nils-herrmann/clean_scirepeval huggingface dataset:
Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image

In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.
DARTS: Differentiable Architecture Search

This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques.
GPU Based Monocular Vision for Obstacle Detection

The need for autonomous navigation has increased in recent years due to the adoption of unmanned aerial vehicles (UAVs) and Micro UAVs (MAVs) for task, such as search and rescue, terrain mapping, and other missions, albeit by different means. MAVs have been less successful at fulfilling these missions as they are unable to carry complex sensors and camera systems for computer vision which larger UAVs routinely use. Monocular vision has been used previously to provide vision capabilities for MAVs. Although, monocular vision has had less success at obstacle detection and avoidance compared to stereo vision and is more computationally expensive. The more expensive computations have there for posed a problem in the past for on board closed MAV systems for autonomous navigation using monocular vision. However, with embedded GPUs recently gaining traction for small yet powerful parallel computations in small form factors show promise for fully closed MAV systems. This paper discusses the future of autonomous navigation with an embedded GPU, NVIDIA’s Jetson TX1 board, and AR.Drone 2.0 MAV drone using a novel obstacle detection algorithm implementing goodFeaturesToTrack, Lucas-Kanade Optical Flow, image segmentation, and size expansion.
Writing the History of Dynamical Systems and Chaos : Longue Durée and Revolution , Disciplines and Cultures

Between the late 1960s and the beginning of the 1980s, the wide recognition that simple dynamical laws could give rise to complex behaviors was sometimes hailed as a true scientific revolution impacting several disciplines, for which a striking label was coined—“chaos.” Mathematicians quickly pointed out that the purported revolution was relying on the abstract theory of dynamical systems founded in the late 19th century by Henri Poincaré who had already reached a similar conclusion. In this paper, we flesh out the historiographical tensions arising from these confrontations: longue-durée history and revolution; abstract mathematics and the use of mathematical techniques in various other domains. After reviewing the historiography of dynamical systems theory from Poincaré to the 1960s, we highlight the pioneering work of a few individuals (Steve Smale, Edward Lorenz, David Ruelle). We then go on to discuss the nature of the chaos phenomenon, which, we argue, was a conceptual reconfiguration as much as a sociodisciplinary convergence.
Contemporary Hormonal Contraception and the Risk of Breast Cancer.

Little is known about whether contemporary hormonal contraception is associated with an increased risk of breast cancer.



We assessed associations between the use of hormonal contraception and the risk of invasive breast cancer in a nationwide prospective cohort study involving all women in Denmark between 15 and 49 years of age who had not had cancer or venous thromboembolism and who had not received treatment for infertility. Nationwide registries provided individually updated information about the use of hormonal contraception, breast-cancer diagnoses, and potential confounders.



Among 1.8 million women who were followed on average for 10.9 years (a total of 19.6 million person-years), 11,517 cases of breast cancer occurred. As compared with women who had never used hormonal contraception, the relative risk of breast cancer among all current and recent users of hormonal contraception was 1.20 (95% confidence interval [CI], 1.14 to 1.26). This risk increased from 1.09 (95% CI, 0.96 to 1.23) with less than 1 year of use to 1.38 (95% CI, 1.26 to 1.51) with more than 10 years of use (P=0.002). After discontinuation of hormonal contraception, the risk of breast cancer was still higher among the women who had used hormonal contraceptives for 5 years or more than among women who had not used hormonal contraceptives. Risk estimates associated with current or recent use of various oral combination (estrogen-progestin) contraceptives varied between 1.0 and 1.6. Women who currently or recently used the progestin-only intrauterine system also had a higher risk of breast cancer than women who had never used hormonal contraceptives (relative risk, 1.21; 95% CI, 1.11 to 1.33). The overall absolute increase in breast cancers diagnosed among current and recent users of any hormonal contraceptive was 13 (95% CI, 10 to 16) per 100,000 person-years, or approximately 1 extra breast cancer for every 7690 women using hormonal contraception for 1 year.



