nils-herrmann/clean_scirepeval huggingface dataset:
EBIC: an open source software for high-dimensional and big data biclustering analyses

In this paper we present the latest release of EBIC, a next-generation biclustering algorithm for mining genetic data. The major contribution of this paper is adding support for big data, making it possible to efficiently run large genomic data mining analyses. Additional enhancements include integration with R and Bioconductor and an option to remove influence of missing value on the final result. 
 EBIC was applied to datasets of different sizes, including a large DNA methylation dataset with 436,444 rows. For the largest dataset we observed over 6.6 fold speedup in computation time on a cluster of 8 GPUs compared to running the method on a single GPU. This proves high scalability of the algorithm. 
Availability: The latest version of EBIC could be downloaded from this http URL . Installation and usage instructions are also available online.
Bottom-up Pose Estimation of Multiple Person with Bounding Box Constraint

In this work, we propose a new method for multi-person pose estimation which combines the traditional bottom-up and the top-down methods. Specifically, we perform the network feed-forwarding in a bottom-up manner, and then parse the poses with bounding box constraints in a top-down manner. In contrast to the previous top-down methods, our method is robust to bounding box shift and tightness. We extract features from an original image by a residual network and train the network to learn both the confidence maps of joints and the connection relationships between joints. During testing, the predicted confidence maps, the connection relationships and the bounding boxes are used to parse the poses of all persons. The experimental results showed that our method learns more accurate human poses especially in challenging situations and gains better time performance, compared with the bottom-up and the top-down methods.
Deriving Information Acquisition Criteria For Sequentially Inferring The Expected Value Of A Black-Box Function

Acquiring information about noisy expensive black-box functions (computer simulations or physical experiments) is a tremendously challenging problem. Finite computational and financial resources restrict the application of traditional methods for design of experiments. The problem is surmounted by hurdles such as numerical errors and stochastic approximations errors, when the quantity of interest (QoI) in a problem depends on an expensive black-box function. Bayesian optimal design of experiments has been reasonably successful in guiding the designer towards the QoI for problems of the above kind. This is usually achieved by sequentially querying the function at designs selected by an infill-sampling criterion compatible with utility theory. However, most current methods are semantically designed to work only on optimizing or inferring the black-box function itself. We aim to construct a heuristic which can unequivocally deal with the above problems irrespective of the QoI. This paper applies the above mentioned heuristic to infer a specific QoI, namely the expectation (expected value) of the function. The Kullback Leibler (KL) divergence is fairly conspicuous among techniques that used to quantify information gain. In this paper, we derive an expression for the expected KL divergence to sequentially infer our QoI. The analytical tractability provided by the Karhunene Loeve expansion around the Gaussian process (GP) representation of the black-box function allows the circumvention around numerical issues associated with sample averaging. The proposed methodology can be extended to any QoI, with reasonable assumptions. The proposed method is verified and validated on two synthetic functions with varying levels of complexity. We demonstrate the methodology on a steel wire manufacturing problem.
Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks

Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicenter of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent neural networks combined with a mixture density network output layer. To consolidate the multi-modal nature of the output probability distribution, we introduce a clustering algorithm that extracts the set of possible paths that exist in the prediction output and ranks them according to probability. To verify the method’s performance and generalizability, we present a real-world dataset that consists of over 23 000 vehicles traversing five different intersections, collected using a vehicle-mounted lidar-based tracking system. An array of metrics is used to demonstrate the performance of the model against several baselines.
A Data-Driven Prior on Facet Orientation for Semantic Mesh Labeling

Mesh labeling is the key problem of classifying the facets of a 3D mesh with a label among a set of possible ones. State-of-the-art methods model mesh labeling as a Markov Random Field over the facets. These algorithms map image segmentations to the mesh by minimizing an energy function that comprises a data term, a smoothness terms, and class-specific priors. The latter favor a labeling with respect to another depending on the orientation of the facet normals. In this paper we propose a novel energy term that acts as a prior, but does not require any prior knowledge about the scene nor scene-specific relationship among classes. It bootstraps from a coarse mapping of the 2D segmentations on the mesh, and it favors the facets to be labeled according to the statistics of the mesh normals in their neighborhood. We tested our approach against five different datasets and, even if we do not inject prior knowledge, our method adapts to the data and overcomes the state-of-the-art.
Instance Segmentation by Deep Coloring

