Miracle-dz/UniFAR-FTUs huggingface dataset:
Internal control vs market force: the dividend policy in China

We explore whether the disappearance of stock dividends and the fluctuation in the popularity of cash dividends in China are driven by internal corporate governance or external market forces, including stock liquidity, risks or investor preferences (catering theory), measured by the dividend premium. This study is of particular interest given the weak minority investor protection and poor enforcement of regulation in the Chinese market. Our results suggest that, while CEO duality and board independence do not affect dividend decisions, larger boards, lower board meeting frequency and higher board ownership are consistently positively related to cash dividends and negatively related to stock dividends. Moreover, we find that among external market forces, systematic and idiosyncratic risks and liquidity play a determinant role in corporate cash dividend policies, while decisions about stock dividends are driven only by systematic risk. More importantly, our results show that investor preferences persistent even after adjusting for board ownership and characteristics, but it disappears when controlling for risks. We further examine whether investor preferences influence dividend substitution, and find that investor preferences for cash dividends do not influence firms’ stock dividend decision and vice versa. Our study provide insights into the determinants of both cash and stock dividend choice and raise potential policy implications in the emerging market context. JEL classification: G15; G30; G34; G35
Beliefs about psychotherapy among adults in La Araucanía, Chile

Cultural factors relevant to psychotherapy, although highly relevant, have been poorly studied in the local context, with little clarity regarding the socially shared meanings about this kind of treatment. The aim of this study was to identify beliefs about psychotherapy among adults from La Araucanía region, Chile. Through a bottom up cultural research approach, 32 semi-structured interviews were conducted and examined using thematic analysis. Participants held mostly positive beliefs about psychotherapy itself and its results. However, understandings that could hinder adherence to psychotherapy were identified, as well as negative emotions associated with attending it. Additionally, participants displayed negative beliefs about the psychotherapy delivered in the public health system. These results provide a characterization of beliefs about psychotherapy among adults, and could serve as an input to understand psychotherapy adherence in this particular context.
Are Stocks Priced to Yield a Non-Resiliency Premium?

This paper examines resiliency, a dynamic dimension of liquidity that we capture using an intraday return autocorrelation measure. Long-short portfolios based on this measure yield a significant non-resiliency premium that ranges from 38 to 63 basis points per month. The premium persists with an extensive list of control variables accounted for, and it remains significant for a sample of S&P 500 stocks that are deemed to be liquid according to more traditional liquidity measures (e.g., spread and price impact). Moreover, the non-resiliency premium is particularly high during periods of accentuated stress. JEL classification: G02, G10, G11, G12, G14
Action Recognition Using Volumetric Motion Representations

Traditional action recognition models are constructed around the paradigm of 2D perspective imagery. Though sophisticated time-series models have pushed the field forward, much of the information is still not exploited by confining the domain to 2D. In this work, we introduce a novel representation of motion as a voxelized 3D vector field and demonstrate how it can be used to improve performance of action recognition networks. This volumetric representation is a natural fit for 3D CNNs, and allows out-of-plane data augmentation techniques during training of these networks. Both the construction of this representation from RGB-D video and inference can be run in real time. We demonstrate superior results using this representation with our network design on the open-source NTU RGB+D dataset where it outperforms state-of-the-art on both of the defined evaluation metrics. Furthermore, we experimentally show how the out-of-plane augmentation techniques create viewpoint invariance and allow the model trained using this representation to generalize to unseen camera angles. Code is available here: this https URL.
Attention Guided Anomaly Detection and Localization in Images

Anomaly detection and localization is a popular computer vision problem involving detecting anomalous images and localizing anomalies within them. However, this task is challenging due to the small sample size and pixel coverage of the anomaly in real-world scenarios. Prior works need to use anomalous training images to compute a threshold to detect and localize anomalies. To remove this need, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information. In the unsupervised setting, we propose an attention expansion loss, where we encourage CAVGA to focus on all normal regions in the image without using any anomalous training image. Furthermore, using only 2% anomalous images in the weakly supervised setting we propose a complementary guided attention loss, where we encourage the normal attention to focus on all normal regions while minimizing the regions covered by the anomalous attention in the normal image. CAVGA outperforms the state-of-the-art (SOTA) anomaly detection methods on the MNIST, CIFAR-10, Fashion-MNIST, MVTec Anomaly Detection (MVTAD), and modified ShanghaiTech Campus (mSTC) datasets. CAVGA also outperforms the SOTA anomaly localization methods on the MVTAD and mSTC datasets.
Demystifying TasNet: A Dissecting Approach

