nils-herrmann/clean_scirepeval huggingface dataset:
Deep Active Learning for Axon-Myelin Segmentation on Histology Data

Semantic segmentation is a crucial task in biomedical image processing, which recent breakthroughs in deep learning have allowed to improve. However, deep learning methods in general are not yet widely used in practice since they require large amount of data for training complex models. This is particularly challenging for biomedical images, because data and ground truths are a scarce resource. Annotation efforts for biomedical images come with a real cost, since experts have to manually label images at pixel-level on samples usually containing many instances of the target anatomy (e.g. in histology samples: neurons, astrocytes, mitochondria, etc.). In this paper we provide a framework for Deep Active Learning applied to a real-world scenario. Our framework relies on the U-Net architecture and overall uncertainty measure to suggest which sample to annotate. It takes advantage of the uncertainty measure obtained by taking Monte Carlo samples while using Dropout regularization scheme. Experiments were done on spinal cord and brain microscopic histology samples to perform a myelin segmentation task. Two realistic small datasets of 14 and 24 images were used, from different acquisition settings (Serial Block-Face Electron Microscopy and Transmitting Electron Microscopy) and showed that our method reached a maximum Dice value after adding 3 uncertainty-selected samples to the initial training set, versus 15 randomly-selected samples, thereby significantly reducing the annotation effort. We focused on a plausible scenario and showed evidence that this straightforward implementation achieves a high segmentation performance with very few labelled samples. We believe our framework may benefit any biomedical researcher willing to obtain fast and accurate image segmentation on their own dataset. The code is freely available at this https URL.
Weizmann Lectures on the Numerical Conformal Bootstrap

These lectures were given at the Weizmann Institute in the spring of 2019. They are intended to familiarize students with the nuts and bolts of the numerical bootstrap as efficiently as possible. After a brief review of the basics of conformal field theory in $d>2$ spacetime dimensions, we discuss how to compute conformal blocks, formulate the crossing equations as a semi-definite programming problem, solve this problem using SDPB on a personal computer, and interpret the results. We include worked examples for all steps, including bounds for 3d CFTs with $\mathbb{Z}_2$ or $O(N)$ global symmetries. Each lecture includes a problem set, which culminate in a precise computation of the 3d Ising model critical exponents using the mixed correlator $\mathbb{Z}_2$ bootstrap. A Mathematica file is included that transforms crossing equations into the proper input form for SDPB.
On Ramsey properties, function spaces, and topological games

An open question of Gruenhage asks if all strategically selectively separable
 spaces are Markov selectively separable, a game-theoretic statement known to
 hold for countable spaces. As a corollary of a result by Berner and Juh?sz,
 we note that the ?strong? version of this statement, where the second player
 is restricted to selecting single points rather than finite subsets, holds
 for all T3 spaces without isolated points. Continuing this investigation, we
 also consider games related to selective sequential separability, and
 demonstrate results analogous to those for selective separability. In
 particular, strong selective sequential separability in the presence of the
 Ramsey property may be reduced to a weaker condition on a countable
 sequentially dense subset. Additionally, ?- and ?-covering properties on X
 are shown to be equivalent to corresponding sequential properties on Cp(X).
 A strengthening of the Ramsey property is also introduced, which is still
 equivalent to ?2 and ?4 in the context of Cp(X).
Amplifying Rényi Differential Privacy via Shuffling

Differential privacy is a useful tool to build machine learning models which do not release too much information about the training data. We study the Renyi differential privacy of stochastic gradient descent when each training example is sampled without replacement (also known as cyclic SGD). Cyclic SGD is typically faster than traditional SGD and is the algorithm of choice in large-scale implementations. We recover privacy guarantees for cyclic SGD which are competitive with those known for sampling with replacement. Our proof techniques make no assumptions on the model or on the data and are hence widely applicable.
Disease classification of macular Optical Coherence Tomography scans using deep learning software: validation on independent, multi-centre data

To evaluate Pegasus optical coherence tomography (OCT), a clinical decision support software for the identification of features of retinal disease from macula OCT scans, across heterogenous populations involving varying patient demographics, device manufacturers, acquisition sites, and operators.



Five thousand five hundred and eighty-eight normal and anomalous macular OCT volumes (162,721 B-scans), acquired at independent centers in five countries, were processed using the software. Results were evaluated against ground truth provided by the data set owners.



Pegasus-OCT performed with areas under the curve of the receiver operating characteristic of at least 98% for all data sets in the detection of general macular anomalies. For scans of sufficient quality, the areas under the curve of the receiver operating characteristic for general age-related macular degeneration and diabetic macular edema detection were found to be at least 99% and 98%, respectively.



