Miracle-dz/UniFAR-FTUs huggingface dataset:
Edinburgh Research Explorer The mRNA m6A reader YTHDF2 supresses pro-inflammatory pathways and sustains hematopoietic stem cell function

, The mRNA N 6 -methyladenosine (m 6 A) modification has emerged as an essential regulator of normal and malignant hematopoiesis. Inactivation of the m 6 A mRNA reader YTHDF2, which recognizes m 6 A-modified transcripts to promote m 6 A-mRNA degradation, results in hematopoietic stem cell (HSC) expansion and compromises acute myeloid leukemia. Here we investigate the long-term impact of YTHDF2 deletion on HSC maintenance and multilineage hematopoiesis. We demonstrate that Ythdf2 -deficient HSCs from young mice fail upon serial transplantation, display increased abundance of multiple m 6 A-modified inflammation-related transcripts, and chronically activate proinflammatory pathways. Consistent with the detrimental consequences of chronic activation of inflammatory pathways in HSCs, hematopoiesis-specific Ythdf2 deficiency results in a progressive myeloid bias, loss of lymphoid potential, HSC expansion, and failure of aged Ythdf2 -deficient HSCs to reconstitute multilineage hematopoiesis. Experimentally induced inflammation increases YTHDF2 expression, and YTHDF2 is required to protect HSCs from this insult. Thus, our study positions YTHDF2 as a repressor of inflammatory pathways in HSCs
An Alternative Study about the Geometry and the First Law of Thermodynamics for AdS Lovelock Gravity, Using the Definition of Conserved Charges

In this work, we introduce an extension of the study of the first law of thermodynamics of black holes based on the geometry of the extended phase space for AdS Lovelock gravities, which includes changes in scales. As expected, the result obtained coincides with the previously known four-dimensional case. For higher dimensions, the result is the rise of two new contributions to the first law of thermodynamics. The first term corresponds to corrections of the usual definition of thermodynamic volumes at the horizon due to the presence of the higher curvature terms. The second term arises in odd dimensions, comes from the asymptotic region, and corresponds to a scale transformation of the form ∝δ^ln(l/ℓ), with l the AdS radius and ℓ a parameter. A particularly interesting case corresponds to the Chern Simons gravity where the change scale does not generate a contribution at the asymptotic region, likely due to the Chern Simons AdS local symmetry.
A derivation of variational message passing (VMP) for latent Dirichlet allocation (LDA)

Latent Dirichlet Allocation (LDA) is a probabilistic model used to uncover latent topics in a corpus of documents. Inference is often performed using variational Bayes (VB) algorithms, which calculate a lower bound to the posterior distribution over the parameters. Deriving the variational update equations for new models requires considerable manual effort; variational message passing (VMP) has emerged as a "black-box" tool to expedite the process of variational inference. But applying VMP in practice still presents subtle challenges, and the existing literature does not contain the steps that are necessary to implement VMP for the standard smoothed LDA model, nor are available black-box probabilistic graphical modelling software able to do the word-topic updates necessary to implement LDA. In this paper, we therefore present a detailed derivation of the VMP update equations for LDA. We see this as a ﬁrst step to enabling other researchers to calculate the VMP updates for similar graphical models.
Dropouts of Fully Stripped Ions in the Solar Wind: A Diagnostic for Wave Heating versus Reconnection

The SWICS instrument on board the ACE satellite has detected frequent intervals in the slow solar wind and interplanetary coronal mass ejections in which C6+ and other fully stripped ions are strongly depleted, though the ionization states of elements such as Si and Fe indicate that those ions should be present. It has been suggested that these “outlier” or “dropout” events can be explained by the resonant cyclotron heating process, because these ions all have the same cyclotron frequency as He2+. We investigate the region in the corona where these outlier events form. It must be above the ionization freeze-in height and the transition to collisionless plasma conditions, but low enough that the wind still feels the effects of solar gravity. We suggest that the dropout events correspond to relatively dense blobs of gas in which the heating is reduced because local variations in the Alfvén speed change the reflection of Alfvén waves and the turbulent cascade. As a result, the wave power at the cyclotron frequency of the fully stripped ions is absorbed by He2+ and may not be able to heat the other fully stripped ions enough to overcome solar gravity. If this picture is borne out, it may help to discriminate between resonant cyclotron heating and stochastic heating models of the solar wind.
Explicit Time-Optimal Acceleration-Constrained Speed Profiles for Planar Paths with Monotone Curvature