The risk of breast cancer was higher among women who currently or recently used contemporary hormonal contraceptives than among women who had never used hormonal contraceptives, and this risk increased with longer durations of use; however, absolute increases in risk were small. (Funded by the Novo Nordisk Foundation.).
Pre-hospital delay and its associated factors in first-ever stroke registered in communities from three cities in China

This study aimed to explore pre-hospital delay and its associated factors in first-ever stroke registered in communities from three cities in China. The rates of delay greater than or equal to 2 hours were calculated and factors associated with delays were determined by non-conditional binary logistic regression, after adjusting for different explanatory factors. Among the 403 cases of stroke with an accurate documented time of prehospital delay, the median time (interquartile range) was 4.00 (1.50-14.00) hours. Among the 544 cases of stroke with an estimated time range of prehospital delay, 24.8% of patients were transferred to the emergency department or hospital within 2 hours, only 16.9% of patients with stroke were aware that the initial symptom represented a stroke, only 18.8% used the emergency medical service and one-third of the stroke cases were not identified by ambulance doctors. In the multivariate analyses, 8 variables or sub-variables were identified. In conclusion, prehospital delay of stroke was common in communities. Thus, intervention measures in communities should focus on education about the early identification of stroke and appropriate emergency medical service (EMS) use, as well as the development of organized stroke care.
A novel low-power, low-offset, and high-speed CMOS dynamic latched comparator

A novel dynamic latched comparator with offset voltage compensation is presented. The proposed comparator uses one phase clock signal for its operation and can drive a larger capacitive load with complementary version of the regenerative output latch stage. As it provides a larger voltage gain up to 22 V/V to the regenerative latch, the inputreferred offset voltage of the latch is reduced and metastability is improved. The proposed comparator is designed using 90 nm PTM technology and 1 V power supply voltage. It demonstrates up to 24.6% less offset voltage and 30.0% less sensitivity of delay to decreasing input voltage difference (17 ps/decade) than the conventional double-tail latched comparator at approximately the same area and power consumption. In addition, with a digitally controlled capacitive offset calibration technique, the offset voltage of the proposed comparator is further reduced from 6.03 to 1.10 mV at 1-sigma at the operating clock frequency of 3 GHz, and it consumes 54 lW/GHz after calibration.
Generalizing to Unseen Domains via Adversarial Data Augmentation

We are concerned with learning models that generalize well to different unseen domains. We consider a worst-case formulation over data distributions that are near the source domain in the feature space. Only using training data from a single source distribution, we propose an iterative procedure that augments the dataset with examples from a fictitious target domain that is "hard" under the current model. We show that our iterative scheme is an adaptive data augmentation method where we append adversarial examples at each iteration. For softmax losses, we show that our method is a data-dependent regularization scheme that behaves differently from classical regularizers that regularize towards zero (e.g., ridge or lasso). On digit recognition and semantic segmentation tasks, our method learns models improve performance across a range of a priori unknown target domains.
Emotional Implications of Metaphor : Consequences of Metaphor Framing for Mindset about Hardship

Do metaphors shape people’s emotional states and mindsets for dealing with hardship? Natural language metaphors may act as frames that encourage people to reappraise an emotional situation, changing the way they respond to it. Recovery from cancer is one type of adversity that many people face, and it can be mediated by the mindset people adopt. We investigate whether two common metaphors for describing a cancer experience – the battle and the journey – encourage people to make different inferences about the patient’s emotional state. After being exposed to the battle metaphor participants inferred that the patient would feel more guilt if he didn’t recover, while after being exposed to the journey metaphor participants felt that he had a better chance of making peace with his situation. We discuss implications of this work for investigations of metaphor and emotion, mindsets, and recovery.
Distilling the Knowledge in a Neural Network

A very simple way to improve the performance of almost any mac hine learning algorithm is to train many different models on the same data a nd then to average their predictions . Unfortunately, making predictions u ing a whole ensemble of models is cumbersome and may be too computationally expen sive to allow deployment to a large number of users, especially if the indivi dual models are large neural nets. Caruana and his collaborators  have shown th at it is possible to compress the knowledge in an ensemble into a single model whi ch is much easier to deploy and we develop this approach further using a dif ferent compression technique. We achieve some surprising results on MNIST and w e show that we can significantly improve the acoustic model of a heavily use d commercial system by distilling the knowledge in an ensemble of models into a si ngle model. We also introduce a new type of ensemble composed of one or more full m odels and many specialist models which learn to distinguish fine-grained c lasses that the full models confuse. Unlike a mixture of experts, these specialist m odels can be trained rapidly and in parallel.
Multi-species simulation of porous sand and water mixtures