In recent years time domain speech separation has excelled over frequency domain separation in single channel scenarios and noise-free environments. In this paper we dissect the gains of the time-domain audio separation network (TasNet) approach by gradually replacing components of an utterance-level permutation invariant training (u-PIT) based separation system in the frequency domain until the TasNet system is reached, thus blending components of frequency domain approaches with those of time domain approaches. Some of the intermediate variants achieve comparable signal-to-distortion ratio (SDR) gains to TasNet, but retain the advantage of frequency domain processing: compatibility with classic signal processing tools such as frequency-domain beamforming and the human interpretability of the masks. Furthermore, we show that the scale invariant signal-to-distortion ratio (si-SDR) criterion used as loss function in TasNet is related to a logarithmic mean square error criterion and that it is this criterion which contributes most reliable to the performance advantage of TasNet. Finally, we critically assess which gains in a noise-free single channel environment generalize to more realistic reverberant conditions.
Strong Excess Faraday Rotation on the Inside of the Sagittarius Spiral Arm

We present first results for Faraday rotation of compact polarized sources (1–2 GHz continuum) in The H i/OH/Recombination line (THOR) survey of the inner Galaxy. In the Galactic longitude range 39° < ℓ < 52°, we find rotation measures (RMs) in the range −310 rad m−2 ≤ RM ≤ +4219 rad m−2, with the highest values concentrated within a degree of ℓ = 48° at the Sagittarius arm tangent. Most of the high RMs arise in diffuse plasma, along lines of sight that do not intersect H ii regions. For ℓ > 49°, RM drops off rapidly, while at ℓ < 47°, the mean RM is higher with a larger standard deviation than at ℓ > 49°. We attribute the RM structure to the compressed diffuse warm ionized medium in the spiral arm, upstream of the major star formation regions. The Sagittarius arm acts as a significant Faraday screen inside the Galaxy. This has implications for models of the Galactic magnetic field and the expected amount of Faraday rotation of fast radio bursts from their host galaxies. We emphasize the importance of sensitivity to high Faraday depth in future polarization surveys.
Deep motion blur removal using noisy/blurry image pairs

Abstract. Removing spatially variant motion blur from a blurry image is a challenging problem as image blur can be complicated and difficult to model accurately. Recent progress in deep neural networks suggests that kernel-free single image deblurring can be achieved, but questions about deblurring performance persist. To improve performance, we proposed a deep convolutional neural network to restore a sharp image from a noisy/blurry image pair captured in quick succession. Two neural network structures, Deblur Long Short-Term Memory (LSTM) and DeblurMerger, are presented to fuse the pair of images in either sequential or parallel manner. To boost the training, gradient loss, adversarial loss, and spectral normalization are leveraged. The training dataset that consists of pairs of noisy/blurry images and the corresponding ground truth sharp image is synthesized based on the benchmark dataset GOPRO. We evaluated the trained networks on a variety of synthetic datasets and real image pairs. The results demonstrate that the proposed approach outperforms the state-of-the-art methods both qualitatively and quantitatively. DeblurLSTM achieves the best debluring performance, while DeblurMerger achieves nearly the same result but with significantly less computation time.
Self-Imitation Learning via Trajectory-Conditioned Policy for Hard-Exploration Tasks

Imitation learning from human-expert demonstrations has been shown to be greatly helpful for challenging reinforcement learning problems with sparse environment rewards. However, it is very difficult to achieve similar success without relying on expert demonstrations. Recent works on self-imitation learning showed that imitating the agent's own past good experience could indirectly drive exploration in some environments, but these methods often lead to sub-optimal and myopic behavior. To address this issue, we argue that exploration in diverse directions by imitating diverse trajectories, instead of focusing on limited good trajectories, is more desirable for the hard-exploration tasks. We propose a new method of learning a trajectory-conditioned policy to imitate diverse trajectories from the agent's own past experience and show that such self-imitation helps avoid myopic behavior and increases the chance of finding a globally optimal solution for hard-exploration tasks, especially when there are misleading rewards. Our method significantly outperforms existing self-imitation learning and count-based exploration methods on various hard-exploration tasks with local optima. In particular, we report a state-of-the-art score of more than 20,000 points on Montezuma's Revenge without using expert demonstrations or resetting to arbitrary states.
Outside the Box: Abstraction-Based Monitoring of Neural Networks