We propose a new and, arguably, a very simple reduction of instance segmentation to semantic segmentation. This reduction allows to train feed-forward non-recurrent deep instance segmentation systems in an end-to-end fashion using architectures that have been proposed for semantic segmentation. Our approach proceeds by introducing a fixed number of labels (colors) and then dynamically assigning object instances to those labels during training (coloring). A standard semantic segmentation objective is then used to train a network that can color previously unseen images. At test time, individual object instances can be recovered from the output of the trained convolutional network using simple connected component analysis. In the experimental validation, the coloring approach is shown to be capable of solving diverse instance segmentation tasks arising in autonomous driving (the Cityscapes benchmark), plant phenotyping (the CVPPP leaf segmentation challenge), and high-throughput microscopy image analysis. 
The source code is publicly available: this https URL.
General Context-Aware Data Matching and Merging Framework

Due to numerous public information sources and services, many methods to combine heterogeneous data were proposed recently. However, general end-to-end solutions are still rare, especially systems taking into account different context dimensions. Therefore, the techniques often prove insufficient or are limited to a certain domain. In this paper we briefly review and rigorously evaluate a general framework for data matching and merging. The framework employs collective entity resolution and redundancy elimination using three dimensions of context types. In order to achieve domain independent results, data is enriched with semantics and trust. However, the main contribution of the paper is evaluation on five public domain-incompatible datasets. Furthermore, we introduce additional attribute, relationship, semantic and trust metrics, which allow complete framework management. Besides overall results improvement within the framework, metrics could be of independent interest.
Towards Optimal Power Control via Ensembling Deep Neural Networks

A deep neural network (DNN) based power control method that aims at solving the non-convex optimization problem of maximizing the sum rate of a fading multi-user interference channel is proposed. Towards this end, we first present PCNet, which is a multi-layer fully connected neural network that is specifically designed for the power control problem. A key challenge in training a DNN for the power control problem is the lack of ground truth, i.e., the optimal power allocation is unknown. To address this issue, PCNet leverages the unsupervised learning strategy and directly maximizes the sum rate in the training phase. We then present PCNet+, which enhances the generalization capacity of PCNet by incorporating noise power as an input to the network. Observing that a single PCNet(+) does not universally outperform the existing solutions, we further propose ePCNet(+), a network ensemble with multiple PCNets(+) trained independently. Simulation results show that for the standard symmetric $K$ -user Gaussian interference channel, the proposed methods can outperform state-of-the-art power control solutions under a variety of system configurations. Furthermore, the performance improvement of ePCNet comes with a reduced computational complexity.
A Tensor Factorization Method for 3-D Super Resolution With Application to Dental CT

Available super-resolution techniques for 3-D images are either computationally inefficient prior-knowledge-based iterative techniques or deep learning methods which require a large database of known low-resolution and high-resolution image pairs. A recently introduced tensor-factorization-based approach offers a fast solution without the use of known image pairs or strict prior assumptions. In this paper, this factorization framework is investigated for single image resolution enhancement with an offline estimate of the system point spread function. The technique is applied to 3-D cone beam computed tomography for dental image resolution enhancement. To demonstrate the efficiency of our method, it is compared to a recent state-of-the-art iterative technique using low-rank and total variation regularizations. In contrast to this comparative technique, the proposed reconstruction technique gives a 2-order-of-magnitude improvement in running time—2 min compared to 2 h for a dental volume of $282\times 266\times392$ voxels. Furthermore, it also offers slightly improved quantitative results (peak signal-to-noise ratio and segmentation quality). Another advantage of the presented technique is the low number of hyperparameters. As demonstrated in this paper, the framework is not sensitive to small changes in its parameters, proposing an ease of use.
AlphaGAN: Generative adversarial networks for natural image matting

