Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Convolutional Capsule Network for Hyperspectral Image Spectral and Spectral-Spatial Classification

Capsule networks can be considered to be the next era of deep learning and have recently shown their advantages in supervised classification. Instead of using scalar values to represent features, the capsule networks use vectors to represent features, which enriches the feature presentation capability. This paper introduces a deep capsule network for hyperspectral image (HSI) classification to improve the performance of the conventional convolutional neural networks (CNNs). Furthermore, a modification of the capsule network named Conv-Capsule is proposed. Instead of using full connections, local connections and shared transform matrices, which are the core ideas of CNNs, are used in the Conv-Capsule network architecture. In Conv-Capsule, the number of trainable parameters is reduced compared to the original capsule, which potentially mitigates the overfitting issue when the number of available training samples is limited. Specifically, we propose two schemes: (1) A 1D deep capsule network is designed for spectral classification, as a combination of principal component analysis, CNN, and the Conv-Capsule network, and (2) a 3D deep capsule network is designed for spectral-spatial classification, as a combination of extended multi-attribute profiles, CNN, and the Conv-Capsule network. The proposed classifiers are tested on three widely-used hyperspectral data sets. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, including kernel support vector machines, CNNs, and recurrent neural network.
BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books

We showcase how dropout variational inference can be applied to a large-scale deep learning model that predicts price movements from limit order books (LOBs), the canonical data source representing trading and pricing movements. We demonstrate that uncertainty information derived from posterior predictive distributions can be utilised for position sizing, avoiding unnecessary trades and improving profits. Further, we test our models by using millions of observations across several instruments and markets from the London Stock Exchange. Our results suggest that those Bayesian techniques not only deliver uncertainty information that can be used for trading but also improve predictive performance as stochastic regularisers. To the best of our knowledge, we are the first to apply Bayesian networks to LOBs.
Dynamics of protein synthesis and degradation through the cell cycle

ABSTRACT Protein expression levels depend on the balance between their synthesis and degradation rates. Even quiescent (G0) cells display a continuous turnover of proteins, despite protein levels remaining largely constant over time. In cycling cells, global protein levels need to be precisely doubled at each cell division in order to maintain cellular homeostasis, but we still lack a quantitative understanding of how this is achieved. Recent studies have shed light on cell cycle-dependent changes in protein synthesis and degradation rates. Here we discuss current population-based and single cell approaches used to assess protein synthesis and degradation, and review the insights they have provided into the dynamics of protein turnover in different cell cycle phases.
Use of UAV Photogrammetric Data for Estimation of Biophysical Properties in Forest Stands Under Regeneration

The objective of this study was to assess the use of unmanned aerial vehicle (UAV) data for modelling tree density and canopy height in young boreal forests stands. The use of UAV data for such tasks can be beneficial thanks to the high resolution and reduction of the time spent in the field. This study included 29 forest stands, within which 580 clustered plots were measured in the field. An area-based approach was adopted to which random forest models were fitted using the plot data and the corresponding UAV data and then applied and validated at plot and stand level. The results were compared to those of models based on airborne laser scanning (ALS) data and those from a traditional field-assessment. The models based on UAV data showed the smallest stand-level RMSE values for mean height (0.56 m) and tree density (1175 trees ha−1). The RMSE of the tree density using UAV data was 50% smaller than what was obtained using ALS data (2355 trees ha−1). Overall, this study highlighted that the use of UAVs for the inventory of forest stands under regeneration can be beneficial both because of the high accuracy of the derived data analytics and the time saving compared to traditional field assessments.
Expanding the Role of Levels of Personality Functioning in Personality Disorder Taxonomy: Commentary on “Criterion A of the AMPD in HiTOP”

Abstract The Alternative Model for Personality Disorders (AMPD) construes personality disorder diagnosis in terms of levels of personality functioning (LPF) as well as dimensions of pathological personality traits. The Hierarchical Taxonomy of Psychopathology (HiTOP), which offers a comprehensive framework for conceptualizing psychopathology, integrates pathological personality traits into a model for understanding different disorders, deficits, and personality problems. Widiger et al. (2018) placed LPF within the HiTOP model, suggesting that LPF might already be subsumed by trait dimensions and other spectra representing severity of psychopathology. This commentary raises a number of questions about their underlying assumptions, and advocates for an expanded role for LPF to more fully capture the complexity and instability inherent in personality disorder pathology.
Actin filaments are dispensable for bulk autophagy in plants

