Miracle-dz/UniFAR-FTUs huggingface dataset:
N
 ‐centered ensemble density‐functional theory for open systems

Two (so-called left and right) variants of N-centered ensemble density-functional theory (DFT) are presented. Unlike the original formulation of the theory, these variants allow for the description of systems with a fractional electron number. While conventional DFT for open systems uses only the true electron density as basic variable, left/right N-centered ensemble DFT relies instead on (a) a fictitious ensemble density that integrates to a central (integral) number N of electrons, and (b) a grand canonical ensemble weight α which is equal to the deviation of the true electron number from N. Within such a formalism, the infamous derivative discontinuity that appears when crossing an integral number of electrons is described exactly through the dependence in α of the left and right N-centered ensemble Hartree-exchange-correlation density functionals. Incorporating N-centered ensembles into existing density-functional embedding theories is expected to pave the way toward the in-principle-exact description of an open fragment by means of a pure-state N-electron many-body wavefunction. Work is currently in progress in this direction.
Self-Play Learning Without a Reward Metric

The AlphaZero algorithm for the learning of strategy games via self-play, which has produced superhuman ability in the games of Go, chess, and shogi, uses a quantitative reward function for game outcomes, requiring the users of the algorithm to explicitly balance different components of the reward against each other, such as the game winner and margin of victory. We present a modification to the AlphaZero algorithm that requires only a total ordering over game outcomes, obviating the need to perform any quantitative balancing of reward components. We demonstrate that this system learns optimal play in a comparable amount of time to AlphaZero on a sample game.
A Unified Framework for Random Forest Prediction Error Estimation

We introduce a unified framework for random forest prediction error estimation based on a novel estimator of the conditional prediction error distribution function. Our framework enables immediate estimation of key prediction uncertainty metrics, including conditional mean squared prediction errors, conditional biases, and conditional quantiles, by a straightforward plug-in routine. Our approach is particularly well-adapted for prediction interval estimation, which has received less attention in the random forest literature despite its practical utility; we show via simulations that our proposed prediction intervals are competitive with, and in some settings outperform, existing methods. To establish theoretical grounding for our framework, we prove pointwise uniform consistency of a more stringent version of our estimator of the conditional prediction error distribution. In addition to providing a suite of measures of prediction uncertainty, our general framework is applicable to many variants of the random forest algorithm. The estimators introduced here are implemented in the R package forestError.
Optimized Tracking of Topic Evolution

Topic evolution modeling has been researched for a long time and has gained considerable interest. A state-of-the-art method has been recently using word modeling algorithms in combination with community detection mechanisms to achieve better results in a more effective way. We analyse results of this approach and discuss the two major challenges that this approach still faces. Although the topics that have resulted from the recent algorithm are good in general, they are very noisy due to many topics that are very unimportant because of their size, words, or ambiguity. Additionally, the number of words defining each topic is too large, making it difficult to analyse them in their unsorted state. In this paper, we propose approaches to tackle these challenges by adding topic filtering and network analysis metrics to define the importance of a topic. We test different combinations of these metrics to see which combination yields the best results. Furthermore, we add word filtering and ranking to each topic to identify the words with the highest novelty automatically. We evaluate our enhancement methods in two ways: human qualitative evaluation and automatic quantitative evaluation. Moreover, we created two case studies to test the quality of the clusters and words. In the quantitative evaluation, we use the pairwise mutual information score to test the coherency of topics. The quantitative evaluation also includes an analysis of execution times for each part of the program. The results of the experimental evaluations show that the two evaluation methods agree on the positive feasibility of the algorithm. We then show possible extensions in the form of usability and future improvements to the algorithm.
Simulation of a compact object with outflows moving through a gaseous background

A compact object moving relative to surrounding gas accretes material and perturbs the density of gas in its vicinity. In the classical picture of Bondi–Hoyle–Lyttleton accretion, the perturbation takes the form of an overdense wake behind the object, which exerts a dynamical friction drag. We use hydrodynamic simulations to investigate how the accretion rate and strength of dynamical friction are modified by the presence of outflow from the compact object. We show that the destruction of the wake by an outflow reduces dynamical friction, and reverses its sign when the outflow is strong enough, in good quantitative agreement with analytic calculations. For a strong isotropic outflow, the outcome on scales that we have simulated is a negative dynamical friction, i.e. net acceleration. For jet-like outflows driven by reprocessed accretion, both the rate of accretion and the magnitude of dynamical friction drop for more powerful jets. The accretion rate is strongly intermittent when the jet points to the same direction as the motion of the compact object. The dynamical effects of outflows may be important for the evolution of compact objects during the common envelope phase of binary systems, and for accreting compact objects and massive stars encountering active galactic nucleus discs.
Incrementally Improving Graph WaveNet Performance on Traffic Prediction

