Miracle-dz/UniFAR-FTUs huggingface dataset:
Uneven Warming Likely Contributed to Declining Near‐Surface Wind Speeds in Northern China Between 1961 and 2016

A decline in mean near‐surface (10 m) wind speed has been widely reported for many land regions over recent decades, yet the underlying cause(s) remains uncertain. This study investigates changes in near‐surface wind speed over northern China from 1961 to 2016, and analyzes the associated physical mechanisms using station observations, reanalysis products and model simulations from the Community Atmosphere Model version 5.1 (CAM5). The homogenized near‐surface wind speed shows a significantly (p < 0.05) decline trend of −0.103 m s−1 decade−1, which stabilized from the 1990s onwards. Similar negative trends are observed for all seasons, with the strongest trends occurring in the central and eastern parts of northern China. Fast warming has occurred at high‐latitudes (i.e., >50°N) in recent decades, which has weakened the annual and seasonal meridional air temperature gradient (−0.33°C to −0.12°C dec−1, p < 0.05, except autumn) between these regions (50°–60°N, 75°–135°E) and the northern China zone (35°–45°N, 75°–135°E). This caused a significant (p < 0.05) decrease in annual and seasonal pressure gradient (−0.43 to −0.20 hPa dec−1) between the two zones, which contributed to the slowdown of winds. CAM5 simulations demonstrate that spatially uneven air temperature increases and near‐surface wind speed decreases over northern China can be realistically reproduced using the so‐called “all forcing” simulation, while the “natural only forcing” simulation fails to realistically simulate the uneven warming patterns and declines in near‐surface wind speed over most of northern China, except for summer.
Distributed IT championing: A process theory

Championing is key to the success of an IT implementation. Recently, changes in the nature of technologies used in organizational contexts and changing organizational structures call for a renewed focus on IT championing to explain its distributed nature. Following an analytic induction approach and drawing from semi-structured interviews with 37 practitioners (physicians, residents, nurses, IT staff, and administrators) in three healthcare-related settings, the study conceptualizes distributed IT championing as a process constituted of multiple individuals’ behaviors, unfolding over time, that proactively go beyond formal job requirements in support of an IT implementation. While multiple individuals may enact similar championing behaviors, the data indicate that multiple individuals may also enact distinct, yet complementary, championing behaviors over the course of the IT implementation. Overall, distributed IT championing evolves through cycles of distinct stages of bridging-in, bonding, and bridging-out, with each stage being shaped by different dimensions of social capital. Also, IT artifacts that are particularly generative appear more conducive to distributed IT championing than closed ones. This article contributes to extant literature on IT championing by developing a process model of distributed IT championing in the context of an IT implementation.
When AWGN-based Denoiser Meets Real Noises

Discriminative learning-based image denoisers have achieved promising performance on synthetic noises such as Additive White Gaussian Noise (AWGN). The synthetic noises adopted in most previous work are pixel-independent, but real noises are mostly spatially/channel-correlated and spatially/channel-variant. This domain gap yields unsatisfied performance on images with real noises if the model is only trained with AWGN. In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN. First, we train a deep model that consists of a noise estimator and a denoiser with mixed AWGN and Random Value Impulse Noise (RVIN). We then investigate Pixel-shuffle Down-sampling (PD) strategy to adapt the trained model to real noises. Extensive experiments demonstrate the effectiveness and generalization of the proposed approach. Notably, our method achieves state-of-the-art performance on real sRGB images in the DND benchmark among models trained with synthetic noises. Codes are available at this https URL.
Time Domain Audio Visual Speech Separation