The ability of a clinical decision support system to cater for different populations is key to its adoption. Pegasus-OCT was shown to be able to detect age-related macular degeneration, diabetic macular edema, and general anomalies in OCT volumes acquired across multiple independent sites with high performance. Its use thus offers substantial promise, with the potential to alleviate the burden of growing demand in eye care services caused by retinal disease.
Self-Regulated Interactive Sequence-to-Sequence Learning

Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost-aware sequence-to-sequence learning. In experiments on interactive neural machine translation, we find that the self-regulator discovers an \epsilon-greedy strategy for the optimal cost-quality trade-off by mixing different feedback types including corrections, error markups, and self-supervision. Furthermore, we demonstrate its robustness under domain shift and identify it as a promising alternative to active learning.
retina-VAE: Variationally Decoding the Spectrum of Macular Disease

In this paper, we seek a clinically-relevant latent code for representing the spectrum of macular disease. Towards this end, we construct retina-VAE, a variational autoencoder-based model that accepts a patient profile vector (pVec) as input. The pVec components include clinical exam findings and demographic information. We evaluate the model on a subspectrum of the retinal maculopathies, in particular, exudative age-related macular degeneration, central serous chorioretinopathy, and polypoidal choroidal vasculopathy. For these three maculopathies, a database of 3000 6-dimensional pVecs (1000 each) was synthetically generated based on known disease statistics in the literature. The database was then used to train the VAE and generate latent vector representations. We found training performance to be best for a 3-dimensional latent vector architecture compared to 2 or 4 dimensional latents. Additionally, for the 3D latent architecture, we discovered that the resulting latent vectors were strongly clustered spontaneously into one of 14 clusters. Kmeans was then used only to identify members of each cluster and to inspect cluster properties. These clusters suggest underlying disease subtypes which may potentially respond better or worse to particular pharmaceutical treatments such as anti-vascular endothelial growth factor variants. The retina-VAE framework will potentially yield new fundamental insights into the mechanisms and manifestations of disease. And will potentially facilitate the development of personalized pharmaceuticals and gene therapies.
Distributing Persistent Homology via Spectral Sequences

We set up the theory for a distributive algorithm for computing persistent homology. For this purpose we develop linear algebra of persistence modules. We present bases of persistence modules, and give motivation as for the advantages of using them. Our focus is on developing efficient methods for the computation of homology of chains of persistence modules. Later we give a brief, self contained presentation of the Mayer-Vietoris spectral sequence. Then we study the Persistent Mayer-Vietoris spectral sequence and present a solution to the extension problem. Finally we outline applications of our results for Vietoris-Rips complexes, cubical complexes and $\alpha$-complexes.
Variance-Based Risk Estimations in Markov Processes via Transformation with State Lumping

Variance plays a key role in risk-sensitive reinforcement learning, and most risk measures can be analyzed via variance. In this paper, we consider two law-invariant risks as examples: mean-variance risk and exponential utility risk. With the aid of the state-augmentation transformation (SAT), we show that the two risks can be estimated in Markov decision processes (MDPs) with a stochastic transition-based reward and a randomized policy. To relieve the enlarged state space, a novel definition of isotopic states is proposed for state lumping, considering the special structure of the transformed transition probability. In the numerical experiment, we illustrate state lumping in the SAT, errors from a naive reward simplification, and the validity of the SAT for the two risk estimations.
A Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehicles

This work regards our preliminary investigation on the problem of path planning for autonomous vehicles that move on a freeway. We approach this problem by proposing a driving policy based on Reinforcement Learning. The proposed policy makes minimal or no assumptions about the environment, since no a priori knowledge about the system dynamics is required. We compare the performance of the proposed policy against an optimal policy derived via Dynamic Programming and against manual driving simulated by SUMO traffic simulator.
(Un)conscious Bias in the Astronomical Profession: Universal Recommendations to improve Fairness, Inclusiveness, and Representation

(Un)conscious bias affects every aspect of the astronomical profession, from scientific activities (e.g., invitations to join collaborations, proposal selections, grant allocations, publication review processes, and invitations to attend and speak at conferences) to activities more strictly related to career advancement (e.g., reference letters, fellowships, hiring, promotion, and tenure). For many, (un)conscious bias is still the main hurdle to achieving excellence, as the most diverse talents encounter bigger challenges and difficulties to reach the same milestones than their more privileged colleagues. Over the past few years, the Space Telescope Science Institute (STScI) has constructed tools to raise awareness of (un)conscious bias and has designed guidelines and goals to increase diversity representation and outcome in its scientific activities, including career-related matters and STScI sponsored fellowships, conferences, workshops, and colloquia. STScI has also addressed (un)conscious bias in the peer-review process by anonymizing submission and evaluation of Hubble Space Telescope (and soon to be James Webb Space Telescope) observing proposals. In this white paper we present a plan to standardize these methods with the expectation that these universal recommendations will truly increase diversity, inclusiveness and fairness in Astronomy if applied consistently throughout all the scientific activities of the Astronomical community.
Complete monotonicity of a ratio of gamma functions and some combinatorial inequalities for multinomial coefficients

