nils-herrmann/clean_scirepeval huggingface dataset:
Imagine That! Leveraging Emergent Affordances for Tool Synthesis in Reaching Tasks

In this paper we investigate an artificial agent's ability to perform task-focused tool synthesis via imagination. Our motivation is to explore the richness of information captured by the latent space of an object-centric generative model -- and how to exploit it. In particular, our approach employs activation maximisation of a task-based performance predictor to optimise the latent variable of a structured latent-space model in order to generate tool geometries appropriate for the task at hand. We evaluate our model using a novel dataset of synthetic reaching tasks inspired by the cognitive sciences and behavioural ecology. In doing so we examine the model's ability to imagine tools for increasingly complex scenario types, beyond those seen during training. Our experiments demonstrate that the synthesis process modifies emergent, task-relevant object affordances in a targeted and deliberate way: the agents often specifically modify aspects of the tools which relate to meaningful (yet implicitly learned) concepts such as a tool's length, width and configuration. Our results therefore suggest that task relevant object affordances are implicitly encoded as directions in a structured latent space shaped by experience.
Meta-learning algorithms for Few-Shot Computer Vision

Few-Shot Learning is the challenge of training a model with only a small amount of data. Many solutions to this problem use meta-learning algorithms, i.e. algorithms that learn to learn. By sampling few-shot tasks from a larger dataset, we can teach these algorithms to solve new, unseen tasks. This document reports my work on meta-learning algorithms for Few-Shot Computer Vision. This work was done during my internship at Sicara, a French company building image recognition solutions for businesses. It contains: 1. an extensive review of the state-of-the-art in few-shot computer vision; 2. a benchmark of meta-learning algorithms for few-shot image classification; 3. the introduction to a novel meta-learning algorithm for few-shot object detection, which is still in development.
A simple continued fraction expansion for $e^n$

In this paper we discuss the continued fraction expansion \[ e = 3 - \cfrac{1}{4 - \cfrac{2}{5 - \cfrac{3}{6 - \cfrac{4}{7 - \cdots}}} }, \] and its convergence properties. We show that this expansion is a particular case of a continued fraction expansion of $e^n$, for positive integer power \(n\) with denominators given by arithmetic progressions, and more generally, it is a special case of a continued fraction expansion of the incomplete gamma function, or equivalently, of the confluent hypergeometric function. In addition, we give a related family of continued fraction expansions of $e^{\ell/n}$ for positive intengers $1\leq\ell< n $.
Partial Uncertainty and Applications to Risk-Averse Valuation

This paper introduces an intermediary between conditional expectation and conditional sublinear expectation, called R-conditioning. The R-conditioning of a random-vector in $L^2$ is defined as the best $L^2$-estimate, given a $\sigma$-subalgebra and a degree of model uncertainty. When the random vector represents the payoff of derivative security in a complete financial market, its R-conditioning with respect to the risk-neutral measure is interpreted as its risk-averse value. The optimization problem defining the optimization R-conditioning is shown to be well-posed. We show that the R-conditioning operators can be used to approximate a large class of sublinear expectations to arbitrary precision. We then introduce a novel numerical algorithm for computing the R-conditioning. This algorithm is shown to be strongly convergent. 
Implementations are used to compare the risk-averse value of a Vanilla option to its traditional risk-neutral value, within the Black-Scholes-Merton framework. Concrete connections to robust finance, sensitivity analysis, and high-dimensional estimation are all treated in this paper.
Recipe: converting concurrent DRAM indexes to persistent-memory indexes

We present Recipe, a principled approach for converting concurrent DRAM indexes into crash-consistent indexes for persistent memory (PM). The main insight behind Recipe is that isolation provided by a certain class of concurrent in-memory indexes can be translated with small changes to crash-consistency when the same index is used in PM. We present a set of conditions that enable the identification of this class of DRAM indexes, and the actions to be taken to convert each index to be persistent. Based on these conditions and conversion actions, we modify five different DRAM indexes based on B+ trees, tries, radix trees, and hash tables to their crash-consistent PM counterparts. The effort involved in this conversion is minimal, requiring 30--200 lines of code. We evaluated the converted PM indexes on Intel DC Persistent Memory, and found that they outperform state-of-the-art, hand-crafted PM indexes in multi-threaded workloads by up-to 5.2x. For example, we built P-CLHT, our PM implementation of the CLHT hash table by modifying only 30 LOC. When running YCSB workloads, P-CLHT performs up to 2.4x better than Cacheline-Conscious Extendible Hashing (CCEH), the state-of-the-art PM hash table.
Non-Euclidean origami.