Audio-visual multi-modal modeling has been demonstrated to be effective in many speech related tasks, such as speech recognition and speech enhancement. This paper introduces a new time-domain audio-visual architecture for target speaker extraction from monaural mixtures. The architecture generalizes the previous TasNet (time-domain speech separation network) to enable multi-modal learning and at meanwhile it extends the classical audio-visual speech separation from frequency-domain to time-domain. The main components of proposed architecture include an audio encoder, a video encoder that extracts lip embedding from video streams, a multi-modal separation network and an audio decoder. Experiments on simulated mixtures based on recently released LRS2 dataset show that our method can bring 3dB+ and 4dB+ Si-SNR improvements on two- and three-speaker cases respectively, compared to audio-only TasNet and frequency-domain audio-visual networks.
Penggunaan M-Learning Sebagai Bahan Bantu Pengajaran dalam Kelas Pendidikan Khas

Penggunaan teknologi moden seperti telefon bimbit dan tablet dapat membantu melaksanakan pengajaran menggunakan teknologi maklumat dan komunikasi. Dalam bidang pendidikan, perkembangan kurikulum dan pedagogi dalam pendidikan adalah dipengaruhi oleh perkembangan teknologi itu sendiri. Oleh hal yang demikian, penggunaan Mobile Learning (M-Learning) sebagai bahan bantu pengajaran guru dalam kelas Pendidikan Khas adalah lebih efektif dan seterusnya dapat meningkatkan kemahiran guru dalam penggunaan teknologi maklumat. Kajian tinjauan ini menggunakan pendekatan kuantitatif berbentuk deskriptif yang melibatkan peratusan, frekuensi, min dan sisihan piawai. Dalam kajian ini, kaedah persampelan bertujuan digunakan dalam kajian ini kerana kajian ini hanya melibatkan guru Pendidikan Khas dalam sebuah daerah di negeri Johor. Seramai 88 orang sampel yang terdiri daripada 28 buah sekolah kebangsaan, 2 buah sekolah jenis kebangsaan dan 14 buah sekolah menengah yang berada di bawah pentadbiran penuh Kementerian Pelajaran Malaysia. Dapatan kajian menunjukkan bahawa penggunaan, kemahiran dan sikap guru Pendidikan Khas terhadap penggunaan M-Learning sebagai bahan bantu pengajaran dan pembelajaran berada pada tahap tinggi. Implikasi daripada kajian ini menunjukkan bahawa hampir semua guru Pendidikan Khas yang terlibat dalam kajian ini menggunakan M-Learning sebagai bahan bantu dalam persediaan pengajaran dan semasa pengajaran. Penggunaan M-Learning juga mampu membantu guru-guru ini meningkatkan pengetahuan dalam teknologi maklumat dan komunikasi terkini. Guru-guru Pendidikan Khas juga menggunakan M-Learning ini sebagai bahan bantu mengajar kerana maklumat dapat diakses dengan lebih mudah dan cepat. Hal ini dapat membantu melancarkan lagi pencapaian objektif dalam sesi pengajaran guru.
Paying More Attention to Motion: Attention Distillation for Learning Video Representations

We address the challenging problem of learning motion representations using deep models for video recognition. To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition. Specifically, we propose to leverage output attention maps as a vehicle to transfer the learned representation from a motion (flow) network to an RGB network. We systematically study the design of attention modules, and develop a novel method for attention distillation. Our method is evaluated on major action benchmarks, and consistently improves the performance of the baseline RGB network by a significant margin. Moreover, we demonstrate that our attention maps can leverage motion cues in learning to identify the location of actions in video frames. We believe our method provides a step towards learning motion-aware representations in deep models.
Jet kinematics of the quasar 4C+21.35 from observations with the KaVA very long baseline interferometry array