Let $m,n\in \mathbb{N}$. For all $i\in \{1,2,\dots,m\}$, $j\in \{1,2,\dots,n\}$, choose constants $X_i,Y_j > 0$, $M\in (0,1]$ and let $(x_{ij})\in (0,\infty)^{m\times n}$ be such that $\sum_{j=1}^n x_{ij} = X_i$, $\sum_{i=1}^m x_{ij} = Y_j$ and $\sum_{i=1}^m X_i = \sum_{j=1}^n Y_j = M$. We prove that the ratio of gamma functions \begin{equation*} a \mapsto \frac{\prod_{i=1}^m \Gamma(a X_i + 1) \prod_{j=1}^n \Gamma(a Y_j + 1)}{\prod_{i=1}^m \prod_{j=1}^n \Gamma(a x_{ij} + 1)} \end{equation*} is logarithmically completely monotonic on $(0,\infty)$. This result complements the logarithmically complete monotonicity of multinomial probabilities shown in Ouimet (2018); Qi et al. (2018), and the recent survey of Qi & Agarwal (2019) on the complete monotonicity of functions related to ratios of gamma functions. As a consequence of the log-convexity, we obtain new combinatorial inequalities for multinomial coefficients.
Perturbation theory approach to study the latent space degeneracy of Variational Autoencoders

The use of Variational Autoencoders in different Machine Learning tasks has drastically increased in the last years. They have been developed as denoising, clustering and generative tools, highlighting a large potential in a wide range of fields. Their embeddings are able to extract relevant information from highly dimensional inputs, but the converged models can differ significantly and lead to degeneracy on the latent space. We leverage the relation between theoretical physics and machine learning to explain this behaviour, and introduce a new approach to correct for degeneration by using perturbation theory. The re-formulation of the embedding as multi-dimensional generative distribution, allows mapping to a new set of functions and their corresponding energy spectrum. We optimise for a perturbed Hamiltonian, with an additional energy potential that is related to the unobserved topology of the data. Our results show the potential of a new theoretical approach that can be used to interpret the latent space and generative nature of unsupervised learning, while the energy landscapes defined by the perturbations can be further used for modelling and dynamical purposes.
Influence of Pointing on Learning to Count: A Neuro-Robotics Model

In this paper a neuro-robotics model capable of counting using gestures is introduced. The contribution of gestures to learning to count is tested with various model and training conditions. Two studies were presented in this article. In the first, we combine different modalities of the robot’s neural network, in the second, a novel training procedure for it is proposed. The model is trained with pointing data from an iCub robot simulator. The behaviour of the model is in line with that of human children in terms of performance change depending on gesture production.
A Deep Neural Network for Finger Counting and Numerosity Estimation

In this paper, we present neuro-robotics models with a deep artificial neural network capable of generating finger counting positions and number estimation. We first train the model in an unsupervised manner where each layer is treated as a Restricted Boltzmann Machine or an autoencoder. Such a model is further trained in a supervised way. This type of pre-training is tested on our baseline model and two methods of pre-training are compared. The network is extended to produce finger counting positions. The performance in number estimation of such an extended model is evaluated. We test the hypothesis if the subitizing process can be obtained by one single model used also for estimation of higher numerosities. The results confirm the importance of unsupervised training in our enumeration task and show some similarities to human behaviour in the case of subitizing.
Introduction to Camera Pose Estimation with Deep Learning

Over the last two decades, deep learning has transformed the field of computer vision. Deep convolutional networks were successfully applied to learn different vision tasks such as image classification, image segmentation, object detection and many more. By transferring the knowledge learned by deep models on large generic datasets, researchers were further able to create fine-tuned models for other more specific tasks. Recently this idea was applied for regressing the absolute camera pose from an RGB image. Although the resulting accuracy was sub-optimal, compared to classic feature-based solutions, this effort led to a surge of learning-based pose estimation methods. Here, we review deep learning approaches for camera pose estimation. We describe key methods in the field and identify trends aiming at improving the original deep pose regression solution. We further provide an extensive cross-comparison of existing learning-based pose estimators, together with practical notes on their execution for reproducibility purposes. Finally, we discuss emerging solutions and potential future research directions.
Affine Disentangled GAN for Interpretable and Robust AV Perception

