Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable and safe synthetic organic electroreduction inspired by Li-ion battery chemistry

Scaled-up sodium-free Birch reductions The so-called Birch reduction is frequently used by chemists despite its daunting conditions: Pyrophoric sodium is dissolved in pure liquified ammonia to achieve partial reduction of aromatics. Peters et al. surveyed and then optimized small-scale electrochemical alternatives to devise a safer protocol that can work on a larger scale with a broad range of functionally complex substrates. Science, this issue p. 838 Optimized electrochemical conditions reduce a wide range of aromatic substrates more safely than sodium dissolved in ammonia. Reductive electrosynthesis has faced long-standing challenges in applications to complex organic substrates at scale. Here, we show how decades of research in lithium-ion battery materials, electrolytes, and additives can serve as an inspiration for achieving practically scalable reductive electrosynthetic conditions for the Birch reduction. Specifically, we demonstrate that using a sacrificial anode material (magnesium or aluminum), combined with a cheap, nontoxic, and water-soluble proton source (dimethylurea), and an overcharge protectant inspired by battery technology [tris(pyrrolidino)phosphoramide] can allow for multigram-scale synthesis of pharmaceutically relevant building blocks. We show how these conditions have a very high level of functional-group tolerance relative to classical electrochemical and chemical dissolving-metal reductions. Finally, we demonstrate that the same electrochemical conditions can be applied to other dissolving metal–type reductive transformations, including McMurry couplings, reductive ketone deoxygenations, and epoxide openings.
L1-norm double backpropagation adversarial defense

Adversarial examples are a challenging open problem for deep neural networks. We propose in this paper to add a penalization term that forces the decision function to be at in some regions of the input space, such that it becomes, at least locally, less sensitive to attacks. Our proposition is theoretically motivated and shows on a first set of carefully conducted experiments that it behaves as expected when used alone, and seems promising when coupled with adversarial training.
Multi-Variable Thermal Modeling of Power Devices Considering Mutual Coupling

In relation to power converter design, power density is increasing while the form factor isdecreasing. This trend generally reduces the rate of the cooling process, which increases the mutualthermal coupling among the surrounding power components. Most of the traditional modelsusually ignore the mutual effects or just focus on the conduction coupling. To deal with these factors,the thermal modeling for a boost converter system has been built to compare the junctiontemperatures (Tj) and the increments under different working conditions in order to consider theconduction coupling. A multi-variable thermal resistances model is proposed in this paper toincorporate the convection thermal coupling into the mutual thermal effects. The couplingresistances, MOSFET to the diode[. ]
Preconditioning Kaczmarz method by sketching

We propose a new method for preconditioning Kaczmarz method by sketching. Kaczmarz method is a stochastic method for solving overdetermined linear systems based on a sampling of matrix rows. The standard approach to speed up convergence of iterative methods is using preconditioner. As we show the best possible preconditioner for this method can be constructed from QR decomposition of the system matrix, but the complexity of this procedure is too high. Therefore, to reduce this complexity, we use random sketching and compare it with the Kaczmarz method without preconditioning. The developed method is applicable for different modifications of classical Kaczmarz method that were proposed recently. We provide numerical experiments to show the performance of the developed methods on solving both random and real overdetermined linear systems.
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future

In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably intertwined. If the model is not able to provide sensible long-term prediction, the executed planner would exploit model flaws, which can yield catastrophic failures. This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration. To this end, we build a latent-variable autoregressive model by leveraging recent ideas in variational inference. We argue that forcing latent variables to carry future information through an auxiliary task substantially improves long-term predictions. Moreover, by planning in the latent space, the planner's solution is ensured to be within regions where the model is valid. An exploration strategy can be devised by searching for unlikely trajectories under the model. Our method achieves higher reward faster compared to baselines on a variety of tasks and environments in both the imitation learning and model-based reinforcement learning settings.
Real-time Multiple People Hand Localization in 4D Point Clouds

We propose novel real-time algorithm to localize hands and find their associations with multiple people in the cluttered 4D volumetric data (dynamic 3D volumes). Different from the traditional multiple view approaches, which find key points in 2D and then triangulate to recover the 3D locations, our method directly processes the dynamic 3D data that involve both clutter and crowd. The volumetric representation is more desirable than the partial observations from different view points and enables more robust and accurate results. However, due to the large amount of data in the volumetric representation brute force 3D schemes are slow. In this paper, we propose novel real-time methods to tackle the problem to achieve both higher accuracy and faster speed than previous approaches. Our method detects the 3D bounding box of each subject and localizes the hands of each person. We develop new 2D features for fast candidate proposals and optimize the trajectory linking using a new max-covering bipartite matching formulation, which is critical for robust performance. We propose a novel decomposition method to reduce the key point localization in each person 3D volume to a sequence of efficient 2D problems. Our experiments show that the proposed method is faster than different competing methods and it gives almost half the localization error.
Quantifying Gait Changes Using Microsoft Kinect and Sample Entropy