We present the jet kinematics of the flat spectrum radio quasar (FSRQ) 4C+21.35 using time-resolved KaVA very long baseline interferometry array radio maps obtained from 2014 September to 2016 July. During two out of three observing campaigns, observations were performed bi-weekly at 22 and 43 GHz quasi-simultaneously. At 22 GHz, we identified three jet components near the core with apparent speeds up to (14.4 ± 2.1)c. The timing of the ejection of a new component detected in 2016 is consistent with a γ-ray flare in 2014 November. At 43 GHz, we found four inner jet (<3 mas) components with speeds from (3.5 ± 1.4)c to (6.8 ± 1.5)c. Jet component speeds tend to be higher with increasing distances from the core. We compared our data with archival Very Long Baseline Array (VLBA) data from the Boston University (BU) 43 GHz and the Monitoring Of Jets in Active galactic nuclei with VLBA Experiments (MOJAVE) 15.4 GHz monitoring programmes. Whereas MOJAVE data and our data are in good agreement, jet speeds obtained from the BU programme data in the same time period are about twice as high as the ones we obtain from the KaVA data. The discrepancy at 43 GHz indicates that radio arrays with different angular resolution identify and trace different jet features even when the data are obtained at the same frequency and at the same time. The flux densities of jet components decay exponentially, in agreement with a synchrotron cooling time-scale of ∼1 yr. Using known electron Lorentz factor values (∼9000), we estimate the magnetic field strength to be ∼1–3 $\mu$T. When adopting a jet viewing angle of 5°, the intrinsic jet speed is of order 0.99c.
How Privacy Concerns and Trust and Risk Beliefs Influence Users' Intentions to Use Privacy-Enhancing Technologies - The Case of Tor

Due to an increasing collection of personal data by internet companies and several data breaches, research related to privacy gained importance in the last years in the information systems domain. Privacy concerns can strongly influence users’ decision to use a service. The Internet Users Information Privacy Concerns (IUIPC) construct is one operationalization to measure the impact of privacy concerns on the use of technologies. However, when applied to a privacy enhancing technology (PET) such as an anonymization service the original rationales do not hold anymore. In particular, an inverted impact of trusting and risk beliefs on behavioral intentions can be expected. We show that the IUIPC model needs to be adapted for the case of PETs. In addition, we extend the original causal model by including trust beliefs in the anonymization service itself. A survey among 124 users of the anonymization service Tor shows that they have a significant effect on the actual use behavior of the PET.
The influences of academic emotion and prior knowledge in the context of online tests

ABSTRACT Online tests offer many advantages but they still belong to assessment, which may make learners have anxiety. Thus, students may experience certain emotion. Academic emotion is a branch of emotion and has great effects on student learning. Such effects can be associated with individual differences, especially prior knowledge. To this end, this study examined the influences of academic emotion and prior knowledge in the context of an online test. The results indicated that the positive academic emotion of high prior knowledge learners (HPKs) and low prior knowledge learners (LPKs) was connected with their pre-test scores while no connection was found for their post-test scores. Additionally, HPKs had higher positive academic emotion and lower negative academic emotion than LPKs. On the other hand, HPKs got better pre-and post-test scores than LPKs whereas LPKs had higher gain scores. Furthermore, HPKs and LPKs exhibited different behavior sequences. More specifically, LPKs tended to acquire small-scaled information from the Chinese hint only while HPKs preferred to obtain both small-scaled information and large-scaled information from the Chinese hint and sentence hint, respectively. According to the findings of this study, we develop a framework, which can be applied to help understand the needs of HPKs and LPKs.
A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition

The automatic recognition of micro-expression has been boosted ever since the successful introduction of deep learning approaches. As researchers working on such topics are moving to learn from the nature of micro-expression, the practice of using deep learning techniques has evolved from processing the entire video clip of micro-expression to the recognition on apex frame. Using the apex frame is able to get rid of redundant video frames, but the relevant temporal evidence of micro-expression would be thereby left out. This paper proposes a novel Apex-Time Network (ATNet)to recognize micro-expression based on spatial information from the apex frame as well as on temporal information from the respective-adjacent frames. Through extensive experiments on three benchmarks, we demonstrate the improvement achieved by learning such temporal information. Specially, the model with such temporal information is more robust in cross-dataset validations.
Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry

Most previous learning-based visual odometry (VO) methods take VO as a pure tracking problem. In contrast, we present a VO framework by incorporating two additional components called Memory and Refining. The Memory component preserves global information by employing an adaptive and efficient selection strategy. The Refining component ameliorates previous results with the contexts stored in the Memory by adopting a spatial-temporal attention mechanism for feature distilling. Experiments on the KITTI and TUM-RGBD benchmark datasets demonstrate that our method outperforms state-of-the-art learning-based methods by a large margin and produces competitive results against classic monocular VO approaches. Especially, our model achieves outstanding performance in challenging scenarios such as texture-less regions and abrupt motions, where classic VO algorithms tend to fail.
Noise-Aware Unsupervised Deep Lidar-Stereo Fusion

In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps. By introducing a novel ``Feedback Loop'' to connect the network input with output, LidarStereoNet could tackle both noisy Lidar points and misalignment between sensors that have been ignored in existing Lidar-stereo fusion work. Besides, we propose to incorporate the piecewise planar model into the network learning to further constrain depths to conform to the underlying 3D geometry. Extensive quantitative and qualitative evaluations on both real and synthetic datasets demonstrate the superiority of our method, which outperforms state-of-the-art stereo matching, depth completion and Lidar-Stereo fusion approaches significantly.
Perturbations of self-similar solutions

We consider the nonlinear heat equation $u_t = \Delta u + |u|^\alpha u$ with $\alpha >0$, either on ${\mathbb R}^N $, $N\ge 1$, or on a bounded domain with Dirichlet boundary conditions. We prove that in the Sobolev subcritical case $(N-2) \alpha <4$, for every $\mu \in {\mathbb R}$, if the initial value $u_0$ satisfies $u_0 (x) = \mu |x-x_0|^{-\frac {2} {\alpha }}$ in a neighborhood of some $x_0\in \Omega $ and is bounded outside that neighborhood, then there exist infinitely many solutions of the heat equation with the initial condition $u(0)= u_0$. The proof uses a fixed-point argument to construct perturbations of self-similar solutions with initial value $\mu |x-x_0|^{-\frac {2} {\alpha }}$ on ${\mathbb R}^N $. Moreover, if $\mu \ge \mu _0$ for a certain $ \mu _0( N, \alpha )\ge 0$, and $u_0 I\ge 0$, then there is no nonnegative local solution of the heat equation with the initial condition $u(0)= u_0$, but there are infinitely many sign-changing solutions.
Online Alternating Direction Method of Multipliers for Online Composite Optimization

In this paper, we investigate regrets of an online semi-proximal alternating direction method of multiplier (Online-spADMM) for solving online linearly constrained convex composite optimization problems. Under mild conditions, we establish ${\rm O}(\sqrt{N})$ objective regret and ${\rm O}(\sqrt{N})$ constraint violation regret at round $N$ when the dual step-length is taken in $(0,(1 +\sqrt{5})/2)$ and penalty parameter $\sigma$ is taken as $\sqrt{N}$. We explain that the optimal value of parameter $\sigma$ is of order ${\rm O}(\sqrt{N})$. Like the semi-proximal alternating direction method of multiplier (spADMM), Online-spADMM has the advantage to resolve the potentially non-solvability issue of the subproblems efficiently. We show the usefulness of the obtained results when applied to different types of online optimization problems and verify the theoretical result by numerical experiments}. The inequalities established for Online-spADMM are also used to develop iteration complexity of the average update of spADMM for solving linearly constrained convex composite optimization problems.
Ultralight boson cloud depletion in binary systems

Ultralight scalars can extract rotational energy from astrophysical black holes through superradiant instabilities, forming macroscopic boson clouds. This process is most efficient when the Compton wavelength of the boson is comparable to the size of the black hole horizon, i.e. when the "gravitational fine structure constant" $\alpha\equiv G \mu M/\hbar c\sim 1$. If the black hole/cloud system is in a binary, tidal perturbations from the companion can produce resonant transitions between the energy levels of the cloud, depleting it by an amount that depends on the nature of the transition and on the parameters of the binary. Previous cloud depletion estimates considered binaries in circular orbit and made the approximation $\alpha\ll 1$. Here we use black hole perturbation theory to compute instability rates and decay widths for generic values of $\alpha$, and we show that this leads to much larger cloud depletion estimates when $\alpha \gtrsim 0.1$. We also study eccentric binary orbits. We show that in this case resonances can occur at all harmonics of the orbital frequency, significantly extending the range of frequencies where cloud depletion may be observable with gravitational wave interferometers.
Alternative Weighting Schemes for ELMo Embeddings