We present a series of modifications which improve upon Graph WaveNet's previously state-of-the-art performance on the METR-LA traffic prediction task. The goal of this task is to predict the future speed of traffic at each sensor in a network using the past hour of sensor readings. Graph WaveNet (GWN) is a spatio-temporal graph neural network which interleaves graph convolution to aggregate information from nearby sensors and dilated convolutions to aggregate information from the past. We improve GWN by (1) using better hyperparameters, (2) adding connections that allow larger gradients to flow back to the early convolutional layers, and (3) pretraining on an easier short-term traffic prediction task. These modifications reduce the mean absolute error by . 06 on the METR-LA task, nearly equal to GWN's improvement over its predecessor. These improvements generalize to the PEMS-BAY dataset, with similar relative magnitude. We also show that ensembling separate models for short-and long-term predictions further improves performance. Code is available at this https URL .
A Survey of Predictive Maintenance: Systems, Purposes and Approaches

This paper provides a comprehensive literature review on Predictive Maintenance (PdM) with emphasis on system architectures, purposes and approaches. In industry, any outages and unplanned downtime of machines or systems would degrade or interrupt a company's core business, potentially resulting in significant penalties and unmeasurable reputation loss. Existing traditional maintenance approaches suffer from some assumptions and limits, such as high prevent/repair costs, inadequate or inaccurate mathematical degradation processes and manual feature extraction. With the trend of smart manufacturing and development of Internet of Things (IoT), data mining and Artificial Intelligence (AI), etc., PdM is proposed as a novel type of maintenance paradigm to perform maintenances only after the analytical models predict certain failures or degradations. In this survey, we first provide a high-level view of the PdM system architectures including the Open System Architecture for Condition Based Monitoring (OSA-CBM), cloud-enhanced PdM system and PdM 4.0, etc. Then, we make clear the specific maintenance purposes/objectives, which mainly comprise cost minimization, availability/reliability maximization and multi-objective optimization. Furthermore, we provide a review of the existing approaches for fault diagnosis and prognosis in PdM systems that include three major subcategories: knowledge based, traditional Machine Learning (ML) based and DL based approaches. We make a brief review on the knowledge based and traditional ML based approaches applied in diverse industrial systems or components with a complete list of references, while providing a comprehensive review of DL based approaches. Finally, important future research directions are introduced.
Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes

The main requisite for fine-grained recognition task is to focus on subtle discriminative details that make the subordinate classes different from each other. We note that existing methods implicitly address this requirement and leave it to a data-driven pipeline to figure out what makes a subordinate class different from the others. This results in two major limitations: First, the network focuses on the most obvious distinctions between classes and overlooks more subtle inter-class variations. Second, the chance of misclassifying a given sample in any of the negative classes is considered equal, while in fact, confusions generally occur among only the most similar classes. Here, we propose to explicitly force the network to find the subtle differences among closely related classes. In this pursuit, we introduce two key novelties that can be easily plugged into existing end-to-end deep learning pipelines. On one hand, we introduce “diversification block” which masks the most salient features for an input to force the network to use more subtle cues for its correct classification. Concurrently, we introduce a “gradient-boosting” loss function that focuses only on the confusing classes for each sample and therefore moves swiftly along the direction on the loss surface that seeks to resolve these ambiguities. The synergy between these two blocks helps the network to learn more effective feature representations. Comprehensive experiments are performed on five challenging datasets. Our approach outperforms existing methods using similar experimental setting on all five datasets.
VEGAS: a VST Early-type GAlaxy Survey

Context. This paper is based on the multi-band VST Early-type GAlaxy Survey (VEGAS) with the VLT Survey Telescope (VST). We present new deep photometry of the IC 1459 group in g and r band. Aims. The main goal of this work is to investigate the photometric properties of the IC 1459 group, and to compare our results with those obtained for other galaxy groups studied in VEGAS, in order to provide an initial view of the variation of their properties as a function of the evolution of the system. Methods. For all galaxies in the IC 1459 group, we fit isophotes and extract the azimuthally averaged surface-brightness profiles, the position angle, and ellipticity profiles as a function of the semi-major axis. We also extract the average colour profile. In each band, we estimate the total magnitude, effective radius, mean colour, and total stellar mass for each galaxy in the group. We then look at the structure of the brightest galaxies and the faint features in their outskirts, considering also the intragroup component. Results. The wide field of view, long integration time, high angular resolution, and arcsec-level seeing of OmegaCAM at VST allow us to map the light distribution of IC 1459 down to a surface brightness level of 29.26 mag arcsec−2 in g band and 28.85 mag arcsec−2 in r band, and out to 7−10Re, and to detect the optical counterpart of HI gas around IC 1459. We also carry out an in-depth exploration of three low-density environments and provide information to understand how galaxy and group properties change with the group evolution stage. Conclusions. Good agreement is found between our results and predictions of numerical simulations regarding the structural properties of the brightest galaxies of the groups. We suggest that the structure of the outer envelope of he brightest cluster galaxies (i.e. the signatures of past mergers and tidal interactions), the intra-group light, and the HI amount and distribution may be used as indicators of the evolutionary stage and mass assembly of galaxy groups.
DAmageNet: A Universal Adversarial Dataset

