Miracle-dz/UniFAR-FTUs huggingface dataset:
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills

Acquiring abilities in the absence of a task-oriented reward function is at the frontier of reinforcement learning research. This problem has been studied through the lens of empowerment, which draws a connection between option discovery and information theory. Information-theoretic skill discovery methods have garnered much interest from the community, but little research has been conducted in understanding their limitations. Through theoretical analysis and empirical evidence, we show that existing algorithms suffer from a common limitation -- they discover options that provide a poor coverage of the state space. In light of this, we propose 'Explore, Discover and Learn' (EDL), an alternative approach to information-theoretic skill discovery. Crucially, EDL optimizes the same information-theoretic objective derived from the empowerment literature, but addresses the optimization problem using different machinery. We perform an extensive evaluation of skill discovery methods on controlled environments and show that EDL offers significant advantages, such as overcoming the coverage problem, reducing the dependence of learned skills on the initial state, and allowing the user to define a prior over which behaviors should be learned. Code is publicly available at this https URL.
LAVA NAT: A Non-Autoregressive Translation Model with Look-Around Decoding and Vocabulary Attention

Non-autoregressive translation (NAT) models generate multiple tokens in one forward pass and is highly efficient at inference stage compared with autoregressive translation (AT) methods. However, NAT models often suffer from the multimodality problem, i.e., generating duplicated tokens or missing tokens. In this paper, we propose two novel methods to address this issue, the Look-Around (LA) strategy and the Vocabulary Attention (VA) mechanism. The Look-Around strategy predicts the neighbor tokens in order to predict the current token, and the Vocabulary Attention models long-term token dependencies inside the decoder by attending the whole vocabulary for each position to acquire knowledge of which token is about to generate. %We also propose a dynamic bidirectional decoding approach to accelerate the inference process of the LAVA model while preserving the high-quality of the generated output. Our proposed model uses significantly less time during inference compared with autoregressive models and most other NAT models. Our experiments on four benchmarks (WMT14 En$\rightarrow$De, WMT14 De$\rightarrow$En, WMT16 Ro$\rightarrow$En and IWSLT14 De$\rightarrow$En) show that the proposed model achieves competitive performance compared with the state-of-the-art non-autoregressive and autoregressive models while significantly reducing the time cost in inference phase.
Ghost imaging based on Y-net: a dynamic coding and conjugate-decoding approach

Ghost imaging incorporating deep learning technology has recently attracted much attention in the optical imaging field. However, deterministic illumination and multiple exposure are still essential in most scenarios. Here we propose a ghost imaging scheme based on a novel conjugate-decoding deep learning framework (Y-net), which works well under both deterministic and indeterministic illumination. Benefited from the end-to-end characteristic of our network, the image of a sample can be achieved directly from a pair of correlated speckles collected by the detectors, and the sample is illuminated only once in the experiment. The spatial distribution of the speckles encoding the sample in the experiment can be completely different from that of the simulation speckles for training, as long as the statistical characteristics of the speckles remain unchanged. This approach is particularly important to high-resolution x-ray ghost imaging applications due to its potential for improving image quality and reducing radiation damage. And the idea of conjugate-decoding network may also be applied to other learning-based imaging. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
An Intelligent Quaternion SVDCKF AHRS Estimation with Variable Adaptive Methods in Complex Conditions

Aimed at solving the problem of Attitude and Heading Reference System(AHRS) in the complex and dynamic conditions for small-UAV, An intelligent Singular Value Decomposition Cubature Kalman Filter(SVDCKF) combined with the Variable Adaptive Methods(VAM) is proposed in this paper. Considering the nonlinearity of quaternion AHRS model and non-positive definite of the state covariance matrix, the SVDCKF algorithm is presented with both the SVD and CKF in order to better obtain the filter accuracy and reliability. Additionally, there are the different changes of the values in the accelerometer measurement resulting from the complex flying conditions. Thus, the VAM is designed to deal with three-axis values of the acceleration and tune intelligently the measurement noise matrix Ra. Moreover, the heading measurement from the three-axis values of the magnetometer is calculated according to the whether to use the three-axis values of the acceleration in the special situations. The simulation and experiment results demonstrate that the proposed filter algorithm has the more excellent attitude solution accuracy and robustness than both the Complementary Filter(CF) and the Error State Kalman Filter(ESKF).
On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions

Recent advances in randomized incremental methods for minimizing $L$-smooth $\mu$-strongly convex finite sums have culminated in tight complexity of $\tilde{O}((n+\sqrt{n L/\mu})\log(1/\epsilon))$ and $O(n+\sqrt{nL/\epsilon})$, where $\mu>0$ and $\mu=0$, respectively, and $n$ denotes the number of individual functions. Unlike incremental methods, stochastic methods for finite sums do not rely on an explicit knowledge of which individual function is being addressed at each iteration, and as such, must perform at least $\Omega(n^2)$ iterations to obtain $O(1/n^2)$-optimal solutions. In this work, we exploit the finite noise structure of finite sums to derive a matching $O(n^2)$-upper bound under the global oracle model, showing that this lower bound is indeed tight. Following a similar approach, we propose a novel adaptation of SVRG which is both \emph{compatible with stochastic oracles}, and achieves complexity bounds of $\tilde{O}((n^2+n\sqrt{L/\mu})\log(1/\epsilon))$ and $O(n\sqrt{L/\epsilon})$, for $\mu>0$ and $\mu=0$, respectively. Our bounds hold w.h. p. and match in part existing lower bounds of $\tilde{\Omega}(n^2+\sqrt{nL/\mu}\log(1/\epsilon))$ and $\tilde{\Omega}(n^2+\sqrt{nL/\epsilon})$, for $\mu>0$ and $\mu=0$, respectively.
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization

Recent work has shown deep learning can accelerate the prediction of physical dynamics relative to numerical solvers. However, limited physical accuracy and an inability to generalize under the distributional shift limit its applicability to the real world. We propose to improve accuracy and generalization by incorporating symmetries into deep neural networks. Specifically, we employ a variety of methods each tailored to enforce a different symmetry. Our models are both theoretically and experimentally robust to distributional shift by the symmetry group transformations and enjoy favorable sample complexity. We demonstrate the advantage of our approach on a variety of physical dynamics including Rayleigh-Benard Convection and real-world ocean currents and temperatures. This is the first time that equivariant neural networks have been used to forecast physical dynamics.
Stress, Endocrine Physiology and Pathophysiology

Stress is a state of threatened homeostasis caused by intrinsic or extrinsic adverse forces (stressors) and is counteracted by an intricate repertoir of physiologic and behavioral responses aiming to maintain/reestablish the optimal body equilibrium (eustasis). The adaptive stress response depends upon a highly interconnected neuroendocrine, cellular and molecular infrastructure, the stress system. Key components of the stress system are the hypothalamic-pituitary-adrenal (HPA) axis and the autonomic nervous system (ANS), which interact with other vital centers in the central nervous system (CNS) and tissues/organs in the periphery to mobilize a successful adaptive response against the imposed stressor(s). Dysregulation of the stress system (hyper- or hypo-activation) in association with potent and/or chronic stress can markedly disrupt the body homeostasis leading to a state of cacostasis or allostasis, with a spectrum of clinical manifestations. This chapter describes the organization and physiology of the stress system, focusing on its interactions with other CNS centers and endocrine axes, and reviews the existing evidence linking stress to pathophysiologic mechanisms implicated in the development of stress-related diseases affecting the endocrine, metabolic, gastrointestinal and immune systems. For complete coverage of this and all related areas of Endocrinology, please visit our FREE web-book, www.endotext. org .
Massive Access for 5G and Beyond

Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. A typical application of massive access is the cellular Internet of Things (IoT). Different from conventional human-type communication, massive access aims at realizing efficient and reliable communications for a massive number of IoT devices. Hence, the main characteristics of massive access include low power, massive connectivity, and broad coverage, which require new concepts, theories, and paradigms for the design of next-generation cellular networks. This paper presents a comprehensive survey of massive access design for B5G wireless networks. Specifically, we provide a detailed review of massive access from the perspectives of theory, protocols, techniques, coverage, energy, and security. Furthermore, several future research directions and challenges are identified.
First Directions for Using Gamification to Motivate for Open Access