This study describes a method to quantify potential gait changes in human subjects. Microsoft Kinect devices were used to provide and track coordinates of fifteen different joints of a subject over time. Three male subjects walk a 10-foot path multiple times with and without motion-restricting devices. Their walking patterns were recorded via two Kinect devices through frontal and sagittal planes. A modified sample entropy (SE) value was computed to quantify the variability of the time series for each joint. The SE values with and without motion-restricting devices were used to compare the changes in each joint. The preliminary results of the experiments show that the proposed quantification method can detect differences in walking patterns with and without motion-restricting devices. The proposed method has the potential to be applied to track personal progress in physical therapy sessions.
A linear-time algorithm and analysis of graph Relative Hausdorff distance

Graph similarity metrics serve far-ranging purposes across many domains in data science. As graph datasets grow in size, scientists need comparative tools that capture meaningful differences, yet are lightweight and scalable. Graph Relative Hausdorff (RH) distance is a promising, recently proposed measure for quantifying degree distribution similarity. In spite of recent interest in RH distance, little is known about its properties. Here, we conduct an algorithmic and analytic study of RH distance. In particular, we provide the first linear-time algorithm for computing RH distance, analyze examples of RH distance between pairs of real-world networks as well as structured families of graphs, and prove several analytic results concerning the range, density, and extremal behavior of RH distance values.
Enso: A general-purpose virtual machine

In this paper we introduce Enso, a virtual machine designed to be used as general-purpose state transition function in blockchains. This design allows the blockchain application logic to be coded into the state, instead of into the state transition function, making it much more flexible and easier to modify. A byproduct is reducing the frequency of forks, concerted or not. Finally, we discuss how Enso can easily be implemented as a runtime module on Parity's Substrate.
Attitudes, Beliefs, and Development Data Concerning Agile Software Development Practices

The perceptions and attitudes of developers impact how software projects are run and which development practices are employed in development teams. Recent agile methodologies have taken this into account, focusing on collaboration and shared team culture. In this research, we investigate the perceptions of agile development practices and their usage in Scrum software development teams. Although perceptions collected through surveys of 42 participating students did not evolve significantly over time, our analyses show that the Scrum role significantly impacted participants' views of employed development practices. We find that using the version control system according to agile ideas was consistently rated most related to the values of the Agile Manifesto. Furthermore, we investigate how common software development artifacts can be used to gain insights into team behavior and present the development data measurements we employed. We show that we can reliably detect well-defined agile practices, such Test-Driven Development, in this data and that usage of these practices coincided with participants' self-assessments.
Exploiting Emotions for Fake News Detection on Social Media

Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that conveyed by the publishers (i.e., publisher emotion). However, fake news often evokes high-arousal or activating emotions of people, so the emotions of news comments aroused in the crowd (i.e., social emotion) should not be ignored. Furthermore, it remains to be explored whether there exists a relationship between publisher emotion and social emotion (i.e., dual emotion), and how the dual emotion appears in fake news. In this paper, we verify that dual emotion is distinctive between fake and real news and propose Dual Emotion Features to represent dual emotion and the relationship between them for fake news detection. Further, we exhibit that our proposed features can be easily plugged into existing fake news detectors as an enhancement. Extensive experiments on three real-world datasets (one in English and the others in Chinese) show that our proposed feature set: 1) outperforms the state-of-the-art task-related emotional features; 2) can be well compatible with existing fake news detectors and effectively improve the performance of detecting fake news.
Optimised curtailment of distributed generators for the provision of congestion management services considering discrete controllability

Grid operation becomes a more challenging task due to the increasing amount of distributed generators in the system. Consequently, critical situations with violations of operational limits occur more frequently. Hence, grid operators have to take corrective countermeasures to ensure the security of supply. This study presents a discrete optimisation approach to determine the optimal amount of distributed generator curtailment regarding multiple objectives. The proposed method is based on non-linear AC load flow analysis of the network. The method is implemented into a modified version of the IEEE 14-bus test system and the necessary amount of generation curtailment to retain predefined operational limits is identified. Simulations are carried out for different contingency scenarios and several possible increments of curtailment levels are compared. Results reveal the potential to reduce the total amount of power adjustments distinctly by using curtailment levels with smaller step sizes compared to todays' applied standards in German grid operation. Furthermore, the developed methodology enables distributed generators to be used for managing congestions of transformers connecting different voltage levels.
The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes

3D multi-object detection and tracking are crucial for traffic scene understanding. However, the community pays less attention to these areas due to the lack of a standardized benchmark dataset to advance the field. Moreover, existing datasets (e.g., KITTI [1]) do not provide sufficient data and labels to tackle challenging scenes where highly interactive and occluded traffic participants are present. To address the issues, we present the Honda Research Institute 3D Dataset (H3D), a large-scale full-surround 3D multi-object detection and tracking dataset collected using a 3D LiDAR scanner. H3D comprises of 160 crowded and highly interactive traffic scenes with a total of 1 million labeled instances in 27,721 frames. With unique dataset size, rich annotations, and complex scenes, H3D is gathered to stimulate research on full-surround 3D multi-object detection and tracking. To effectively and efficiently annotate a large-scale 3D point cloud dataset, we propose a labeling methodology to speed up the overall annotation cycle. A standardized benchmark is created to evaluate full-surround 3D multi-object detection and tracking algorithms. 3D object detection and tracking algorithms are trained and tested on H3D. Finally, sources of errors are discussed for the development of future algorithms.
A Novel Efficient Approach with Data-Adaptive Capability for OMP-based Sparse Subspace Clustering

Orthogonal Matching Pursuit (OMP) plays an important role in data science and its applications such as sparse subspace clustering and image processing. However, the existing OMP-based approaches lack of data adaptiveness so that the data cannot be represented well enough and may lose the accuracy. This paper proposes a novel approach to enhance the data-adaptive capability for OMP-based sparse subspace clustering. In our method a parameter selection process is developed to adjust the parameters based on the data distribution for information representation. Our theoretical analysis indicates that the parameter selection process can efficiently coordinate with any OMP-based methods to improve the clustering performance. Also a new Self-Expressive-Affinity (SEA) ratio metric is defined to measure the sparse representation conversion efficiency for spectral clustering to obtain data segmentations. Our experiments show that proposed approach can achieve better performances compared with other OMP-based sparse subspace clustering algorithms in terms of clustering accuracy, SEA ratio and representation quality, also keep the time efficiency and anti-noise ability.
TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning

TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production. TensorFlow, which TensorFlow Eager extends, requires users to represent computations as dataflow graphs; this permits compiler optimizations and simplifies deployment but hinders rapid prototyping and run-time dynamism. TensorFlow Eager eliminates these usability costs without sacrificing the benefits furnished by graphs: It provides an imperative front-end to TensorFlow that executes operations immediately and a JIT tracer that translates Python functions composed of TensorFlow operations into executable dataflow graphs. TensorFlow Eager thus offers a multi-stage programming model that makes it easy to interpolate between imperative and staged execution in a single package.
FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms

We present an implementation of a new approach to diffeomorphic non-rigid registration of medical images. The method is based on optical flow and warps images via gradient flow with the standard $L^2$ inner product. To compute the transformation, we rely on accelerated optimisation on the manifold of diffeomorphisms. We achieve regularity properties of Sobolev gradient flows, which are expensive to compute, owing to a novel method of averaging the gradients in time rather than space. We successfully register brain MRI and challenging abdominal CT scans at speeds orders of magnitude faster than previous approaches. We make our code available in a public repository: this https URL
An Explorative Study of GitHub Repositories of AI Papers

With the rapid development of AI technologies, thousands of AI papers are being published each year. Many of these papers have released sample code to facilitate follow-up researchers. This paper presents an explorative study of over 1700 code repositories of AI papers hosted on GitHub. We find that these repositories are often poorly written, lack of documents, lack of maintenance, and hard to configure the underlying runtime environment. Thus, many code repositories become inactive and abandoned. Such a situation makes follow-up researchers hard to reproduce the results or do further research. In addition, these hard-to-reuse code makes a gap between academia and industry. Based on the findings, we give some recommendations on how to improve the quality of code repositories of AI papers.
Ultra-Wideband Dual-Mode Doherty Power Amplifier Using Reciprocal Gate Bias for 5G Applications

