Miracle-dz/UniFAR-FTUs huggingface dataset:
Near-wake dynamics of a vertical-axis turbine

Abstract Cross-flow, or vertical-axis, turbines are a promising technology for capturing kinetic energy in wind or flowing water and their inherently unsteady fluid mechanics present unique opportunities for control optimization of individual rotors or arrays. To explore the potential for beneficial interactions between turbines in an array, as well as to characterize important cycle-to-cycle variations, coherent structures in the wake of a single two-bladed cross-flow turbine are examined using planar stereo particle image velocimetry in a water channel experiment. There are three main objectives in the present work. First, the mean wake structure of this high chord-to-radius ratio rotor is described, compared with previous studies, and a simple explanation for observed wake deflection is presented. Second, the unsteady flow is then analysed via the triple decomposition, with the periodic component extracted using a combination of traditional techniques and a novel implementation of the optimized dynamic mode decomposition. The latter method is shown to outperform conditional averaging and Fourier methods, as well as uncover frequencies suggesting a transition to bluff-body shedding in the far wake. Third, vorticity and finite-time Lyapunov exponents are then employed to further analyse the oscillatory wake component. Vortex streets on both sides of the wake are identified, and their formation mechanisms and effects on the mean flow are discussed. Strong axial (vertical) flow is observed in vortical structures shed on the retreating side of the rotor where the blades travel downstream. Time-resolved tracking of these vortices is performed, which demonstrates that vortex trajectories have significant rotation-to-rotation variation within one diameter downstream. This variability suggests it would be challenging to harness or avoid such structures at greater downstream distances.
Learning-Based Phase Compression and Quantization for Massive MIMO CSI Feedback with Magnitude-Aided Information

—In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) wireless systems, deep learning techniques are regarded as one of the most efﬁcient solutions for CSI recovery. In recent times, to achieve better CSI magnitude recovery at base stations, advanced learning-based CSI feedback solutions decouple magnitude and phase recovery to fully leverage the strong correlation between current CSI magnitudes and those of previous time slots, uplink band, and near locations. However, the CSI phase recovery is a major challenge to further enhance the CSI recovery owing to its complicated patterns. In this letter, we propose a learning-based CSI feedback framework based on limited feedback and magnitude-aided information. In contrast to previous works, our proposed framework with a proposed loss function enables end-to-end learning to jointly optimize the CSI magnitude and phase recovery performance. Numerical simulations show that, the proposed loss function outperform alternate approaches for phase recovery over the overall CSI recovery in both indoor and outdoor scenarios. The performance of the proposed framework was also examined using different core layer designs.
No Dependence of Radio Properties of Brightest Group Galaxies on the Luminosity Gap

We study the radio and optical properties of the brightest group galaxies (BGGs) in a sample of galaxy groups from the Sloan Digital Sky Survey (SDSS) DR7. The luminosity difference between the BGG and the second-ranked galaxy in the group (known as the luminosity, or magnitude, gap) has been used as a probe for the level of galaxy interaction for the BGG within the group. We study the properties of BGGs with magnitude gaps in the range of 0–2.7 mag, in order to investigate any relation between the luminosity gap and the radio properties of the BGG. In order to eliminate selection biases, we ensure that all variations in stellar mass are accounted for. We then confirm that, at fixed stellar mass, there are no significant variations in the optical properties of the BGGs over the full range of luminosity gaps studied. We compare these optical results with the Evolution and Assembly of GaLaxies and their Environments (EAGLE) hydrodynamical simulations and find broad consistency with the observational data. Using EAGLE we also confirm that no trends begin to arise in the simulated data at luminosity gaps beyond our observational limits. Finally, we find that, at a fixed stellar mass, the fraction of BGGs that are radio-loud also shows no trend as a function of luminosity gap. We examine how the BGG offset from the center of the group may affect the radio results and find no significant trend for the fraction of radio-loud BGGs with a magnitude gap in either the BGG samples with greater or less than 100 kpc offset from the center of the group.
Conformational analysis in carbohydrate chemistry. III. The 13C N.M.R. spectra of the hexuloses

All the resonances have been assigned in the 13C N.M. R. spectra of the hexuloses and some of their derivatives by the use of specifically deuterated compounds and comparison with related compounds. The composition of the hexuloses at equilibrium in aqueous solution has been determined and is discussed in terms of conformational analysis. The synthesis of D-fructose[l-D], D-fructose[3-D] and D-fructose[4-D] is described.
CogDL: An Extensive Toolkit for Deep Learning on Graphs