It is now well known that deep neural networks (DNNs) are vulnerable to adversarial attack. Adversarial samples are similar to the clean ones, but are able to cheat the attacked DNN to produce incorrect predictions in high confidence. But most of the existing adversarial attacks have high success rate only when the information of the attacked DNN is well-known or could be estimated by massive queries. A promising way is to generate adversarial samples with high transferability. By this way, we generate 96020 transferable adversarial samples from original ones in ImageNet. The average difference, measured by root means squared deviation, is only around 3.8 on average. However, the adversarial samples are misclassified by various models with an error rate up to 90\%. Since the images are generated independently with the attacked DNNs, this is essentially zero-query adversarial attack. We call the dataset \emph{DAmageNet}, which is the first universal adversarial dataset that beats many models trained in ImageNet. By finding the drawbacks, DAmageNet could serve as a benchmark to study and improve robustness of DNNs. DAmageNet could be downloaded in this http URL.
The NGC 346 massive star census

Previous analyses of two large spectroscopic surveys of early-type stars in the Large Magellanic Cloud (LMC) have found an excess of nitrogen enriched B-type targets with a ve sin i ≤ 40 km s−1 compared with the predictions of single star evolutionary models that incorporate rotational mixing. By contrast, the number of such targets with 40 < ve sin i ≤ 80 km s−1 was consistent with such models. We have undertaken a similar analysis for 61 B-type targets which lie towards the young cluster, NGC 346 in the Small Magellanic Cloud (SMC). These again have projected rotational velocities, ve sin i ≤ 80 km s−1, are not classified as supergiants, and are apparently single. Approximately 65% of these SMC targets could have nitrogen enhancements of less than 0.3 dex, which is consistent with them having experienced only small amounts of mixing due to their low rotational velocities. However, as with the previous LMC surveys, an excess of stars with low projected rotational velocities, ve sin i ≤ 40 km s−1, and significant nitrogen enrichments is found. This is estimated to be approximately 5% of the total population of apparently single B-type stars or 40% of all stars with current rotational velocities of less than 40 km s−1; these percentages are similar to those found previously for the two LMC samples. For all three surveys, the presence of undetected binaries and other uncertainties imply that these percentages might be underestimated and that it is indeed possible for all the single stars with current rotational velocities of less than 40 km s−1 to be nitrogen enriched. Two possible explanations incorporate the effects of the magnetic field, via either a stellar merger followed by magnetic braking or the evolution of a single star with a large magnetic field. Both mechanisms would appear to be compatible with the observed frequency of nitrogen-enriched stars in the Magellanic Clouds. Differences in the properties of the nitrogen-enriched stars compared with the remainder of the sample would be consistent with the former mechanism. For the latter, a qualitative comparison with Galactic evolutionary models that incorporate magnetic fields is encouraging in terms of the amount of nitrogen enrichment and its presence in stars near the zero-age main sequence.
A flexible FPGA accelerator for convolutional neural networks

Though CNNs are highly parallel workloads, in the absence of efficient on-chip memory reuse techniques, an accelerator for them quickly becomes memory bound. In this paper, we propose a CNN accelerator design for inference that is able to exploit all forms of reuse available to minimize off-chip memory access while increasing utilization of available resources. The proposed design is composed of cores, each of which contains a one-dimensional array of processing elements. These cores can exploit different types of reuse available in CNN layers of varying shapes without requiring any reconfiguration; in particular, our design minimizes underutilization due to problem sizes that are not perfect multiples of the underlying hardware array dimensions. A major obstacle in the adoption of FPGAs as a platform for CNN inference is the difficulty to program these devices using hardware description languages. Our end goal is to also address this, and we develop preliminary software support via a codesign in order to leverage the accelerator through TensorFlow, a dominant high-level programming model. Our framework takes care of tiling and scheduling of neural network layers and generates necessary low-level commands to execute the CNN. Experimental evaluation on a real system with a PCI-express based Xilinx VC709 board demonstrates the effectiveness of our approach. As a result of an effective interconnection, the design maintains a high frequency when we scale the number of PEs. The sustained performance overall is a good fraction of the accelerator's theoretical peak performance.
A self-consistent leptonic–hadronic interpretation of the electromagnetic and neutrino emissions from blazar TXS 0506+056