Most scientists are aware that, in addition to the traditional and subscription-based publication model, there is also the possibility of publishing their research in open access. Various surveys show that scientists are in favour of this new model. Nevertheless, the transition to open access has been very slow so far. In order to accelerate this process, we are looking for new opportunities to create incentives for researchers to deal with the topic of open access. In a field study with 28 participants the effects of the game design elements badge and progress bar on the motivation when working on an online quiz on the topic of open access are examined. In our study both game design elements provide a statistically significant increase in the number of questions answered compared to a control group. This suggests that gamification is useful to motivate for open access.
Autonomous planning based on spatial concepts to tidy up home environments with service robots

ABSTRACT Tidy-up tasks by service robots in home environments are challenging in robotics applications because they involve various interactions with the environment. In particular, robots are required not only to grasp, move, and release various home objects but also to plan the order and positions for placing the objects. In this paper, we propose a novel planning method that can efficiently estimate the order and positions of the objects to be tidied up by learning the parameters of a probabilistic generative model. The model allows a robot to learn the distributions of the co-occurrence probability of the objects and places to tidy up using the multimodal sensor information collected in a tidied environment. Additionally, we develop an autonomous robotic system to perform the tidy-up operation. We evaluate the effectiveness of the proposed method by an experimental simulation that reproduces the conditions of the Tidy Up Here task of the World Robot Summit 2018 international robotics competition. The simulation results show that the proposed method enables the robot to successively tidy up several objects and achieves the best task score among the considered baseline tidy-up methods. GRAPHICAL ABSTRACT
Pulsar timing array signals induced by black hole binaries in relativistic eccentric orbits

Individual supermassive black hole binaries in non-circular orbits are possible nanohertz gravitational wave sources for the rapidly maturing Pulsar Timing Array experiments. We develop an accurate and efficient approach to compute Pulsar Timing Array signals due to gravitational waves from inspiraling supermassive black hole binaries in relativistic eccentric orbits. Our approach employs a Keplerian-type parametric solution to model third post-Newtonian accurate precessing eccentric orbits while a novel semi-analytic prescription is provided to model the effects of quadrupolar order gravitational wave emission. These inputs lead to a semi-analytic prescription to model such signals, induced by non-spinning black hole binaries inspiralling along arbitrary eccentricity orbits. Additionally, we provide a fully analytic prescription to model Pulsar Timing Array signals from black hole binaries inspiraling along moderately eccentric orbits, influenced by Boetzelet al.[Phys. Rev. D 96,044011(2017)]. These approaches are being incorporated into Enterprise and TEMPO2 for searching the presence of such binaries in Pulsar Timing Array datasets.
The Great Markarian 421 Flare of 2010 February: Multiwavelength Variability and Correlation Studies

We report on variability and correlation studies using multiwavelength observations of the blazar Mrk 421 during the month of 2010 February, when an extraordinary flare reaching a level of ∼27 Crab Units above 1 TeV was measured in very high energy (VHE) γ-rays with the Very Energetic Radiation Imaging Telescope Array System (VERITAS) observatory. This is the highest flux state for Mrk 421 ever observed in VHE γ-rays. Data are analyzed from a coordinated campaign across multiple instruments, including VHE γ-ray (VERITAS, Major Atmospheric Gamma-ray Imaging Cherenkov), high-energy γ-ray (Fermi-LAT), X-ray (Swift, Rossi X-ray Timing Experiment, MAXI), optical (including the GASP-WEBT collaboration and polarization data), and radio (Metsähovi, Owens Valley Radio Observatory, University of Michigan Radio Astronomy Observatory). Light curves are produced spanning multiple days before and after the peak of the VHE flare, including over several flare “decline” epochs. The main flare statistics allow 2 minute time bins to be constructed in both the VHE and optical bands enabling a cross-correlation analysis that shows evidence for an optical lag of ∼25–55 minutes, the first time-lagged correlation between these bands reported on such short timescales. Limits on the Doppler factor (δ ≳ 33) and the size of the emission region ( ) are obtained from the fast variability observed by VERITAS during the main flare. Analysis of 10 minute binned VHE and X-ray data over the decline epochs shows an extraordinary range of behavior in the flux–flux relationship, from linear to quadratic to lack of correlation to anticorrelation. Taken together, these detailed observations of an unprecedented flare seen in Mrk 421 are difficult to explain with the classic single-zone synchrotron self-Compton model.
Impact of Data Quality on Deep Neural Network Training