It is shown how to construct minimum-time speed proﬁles for planar paths with smooth strictly-monotonic signed curvature, subject to constraints on velocity, normal acceleration and tangential acceleration. The construction is explicit and exact, and global optimality is rigorously established from ﬁrst principles under mild regularity conditions on the path. Free, ﬁxed, and inequality-constrained boundary speeds are all accommodated. Numerical implementation is straightforward.
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation

Wasserstein-Fisher-Rao (WFR) distance is a family of metrics to gauge the discrepancy of two Radon measures, which takes into account both transportation and weight change. Spherical WFR distance is a projected version of WFR distance for probability measures so that the space of Radon measures equipped with WFR can be viewed as metric cone over the space of probability measures with spherical WFR. Compared to the case for Wasserstein distance, the understanding of geodesics under the spherical WFR is less clear and still an ongoing research focus. In this paper, we develop a deep learning framework to compute the geodesics under the spherical WFR metric, and the learned geodesics can be adopted to generate weighted samples. Our approach is based on a Benamou-Brenier type dynamic formulation for spherical WFR. To overcome the difficulty in enforcing the boundary constraint brought by the weight change, a Kullback-Leibler (KL) divergence term based on the inverse map is introduced into the cost function. Moreover, a new regularization term using the particle velocity is introduced as a substitute for the Hamilton-Jacobi equation for the potential in dynamic formula. When used for sample generation, our framework can be beneficial for applications with given weighted samples, especially in the Bayesian inference, compared to sample generation with previous flow models.
Demystifying data evaluation in the measurement of periodic structures

Periodic structures are often found in various areas of nanoscience and nanotechnology with many of them being used for metrological purposes either to calibrate instruments, or forming the basis of measuring devices such as encoders. Evaluating the period of one or two-dimensional periodic structures from topography measurements, e.g. performed using scanning probe microscopy, can be achieved using different methodologies with many grating evaluation methods having been proposed in the past and applied to a handful of examples. The optimum methodology for determining the grating period/pitch is not immediately obvious. This paper reports the results of extensive large-scale simulations and analysis to evaluate the performance of both direct and Fourier space data processing methods. Many thousands of simulations have been performed on a variety of different gratings under different measurement conditions and including the simulation of defects encountered in real life situations. The paper concludes with a summary of the merits and disadvantages of the methods together with practical recommendations for the measurements of periodic structures and for developing algorithms for processing them.
Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback

We study the online inﬂuence maximization (OIM) problem in social networks, where the learner repeatedly chooses seed nodes to generate cascades, observes the cascade feedback, and gradually learns the best seeds that generate the largest cascade in multiple rounds. In the demand of the real world, we work with node-level feedback instead of the common edge-level feedback in the literature. The edge-level feedback reveals all edges that pass through information in a cascade, whereas the node-level feedback only reveals the activated nodes with timestamps. The node-level feedback is arguably more realistic since in practice it is relatively easy to ob- serve who is inﬂuenced but very difﬁcult to observe from which relationship (edge) the inﬂuence comes. Previously, there is a nearly optimal ˜ O ( √ T ) -regret algorithm for OIM problem under the linear threshold (LT) diffusion model with node-level feedback. It remains unknown whether the same algorithm exists for the independent cascade (IC) diffusion model. In this paper, we resolve this open problem by presenting an ˜ O ( √ T ) -regret algorithm for OIM problem under the IC model with node-level feedback.
A Globally Convergent Gradient-based Bilevel Hyperparameter Optimization Method

Hyperparameter optimization in machine learning is often achieved using naive techniques that only lead to an approximate set of hyperparameters. Although techniques such as Bayesian optimization perform an intelligent search on a given domain of hyperparameters, it does not guarantee an optimal solution. A major drawback of most of these approaches is an exponential increase of their search domain with number of hyperparameters, increasing the computational cost and making the approaches slow. The hyperparameter optimization problem is inherently a bilevel optimization task, and some studies have attempted bilevel solution methodologies for solving this problem. However, these studies assume a unique set of model weights that minimize the training loss, which is generally violated by deep learning architectures. This paper discusses a gradient-based bilevel method addressing these drawbacks for solving the hyperparameter optimization problem. The proposed method can handle continuous hyperparameters for which we have chosen the regularization hyperparameter in our experiments. The method guarantees convergence to the set of optimal hyperparameters that this study has theoretically proven. The idea is based on approximating the lower-level optimal value function using Gaussian process regression. As a result, the bilevel problem is reduced to a single level constrained optimization task that is solved using the augmented Lagrangian method. We have performed an extensive computational study on the MNIST and CIFAR-10 datasets on multi-layer perceptron and LeNet architectures that confirms the efficiency of the proposed method. A comparative study against grid search, random search, Bayesian optimization, and HyberBand method on various hyperparameter problems shows that the proposed algorithm converges with lower computation and leads to models that generalize better on the testing set.
Elly: A Real-Time Failure Recovery and Data Collection System for Robotic Manipulation