Autonomous vehicles (AV) have progressed rapidly with the advancements in computer vision algorithms. The deep convolutional neural network as the main contributor to this advancement has boosted the classification accuracy dramatically. However, the discovery of adversarial examples reveals the generalization gap between dataset and the real world. Furthermore, affine transformations may also confuse computer vision based object detectors. The degradation of the perception system is undesirable for safety critical systems such as autonomous vehicles. In this paper, a deep learning system is proposed: Affine Disentangled GAN (ADIS-GAN), which is robust against affine transformations and adversarial attacks. It is demonstrated that conventional data augmentation for affine transformation and adversarial attacks are orthogonal, while ADIS-GAN can handle both attacks at the same time. Useful information such as image rotation angle and scaling factor are also generated in ADIS-GAN. On MNIST dataset, ADIS-GAN can achieve over 98 percent classification accuracy within 30 degrees rotation, and over 90 percent classification accuracy against FGSM and PGD adversarial attack.
Improving the resolution of microscope by deconvolution after dense scan

Super-resolution microscopes (such as STED) illuminate samples with a tiny spot, and achieve very high resolution. But structures smaller than the spot cannot be resolved in this way. Therefore, we propose a technique to solve this problem. It is termed “Deconvolution after Dense Scan (DDS)”. First, a preprocessing stage is introduced to eliminate the optical uncertainty of the peripheral areas around the sample’s ROI (Region of Interest). Then, the ROI is scanned densely together with its peripheral areas. Finally, the high resolution image is recovered by deconvolution. The proposed technique does not need to modify the apparatus much, and is mainly performed by algorithm. Simulation experiments show that the technique can further improve the resolution of super-resolution microscopes.
City-GAN: Learning architectural styles using a custom Conditional GAN architecture

Generative Adversarial Networks (GANs) are a well-known technique that is trained on samples (e.g. pictures of fruits) and which after training is able to generate realistic new samples. Conditional GANs (CGANs) additionally provide label information for subclasses (e.g. apple, orange, pear) which enables the GAN to learn more easily and increase the quality of its output samples. We use GANs to learn architectural features of major cities and to generate images of buildings which do not exist. We show that currently available GAN and CGAN architectures are unsuited for this task and propose a custom architecture and demonstrate that our architecture has superior performance for this task and verify its capabilities with extensive experiments.
A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data

As Super-Resolution (SR) has matured as a research topic, it has been applied to additional topics beyond image reconstruction. In particular, combining classification or object detection tasks with a super-resolution preprocessing stage has yielded improvements in accuracy especially with objects that are small relative to the scene. While SR has shown promise, a study comparing SR and naive upscaling methods such as Nearest Neighbors (NN) interpolation when applied as a preprocessing step for object detection has not been performed. We apply the topic to satellite data and compare the Multi-scale Deep Super-Resolution (MDSR) system to NN on the xView challenge dataset. To do so, we propose a pipeline for processing satellite data that combines multi-stage image tiling and upscaling, the YOLOv2 object detection architecture, and label stitching. We compare the effects of training models using an upscaling factor of 4, upscaling images from 30cm Ground Sample Distance (GSD) to an effective GSD of 7.5cm. Upscaling by this factor significantly improves detection results, increasing Average Precision (AP) of a generalized vehicle class by 23 percent. We demonstrate that while SR produces upscaled images that are more visually pleasing than their NN counterparts, object detection networks see little difference in accuracy with images upsampled using NN obtaining nearly identical results to the MDSRx4 enhanced images with a difference of 0.0002 AP between the two methods.
Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds

Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-FPN, a novel one-stage 3D object detector that utilizes raw data from LIDAR sensors only. The core framework consists of an encoder network and a corresponding decoder followed by a region proposal network. Encoder extracts multi-scale voxel information in a bottom-up manner while decoder fuses multiple feature maps from various scales in a top-down way. Extensive experiments show that the proposed method has better performance on extracting features from point data and demonstrates its superiority over some baselines on the challenging KITTI-3D benchmark, obtaining good performance on both speed and accuracy in real-world scenarios.
Non-Gaussian CMB and LSS statistics beyond polyspectra

Cosmic inflation may have led to non-Gaussian initial conditions that cannot be fully parametrised by 3- and/or 4-point functions. In this work, we discuss various strategies to search for primordial non-Gaussianity beyond polyspectra with the help of cosmological data. Our starting point is a generalised local ansatz for the primordial curvature perturbation ζ of the form ζ = ζG + ℱNG (ζG), where ζG is a Gaussian random field and ℱNG is an arbitrary function parametrising non-Gaussianity that, in principle, could be reconstructed from data. Noteworthily, in the case of multi-field inflation, the function ℱNG can be shown to be determined by the shape of tomographic sections of the landscape potential responsible for driving inflation. We discuss how this generalised local ansatz leads to a probability distribution functional that may be used to extract information about inflation from current and future observations. In particular, we derive various classes of probability distribution functions suitable for the statistical analysis of the cosmic microwave background and large-scale structure.