STIC shows that mixing of the hard fake samples (i.e. those synthesized by the one-hot class conditioning), and the soft fake samples (which are synthesized as a convex combination of classes, i.e. a mixup of classes [36]) improves class interpolation. We demonstrate an Attentive-STIC network that shows iterative drawing of synthesized images on the ImageNet dataset that has thousands of classes. In addition, we introduce the synthesis using a class conditional score classifier (Score-STIC) instead of a normal image classifier and show improved results on several real world datasets, i.e. ImageNet, LSUN and CIFAR 10.
Massive quarks in NLO dipole factorization for DIS: Longitudinal photon

In this work, we will present the ﬁrst complete calculation of the one-loop longitudinal photon-to-quark-antiquark light cone wave function, with massive quarks. The quark masses are renormalized in the pole mass scheme. The result is used to calculate the next-to-leading order correction to the high energy Deep Inelastic Scattering longitudinal structure function on a dense target in the dipole factorization framework. For massless quarks the next-to-leading order correction was already known to be sizeable, and our result makes it possible to evaluate it also for massive quarks.
Detection of aflatoxin M1 in milk and dairy products consumed in Adana, Turkey

A total of 70 dairy products consisting of 20 sterilized milk, 10 butter, 20 white cheese and 20 Kashar cheese samples were analysed for aflatoxin M₁ (AFM₁) by enzyme-linked immunosorbent assay (ELISA). The detection limit was 5 ng/L for milk and 25 ng/kg for butter, white cheese and Kashar cheese. Of the 70 dairy products analysed, AFM₁ in 49 samples (70%) was found to range from 10 to 388 ng/kg. Moreover, AFM₁ levels in three samples of milk, two samples of butter, one sample of white cheese and one sample of Kashar cheese were found to be higher than the Turkish legal limits.
IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approach

In this work, we propose a novel method for performing inertial aided navigation, by using deep neural net-works (DNNs). To date, most DNN inertial navigation methods focus on the task of inertial odometry, by taking gyroscope and accelerometer readings as input and regressing for integrated IMU poses (i.e., position and orientation). While this design has been successfully applied on a number of applications, it is not of theoretical performance guarantee unless patterned motion is involved. This inevitably leads to significantly reduced accuracy and robustness in certain use cases. To solve this problem, we design a framework to compute observable IMU integration terms using DNNs, followed by the numerical pose integration and sensor fusion to achieve the performance gain. Specifically, we perform detailed analysis on the motion terms in IMU kinematic equations, propose a dedicated network design, loss functions, and training strategies for the IMU data processing, and conduct extensive experiments. The results show that our method is generally applicable and outperforms both traditional and DNN methods by wide margins.
Learning Camera Performance Models for Active Multi-Camera Visual Teach and Repeat

In dynamic and cramped industrial environments, achieving reliable Visual Teach and Repeat (VT&R) with a single-camera is challenging. In this work, we develop a robust method for non-synchronized multi-camera VT&R. Our contribution are expected Camera Performance Models (CPM) which evaluate the camera streams from the teach step to determine the most informative one for localization during the repeat step. By actively selecting the most suitable camera for localization, we are able to successfully complete missions when one of the cameras is occluded, faces into feature poor locations or if the environment has changed. Furthermore, we explore the specific challenges of achieving VT&R on a dynamic quadruped robot, ANYmal. The camera does not follow a linear path (due to the walking gait and holonomicity) such that precise path-following cannot be achieved. Our experiments feature forward and backward facing stereo cameras showing VT&R performance in cluttered indoor and outdoor scenarios. We compared the trajectories the robot executed during the repeat steps demonstrating typical tracking precision of less than 10 cm on average. With a view towards omni-directional localization, we show how the approach generalizes to four cameras in simulation.
aDWI-BIDS: an extension to the brain imaging data structure for advanced diffusion weighted imaging

Diffusion weighted imaging techniques permit us to infer microstructural detail in biological tissue in vivo and noninvasively. Modern sequences are based on advanced diffusion encoding schemes, allowing probing of more revealing measures of tissue microstructure than the standard apparent diffusion coefficient or fractional anisotropy. Though these methods may result in faster or more revealing acquisitions, they generally demand prior knowledge of sequence-specific parameters for which there is no accepted sharing standard. Here, we present a metadata labelling scheme suitable for the needs of developers and users within the diffusion neuroimaging community alike: a lightweight, unambiguous parametric map relaying acqusition parameters. This extensible scheme supports a wide spectrum of diffusion encoding methods, from single diffusion encoding to highly complex sequences involving arbitrary gradient waveforms. Built under the brain imaging data structure (BIDS), it allows storage of advanced diffusion MRI data comprehensively alongside any other neuroimaging information, facilitating processing pipelines and multimodal analyses. We illustrate the usefulness of this BIDS-extension with a range of example data, and discuss the extension's impact on pre- and post-processing software.
Neural Network Training as an Optimal Control Problem : — An Augmented Lagrangian Approach —