Even the most robust autonomous behaviors can fail. The goal of this research is to both recover and collect data from failures, during autonomous task execution, so they can be prevented in the future. We propose haptic intervention for real-time failure recovery and data collection. Elly is a system that allows for seamless transitions between autonomous robot behaviors and human intervention while collecting sensory information from the human's recovery strategy. The system and our design choices were experimentally validated on a single arm task -- installing a lightbulb in a socket -- and a bimanual task -- screwing a cap on a bottle -- using two 7-DOF manipulators equipped 4-finger grippers. In these examples, Elly achieved over 80% task completion during a total of 40 runs.
UAS Navigation in the Real World Using Visual Observation

This paper presents a novel end-to-end Unmanned Aerial System (UAS) navigation approach for long-range visual navigation in the real world. Inspired by dual-process visual navigation system of human's instinct: environment understanding and landmark recognition, we formulate the UAS navigation task into two same phases. Our system combines the reinforce-ment learning (RL) and image matching approaches. First, the agent learns the navigation policy using RL in the specified environment. To achieve this, we design an interactive UASNAV environment for the training process. Once the agent learns the navigation policy, which means ‘familiarized themselves with the environment’, we let the UAS fly in the real world to recognize the landmarks using image matching method and take action according to the learned policy. During the navigation process, the UAS is embedded with single camera as the only visual sensor. We demonstrate that the UAS can learn navigating to the destination hundreds meters away from the starting point with the shortest path in the real world scenario.
Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation

Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, overparameterization poses fundamental challenges to the cost-sensitive modeling of deep neural networks (DNNs). The ability of a DNN to fully interpolate a training data set can render a DNN, evaluated purely on the training set, ineffective in distinguishing a cost-sensitive solution from its overall accuracy maximization counterpart. This necessitates rethinking cost-sensitive classification in DNNs. To address this challenge, this paper proposes a cost-sensitive adversarial data augmentation (CSADA) framework to make overparameterized models cost sensitive. The overarching idea is to generate targeted adversarial examples that push the decision boundary in cost-aware directions. These targeted adversarial samples are generated by maximizing the probability of critical misclassifications and used to train a model with more conservative decisions on costly pairs. Experiments on well-known data sets and a pharmacy medication image (PMI) data set, made publicly available, show that our method can effectively minimize the overall cost and reduce critical errors while achieving comparable overall accuracy. Funding: Research reported in this publication was supported by the National Library of medicine of the National Institutes of Health in the United States under award number R01LM013624. Data Ethics & Reproducibility Note: This paper abides by data ethics requirements. Data used are publicly available online at https://deepblue.lib. umich.edu/data/concern/data_sets/6d56zw997 . Codes to replicate the results of this paper are available at https://doi.org/10.24433/CO.2139841. v1 .
Learning to Construct 3D Building Wireframes from 3D Line Clouds

Line clouds, though under-investigated in the previous work, potentially encode more compact structural information of buildings than point clouds extracted from multi-view images. In this work, we propose the first network to process line clouds for building wireframe abstraction. The network takes a line cloud as input , i.e., a nonstructural and unordered set of 3D line segments extracted from multi-view images, and outputs a 3D wireframe of the underlying building, which consists of a sparse set of 3D junctions connected by line segments. We observe that a line patch, i.e., a group of neighboring line segments, encodes sufficient contour information to predict the existence and even the 3D position of a potential junction, as well as the likelihood of connectivity between two query junctions. We therefore introduce a two-layer Line-Patch Transformer to extract junctions and connectivities from sampled line patches to form a 3D building wireframe model. We also introduce a synthetic dataset of multi-view images with ground-truth 3D wireframe. We extensively justify that our reconstructed 3D wireframe models significantly improve upon multiple baseline building reconstruction methods. The code and data can be found at https://github.com/Luo1Cheng/LC2WF.
Digital Audio Tampering Detection Based on ENF Spatio-temporal Features Representation Learning