Neural networks have demonstrated unmatched performance in a range of classification tasks. Despite numerous efforts of the research community, novelty detection remains one of the significant limitations of neural networks. The ability to identify previously unseen inputs as novel is crucial for our understanding of the decisions made by neural networks. At runtime, inputs not falling into any of the categories learned during training cannot be classified correctly by the neural network. Existing approaches treat the neural network as a black box and try to detect novel inputs based on the confidence of the output predictions. However, neural networks are not trained to reduce their confidence for novel inputs, which limits the effectiveness of these approaches. We propose a framework to monitor a neural network by observing the hidden layers. We employ a common abstraction from program analysis - boxes - to identify novel behaviors in the monitored layers, i.e., inputs that cause behaviors outside the box. For each neuron, the boxes range over the values seen in training. The framework is efficient and flexible to achieve a desired trade-off between raising false warnings and detecting novel inputs. We illustrate the performance and the robustness to variability in the unknown classes on popular image-classification benchmarks.
Unsupervised Monocular Depth Prediction for Indoor Continuous Video Streams

This paper studies unsupervised monocular depth prediction problem. Most of existing unsupervised depth prediction algorithms are developed for outdoor scenarios, while the depth prediction work in the indoor environment is still very scarce to our knowledge. Therefore, this work focuses on narrowing the gap by firstly evaluating existing approaches in the indoor environments and then improving the state-of-the-art design of architecture. Unlike typical outdoor training dataset, such as KITTI with motion constraints, data for indoor environment contains more arbitrary camera movement and short baseline between two consecutive images, which deteriorates the network training for the pose estimation. To address this issue, we propose two methods: Firstly, we propose a novel reconstruction loss function to constraint pose estimation, resulting in accuracy improvement of the predicted disparity map; secondly, we use an ensemble learning with a flipping strategy along with a median filter, directly taking operation on the output disparity map. We evaluate our approaches on the TUM RGB-D and self-collected datasets. The results have shown that both approaches outperform the previous state-of-the-art unsupervised learning approaches.
Characteristics and Outcomes of Adult Patients Receiving Mechanical Ventilation Due to Acute Poisoning Akut Zehirlenme Nedeniyle Mekanik Ventilasyon Uygulanan Hastalarin Karakteristik Özellikleri ve Sonuçlari

SUMMARY Objective: Poisoning is an important cause of admission to intensive care units (ICUs) and prolongs the length of stay (LOS) in the ICU. The patients whose admitted to the ICU with poisoning may require mechanical ventilation (MV). The need of prolonged MV causes a significantly longer LOS and higher mortality in the ICU. The analysis of the characteristics and outcomes of patients receiving MV after poisoning may be useful in the clinical management of these patients, allowing for better counseling of the patients and their families. The objective of this retrospective study to evaluate the relationship between demographic and etiological characteristics with need of MV in patients admitted to ICU due to poisoning. Material and Method: 211 patients diagnosed with poisoning were analyzed from January 2010 to December 2011. Data regarding age, gender, the time between exposure and ICU admission, type of poisoning, exposure to toxic agents, the route of exposure, a history of previous toxicity, consciousness status on arrival, the length of the ICU stay, need of MV, complications associated with MV, the Glasgow coma scale and outcome were obtained. Results: Of the 211 patients, 143 (67.8%) were female. The most common cause of poisoning was suicide (87.7%), and the main route of exposure was oral (93.4%). The greatest number of agents which patients were exposed was drugs. (74.4%). The most common drugs causing poisoning were antidepressants (including tricyclic antidepressants) (38.3%). The need of MV was significantly related to both gender (p=0.04) and the route of exposure (p<0.001). The need of MV was also significantly related to type of poisoning (p=0.01) and the toxic agents (p<0.001). There were very strong relationships between the drug poisoning and gender (p=0.002) and between type of poisoning and gender (p=0.006). Conclusion: The present study demonstrated that there were significant relationships between the need of MV and gender, type of poisoning, the toxic agents, the route of exposure and the ICU LOS. The need of MV may lead to poor outcome as it is associated with higher incidence of ventilator related complications and longer ICU stay.
Issues with SZZ: An empirical assessment of the state of practice of defect prediction data collection

Defect prediction research has a strong reliance on published data sets that are shared between researchers. The SZZ algorithm is the de facto standard for collecting defect labels for this kind of data and is used by most public data sets. Thus, problems with the SZZ algorithm may have a strong indirect impact on almost the complete state of the art of defect prediction. Recent research uncovered potential problems in different parts of the SZZ algorithm. Within this article, we provide an extensive empirical analysis of the defect labels created with the SZZ algorithm. We used a combination of manual validation and adopted or improved heuristics for the collection of defect data to establish ground truth data for bug fixing commits, improved the heuristic for the identification of inducing changes for defects, as well as the assignment of bugs to releases. We conducted an empirical study on 398 releases of 38 Apache projects and found that only half of the bug fixing commits determined by SZZ are actually bug fixing. Moreover, if a six month time frame is used in combination with SZZ to determine which bugs affect a release, one file is incorrectly labeled as defective for every file that is correctly labeled as defective. In addition, two defective files are missed. We also explored the impact of the relatively small set of features that are available in most defect prediction data sets, as there are multiple publications that indicate that, e.g., churn related features are important for defect prediction. We found that the difference of using more features is negligible.
Bayesian interpretation of SGD as Ito process