ABSTRACT Actin filament, also known as microfilament, is one of two major cytoskeletal elements in plants and plays important roles in various biological processes. Like in animal cells, actin filaments have been thought to participate in autophagy in plants. However, surprisingly, in this study we found that actin filaments are dispensable for the occurrence of autophagy in plants. Disruption of actin filaments by short term treatment with actin polymerization inhibitors, cytochalasin D and latrunculin B, or transient overexpression of Profilin 3 in Nicotiana benthamiana had no effect on basal autophagy as well as the upregulation of nocturnal autophagy and salt stress-induced autophagy. Furthermore, anti-microfilament drug treatment affected neither basal nor salt stress-induced autophagy in Arabidopsis. In addition, prolonged perturbation of actin filaments by silencing Actin7 or 24-h treatment with microfilament-disrupting agents in N. benthamiana caused endoplasmic reticulum (ER) disorganization and subsequent degradation via autophagy involving ATG2, 3, 5, 6 and 7. Our findings reveal that, unlike mammalian cells, actin filaments are unnecessary for bulk autophagy in plants. Abbreviations: ATG: autophagy-related; CD: cytochalasin D; Cvt pathway: cytoplasm to vacuole targeting pathway; DMSO: dimethyl sulfoxide; ER: endoplasmic reticulum; LatB: latrunculin B; Nb: Nicotiana benthamiana; PAS: phagophore assembly site; PRF3: Profilin 3; RER: rough ER; SER: smooth ER; TEM: transmission electron microscopy; TRV: Tobacco rattle virus; VIGS: virus-induced gene silencing; wpi: weeks post-agroinfiltration
The use of Hurst and effective return in investing

In this paper, we demonstrate the use of long-range dependence in forming portfolios of mutual funds. We review the computation Hurst (how we identify long-range dependence) and stress the importance of using two different methodologies to calculate Hurst. We show that the methodologies we use—DFA and the Whittle estimator—are robust to data size. We use goodness-of-fit and shuffle tests to further confirm our measurements of long-range dependence. We also demonstrate the use of effective return (Dacorogna et al. 1999) in portfolio formation problems. We determine the slope of a fund’s P&L using effective return (ER), a risk-adjustment tool that marries computational finance and prospect theory. We do this because though Hurst (H) is good at computing the roughness or volatility of a fund, it tells you nothing about its direction (if the fund is moving up, down or sideways). In our tests of H on its own, we find that H is a good tool for forming portfolios of mutual funds meant to follow the complete US securities markets (defined here as stocks, bonds, real estate and cash). In related tests where we form portfolios of US equity mutual funds using H and ER together, the results are a little more mixed, with H and ER besting a Sharpe ratio based methodology, while drawing even to the recently developed generalized CAPM of Malevergne and Sornette (2002, 2003). We conduct follow up tests and find that screening funds first using H and ER, and then forming portfolios based on generalized CAPM improved each methodology’s performance.
Fast Underwater Image Enhancement for Improved Visual Perception

In this letter, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large-scale dataset of a paired and an unpaired collection of underwater images (of ‘poor’ and ‘good’ quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available at https://github.com/xahidbuffon/funie-gan.
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models

Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of equipment. We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios where: (i) access to labeled failure data is scarce due to rarity of failures (ii) inherent noise ispresent in the sensor readings. The two scenarios mentioned are unavoidable sources of uncertainty in the RUL estimation process, often resulting in unreliable RUL estimates. To address (i), we formulate RUL estimation as an Ordinal Regression (OR) problem and propose LSTM-OR: deep Long Short Term Memory (LSTM) network-based approach to learn the OR function. We show that LSTM-OR naturally allows for the incorporation of censored operational instances in training along with the failed instances, leading to more robust learning. To address (ii), we propose a simple yet effectiveapproach to quantify predictive uncertainty in the RUL estimation models by training an ensemble of LSTM-OR models. Through empirical evaluation on the publicly available turbofan engine benchmark datasets, we demonstrate that LSTMOR is at par with commonly used deep metric regressionbased approaches for RUL estimation when sufficient failed instances are available for training. Importantly, LSTM-OR outperforms these metric regression-based approaches in the practical scenario where failed training instances are scarce, but sufficient operational (censored) instances are additionally available. Furthermore, our uncertainty quantification approach yields high-quality predictive uncertainty estimateswhile also leading to improved RUL estimates compared to single best LSTM-OR models.
Changing the perception of premenstrual dysphoric disorder - An online-experiment using the Stereotype Content Model