Traditional origami starts from flat surfaces, leading to crease patterns consisting of Euclidean vertices. However, Euclidean vertices are limited in their folding motions, are degenerate, and suffer from misfolding. Here we show how non-Euclidean 4-vertices overcome these limitations by lifting this degeneracy, and that when the elasticity of the hinges is taken into account, non-Euclidean 4-vertices permit higher order multistability. We harness these advantages to design an origami inverter that does not suffer from misfolding and to physically realize a tristable vertex.
Depth Estimation in Nighttime using Stereo-Consistent Cyclic Translations

Most existing methods of depth from stereo are designed for daytime scenes, where the lighting can be assumed to be sufficiently bright and more or less uniform. Unfortunately, this assumption does not hold for nighttime scenes, causing the existing methods to be erroneous when deployed in nighttime. Nighttime is not only about low light, but also about glow, glare, non-uniform distribution of light, etc. One of the possible solutions is to train a network on nighttime images in a fully supervised manner. However, to obtain proper disparity ground-truths that are dense, independent from glare/glow, and can have sufficiently far depth ranges is extremely intractable. In this paper, to address the problem of depth from stereo in nighttime, we introduce a joint translation and stereo network that is robust to nighttime conditions. Our method uses no direct supervision and does not require ground-truth disparities of the nighttime training images. First, we utilize a translation network that can render realistic nighttime stereo images from given daytime stereo images. Second, we train a stereo network on the rendered nighttime images using the available disparity supervision from the daytime images, and simultaneously also train the translation network to gradually improve the rendered nighttime images. We introduce a stereo-consistency constraint into our translation network to ensure that the translated pairs are stereo-consistent. Our experiments show that our joint translation-stereo network outperforms the state-of-the-art methods.
Randaugment: Practical automated data augmentation with a reduced search space

Recent work on automated augmentation strategies has led to state-of-the-art results in image classification and object detection. An obstacle to a large-scale adoption of these methods is that they require a separate and expensive search phase. A common way to overcome the expense of the search phase was to use a smaller proxy task. However, it was not clear if the optimized hyperparameters found on the proxy task are also optimal for the actual task. In this work, we rethink the process of designing automated augmentation strategies. We find that while previous work required a search for both magnitude and probability of each operation independently, it is sufficient to only search for a single distortion magnitude that jointly controls all operations. We hence propose a simplified search space that vastly reduces the computational expense of automated augmentation, and permits the removal of a separate proxy task.Despite the simplifications, our method achieves equal or better performance over previous automated augmentation strategies on on CIFAR-10/100, SVHN, ImageNet and COCO datasets. EfficientNet-B7, we achieve 85.0% accuracy, a 1.0% increase over baseline augmentation, a 0.6% improvement over AutoAugment on the ImageNet dataset. With EfficientNet-B8, we achieve 85.4% accuracy on ImageNet, which matches a previous result that used 3.5B extra images. On object detection, the same method as classification leads to 1.0-1.3% improvement over baseline augmentation. Code will be made available online.
IPC-Net: 3D Point-Cloud Segmentation Using Deep Inter-Point Convolutional Layers

Over the last decade, the demand for better segmentation and classification algorithms in 3D spaces has significantly grown due to the popularity of new 3D sensor technologies and advancements in the field of robotics. Point-clouds are one of the most popular representations to store a digital description of 3D shapes. However, point-clouds are stored in irregular and unordered structures, which limits the direct use of segmentation algorithms such as Convolutional Neural Networks. The objective of our work is twofold: First, we aim to provide a full analysis of the PointNet architecture to illustrate which features are being extracted from the point-clouds. Second, to propose a new network architecture called IPC-Net to improve the state-of-the-art point cloud architectures. We show that IPC-Net extracts a larger set of unique features allowing the model to produce more accurate segmentations compared to the PointNet architecture. In general, our approach outperforms PointNet on every family of 3D geometries on which the models were tested. A high generalisation improvement was observed on every 3D shape, especially on the rockets dataset. Our experiments demonstrate that our main contribution, inter-point activation on the network's layers, is essential to accurately segment 3D point-clouds.
X-ray and Visible Spectra Circular Motion Images Dataset

We present the collections of images of the same rotating plastic object made in X-ray and visible spectra. Both parts of the dataset contain 400 images. The images are maid every 0.5 degrees of the object axial rotation. The collection of images is designed for evaluation of the performance of circular motion estimation algorithms as well as for the study of X-ray nature influence on the image analysis algorithms such as keypoints detection and description. The dataset is available at this https URL.
Hamiltonian Generative Networks