Graph representation learning aims to learn low-dimensional node embeddings for graphs. It is used in several real-world applications such as social network analysis and large-scale recommender systems. In this paper, we introduce CogDL 1 , an extensive research toolkit for deep learning on graphs that allows researchers and developers to easily conduct experiments and build applications. It provides standard training and evaluation for the most important tasks in the graph domain, including node classification, link prediction, graph classification, and other graph tasks. For each task, it offers implementations of state-of-the-art models. The models in our toolkit are divided into two major parts, graph embedding methods and graph neural networks. Most of the graph embedding methods learn node-level or graph-level representations in an unsupervised way and preserves the graph properties such as structural information, while graph neural networks capture node features and work in semi-supervised or self-supervised settings. All models implemented in our toolkit can be easily reproducible for leaderboard results. Most models in CogDL are developed on top of PyTorch, and users can leverage the advantages of PyTorch to implement their own models. Furthermore, we demonstrate the effectiveness of CogDL for real-world applications in AMiner 2 , which is a large academic database and system.
Hybrid model of proton structure functions

We develop a “hybrid” model of the proton inelastic structure functions applicable in a wide region of invariant mass of produced states 𝑊 and invariant momentum transfer 𝑄 including deep inelastic scattering (DIS), nucleon resonance production as well as the region close to inelastic threshold. DIS is described in terms of the parton distributions together with higher-twist corrections from an available global QCD fit. The resonant part is addressed in terms of the Breit-Wiegner contributions from five states including the Δ(1232) resonance, the 𝑁 (1440) Roper resonance, and three effective resonances describing the second and third resonance regions. The couplings of the nucleon resonances to photon are described in terms of helicity amplitudes. The nonresonant background is addressed in terms of DIS structure functions smoothly extrapolated to low- 𝑊 and low- 𝑄 values with the proper behavior at the real photon limit 𝑄 2 = 0 as well as near the inelastic threshold. We independently treat the transverse 𝐹 𝑇 and the longitudinal 𝐹 𝐿 structure function and fix the model parameters from a global analysis of the world hydrogen electroproduction and photoproduction cross-section data. We demonstrate a very good performance of the model by comparing our predictions with data on differential cross sections and the structure functions 𝐹 2 and 𝑅 = 𝐹 𝐿 /𝐹 𝑇 .
The Log-Normal Size Distribution Theory for Brownian Coagulation in the Low Knudsen Number Regime

Abstract An analytical solution to the Brownian coagulation of aerosol particles in the low Knudsen number regime is presented which provides time evolution of the particle size distribution. The theoretical analysis used is based on representation of the size distribution of a coagulating aerosol with a time-dependent log-normal size distribution function and employs the method of moments together with suitable simplifications. The results are found in the form that extends the continuum coagulation solution that was previously obtained by Lee (J. Colloid Interface Sci.92,315 (1983)). The results show that the Knudsen number has a significant effect on the size distribution evolution during coagulation. In addition, it was confirmed that a self-preserving size distribution does not exist in the low Knudsen number regime. The obtained solution was compared with numerical results and good agreement was obtained. The present work represents the first analytic solution to the low Knudsen number regime Brownian coagulation problem.
Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images

Security inspection is X-ray scanning for personal belongings in suitcases, which is significantly important for the public security but highly time-consuming for human inspectors. Fortunately, deep learning has greatly promoted the development of computer vision, offering a possible way of automatic security inspection. However, items within a luggage are randomly overlapped resulting in noisy X-ray images with heavy occlusions. Thus, traditional CNN-based models trained through common image recognition datasets fail to achieve satisfactory performance in this scenario. To address these problems, we contribute the first high-quality prohibited X-ray object detection dataset named OPIXray, which contains 8885 X-ray images from 5 categories of the widely-occurred prohibited item ``cutters''. The images are gathered from an airport and these prohibited items are annotated manually by professional inspectors, which can be used as a benchmark for model training and further facilitate future research. To better improve occluded X-ray object detection, we further propose an over-sampling de-occlusion attention network (DOAM-O), which consists of a novel de-occlusion attention module and a new over-sampling training strategy. Specifically, our de-occlusion module, namely DOAM, simultaneously leverages the different appearance information of the prohibited items; the over-sampling training strategy forces the model to put more emphasis on these hard samples consisting these items of high occlusion levels, which is more suitable for this scenario. We comprehensively evaluated DOAM-O on the OPIXray dataset, which proves that our model can stably improve the performance of the famous detection models such as SSD, YOLOv3, and FCOS, and outperform many extensively-used attention mechanisms.
A Simple But Effective Approach to n-shot Task-Oriented Dialogue Augmentation