ABSTRACT Women with Premenstrual Dysphoric Disorder (PMDD) are often faced with prejudices about the premenstrual phase. The aim of this study was to investigate whether providing information (psychoeducation) could improve study participants’ perception of a PMDD-patient and whether experimentally-induced prejudices about PMDD resulted in stigmatization. Two hundred sixteen students (50% female; aged 18–42 years) from Philipps University Marburg participated in January 2014. Participants were randomly assigned to one of two experimental groups (EG1, EG2) or to a control group (CG). EG1 read a text informing about PMDD. EG2 read a text with stereotypic PMDD-information. CG received a text with information unrelated to PMDD. Then, all participants watched a video of a woman reporting about her PMDD. Finally, participants appraised the woman on the cognitive dimensions warmth and competence as well as on PMDD-related attributes (depressive symptoms, emotional regulation). Participants of EG1 rated the woman as warmer (p < .001), more competent (p = .006), and with less depressive symptoms (p < .001) than the CG. The results by study group did not differ by gender. Stereotypic information did not differ significantly among the study groups. Psychoeducation can facilitate the understanding of PMDD-patients and should be integrated in future research on PMDD-treatments.
GNSS RTK Positioning Augmented with Large LEO Constellation

It is widely known that in real-time kinematic (RTK) solution, the convergence and ambiguity-fixed speeds are critical requirements to achieve centimeter-level positioning, especially in medium-to-long baselines. Recently, the current status of the global navigation satellite systems (GNSS) can be improved by employing low earth orbit (LEO) satellites. In this study, an initial assessment is applied for LEO constellations augmented GNSS RTK positioning, where four designed LEO constellations with different satellite numbers, as well as the nominal GPS constellation, are simulated and adopted for analysis. In terms of aforementioned constellations solutions, the statistical results of a 68.7-km baseline show that when introducing 60, 96, 192, and 288 polar-orbiting LEO constellations, the RTK convergence time can be shortened from 4.94 to 2.73, 1.47, 0.92, and 0.73 min, respectively. In addition, the average time to first fix (TTFF) can be decreased from 7.28 to 3.33, 2.38, 1.22, and 0.87 min, respectively. Meanwhile, further improvements could be satisfied in several elements such as corresponding fixing ratio, number of visible satellites, position dilution of precision (PDOP) and baseline solution precision. Furthermore, the performance of the combined GPS/LEO RTK is evaluated over various-length baselines, based on convergence time and TTFF. The research findings show that the medium-to-long baseline schemes confirm that LEO satellites do helpfully obtain faster convergence and fixing, especially in the case of long baselines, using large LEO constellations, subsequently, the average TTFF for long baselines has a substantial shortened about 90%, in other words from 12 to 2 min approximately by combining with the larger LEO constellation of 192 or 288 satellites. It is interesting to denote that similar improvements can be observed from the convergence time.
DeepRED: Deep Image Prior Powered by RED

Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. One such contribution, which is the focus of this paper, is the Deep Image Prior (DIP) work by Ulyanov, Vedaldi, and Lempitsky (2018). DIP offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture. While DIP has been shown to be quite an effective unsupervised approach, its results still fall short when compared to state-of-the-art alternatives. In this work, we aim to boost DIP by adding an explicit prior, which enriches the overall regularization effect in order to lead to better-recovered images. More specifically, we propose to bring-in the concept of Regularization by Denoising (RED), which leverages existing denoisers for regularizing inverse problems. Our work shows how the two (DIP and RED) can be merged into a highly effective unsupervised recovery process while avoiding the need to differentiate the chosen denoiser, and leading to very effective results, demonstrated for several tested problems.
Families grieving together: Integrating the loss of a child through ongoing relational connections

Abstract This study explored the relational dimensions of grieving within the family unit. Three families bereaved of a child, participated. Using the Qualitative Action-Project Method, individual and joint interviews were conducted with family members. Data analysis illuminated family grieving processes and demonstrated that grieving was an interactive process with individual, dyadic, multi-adic, and community levels of processing. The family grieving process included intentionality in grieving together and separately, recognition of differing grieving styles, and meaning came through the incorporation of ongoing rituals and remembrances. A finding emerged of family connection facilitated through an ongoing, shared, continuing bond with the deceased child.
Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data