The Hamiltonian formalism plays a central role in classical and quantum physics. Hamiltonians are the main tool for modelling the continuous time evolution of systems with conserved quantities, and they come equipped with many useful properties, like time reversibility and smooth interpolation in time. These properties are important for many machine learning problems - from sequence prediction to reinforcement learning and density modelling - but are not typically provided out of the box by standard tools such as recurrent neural networks. In this paper, we introduce the Hamiltonian Generative Network (HGN), the first approach capable of consistently learning Hamiltonian dynamics from high-dimensional observations (such as images) without restrictive domain assumptions. Once trained, we can use HGN to sample new trajectories, perform rollouts both forward and backward in time and even speed up or slow down the learned dynamics. We demonstrate how a simple modification of the network architecture turns HGN into a powerful normalising flow model, called Neural Hamiltonian Flow (NHF), that uses Hamiltonian dynamics to model expressive densities. We hope that our work serves as a first practical demonstration of the value that the Hamiltonian formalism can bring to deep learning.
Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes

Applications based on synergistic integration of optical imagery and LiDAR data are receiving a growing interest from the remote sensing community. However, a misaligned integration of these datasets fails to fully profit from the potential of both sensors. An optimum fusion of optical imagery and LiDAR data requires an accurate registration. This is a complex problem since a versatile solution is still missing, especially when data are collected at different times, from different platforms, under different acquisition configurations. This article presents a coarse-to-fine registration method of optical imagery with airborne LiDAR data acquired in such context. First, a coarse registration involves processes of extraction and matching of building candidates from the two datasets. Then, a mutual-information-based fine registration is carried out. It involves a superresolution approach applied to LiDAR data to generate images with the same resolution as the optical image, and a local approach of transformation model estimation. The proposed method succeeds at overcoming the challenges associated with this difficult context. For instance, considering the experimented airborne LiDAR (2011) and orthorectified aerial imagery (2016) datasets, their spatial shift is reduced by 48.15% after the proposed coarse registration. Moreover, the incompatibility of size and spatial resolution is well addressed by the superresolution. Finally, a high accuracy of dataset alignment is also achieved, highlighted by a 40-cm error based on a check-point assessment and a 64-cm error based on a check-pair-line assessment. These promising results enable further researches for a complete fusion methodology between these datasets in this challenging context.
A Reanalysis of the Fundamental Parameters and Age of TRAPPIST-1

We present the distance-calibrated spectral energy distribution (SED) of TRAPPIST-1 using a new medium-resolution (R ∼ 6000) near-infrared (NIR) Folded-port InfraRed Echellette (FIRE) spectrum and its Gaia parallax. We report an updated bolometric luminosity (Lbol) of −3.216 ± 0.016, along with semiempirical fundamental parameters: effective temperature Teff = 2628 ± 42 K, mass = 90 ± 8 MJup, radius = 1.16 ± 0.03 RJup, and log g = 5.21 ± 0.06 dex. Its kinematics point toward an older age, while spectral indices indicate youth; therefore, we compare the overall SED and NIR bands of TRAPPIST-1 to field-age, low-gravity, and low-metallicity dwarfs of similar Teff and Lbol. We find field dwarfs of similar Teff and Lbol best fit the overall and band-by-band features of TRAPPIST-1. Additionally, we present new Allers & Liu spectral indices for the SpeX SXD and FIRE spectra of TRAPPIST-1, both classifying it as intermediate gravity. Examining Teff, Lbol, and absolute JHKW1W2 magnitudes versus optical spectral type places TRAPPIST-1 in an ambiguous location containing both field and intermediate-gravity sources. Kinematics place TRAPPIST-1 within a subpopulation of intermediate-gravity sources lacking bona fide membership in a moving group with higher tangential and UVW velocities. We conclude that TRAPPIST-1 is a field-age source with subtle spectral features reminiscent of a low surface gravity object. To resolve the cause of TRAPPIST-1's intermediate-gravity indicators we speculate on two avenues that might be correlated to inflate the radius: (1) magnetic activity or (2) tidal interactions from planets. We find the M8 dwarf LHS 132 is an excellent match to TRAPPIST-1's spectral peculiarities along with the M9 β dwarf 2MASS J10220489+0200477, the L1 β 2MASS J10224821+5825453, and the L0 β 2MASS J23224684−3133231, which have distinct kinematics, making all four intriguing targets for future exoplanet studies.
XNOR-Net++: Improved binary neural networks

This paper proposes an improved training algorithm for binary neural networks in which both weights and activations are binary numbers. A key but fairly overlooked feature of the current state-of-the-art method of XNOR-Net is the use of analytically calculated real-valued scaling factors for re-weighting the output of binary convolutions. We argue that analytic calculation of these factors is sub-optimal. Instead, in this work, we make the following contributions: (a) we propose to fuse the activation and weight scaling factors into a single one that is learned discriminatively via backpropagation. (b) More importantly, we explore several ways of constructing the shape of the scale factors while keeping the computational budget fixed. (c) We empirically measure the accuracy of our approximations and show that they are significantly more accurate than the analytically calculated one. (d) We show that our approach significantly outperforms XNOR-Net within the same computational budget when tested on the challenging task of ImageNet classification, offering up to 6\% accuracy gain.
Efficient Bimanual Manipulation Using Learned Task Schemas