Training of neural networks amounts to nonconvex optimization problems that are typically solved by using backpropagation and (variants of) stochastic gradient descent. In this work, we propose an alternative approach by viewing the training task as a nonlinear optimal control problem. Under this lens, backpropagation amounts to the sequential approach (single shooting) to optimal control, where the states variables have been eliminated. It is well known that single shooting may lead to ill-conditioning, and for this reason the simultaneous approach (multiple shooting) is typically preferred. Motivated by this hypothesis, an augmented Lagrangian algorithm is developed that only requires an approximate solution to the Lagrangian subproblems up to a user-defined accuracy. By applying this framework to the training of neural networks, it is shown that the inner Lagrangian subproblems are amenable to be solved using Gauss-Newton iterations. To fully exploit the structure of neural networks, the resulting linear least-squares problems are addressed by employing an approach based on forward dynamic programming. Finally, the effectiveness of our method is showcased on regression datasets.
Bidirectional Projection Network for Cross Dimension Scene Understanding

2D image representations are in regular grids and can be processed efficiently, whereas 3D point clouds are unordered and scattered in 3D space. The information inside these two visual domains is well complementary, e.g., 2D images have fine-grained texture while 3D point clouds contain plentiful geometry information. However, most current visual recognition systems process them individually. In this paper, we present a bidirectional projection network (BPNet) for joint 2D and 3D reasoning in an end-to-end manner. It contains 2D and 3D sub-networks with symmetric architectures, that are connected by our proposed bidirectional projection module (BPM). Via the BPM, complementary 2D and 3D information can interact with each other in multiple architectural levels, such that advantages in these two visual domains can be combined for better scene recognition. Extensive quantitative and qualitative experimental evaluations show that joint reasoning over 2D and 3D visual domains can benefit both 2D and 3D scene understanding simultaneously. Our BPNet achieves top performance on the ScanNetV2 benchmark for both 2D and 3D semantic segmentation. Code is available at https://github.com/wbhu/BPNet.
Prior-Free Auctions for the Demand Side of Federated Learning

Federated learning (FL) is a paradigm that allows distributed clients to learn a shared machine learning model without sharing their sensitive training data. While largely decentralized, FL requires resources to fund a central orchestrator or to reimburse contributors of datasets to incentivize participation. Inspired by insights from prior-free auction design, we propose a mechanism, FIPFA (Federated Incentive Payments via Prior-Free Auctions), to collect monetary contributions from self-interested clients. The mechanism operates in the semi-honest trust model and works even if clients have a heterogeneous interest in receiving high-quality models, and the server does not know the clients’ level of interest. We run experiments on the MNIST dataset to test clients’ model quality under FIPFA and FIPFA’s incentive properties.
NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction

We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit.
CGPart: A Part Segmentation Dataset Based on 3D Computer Graphics Models

Part segmentations provide a rich and detailed part-level description of objects, but their annotation requires an enormous amount of work. In this paper, we introduce CGPart, a comprehensive part segmentation dataset that provides detailed annotations on 3D CAD models, synthetic images, and real test images. CGPart includes 21 3D CAD models covering 5 vehicle categories, each with detailed per-mesh part labeling. The average number of parts per category is 24 , which is larger than any existing datasets for part segmentation on vehicle objects. By varying the rendering parameters, we make 168 , 000 synthetic images from these CAD models, each with automatically generated part segmentation ground-truth. We also anno-tate part segmentations on 200 real images for evaluation purposes. To illustrate the value of CGPart, we apply it to image part segmentation through unsupervised domain adaptation (UDA). We evaluate several baseline methods by adapting top-performing UDA algorithms from related tasks to part segmentation. Moreover, we introduce a new method called Geometric-Matching Guided domain adaptation (GMG), which leverages the spatial object structure to guide the knowledge transfer from the synthetic to the real images. Experimental results demonstrate the advantage
A new method for the evaluation of small‐angle scattering data

A new numerical method is presented for simultaneous smoothing, desmearing and Fourier transformation of X-ray and neutron small-angle scattering data. The method can only be applied to scattering curves from dilute particle systems, i.e. for scattering media whose distance distributions are zero beyond a certain value. The distance distribution of the scattering medium is approximated by a linear combination of about 20 to 30 cubic B-splines. These spline functions have a restricted extension in real space. Their coefficients are adjusted by a weighted least-squares operation so that the series, after being Fourier transformed and smeared according to the geometry and wavelength distribution, represents an optimum smoothed approximation of the experimental data. Tendencies towards oscillations in the least-squares operation are suppressed by a new stabilization routine. The method offers a new possibility for the estimation of the radius of gyration, which is generally superior to the Guinier approximation.
GNSS-denied geolocalization of UAVs by visual matching of onboard camera images with orthophotos

Localization of low-cost unmanned aerial vehicles (UAVs) often relies on Global Navigation Satellite Systems (GNSS). GNSS are susceptible to both natural disruptions to radio signal and intentional jamming and spoofing by an adversary. A typical way to provide georeferenced localization without GNSS for small UAVs is to have a downward-facing camera and match camera images to a map. The downward-facing camera adds cost, size, and weight to the UAV platform and the orientation limits its usability for other purposes. In this work, we propose a Monte-Carlo localization method for georeferenced localization of an UAV requiring no infrastructure using only inertial measurements, a camera facing an arbitrary direction, and an orthoimage map. We perform orthorectification of the UAV image, relying on a local planarity assumption of the environment, relaxing the requirement of downward-pointing camera. We propose a measure of goodness for the matching score of an orthorectified UAV image and a map. We demonstrate that the system is able to localize globally an UAV with modest requirements for initialization and map resolution.
Distilling Object Detectors via Decoupled Features