The current interpretation of stochastic gradient descent (SGD) as a stochastic process lacks generality in that its numerical scheme restricts continuous-time dynamics as well as the loss function and the distribution of gradient noise. We introduce a simplified scheme with milder conditions that flexibly interprets SGD as a discrete-time approximation of an Ito process. The scheme also works as a common foundation of SGD and stochastic gradient Langevin dynamics (SGLD), providing insights into their asymptotic properties. We investigate the convergence of SGD with biased gradient in terms of the equilibrium mode and the overestimation problem of the second moment of SGLD.
Effects of Planck-scale-modified dispersion relations on the thermodynamics of charged black holes

Considering corrections produced by modified dispersion relations on the equation of state parameter of radiation, we study the induced the black hole metric inspired by Kiselev's ansatz, thus defining a deformed Reissner-Nordstr\"{o}m metric. In particular, we consider thermodynamic properties of such black hole from the combined viewpoints of the modified equation of state parameter and the phenomenological approach to the quantum gravity problem called rainbow gravity.
Fine-grained Synthesis of Unrestricted Adversarial Examples

We propose a novel approach for generating unrestricted adversarial examples by manipulating fine-grained aspects of image generation. Unlike existing unrestricted attacks that typically hand-craft geometric transformations, we learn stylistic and stochastic modifications leveraging state-of-the-art generative models. This allows us to manipulate an image in a controlled, fine-grained manner without being bounded by a norm threshold. Our approach can be used for targeted and non-targeted unrestricted attacks on classification, semantic segmentation and object detection models. Our attacks can bypass certified defenses, yet our adversarial images look indistinguishable from natural images as verified by human evaluation. Moreover, we demonstrate that adversarial training with our examples improves performance of the model on clean images without requiring any modifications to the architecture. We perform experiments on LSUN, CelebA-HQ and COCO-Stuff as high resolution datasets to validate efficacy of our proposed approach.
Efficient Derivative Computation for Cumulative B-Splines on Lie Groups

Continuous-time trajectory representation has recently gained popularity for tasks where the fusion of high-frame-rate sensors and multiple unsynchronized devices is required. Lie group cumulative B-splines are a popular way of representing continuous trajectories without singularities. They have been used in near real-time SLAM and odometry systems with IMU, LiDAR, regular, RGB-D and event cameras, as well as for offline calibration. These applications require efficient computation of time derivatives (velocity, acceleration), but all prior works rely on a computationally suboptimal formulation. In this work we present an alternative derivation of time derivatives based on recurrence relations that needs O(k) instead of O(k^2) matrix operations (for a spline of order k) and results in simple and elegant expressions. While producing the same result, the proposed approach significantly speeds up the trajectory optimization and allows for computing simple analytic derivatives with respect to spline knots. The results presented in this paper pave the way for incorporating continuous-time trajectory representations into more applications where real-time performance is required.
Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN

Shadow removal is an essential task for scene understanding. Many studies consider only matching the image contents, which often causes two types of ghosts: color in-consistencies in shadow regions or artifacts on shadow boundaries (as shown in Figure. 1). In this paper, we tackle these issues in two ways. First, to carefully learn the border artifacts-free image, we propose a novel network structure named the dual hierarchically aggregation network (DHAN). It contains a series of growth dilated convolutions as the backbone without any down-samplings, and we hierarchically aggregate multi-context features for attention and prediction, respectively. Second, we argue that training on a limited dataset restricts the textural understanding of the network, which leads to the shadow region color in-consistencies. Currently, the largest dataset contains 2k+ shadow/shadow-free image pairs. However, it has only 0.1k+ unique scenes since many samples share exactly the same background with different shadow positions. Thus, we design a shadow matting generative adversarial network (SMGAN) to synthesize realistic shadow mattings from a given shadow mask and shadow-free image. With the help of novel masks or scenes, we enhance the current datasets using synthesized shadow images. Experiments show that our DHAN can erase the shadows and produce high-quality ghost-free images. After training on the synthesized and real datasets, our network outperforms other state-of-the-art methods by a large margin. The code is available: http://github.com/vinthony/ghost-free-shadow-removal/