We present the first generative adversarial network (GAN) for natural image matting. Our novel generator network is trained to predict visually appealing alphas with the addition of the adversarial loss from the discriminator that is trained to classify well-composited images. Further, we improve existing encoder-decoder architectures to better deal with the spatial localization issues inherited in convolutional neural networks (CNN) by using dilated convolutions to capture global context information without downscaling feature maps and losing spatial information. We present state-of-the-art results on the alphamatting online benchmark for the gradient error and give comparable results in others. Our method is particularly well suited for fine structures like hair, which is of great importance in practical matting applications, e.g. in film/TV production.
A Unified Approximation Framework for Deep Neural Networks

Deep neural networks (DNNs) have achieved significant success in a variety of real world applications. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various compression and acceleration techniques have been investigated, among which low-rank filters and sparse filters are heavily studied. In this paper we propose a unified framework to compress the convolutional neural networks by combining these two strategies, while taking the nonlinear activation into consideration. The filer of a layer is approximated by the sum of a sparse component and a low-rank component, both of which are in favor of model compression. Especially, we constrain the sparse component to be structured sparse which facilitates acceleration. The performance of the network is retained by minimizing the reconstruction error of the feature maps after activation of each layer, using the alternating direction method of multipliers (ADMM). The experimental results show that our proposed approach can compress VGG-16 and AlexNet by over 4X. In addition, 2.2X and 1.1X speedup are achieved on VGG-16 and AlexNet, respectively, at a cost of less increase on error rate.
From handcrafted to deep local invariant features

This paper presents an overview of the evolution of local features from handcrafted to deeplearning-based methods, followed by a discussion of several benchmarks and papers evaluating such local features. Our investigations are motivated by 3D reconstruction problems, where the precise location of the features is important. As we describe these methods, we highlight and explain the challenges of feature extraction and potential ways to overcome them. We first present handcrafted methods, followed by methods based on classical machine learning and finally we discuss methods based on deep-learning. This largely chronologically-ordered presentation will help the reader to fully understand the topic of image and region description in order to make best use of it in modern computer vision applications. In particular, understanding handcrafted methods and their motivation can help to understand modern approaches and how machine learning is used to improve the results. We also provide references to most of the relevant literature and code.
Evaluating and Understanding the Robustness of Adversarial Logit Pairing

We evaluate the robustness of Adversarial Logit Pairing, a recently proposed defense against adversarial examples. We find that a network trained with Adversarial Logit Pairing achieves 0.6% accuracy in the threat model in which the defense is considered. We provide a brief overview of the defense and the threat models/claims considered, as well as a discussion of the methodology and results of our attack, which may offer insights into the reasons underlying the vulnerability of ALP to adversarial attack.
Auto-Encoding Variational Neural Machine Translation

We present a deep generative model of bilingual sentence pairs for machine translation. The model generates source and target sentences jointly from a shared latent representation and is parameterised by neural networks. We perform efficient training using amortised variational inference and reparameterised gradients. Additionally, we discuss the statistical implications of joint modelling and propose an efficient approximation to maximum a posteriori decoding for fast test-time predictions. We demonstrate the effectiveness of our model in three machine translation scenarios: in-domain training, mixed-domain training, and learning from a mix of gold-standard and synthetic data. Our experiments show consistently that our joint formulation outperforms conditional modelling (i.e. standard neural machine translation) in all such scenarios.
Indexing Electron Backscatter Diffraction Patterns with a Refined Template Matching Approach

Electron backscatter diffraction (EBSD) is a well-established method of characterisation for crystalline materials. This technique can rapidly acquire and index diffraction patterns to provide phase and orientation information about the crystals on the material surface. The conventional analysis method uses signal processing based on a Hough/Radon transform to index each diffraction pattern. This method is limited to the analysis of simple geometric features and ignores subtle characteristics of diffraction patterns, such as variations in relative band intensities. A second method, developed to address the shortcomings of the Hough/Radon transform, is based on template matching of a test experimental pattern with a large library of potential patterns. In the present work, the template matching approach has been refined with a new cross correlation function that allows for a smaller library and enables a dramatic speed up in pattern indexing. Refinement of the indexed orientation is performed with a follow-up step to allow for small alterations to the best match from the library search. The orientation is further refined with rapid measurement of misorientation using whole pattern matching. The refined template matching approach is shown to be comparable in accuracy, precision and sensitivity to the Hough based method, even exceeding it in some cases, via the use of simulations and experimental data collected from a silicon single crystal and a deformed {\alpha}-iron sample. The drastic speed up and pattern refinement approaches should increase the widespread utility of pattern matching approaches.
Learning dexterous in-hand manipulation