Knowledge distillation is a widely used paradigm for inheriting information from a complicated teacher network to a compact student network and maintaining the strong performance. Different from image classification, object detectors are much more sophisticated with multiple loss functions in which features that semantic information rely on are tangled. In this paper, we point out that the information of features derived from regions excluding objects are also essential for distilling the student detector, which is usually ignored in existing approaches. In addition, we elucidate that features from different regions should be assigned with different importance during distillation. To this end, we present a novel distillation algorithm via decoupled features (DeFeat) for learning a better student detector. Specifically, two levels of decoupled features will be processed for embedding useful information into the student, i.e., decoupled features from neck and decoupled proposals from classification head. Extensive experiments on various detectors with different backbones show that the proposed DeFeat is able to surpass the state-of-the-art distillation methods for object detection. For example, DeFeat improves ResNet50 based Faster R-CNN from 37.4% to 40.9% mAP, and improves ResNet50 based RetinaNet from 36.5% to 39.7% mAP on COCO benchmark. Code will be released1,2.
Information theoretic parameters of non-commutative graphs and convex corners

We establish a second anti-blocker theorem for non-commutative convex corners, show that the anti-blocking operation is continuous on bounded sets of convex corners, and define optimisation parameters for a given convex corner that generalise well-known graph theoretic quantities. We define the entropy of a state with respect to a convex corner, characterise its maximum value in terms of a generalised fractional chromatic number and establish entropy splitting results that demonstrate the entropic complementarity between a convex corner and its anti-blocker. We identify two extremal tensor products of convex corners and examine the behaviour of the introduced parameters with respect to tensoring. Specialising to non-commutative graphs, we obtain quantum versions of the fractional chromatic number and the clique covering number, as well as a notion of non-commutative graph entropy of a state, which we show to be continuous with respect to the state and the graph. We define the Witsenhausen rate of a non-commutative graph and compute the values of our parameters in some specific cases.
Single-crystal investigations on the multiferroic material 
LiFe(WO4)2

S. Biesenkamp, ∗ D. Gorkov, 2 D. Brüning, A. Bertin, T. Fröhlich, X. Fabrèges, A. Gukasov, M. Meven, 5 P. Becker, L. Bohatý, T. Lorenz, and M. Braden † II. Physikalisches Institut, Universität zu Köln, Zülpicher Straße 77, D-50937 Köln, Germany FRM-II Munich, Germany Laboratoire Léon Brillouin, C.E. A. /C.N. R.S. , F-91191 Gif-sur-Yvette CEDEX, France RWTH Aachen University, Institut für Kristallographie, 52056 Aachen, Germany Jülich Centre for Neutron Science JCNS at Heinz Maier-Leibnitz Zentrum (MLZ) Abteilung Kristallographie, Institut für Geologie und Mineralogie, Universität zu Köln, Zülpicher Straße 49b, 50674 Köln, Germany (Dated: March 29, 2021)
Drag-Based Model (DBM) Tools for Forecast of Coronal Mass Ejection Arrival Time and Speed

Forecasting the arrival time of coronal mass ejections (CMEs) and their associated shocks is one of the key aspects of space weather research. One of the commonly used models is the analytical drag-based model (DBM) for heliospheric propagation of CMEs due to its simplicity and calculation speed. The DBM relies on the observational fact that slow CMEs accelerate whereas fast CMEs decelerate and is based on the concept of magnetohydrodynamic (MHD) drag, which acts to adjust the CME speed to the ambient solar wind. Although physically DBM is applicable only to the CME magnetic structure, it is often used as a proxy for shock arrival. In recent years, the DBM equation has been used in many studies to describe the propagation of CMEs and shocks with different geometries and assumptions. In this study, we provide an overview of the five DBM versions currently available and their respective tools, developed at Hvar Observatory and frequently used by researchers and forecasters (1) basic 1D DBM, a 1D model describing the propagation of a single point (i.e., the apex of the CME) or a concentric arc (where all points propagate identically); (2) advanced 2D self-similar cone DBM, a 2D model which combines basic DBM and cone geometry describing the propagation of the CME leading edge which evolves in a self-similar manner; (3) 2D flattening cone DBM, a 2D model which combines basic DBM and cone geometry describing the propagation of the CME leading edge which does not evolve in a self-similar manner; (4) DBEM, an ensemble version of the 2D flattening cone DBM which uses CME ensembles as an input; and (5) DBEMv3, an ensemble version of the 2D flattening cone DBM which creates CME ensembles based on the input uncertainties. All five versions have been tested and published in recent years and are available online or upon request. We provide an overview of these five tools, as well as of their similarities and differences, and discuss and demonstrate their application.