The collection and annotation of task-oriented conversational data is a costly and time-consuming manner. Many augmentation techniques have been proposed to improve the performance of state-of-the-art (SOTA) systems in new domains that lack the necessary amount of data for training. However, these augmentation techniques ( e.g. paraphrasing) also require some mediocre amount of data since they use learning-based approaches. This makes us-ing SOTA systems in emerging low-resource domains infeasible. We, to tackle this problem, introduce a framework, that creates synthetic task-oriented dialogues in a fully automatic manner, which operates with input sizes of as small as a few dialogues. Our framework uses the simple idea that each turn-pair in a task-oriented dialogue has a certain function and exploits this idea to mix them creating new dialogues. We evaluate our framework within a low-resource setting by integrating it with a SOTA model TRADE in the dialogue state tracking task and observe signiﬁcant improvements in the ﬁne-tuning scenarios in several domains. We conclude that this end-to-end dialogue augmentation framework can be a crucial tool for natural language understanding performance in emerging task-oriented dialogue domains.
Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities

Significance Mastering diverse cognitive tasks is crucial for humans. We study how the brain’s functional organization at rest is configured to support diverse cognitive phenotypes. Emphasizing the multilevel, hierarchical modular structure of brain’s functional connectivity to derive eigenmode-based measures, we demonstrate that the resting brain’s functional organization in healthy young adults is configured to maintain a balance between network segregation and integration. This functional balance is associated with better memory. Furthermore, brains tending toward stronger segregation versus integration foster different cognitive abilities. Thus, the segregation–integration balance empowers the brain to support diverse cognitive abilities. These findings yield high potential to understand the role of whole-brain resting state dynamics in human cognition and to develop neural biomarkers of atypical cognition. Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains an open question how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here we use an eigenmode-based approach to identify hierarchical modules in functional brain networks and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n = 991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations and highly flexible switching between them. Furthermore, we employ structural equation modeling to estimate general and domain-specific cognitive phenotypes from nine tasks and demonstrate that network segregation, integration, and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and an individual’s tendency toward balance supports better memory. Our findings provide a comprehensive and deep understanding of the brain’s functioning principles in supporting diverse functional demands and cognitive abilities and advance modern network neuroscience theories of human cognition.
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks

Spiking neural networks (SNNs) are biology-inspired artificial neural networks (ANNs) that comprise of spiking neurons to process asynchronous discrete signals. While more efficient in power consumption and inference speed on the neuromorphic hardware, SNNs are usually difficult to train directly from scratch with spikes due to the discreteness. As an alternative, many efforts have been devoted to converting conventional ANNs into SNNs by copying the weights from ANNs and adjusting the spiking threshold potential of neurons in SNNs. Researchers have designed new SNN architectures and conversion algorithms to diminish the conversion error. However, an effective conversion should address the difference between the SNN and ANN architectures with an efficient approximation \DSK{of} the loss function, which is missing in the field. In this work, we analyze the conversion error by recursive reduction to layer-wise summation and propose a novel strategic pipeline that transfers the weights to the target SNN by combining threshold balance and soft-reset mechanisms. This pipeline enables almost no accuracy loss between the converted SNNs and conventional ANNs with only $\sim1/10$ of the typical SNN simulation time. Our method is promising to get implanted onto embedded platforms with better support of SNNs with limited energy and memory.
Multiscale Solar Wind Turbulence Properties inside and near Switchbacks Measured by the Parker Solar Probe

The Parker Solar Probe (PSP) routinely observes magnetic field deflections in the solar wind at distances less than 0.3 au from the Sun. These deflections are related to structures commonly called “switchbacks” (SBs), whose origins and characteristic properties are currently debated. Here, we use a database of visually selected SB intervals—and regions of solar wind plasma measured just before and after each SB—to examine plasma parameters, turbulent spectra from inertial to dissipation scales, and intermittency effects in these intervals. We find that many features, such as perpendicular stochastic heating rates and turbulence spectral slopes are fairly similar inside and outside of SBs. However, important kinetic properties, such as the characteristic break scale between the inertial to dissipation ranges differ inside and outside these intervals, as does the level of intermittency, which is notably enhanced inside SBs and in their close proximity, most likely due to magnetic field and velocity shears observed at the edges. We conclude that the plasma inside and outside of an SB, in most of the observed cases, belongs to the same stream, and that the evolution of these structures is most likely regulated by kinetic processes, which dominate small-scale structures at the SB edges.
A Context-Based Automated Approach for Method Name Consistency Checking and Suggestion