A novel architecture to extend the bandwidth of the Doherty power amplifier (DPA) is presented in this article. It is illustrated that two DPA modes at different frequency bands can be realized by simply swapping the gate biases of the transistors without changing the matching circuits, and hence, ultrawide bandwidth can be achieved by using a single load modulation network in DPA. A dual-mode DPA with 2.8–4.1-GHz bandwidth for Mode I and 2.2–2.7-GHz/4.2–4.8-GHz bandwidth for Mode II using commercial GaN transistors is designed and implemented to validate the proposed architecture. The fabricated DPA attains a measured 7.5–11.7-dB gain and 39.2–41-dBm saturated power. 35.0%–49.7% drain efficiency is obtained at 6-dB output power back-off for the designed dual-mode bands. When driven by a ten-carrier 200-MHz OFDM signal with 7.7-dB peak-to-average power ratio, the proposed DPA achieves adjacent channel leakage ratio of better than −50 dBc after digital predistortion at 2.5 GHz/3.5 GHz/4.5 GHz with an average efficiency of 46.0%/35.7%/33.0%. This simple configuration provides a promising solution for 5G, where multiple frequency bands in sub-6 GHz will be deployed.
Joint Subcarrier Assignment and Power Allocation Strategy for Integrated Radar and Communications System Based on Power Minimization

This paper investigates the problem of power minimization-based joint subcarrier assignment and power allocation (PM-JSAPA) for an integrated radar and communications system (IRCS). In the considered system, the integrated transmitter is responsible to fulfill both radar and communications purposes simultaneously. The proposed PM-JSAPA strategy aims to minimize the total radiated power of the IRCS by jointly optimizing the available subcarriers and power resource allocation in order to achieve improved power-saving performance, subject to a predetermined mutual information (MI) for target parameter estimation and a specified data information rate (DIR) for wireless communications. The formulated problem falls into a mixed integer nonlinear programming (MINLP) problem, which is generally NP-hard. Then, an efficient three-step resource allocation framework is designed to solve the resulting optimization problem. Specifically, in Step 1, the subcarrier assignment for both radar and communications purposes is determined. In Step 2 and Step 3, we perform power allocation for different applications with the obtained subcarrier assignment results in Step 1, respectively. Finally, numerical simulation results are presented to demonstrate the superior power-saving performance of the proposed PM-JSAPA strategy and the effectiveness of the proposed solutions.
Reactive Power Optimization of a Distribution System Based on Scene Matching and Deep Belief Network

With a large number of distributed generators (DGs) and electrical vehicles (EVs) integrated into the power distribution system, the complexity of distribution system operation is increased, which arises to higher requirements for online reactive power optimization. This paper proposes two methods for online reactive power optimization, a scene-matching method based on Random Matrix (RM) features and a deep learning method based on Deep Belief Network (DBN). Firstly, utilizing the operation and ambient Big Data (BD) of the distribution system, we construct the high-dimension Random Matrices and extract 57 state features for the subsequent scene-matching and DBN training. Secondly, the feature-based scene-matching method is proposed. Furtherly, to effectively deal with the uncertainty of DGs and to avoid the performance deterioration of the scene-matching method under a new unknown scene, the DBN-based model is constructed and trained, with the former features as the inputs and the conventional reactive power control solutions as the outputs. This DBN model can learn the nonlinear complicated relationship between the system features and the reactive power control solutions. Finally, the comprehensive case studies have been conducted on the modified IEEE-37 nodes active distribution system, and the performances of the proposed two methods are compared with the conventional method. The results show that the DBN-based method possesses the better performance than the others, and it can reduce the network losses and node voltage deviations obviously, even under the new unknown and unmatched scenes. It does not depend on the distribution system model and parameters anymore and can provide online decision-making more quickly. The discussions of the two methods under different DG penetrations and the historical data volume were given, verifying the adaptability, robustness and generalization ability of the DBN-based method.
Decoupled Torque Control of Series Elastic Actuator With Adaptive Robust Compensation of Time-Varying Load-Side Dynamics

Modern robots are increasingly being designed to collaborate with human, and the safety of human-robot interaction has become more significant than ever. Series elastic actuator (SEA) is a potential actuator system in force control for various robotic applications with inherent safe property. However, achieving the good performances of SEA torque control especially with unknown load-side dynamics is still challenging. In this article, a high performance decoupled torque controller is proposed to control the SEA as an ideal torque source that is suitable for various application scenarios. A more general decoupled dynamics of the SEA system is first established to fit the various scenarios by considering the large parametric uncertainties, nonlinearities, and unmodeled uncertainties. The adaptive robust control algorithm with an effective compensation mechanism is then developed to attenuate the uncertainties. Theoretically, the proposed approach guarantees the stability and high performance of the SEA system. Comparative experiments are carried out on a SEA testbed, and the experimental results demonstrate that the proposed approach has significant performance improvements over previous approaches.