ELMo embeddings (Peters et. al, 2018) had a huge impact on the NLP community and may recent publications use these embeddings to boost the performance for downstream NLP tasks. However, integration of ELMo embeddings in existent NLP architectures is not straightforward. In contrast to traditional word embeddings, like GloVe or word2vec embeddings, the bi-directional language model of ELMo produces three 1024 dimensional vectors per token in a sentence. Peters et al. proposed to learn a task-specific weighting of these three vectors for downstream tasks. However, this proposed weighting scheme is not feasible for certain tasks, and, as we will show, it does not necessarily yield optimal performance. We evaluate different methods that combine the three vectors from the language model in order to achieve the best possible performance in downstream NLP tasks. We notice that the third layer of the published language model often decreases the performance. By learning a weighted average of only the first two layers, we are able to improve the performance for many datasets. Due to the reduced complexity of the language model, we have a training speed-up of 19-44% for the downstream task.
Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling

Geometric deep learning is increasingly important thanks to the popularity of 3D sensors. Inspired by the recent advances in NLP domain, the self-attention transformer is introduced to consume the point clouds. We develop Point Attention Transformers (PATs), using a parameter-efficient Group Shuffle Attention (GSA) to replace the costly Multi-Head Attention. We demonstrate its ability to process size-varying inputs, and prove its permutation equivariance. Besides, prior work uses heuristics dependence on the input data (e.g., Furthest Point Sampling) to hierarchically select subsets of input points. Thereby, we for the first time propose an end-to-end learnable and task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points. Equipped with Gumbel-Softmax, it produces a "soft" continuous subset in training phase, and a "hard" discrete subset in test phase. By selecting representative subsets in a hierarchical fashion, the networks learn a stronger representation of the input sets with lower computation cost. Experiments on classification and segmentation benchmarks show the effectiveness and efficiency of our methods. Furthermore, we propose a novel application, to process event camera stream as point clouds, and achieve a state-of-the-art performance on DVS128 Gesture Dataset.
A Closer Look at Few-shot Classification

Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difficult. In this paper, we present 1) a consistent comparative analysis of several representative few-shot classification algorithms, with results showing that deeper backbones significantly reduce the performance differences among methods on datasets with limited domain differences, 2) a modified baseline method that surprisingly achieves competitive performance when compared with the state-of-the-art on both the \miniI and the CUB datasets, and 3) a new experimental setting for evaluating the cross-domain generalization ability for few-shot classification algorithms. Our results reveal that reducing intra-class variation is an important factor when the feature backbone is shallow, but not as critical when using deeper backbones. In a realistic cross-domain evaluation setting, we show that a baseline method with a standard fine-tuning practice compares favorably against other state-of-the-art few-shot learning algorithms.
A nonintrusive nonlinear model reduction method for structural dynamical problems based on machine learning

Model order reduction (MOR) has become one of the most widely used tools to create efficient surrogate models for time‐critical applications. For nonlinear models, however, linear MOR approaches are only practicable to a limited extent. Nonlinear approaches, on the contrary, often require intrusive manipulations of the used simulation code. Hence, nonintrusive MOR approaches using classic model order reduction along with machine learning (ML) algorithms can provide remedy. Such approaches have drawn a lot of attention in the recent years. They rely on the idea to learn the dynamics not in a high dimensional but in a reduced space, that is, they predict the discrete sequence of reduced basis' coefficients. Open questions are the suitability of such methods in the field of structural dynamics and the best choice of the used ML algorithm. Both are addressed in this article in addition to the integration of the methodology into a modular and flexible framework that can effortless be adapted to various requirements. By applying the methodology to a dynamic mechanical system, accurate surrogate models are received, which can speed up the simulation time significantly, while still providing high‐quality state approximations.