Most digital audio tampering detection methods based on electrical network frequency (ENF) only utilize the static spatial information of ENF, ignoring the variation of ENF in time series, which limit the ability of ENF feature representation and reduce the accuracy of tampering detection. This paper proposes a new method for digital audio tampering detection based on ENF spatio-temporal features representation learning. A parallel spatio-temporal network model is constructed using CNN and BiLSTM, which deeply extracts ENF spatial feature information and ENF temporal feature information to enhance the feature representation capability to improve the tampering detection accuracy. In order to extract the spatial and temporal features of the ENF, this paper firstly uses digital audio high-precision Discrete Fourier Transform analysis to extract the phase sequences of the ENF. The unequal phase series is divided into frames by adaptive frame shifting to obtain feature matrices of the same size to represent the spatial features of the ENF. At the same time, the phase sequences are divided into frames based on ENF time changes information to represent the temporal features of the ENF. Then deep spatial and temporal features are further extracted using CNN and BiLSTM respectively, and an attention mechanism is used to adaptively assign weights to the deep spatial and temporal features to obtain spatio-temporal features with stronger representation capability. Finally, the deep neural network is used to determine whether the audio has been tampered with. The experimental results show that the proposed method improves the accuracy by 2.12%-7.12% compared with state-of-the-art methods under the public database Carioca, New Spanish.
Nonparametric Gaussian Mixture Models for the Multi-Armed Bandit

We here adopt Bayesian nonparametric mixture models to extend multi-armed bandits in general, and Thompson sampling in particular, to scenarios where there is reward model uncertainty. In the stochastic multi-armed bandit, the reward for the played arm is generated from an unknown distribution. Reward uncertainty, i.e., the lack of knowledge about the reward-generating distribution, induces the exploration-exploitation trade-off: a bandit agent needs to simultaneously learn the properties of the reward distribution and sequentially decide which action to take next. In this work, we extend Thompson sampling to scenarios where there is reward model uncertainty by adopting Bayesian nonparametric Gaussian mixture models for flexible reward density estimation. The proposed Bayesian nonparametric mixture model Thompson sampling sequentially learns the reward model that best approximates the true, yet unknown, per-arm reward distribution, achieving successful regret performance. We derive, based on a novel posterior convergence based analysis, an asymptotic regret bound for the proposed method. In addition, we empirically evaluate its performance in diverse and previously elusive bandit environments, e.g., with rewards not in the exponential family, subject to outliers, and with different per-arm reward distributions. We show that the proposed Bayesian nonparametric Thompson sampling outperforms, both in averaged cumulative regret and in regret volatility, state-of-the-art alternatives. The proposed method is valuable in the presence of bandit reward model uncertainty, as it avoids stringent case-by-case model design choices, yet provides important regret savings.
Towards Unsupervised HPO for Outlier Detection

Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without any labels ? In this work, we address this challenging hyperparameter optimization for unsupervised OD problem, and propose the ﬁrst systematic approach called HPOD that is based on meta-learning. HPOD capitalizes on the prior performance of a large collection of HPs on existing OD benchmark datasets, and transfers this information to enable HP evaluation on a new dataset without labels. Moreover, HPOD adapts (originally supervised) sequential model-based optimization to identify promising HPs efﬁciently. Extensive experiments show that HPOD works with both deep (e.g., Ro-bust AutoEncoder) and shallow (e.g., Local Outlier Factor (LOF) and Isolation Forest (iForest)) OD algorithms on both discrete and continuous HP spaces, and outperforms a wide range of baselines with on average 58% and 66% performance improvement over the default HPs of LOF and iForest.
Super-resolution wavefront reconstruction

Super-Resolution (SR) is a technique that seeks to upscale the resolution of a set of measured signals. SR retrieves higher-frequency signal content by combining multiple lower resolution sampled data sets. SR is well known both in the temporal and spatial domains. It is widely used in imaging to reduce aliasing and enhance the resolution of coarsely sampled images.This paper applies the SR technique to the bi-dimensional wavefront reconstruction. In particular, we show how SR is intrinsically suited for tomographic multi WaveFront Sensor (WFS) AO systems revealing many of its advantages with minimal design effort. This paper provides a direct space and Fourier-optics description of the wavefront sensing operation and demonstrate how SR can be exploited through signal reconstruction, especially in the framework of Periodic Nonuniform Sampling. Both meta uniform and nonuniform sampling schemes are investigated. Then, the SR bi-dimensional model for a Shack Hartmann (SH) WFS is provided and the characteristics of the sensitivity function are analyzed. The SR concept is finally validated with numerical simulations of representative multi WFS SH AO systems. Our results show that combining several WFS samples in a SR framework grants access to a larger number of modes than the native one offered by a single WFS and that despite the fixed sub-aperture size across samples. Furthermore, we show that the associated noise propagation is not degraded under SR. Finally, the concept is extended to the signal produced by single Pyramid WFS. In conclusion, SR applied to wavefront reconstruction offers a new parameter space to explore as it decouples the size of the subaperture from the desired wavefront sampling resolution. By cutting short with old assumptions, new, more flexible and better performing AO designs become now possible.