nils-herrmann/clean_scirepeval huggingface dataset:
UNITER: Learning UNiversal Image-TExt Representations

Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are jointly processed for visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings. We design three pre-training tasks: Masked Language Modeling (MLM), Image-Text Matching (ITM), and Masked Region Modeling (MRM, with three variants). Different from concurrent work on multimodal pre-training that apply joint random masking to both modalities, we use conditioned masking on pre-training tasks (i.e., masked language/region modeling is conditioned on full observation of image/text). Comprehensive analysis shows that conditioned masking yields better performance than unconditioned masking. We also conduct a thorough ablation study to find an optimal setting for the combination of pre-training tasks. Extensive experiments show that UNITER achieves new state of the art across six V+L tasks (over nine datasets), including Visual Question Answering, Image-Text Retrieval, Referring Expression Comprehension, Visual Commonsense Reasoning, Visual Entailment, and NLVR2.
Challenges of Designing and Developing Tangible Interfaces for Mental Well-being

Mental well-being technologies possess many qualities that give them the potential to help people receive assessment and treatment who may otherwise not receive help due to fear of stigma or lack of resources. The combination of advances in sensors, microcontrollers and machine learning is leading to the emergence of dedicated tangible interfaces to monitor and promote positive mental well-being. However, there are key technical, ergonomic and aesthetic challenges to be overcome in order to make these interfaces effective and respond to users’ needs. In this paper, the barriers to develop mental well-being tangible interfaces are discussed by identifying and examining the recent technological challenges machine learning, sensors, microcontrollers and batteries create. 
User-oriented challenges that face the development of mental well-being technologies are then considered ranging from user engagement during co-design and trials to ethical and privacy concerns
Searches for gluinos and squarks

Despite the great success of the Standard Model, it still does not explain Dark Matter, matter/antimatter asymmetry and it does not unite the electroweak and the strong forces. As a possible solution, the theory of Supersymmetry was proposed. In this paper, three searches for strong production Supersymmetry are presented. The data correspond to a total integrated luminosity of 137 fb$^{-1}$ at a center-of-mass energy of 13 TeV, recorded at the Large Hadron Collider during the years 2015-2018. There has been no significant excess observed relative to the Standard Model predictions. The data gave the following best 95\% exclusion limits for the masses of squarks and gluinos: 2250 GeV, 1260 GeV and 1225 GeV which are obtained from the inclusive search for gluinos, bottom squarks and top squarks, respectively. The search for disappearing tracks extends the gluino mass limit to as much as 2460 GeV, and the neutralino mass limit to as much as 2000 GeV.
Cosmological attractors to general relativity and spontaneous scalarization with disformal coupling

The canonical scalar-tensor theory model which exhibits spontaneous scalarization in the strong-gravity regime of neutron stars has long been known to predict a cosmological evolution for the scalar field which generically results in severe violations of present-day Solar System constraints on deviations from general relativity. We study if this tension can be alleviated by generalizing this model to include a disformal coupling between the scalar field $\varphi$ and matter, where the Jordan frame metric ${\tilde g}_{\mu\nu}$ is related to the Einstein frame one $g_{\mu\nu}$ by ${\tilde g}_{\mu\nu}=A(\varphi)^2 (g_{\mu\nu}+\Lambda\, \partial_\mu \varphi \, \partial_\nu\varphi)$. We find that this broader theory admits a late-time attractor mechanism towards general relativity. However, the existence of this attractor requires a value of disformal scale of the order $\Lambda\gtrsim H_0^{-2}$, where $H_0$ is the Hubble parameter of today, which is much larger than the scale relevant for spontaneous scalarization of neutron stars $\Lambda \sim R_s^{2}$ with $R_s (\sim 10^{-22} H_0^{-1})$ being the typical radius of these stars. The large values of $\Lambda$ necessary for the attractor mechanism (i) suppress spontaneous scalarization altogether inside neutron stars and (ii) induce ghost instabilities on scalar field fluctuations, thus preventing a resolution of the tension. We argue that the problem arises because our disformal coupling involves a dimensionful parameter.
Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient

Current deep neural networks can achieve remarkable performance on a single task. However, when the deep neural network is continually trained on a sequence of tasks, it seems to gradually forget the previous learned knowledge. This phenomenon is referred to as catastrophic forgetting and motivates the field called lifelong learning. The central question in lifelong learning is how to enable deep neural networks to maintain performance on old tasks while learning a new this http URL this paper, we introduce a novel and effective lifelong learning algorithm, calledMixEd stochastic GrAdient (MEGA), which allows deep neural networks to ac-quire the ability of retaining performance on old tasks while learning new tasks.MEGA modulates the balance between old tasks and the new task by integrating the current gradient with the gradient computed on a small reference episodic memory. Extensive experimental results show that the proposed MEGA algorithm significantly advances the state-of-the-art on all four commonly used life-long learning benchmarks, reducing the error by up to 18%.
FreeLB: Enhanced Adversarial Training for Natural Language Understanding

Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models. In this work, we propose a novel adversarial training algorithm, FreeLB, that promotes higher invariance in the embedding space, by adding adversarial perturbations to word embeddings and minimizing the resultant adversarial risk inside different regions around input samples. To validate the effectiveness of the proposed approach, we apply it to Transformer-based models for natural language understanding and commonsense reasoning tasks. Experiments on the GLUE benchmark show that when applied only to the finetuning stage, it is able to improve the overall test scores of BERT-base model from 78.3 to 79.4, and RoBERTa-large model from 88.5 to 88.8. In addition, the proposed approach achieves state-of-the-art single-model test accuracies of 85.44\% and 67.75\% on ARC-Easy and ARC-Challenge. Experiments on CommonsenseQA benchmark further demonstrate that FreeLB can be generalized and boost the performance of RoBERTa-large model on other tasks as well. Code is available at \url{this https URL .
An Automated Vehicle (AV) like Me? The Impact of Personality Similarities and Differences between Humans and AVs

To better understand the impacts of similarities and dissimilarities in human and AV personalities we conducted an experimental study with 443 individuals. Generally, similarities in human and AV personalities led to a higher perception of AV safety only when both were high in specific personality traits. Dissimilarities in human and AV personalities also yielded a higher perception of AV safety, but only when the AV was higher than the human in a particular personality trait.
Design of Algorithms under Policy-Aware Local Differential Privacy: Utility-Privacy Trade-offs

Local differential privacy (LDP) enables private data sharing and analytics without the need of a trusted data collector. Error-optimal primitives (for, e.g., estimating means and item frequencies) under LDP have been well studied. For analytical tasks such as range queries, however, the error is often dependent on the domain size of private data, which is potentially prohibitive. This deficiency is inherent as LDP protects the same level of indistinguishability between any pair of private data values. In this paper, we investigate a {policy-aware} extension of eps-LDP, where a {policy} is customizable and defines heterogeneous privacy guarantees for different pairs of private data values. The policy provides another knob besides eps to tune utility-privacy trade-offs in analytical workloads. We show that, under realistic relaxed LDP policies, for analytical workloads such as linear counting queries, multi-dimensional range queries, and quantile queries, we can achieve significant gains in utility. In particular, for range queries under relaxed LDP, we design mechanisms with errors independent on the domain sizes; instead, their errors depends on the policy, which specifies in what granularity the private data is protected. We believe that the primitives we design for policy-aware LDP will be useful in developing mechanisms for other non-trivial analytical tasks with lower errors, and encourage the adoption of LDP in practice.
A $U(1)_{X}$ extension to the SM with three families and Peccei Quinn symmetry

We propose a non-universal $U(1)_{X}$ extension to the Standard Model with three families and an additional global anomala Peccei-Quinn (PQ) symmetry. The breaking of the former allows us to give masses to the exotic fermionic sector and the later generates the necessary zeros in the mass matrices to explain the fermionic mass hierarchy. In addition, the large energy scale associated with the spontaneously breaking (SSB) of the PQ symmetry provides a solution to the strong CP-problem and an axion that could be a possible dark matter candidate. Also, the SSB allows to generate right-handed neutrino masses, so the active neutrinos acquire $eV$-mass values due to the see-saw mechanism implementation.
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)

Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible model specifications are needed as big(ger) data become available, facilitated by more powerful computing infrastructure. (2) Full probabilistic modeling rather than predicting just means or expectations is crucial in many applications. (3) Interest in Bayesian inference has been increasing both as an appealing framework for regularizing or penalizing model estimation as well as a natural alternative to classical frequentist inference. However, while there has been a lot of research in all three areas, also leading to associated software packages, a modular software implementation that allows to easily combine all three aspects has not yet been available. For filling this gap, the R package bamlss is introduced for Bayesian additive models for location, scale, and shape (and beyond). At the core of the package are algorithms for highly-efficient Bayesian estimation and inference that can be applied to generalized additive models (GAMs) or generalized additive models for location, scale, and shape (GAMLSS), also known as distributional regression. However, its building blocks are designed as "Lego bricks" encompassing various distributions (exponential family, Cox, joint models, ...), regression terms (linear, splines, random effects, tensor products, spatial fields, ...), and estimators (MCMC, backfitting, gradient boosting, lasso, ...). It is demonstrated how these can be easily recombined to make classical models more flexible or create new custom models for specific modeling challenges.
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection

We present a principled approach for detecting out-of-distribution (OOD) and adversarial samples in deep neural networks. Our approach consists in modeling the outputs of the various layers (deep features) with parametric probability distributions once training is completed. At inference, the likelihoods of the deep features w.r.t the previously learnt distributions are calculated and used to derive uncertainty estimates that can discriminate in-distribution samples from OOD samples. We explore the use of two classes of multivariate distributions for modeling the deep features - Gaussian and Gaussian mixture - and study the trade-off between accuracy and computational complexity. We demonstrate benefits of our approach on image features by detecting OOD images and adversarially-generated images, using popular DNN architectures on MNIST and CIFAR10 datasets. We show that more precise modeling of the feature distributions result in significantly improved detection of OOD and adversarial samples; up to 12 percentage points in AUPR and AUROC metrics. We further show that our approach remains extremely effective when applied to video data and associated spatio-temporal features by detecting adversarial samples on activity classification tasks using UCF101 dataset, and the C3D network. To our knowledge, our methodology is the first one reported for reliably detecting white-box adversarial framing, a state-of-the-art adversarial attack for video classifiers.
Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks

This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multi- modal biosignals. Most of the current work in the literature are either rule-based methods, requiring prior knowledge of arrhythmia analysis to build rules, or classical machine learning approaches, depending on hand-engineered features. In this work, we apply convolutional neural networks to automatically extract time-invariant features, an attention mechanism to put more emphasis on the important regions of the segmented input signal(s) that are more likely to contribute to an alarm, and long short-term memory units to capture the temporal information presented in the signal segments. We trained our method efficiently using a two-step training algorithm (i.e., pre-training and fine-tuning the proposed network) on the dataset provided by the PhysioNet computing in cardiology challenge 2015. The evaluation results demonstrate that the proposed method obtains better results compared to other existing algorithms for the false alarm reduction task in ICUs. The proposed method achieves a sensitivity of 93.88% and a specificity of 92.05% for the alarm classification, considering three different signals. In addition, our experiments for 5 separate alarm types leads significant results, where we just consider a single-lead ECG (e.g., a sensitivity of 90.71%, a specificity of 88.30%, an AUC of 89.51 for alarm type of Ventricular Tachycardia arrhythmia).
MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit

Are Graph Neural Networks (GNNs) fair? In many real world graphs, the formation of edges is related to certain node attributes (e.g. gender, community, reputation). In this case, standard GNNs using these edges will be biased by this information, as it is encoded in the structure of the adjacency matrix itself. In this paper, we show that when metadata is correlated with the formation of node neighborhoods, unsupervised node embedding dimensions learn this metadata. This bias implies an inability to control for important covariates in real-world applications, such as recommendation systems. To solve these issues, we introduce the Metadata-Orthogonal Node Embedding Training (MONET) unit, a general model for debiasing embeddings of nodes in a graph. MONET achieves this by ensuring that the node embeddings are trained on a hyperplane orthogonal to that of the node metadata. This effectively organizes unstructured embedding dimensions into an interpretable topology-only, metadata-only division with no linear interactions. We illustrate the effectiveness of MONET though our experiments on a variety of real world graphs, which shows that our method can learn and remove the effect of arbitrary covariates in tasks such as preventing the leakage of political party affiliation in a blog network, and thwarting the gaming of embedding-based recommendation systems.
Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations

In this paper, we propose a novel framework for estimating systemic risk measures and risk allocations based on Markov Chain Monte Carlo (MCMC) methods. We consider a class of allocations whose jth component can be written as some risk measure of the jth conditional marginal loss distribution given the so-called crisis event. By considering a crisis event as an intersection of linear constraints, this class of allocations covers, for example, conditional Value-at-Risk (CoVaR), conditional expected shortfall (CoES), VaR contributions, and range VaR (RVaR) contributions as special cases. For this class of allocations, analytical calculations are rarely available, and numerical computations based on Monte Carlo (MC) methods often provide inefficient estimates due to the rare-event character of the crisis events. We propose an MCMC estimator constructed from a sample path of a Markov chain whose stationary distribution is the conditional distribution given the crisis event. Efficient constructions of Markov chains, such as the Hamiltonian Monte Carlo and Gibbs sampler, are suggested and studied depending on the crisis event and the underlying loss distribution. The efficiency of the MCMC estimators is demonstrated in a series of numerical experiments.
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks

Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-ofthe-art accuracy compared to other methods in many supervised learning scenarios. In particular, Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of the feature indices. However, in structured data lying on a manifold—such as images, text, and speech—deep networks (Networks), specifically convolutional deep networks (ConvNets), tend to outperform Forests. We conjecture that at least part of the reason for this is that the input to Networks is not simply the feature magnitudes, but also their indices (for example, the convolution operation uses “feature locality”). In contrast, naïve Forest implementations fail to explicitly consider feature indices. A recently proposed Forest approach demonstrates that Forests, for each node, implicitly sample a random matrix from some specific distribution. These Forests, like some classes of Networks, learn by partitioning the feature space into convex polytopes corresponding to linear functions. We build on that approach and show that one can choose distributions in a manifold-aware fashion to incorporate feature locality. We demonstrate the empirical performance on data whose features live on three different manifolds: a torus, images, and time-series. Moreover, we demonstrate its strength in multivariate simulated settings and also show superiority in predicting surgical outcome in epilepsy patients and predicting movement direction from raw stereotactic EEG data from non-motor brain regions. In all simulations and real data, Manifold Oblique Random Forest (Morf) algorithm outperforms approaches that ignore feature space structure and challenges the performance of ConvNets. Moreover, Morf runs fast and maintains interpretability and theoretical justification. Our code is open source and available as part of the package at https:///neurodata.io/code/.
Function Preserving Projection for Scalable Exploration of High-Dimensional Data

We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimension reduction methods aim to maximally preserve the global and/or local geometric structure of a dataset. However, in practice one is often more interested in determining how one or multiple user-selected response function(s) can be explained by the data. To intuitively connect the responses to the data, FPP constructs 2D linear embeddings optimized to reveal interpretable yet potentially non-linear patterns of the response functions. More specifically, FPP is designed to (i) produce human-interpretable embeddings; (ii) capture non-linear relationships; (iii) allow the simultaneous use of multiple response functions; and (iv) scale to millions of samples. Using FPP on real-world datasets, one can obtain fundamentally new insights about high-dimensional relationships in large-scale data that could not be achieved using existing dimension reduction methods.
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems

Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive - potentially occupying hundreds of gigabytes in large-scale settings. To help manage this outsized memory consumption, we explore mixed dimension embeddings, an embedding layer architecture in which a particular embedding vector's dimension scales with its query frequency. Through theoretical analysis and systematic experiments, we demonstrate that using mixed dimensions can drastically reduce the memory usage, while maintaining and even improving the ML performance. Empirically, we show that the proposed mixed dimension layers improve accuracy by 0.1 % using half as many parameters or maintain it using 16 x fewer parameters for click-through rate prediction on the Criteo Kaggle dataset. They also train over 2x faster on a GPU.
A fast, complete, point cloud based loop closure for LiDAR odometry and mapping

This paper presents a loop closure method to correct the long-term drift in LiDAR odometry and mapping (LOAM). Our proposed method computes the 2D histogram of keyframes, a local map patch, and uses the normalized cross-correlation of the 2D histograms as the similarity metric between the current keyframe and those in the map. We show that this method is fast, invariant to rotation, and produces reliable and accurate loop detection. The proposed method is implemented with careful engineering and integrated into the LOAM algorithm, forming a complete and practical system ready to use. To benefit the community by serving a benchmark for loop closure, the entire system is made open source on Github
LAVAE: Disentangling Location and Appearance

We propose a probabilistic generative model for unsupervised learning of structured, interpretable, object-based representations of visual scenes. We use amortized variational inference to train the generative model end-to-end. The learned representations of object location and appearance are fully disentangled, and objects are represented independently of each other in the latent space. Unlike previous approaches that disentangle location and appearance, ours generalizes seamlessly to scenes with many more objects than encountered in the training regime. We evaluate the proposed model on multi-MNIST and multi-dSprites data sets.