We address the problem of effectively composing skills to solve sparse-reward tasks in the real world. Given a set of parameterized skills (such as exerting a force or doing a top grasp at a location), our goal is to learn policies that invoke these skills to efficiently solve such tasks. Our insight is that for many tasks, the learning process can be decomposed into learning a state-independent task schema (a sequence of skills to execute) and a policy to choose the parameterizations of the skills in a state-dependent manner. For such tasks, we show that explicitly modeling the schema’s state-independence can yield significant improvements in sample efficiency for model-free reinforcement learning algorithms. Furthermore, these schemas can be transferred to solve related tasks, by simply re-learning the parameterizations with which the skills are invoked. We find that doing so enables learning to solve sparse-reward tasks on real-world robotic systems very efficiently. We validate our approach experimentally over a suite of robotic bimanual manipulation tasks, both in simulation and on real hardware. See videos at
Spatial methods and their applications to environmental and climate data

Environmental and climate processes are often distributed over large space-time domains. Their complexity and the amount of available data make modelling and analysis a challenging task. Statistical modelling of environment and climate data can have several different motivations including interpretation or characterisation of the data. Results from statistical analysis are often used as a integral part of larger environmental studies. Spatial statistics is an active and modern statistical field, concerned with the quantitative analysis of spatial data; their dependencies and uncertainties. Spatiotemporal statistics extends spatial statistics through the addition of time to the, two or three, spatial dimensions. The focus of this introductory paper is to provide an overview of spatial methods and their application to environmental and climate data. This paper also gives an overview of several important topics including large data sets and non-stationary covariance structures. Further, it is discussed how Bayesian hierarchical models can provide a flexible way of constructing models. Hierarchical models may seem to be a good solution, but they have challenges of their own such as, parameter estimation. Finally, the application of spatio-temporal models to the LANDCLIM data (LAND cover CLIMate interactions in NW Europe during the Holocene) will be discussed.
Higgs boson pair production at colliders: status and perspectives

This document summarises the current theoretical and experimental status of the di-Higgs boson production searches, and of the direct and indirect constraints on the Higgs boson self-coupling, with the wish to serve as a useful guide for the next years. The document discusses the theoretical status, including state-of-the-art predictions for di-Higgs cross sections, developments on the effective field theory approach, and studies on specific new physics scenarios that can show up in the di-Higgs final state. The status of di-Higgs searches and the direct and indirect constraints on the Higgs self-coupling at the LHC are presented, with an overview of the relevant experimental techniques, and covering all the variety of relevant signatures. Finally, the capabilities of future colliders in determining the Higgs self-coupling are addressed, comparing the projected precision that can be obtained in such facilities. The work has started as the proceedings of the Di-Higgs workshop at Colliders, held at Fermilab from the 4th to the 9th of September 2018, but it went beyond the topics discussed at that workshop and included further developments.
Direct detection of dark matter substructure in strong lens images with convolutional neural networks

Strong gravitational lensing is a promising way of uncovering the nature of dark matter, by finding perturbations to images that cannot be well accounted for by modeling the lens galaxy without additional structure, be it subhalos (smaller halos within the smooth lens) or line-of-sight (LOS) halos. We present results attempting to infer the presence of substructure from images, without doing any intermediate lens modeling, using a simple convolutional neural network (CNN). We find that the network is only able to infer the presence of subhalos with $>75\%$ accuracy when they have masses of $\geq 5 \times 10^9$M$_{\odot}$ if they lie within the main lens galaxy. Since less massive foreground LOS halos can have the same effect as higher mass subhalos, the CNN can probe lower masses in the halo mass function. The accuracy does not improve significantly if we add a population of less massive subhalos. With the expectation of experiments such as HST and Euclid yielding thousands of high-quality strong lensing images in the next years, having a way of analyzing images quickly to identify candidates that merit further analysis to determine individual subhalo properties while preventing extensive resources being used for images that would yield null detections could be very useful. By understanding the sensitivity as a function of substructure mass, non-detections could be combined with the information from images with substructure to constrain the cold dark matter scenario, in particular if the sensitivity can be pushed to lower masses.
Non-Gaussian processes and neural networks at finite widths

Gaussian processes are ubiquitous in nature and engineering. A case in point is a class of neural networks in the infinite-width limit, whose priors correspond to Gaussian processes. Here we perturbatively extend this correspondence to finite-width neural networks, yielding non-Gaussian processes as priors. The methodology developed herein allows us to track the flow of preactivation distributions by progressively integrating out random variables from lower to higher layers, reminiscent of renormalization-group flow. We further develop a perturbative procedure to perform Bayesian inference with weakly non-Gaussian priors.