It is well known that data is critical for training neural networks. Lot have been written about quantities of data required to train networks well. However, there is not much publications on how data quality effects convergence of such networks. There is dearth of information on what is considered good data ( for the task ). This empirical experimental study explores some impacts of data quality. Specific results are shown in the paper how simple changes can have impact on Mean Average Precision (mAP).
Vortices over Riemann surfaces and dominated splittings

Abstract We associate a flow $\phi $ with a solution of the vortex equations on a closed oriented Riemannian 2-manifold $(M,g)$ of negative Euler characteristic and investigate its properties. We show that $\phi $ always admits a dominated splitting and identify special cases in which $\phi $ is Anosov. In particular, starting from holomorphic differentials of fractional degree, we produce novel examples of Anosov flows on suitable roots of the unit tangent bundle of $(M,g)$ .
Alternating Tree Automata with Qualitative Semantics

We study alternating automata with qualitative semantics over infinite binary trees: Alternation means that two opposing players construct a decoration of the input tree called a run, and the qualitative semantics says that a run of the automaton is accepting if almost all branches of the run are accepting. In this article, we prove a positive and a negative result for the emptiness problem of alternating automata with qualitative semantics. The positive result is the decidability of the emptiness problem for the case of Büchi acceptance condition. An interesting aspect of our approach is that we do not extend the classical solution for solving the emptiness problem of alternating automata, which first constructs an equivalent non-deterministic automaton. Instead, we directly construct an emptiness game making use of imperfect information. The negative result is the undecidability of the emptiness problem for the case of co-Büchi acceptance condition. This result has two direct consequences: the undecidability of monadic second-order logic extended with the qualitative path-measure quantifier and the undecidability of the emptiness problem for alternating tree automata with non-zero semantics, a recently introduced probabilistic model of alternating tree automata.
Learning efficient structured dictionary for image classification

Abstract. Recent years have witnessed the success of dictionary learning (DL)-based approaches in the domain of pattern classification. We present an efficient structured dictionary learning (ESDL) method that takes both the diversity and label information of training samples into account. Specifically, ESDL introduces alternative training samples into the process of DL. To increase the discriminative capability of representation coefficients for classification, an ideal regularization term is incorporated into the objective function of ESDL. Moreover, in contrast with conventional DL approaches, which impose a computationally expensive ℓ1-norm constraint on the coefficient matrix, ESDL employs an ℓ2-norm regularization term. Experimental results on benchmark databases (including four face databases and one scene dataset) demonstrate that ESDL outperforms previous DL approaches. More importantly, ESDL can be applied in a wide range of pattern classification tasks.
Segmenting Unseen Industrial Components In A Heavy Clutter Using RGB-D Fusion And Synthetic Data

Segmentation of unseen industrial parts is essential for autonomous industrial systems. However, industrial components are texture-less, reflective, and often found in cluttered and unstructured environments with heavy occlusion, which makes it more challenging to deal with unseen objects. To tackle this problem, we present a synthetic data generation pipeline that randomizes textures via domain randomization to focus on the shape information. In addition, we propose an RGB-D Fusion Mask R-CNN with a confidence map estimator, which exploits reliable depth information in multiple feature levels. We transferred the trained model to real-world scenarios and evaluated its performance by making comparisons with baselines and ablation studies. We demonstrate that our methods, which use only synthetic data, could be effective solutions for unseen industrial components segmentation.
Towards Mixture Proportion Estimation without Irreducibility

\textit{Mixture proportion estimation} (MPE) is a fundamental problem of practical significance, where we are given data from only a \textit{mixture} and one of its two \textit{components} to identify the proportion of each component. All existing MPE methods that are distribution-independent explicitly or implicitly rely on the \textit{irreducible} assumption---the unobserved component is not a mixture containing the observable component. If this is not satisfied, those methods will lead to a critical estimation bias. In this paper, we propose \textit{Regrouping-MPE} that works without irreducible assumption: it builds a new irreducible MPE problem and solves the new problem. It is worthwhile to change the problem: we prove that if the assumption holds, our method will not affect anything; if the assumption does not hold, the bias from problem changing is less than the bias from violation of the irreducible assumption in the original problem. Experiments show that our method outperforms all state-of-the-art MPE methods on various real-world datasets.