The potential association between the blazar TXS 0506+056 and the neutrino event IceCube-170922A provides a unique opportunity to study the possible physical connection between high-energy photons and neutrinos. We explore the correlated electromagnetic and neutrino emissions of blazar TXS 0506+056 by a self-consistent leptonic–hadronic model, taking into account particle stochastic acceleration and all relevant radiative processes self-consistently. The electromagnetic and neutrino spectra of blazar TXS 0506+056 are reproduced by the proton synchrotron and hybrid leptonic–hadronic models based on the proton–photon interactions. It is found that the hybrid leptonic–hadronic model can be used to better explain the observed X-ray and $\gamma$-ray spectra of blazar TXS 0506+056 than the proton synchrotron model. Moreover, the predicted neutrino spectrum of the hybrid leptonic–hadronic model is closer to the observed one compared to the proton synchrotron model. We suggest that the hybrid leptonic–hadronic model is more favored if the neutrino event IceCube-170922A is associated with the blazar TXS 0506+056.
Predicting detection filters for small footprint open-vocabulary keyword spotting

In many scenarios, detecting keywords from natural language queries is sufficient to understand the intent of the user. In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, allowing a user to include a voice interface to its device without having to retrain a model on task-specific data. We present a keyword detection neural network weighing less than 550KB, in which the topmost layer performing keyword detection is predicted by an auxiliary network, that may be run offline to generate a detector for any keyword.
Bounded haptic teleoperation of a quadruped robot’s foot posture for sensing and manipulation

This paper presents a control framework to teleoperate a quadruped robot’s foot for operator-guided haptic exploration of the environment. Since one leg of a quadruped robot typically only has 3 actuated degrees of freedom (DoFs), the torso is employed to assist foot posture control via a hierarchical whole-body controller. The foot and torso postures are controlled by two analytical Cartesian impedance controllers cascaded by a null space projector. The contact forces acting on supporting feet are optimized by quadratic programming (QP). The foot’s Cartesian impedance controller may also estimate contact forces from trajectory tracking errors, and relay the force-feedback to the operator. A 7D haptic joystick, Sigma.7, transmits motion commands to the quadruped robot ANYmal, and renders the force feedback. Furthermore, the joystick’s motion is bounded by mapping the foot’s feasible force polytope constrained by the friction cones and torque limits in order to prevent the operator from driving the robot to slipping or falling over. Experimental results demonstrate the efficiency of the proposed framework.
Deep learning using a biophysical model for Robust and Accelerated Reconstruction (RoAR) of quantitative and artifact-free R2* images

Purpose: To introduce a novel deep learning method for Robust and Accelerated Reconstruction (RoAR) of quantitative and B0-inhomogeneity-corrected R2* maps from multi-gradient recalled echo (mGRE) MRI data. Methods: RoAR trains a convolutional neural network (CNN) to generate quantitative R2* maps free from field inhomogeneity artifacts by adopting a self-supervised learning strategy given (a) mGRE magnitude images, (b) the biophysical model describing mGRE signal decay, and (c) preliminary-evaluated F-function accounting for contribution of macroscopic B0 field inhomogeneities. Importantly, no ground-truth R2* images are required and F-function is only needed during RoAR training but not application. Results: We show that RoAR preserves all features of R2* maps while offering significant improvements over existing methods in computation speed (seconds vs. hours) and reduced sensitivity to noise. Even for data with SNR=5 RoAR produced R2* maps with accuracy of 22% while voxel-wise analysis accuracy was 47%. For SNR=10 the RoAR accuracy increased to 17% vs. 24% for direct voxel-wise analysis. Conclusion: RoAR is trained to recognize the macroscopic magnetic field inhomogeneities directly from the input magnitude-only mGRE data and eliminate their effect on R2* measurements. RoAR training is based on the biophysical model and does not require ground-truth R2* maps. Since RoAR utilizes signal information not just from individual voxels but also accounts for spatial patterns of the signals in the images, it reduces the sensitivity of R2* maps to the noise in the data. These features plus high computational speed provide significant benefits for the potential usage of RoAR in clinical settings.
RTj: a Java Framework for Detecting and Refactoring Rotten Green Test Cases

Rotten green tests are passing tests which have at least one assertion that is not executed. They give developers a false sense of trust in the code. In this paper, we present RTj, a framework that analyzes test cases from Java projects with the goal of detecting and refactoring rotten test cases. RTj automatically discovered 418 rotten tests from 26 open-source Java projects hosted on GitHub. Using RTj, developers have an automated recommendation of the tests that need to be modified for improving the quality of the applications under test. A video is available at: https://youtu.be/Uqxf-Wzp3Mg