Automatic extraction of building footprints from high-resolution satellite imagery has become an important and challenging research issue receiving greater attention. Many recent studies have explored different deep learning-based semantic segmentation methods for improving the accuracy of building extraction. Although they record substantial land cover and land use information (e.g., buildings, roads, water, etc.), public geographic information system (GIS) map datasets have rarely been utilized to improve building extraction results in existing studies. In this research, we propose a U-Net-based semantic segmentation method for the extraction of building footprints from high-resolution multispectral satellite images using the SpaceNet building dataset provided in the DeepGlobe Satellite Challenge of IEEE Conference on Computer Vision and Pattern Recognition 2018 (CVPR 2018). We explore the potential of multiple public GIS map datasets (OpenStreetMap, Google Maps, and MapWorld) through integration with the WorldView-3 satellite datasets in four cities (Las Vegas, Paris, Shanghai, and Khartoum). Several strategies are designed and combined with the U-Net–based semantic segmentation model, including data augmentation, post-processing, and integration of the GIS map data and satellite images. The proposed method achieves a total F1-score of 0.704, which is an improvement of 1.1% to 12.5% compared with the top three solutions in the SpaceNet Building Detection Competition and 3.0% to 9.2% compared with the standard U-Net–based method. Moreover, the effect of each proposed strategy and the possible reasons for the building footprint extraction results are analyzed substantially considering the actual situation of the four cities.
Radial Force Density Analysis of Switched Reluctance Machines: The Source of Acoustic Noise

Closer attention has been given to the acoustic noise performance of electric motors as electrified powertrains penetrate into our transportation system. In particular, switched reluctance machine introduces a new challenge to acoustic noise aspect given that the temporal harmonics of high amplitudes on the radial direction can excite the natural frequencies of the main circumferential modes. A practical understanding of the radial force density decomposition is crucial in identifying the primary source of acoustic noise at different operating points, and it is the main contribution of this paper. An analytical expression is introduced to identify the temporal harmonic orders that excite different spatial mode shapes. The combination of the circumferential and temporal harmonic orders results in a force density surface wave that travels in the clockwise (CW) or counter CW direction. The mode excitation is investigated along with the sound pressure level produced by the primary vibrating mode shapes. Acoustic noise characteristics for each mode and the corresponding natural frequency at different speeds have been analyzed by using a waterfall plot.
A Vertex-to-Edge Weighted Closed-Form Method for Dense RGB-D Indoor SLAM

RGB-D dense mapping systems are widely used for indoor navigation and augmented reality. However, the drift in egomotion estimation systems accumulates as the systems expand over space and time. In this paper, we develop a vertex-to-edge weighted closed-form algorithm to reduce camera drift for dense RGB-D indoor simultaneous localization and mapping (SLAM). First, due to the high-frame rate of common RGB-D sensors, in our approach, we employ a robust key-frame selection strategy. We then consider the typical depth noise properties of the sensor and exploit both visual and geometric information to determine the camera motion of the key frames. To enforce global consistency, we employ a combination of techniques to enhance the efficiency and accuracy of loop closure detection and then factor graph optimization is used to mitigate trajectory drift; specifically, the graph edges are weighted by residual errors. To demonstrate its accuracy and robustness, our SLAM system is tested on seven sets of sequences from publicly available benchmark datasets collected with Kinect v1 and on three sets of sequences taken with a handheld structure sensor. In a direct comparison, our approach yields significantly less trajectory error than several other state-of-the-art methods.
Needle in a Haystack: A Framework for Seeking Small Objects in Big Datasets

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity --- adding new complexity to search tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their search algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the results of a search query should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. We propose a new framework for image retrieval that models object-level regions using image keypoints retrieved from an image index, which are then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method Needle-Haystack (NH) scoring, and it is optimized for fast matrix operations on CPUs. We show that this method not only performs comparably to state-of-the-art methods in classic CBIR problems, but also outperforms them in fine-grained object- and instance-level retrieval on the Oxford 5K, Paris 6K, Google-Landmarks, and NIST MFC2018 datasets, as well as meme-style imagery from Reddit.