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies that can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system such as friction coefficients and an object’s appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results:
Mixed‐effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: Patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with health care data, such assumptions unlikely hold. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user‐friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome.
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

This work demonstrates the potential of deep reinforcement learning techniques for transmit power control in wireless networks. Existing techniques typically find near-optimal power allocations by solving a challenging optimization problem. Most of these algorithms are not scalable to large networks in real-world scenarios because of their computational complexity and instantaneous cross-cell channel state information (CSI) requirement. In this paper, a distributively executed dynamic power allocation scheme is developed based on model-free deep reinforcement learning. Each transmitter collects CSI and quality of service (QoS) information from several neighbors and adapts its own transmit power accordingly. The objective is to maximize a weighted sum-rate utility function, which can be particularized to achieve maximum sum-rate or proportionally fair scheduling. Both random variations and delays in the CSI are inherently addressed using deep ${Q}$ -learning. For a typical network architecture, the proposed algorithm is shown to achieve near-optimal power allocation in real time based on delayed CSI measurements available to the agents. The proposed scheme is especially suitable for practical scenarios where the system model is inaccurate and CSI delay is non-negligible.
The Spectral Content of SDO/AIA 1600 and 1700 Å Filters from Flare and Plage Observations

The strong enhancement of the ultraviolet emission during solar flares is usually taken as an indication of plasma heating in the lower solar atmosphere caused by the deposition of the energy released during these events. Images taken with broadband ultraviolet filters by the Transition Region and Coronal Explorer and Atmospheric Imaging Assembly (AIA; 1600 and 1700 Å) have revealed the morphology and evolution of flare ribbons in great detail. However, the spectral content of these images is still largely unknown. Without knowledge of the spectral contribution to these UV filters, the use of these rich imaging data sets is severely limited. Aiming to solve this issue, we estimate the spectral contributions of the AIA UV flare and plage images using high-resolution spectra in the range 1300–1900 Å from the Skylab NRL SO82B spectrograph. We find that the flare excess emission in AIA 1600 Å is dominated by the C iv 1550 Å doublet (26%), Si i continua (20%), with smaller contributions from many other chromospheric lines such as C i 1561 and 1656 Å multiplets, He ii 1640 Å, and Si ii 1526 and 1533 Å. For the AIA 1700 Å band, the C i 1656 Å multiplet is the main contributor (38%), followed by He ii 1640 (17%), and accompanied by a multitude of other, weaker chromospheric lines, with minimal contribution from the continuum. Our results can be generalized to state that the AIA UV flare excess emission is of chromospheric origin, while plage emission is dominated by photospheric continuum emission in both channels.
Draining the swampland

We discuss some implications of the recently suggested Swampland conjecture $\frac{|\nabla V|}{V}\gtrsim c\sim 1$, together with a previous one $\Delta \phi \lesssim 1$. We list some implications for particle phenomenology and the Early Universe. The most intriguing implication of the conjecture could be a significant shift in allowed inflationary models, if not ruling out slow-roll (single field) inflation altogether. The tension of inflation and the conjecture does not only regard the amplitude of the tensor spectrum, but also its tilt, as $c\gtrsim 1$ implies both a yet unobserved tensor to scalar ratio, and an enhancement of the observed scalar power spectrum on large scales in discord with current data that favors a suppression on these scales. Scalar fields are abundant in theories of quantum gravity. Considering a second scalar field, its dynamics are dictated by the relation between its mass, $m$, and the Hubble parameter, $H$, at different epochs in the history of the Universe. This scalar field, a drainon, fulfills the conjecture draining up the swampland. For Inflation, this drainon requires a modest hierarchy compared to the inflaton. For the rest of the thermal history of the Universe, the drainon can be a coherently oscillating scalar field strengthening the case of Dark Matter candidates of that sort.