Misleading method names in software projects can confuse developers, which may lead to software defects and affect code understandability. In this paper, we present DeepName, a context-based, deep learning approach to detect method name inconsistencies and suggest a proper name for a method. The key departure point is the philosophy of "Show Me Your Friends, I'll Tell You Who You Are". Unlike the state-of-the-art approaches, in addition to the method's body, we also consider the interactions of the current method under study with the other ones including the caller and callee methods, and the sibling methods in the same enclosing class. The sequences of sub-tokens in the program entities' names in the contexts are extracted and used as the input for an RNN-based encoder-decoder to produce the representations for the current method. We modify that RNN model to integrate the copy mechanism and our newly developed component, called the non-copy mechanism, to emphasize on the possibility of a certain sub-token not to be copied to follow the current sub-token in the currently generated method name. We conducted several experiments to evaluate DeepName on large datasets with +14M methods. For consistency checking, DeepName improves the state-of-the-art approach by 2.1%, 19.6%, and 11.9% relatively in recall, precision, and F-score, respectively. For name suggestion, DeepName improves relatively over the state-of-the-art approaches in precision (1.8%–30.5%), recall (8.8%–46.1%), and F-score (5.2%–38.2%). To assess DeepName's usefulness, we detected inconsistent methods and suggested new method names in active projects. Among 50 pull requests, 12 were merged into the main branch. In total, in 30/50 cases, the team members agree that our suggested method names are more meaningful than the current names.
Finding Bugs with Specification-Based Testing is Easy!

Automated specification-based testing has a long history with several notable tools having emerged. For example, QuickCheck for Haskell focuses on testing against user-provided properties. Others, such as JMLUnit, use specifications in the form of preand post-conditions to drive testing. An interesting (and under-explored) question is how effective this approach is at finding bugs in practice. In general, one would assume automated testing is less effective at bug finding than static verification. But, how much less effective? To shed light on this question, we consider automated testing of programs written in Whiley—a language with first-class support for specifications. Whilst originally designed with static verification in mind, we have anecdotally found automated testing for Whiley surprisingly useful and cost-effective. For example, when an error is detected with automated testing, a counterexample is always provided. This has motivated the more rigorous empirical examination presented in this paper. To that end, we provide a technical discussion of the implementation behind an automated testing tool for Whiley. Here, a key usability concern is the ability to parameterise the input space, and we present novel approaches for references and lambdas. We then report on several large experiments investigating the tool’s effectiveness at bug finding using a range of benchmarks, including a suite of 1800+ mutants. The results indicate the automated testing is effective in many cases, and that sampling offers useful performance benefits with only modest reductions in bug-finding capability. Finally, we report on some real-world uses of the tool where it has proved effective at finding bugs (such as in the standard library).
Statistical Measures For Defining Curriculum Scoring Function

Curriculum learning is a training strategy that sorts the training examples by some measure of their difficulty and gradually exposes them to the learner to improve the network performance. Motivated by our insights from implicit curriculum ordering, we first introduce a simple curriculum learning strategy that uses statistical measures such as standard deviation and entropy values to score the difficulty of data points for real image classification tasks. We empirically show its improvements in performance with convolutional and fully-connected neural networks on multiple real image datasets. We also propose and study the performance of a dynamic curriculum learning algorithm. Our dynamic curriculum algorithm tries to reduce the distance between the network weight and an optimal weight at any training step by greedily sampling examples with gradients that are directed towards the optimal weight. Further, we use our algorithms to discuss why curriculum learning is helpful.
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability

We empirically demonstrate that full-batch gradient descent on neural network training objectives typically operates in a regime we call the Edge of Stability. In this regime, the maximum eigenvalue of the training loss Hessian hovers just above the numerical value $2 / \text{(step size)}$, and the training loss behaves non-monotonically over short timescales, yet consistently decreases over long timescales. Since this behavior is inconsistent with several widespread presumptions in the field of optimization, our findings raise questions as to whether these presumptions are relevant to neural network training. We hope that our findings will inspire future efforts aimed at rigorously understanding optimization at the Edge of Stability. Code is available at https://github.com/locuslab/edge-of-stability.