We present a multi-species model for the simulation of gravity driven landslides and debris flows with porous sand and water interactions. We use continuum mixture theory to describe individual phases where each species individually obeys conservation of mass and momentum and they are coupled through a momentum exchange term. Water is modeled as a weakly compressible fluid and sand is modeled with an elastoplastic law whose cohesion varies with water saturation. We use a two-grid Material Point Method to discretize the governing equations. The momentum exchange term in the mixture theory is relatively stiff and we use semi-implicit time stepping to avoid associated small time steps. Our semi-implicit treatment is explicit in plasticity and preserves symmetry of force linearizations. We develop a novel regularization of the elastic part of the sand constitutive model that better mimics plasticity during the implicit solve to prevent numerical cohesion artifacts that would otherwise have occurred. Lastly, we develop an improved return mapping for sand plasticity that prevents volume gain artifacts in the traditional Drucker-Prager model.
Vehicle Logo Recognition and attributes prediction by multi-task learning with CNN

Vehicle Logo Recognition(VLR) has been an important study field in intelligent Transportation system (ITS). This paper proposes to recognize vehicle logo and predict logo attributes by combining Convolutional Neural Network (CNN) with Multi-Task Learning(MTL). In order to accelerate convergence of multi-task model, an adaptive weight training strategy is employed. To verify the algorithm, the Xiamen University Vehicle logo recognition dataset is extended into a larger vehicle logo dataset including 15 brands, 6 visual attributes and 3 no-visual attributes. The experiment results indicate that the proposed multi-task CNN model perform well for both of logo classification and attribution prediction with overall accuracy 98.14%.
Speech discrimination in 11-month-old bilingual and monolingual infants: a magnetoencephalography study.

Language experience shapes infants' abilities to process speech sounds, with universal phonetic discrimination abilities narrowing in the second half of the first year. Brain measures reveal a corresponding change in neural discrimination as the infant brain becomes selectively sensitive to its native language(s). Whether and how bilingual experience alters the transition to native language specific phonetic discrimination is important both theoretically and from a practical standpoint. Using whole head magnetoencephalography (MEG), we examined brain responses to Spanish and English syllables in Spanish-English bilingual and English monolingual 11-month-old infants. Monolingual infants showed sensitivity to English, while bilingual infants were sensitive to both languages. Neural responses indicate that the dual sensitivity of the bilingual brain is achieved by a slower transition from acoustic to phonetic sound analysis, an adaptive and advantageous response to increased variability in language input. Bilingual neural responses extend into the prefrontal and orbitofrontal cortex, which may be related to their previously described bilingual advantage in executive function skills. A video abstract of this article can be viewed at: https://youtu.be/TAYhj-gekqw.
Meta-alinhamento de Ontologias Utilizando a Abordagem Presa-predador

Every year, several new ontology matchers are proposed in the literature, each one using a different heuristic, which implies in different performances according to the characteristics of the ontologies. An ontology metamatcher consists of an algorithm that combines several approaches in order to obtain better results in different scenarios. To achieve this goal, it is necessary to define a criterion for the use of matchers. We presented in this work an ontology meta-matcher that combines several ontology matchers making use of the evolutionary meta-heuristic prey-predator as a means of parameterization of the same.  Todo ano, diversos novos alinhadores de ontologias são propostos na literatura, cada um utilizando uma heurı́stica diferente, o que implica em desempenhos distintos de acordo com as caracterı́sticas das ontologias. Um meta-alinhador consiste de um algoritmo que combina diversas abordagens a fim de obter melhores resultados em diferentes cenários. Para atingir esse objetivo, é necessária a definição de um critério para melhor uso de alinhadores. Neste trabalho, é apresentado um meta-alinhador de ontologias que combina vários alinhadores através da meta-heurı́stica evolutiva presa-predador como meio de parametrização das mesmas.
Deep Multispectral Semantic Scene Understanding of Forested Environments Using Multimodal Fusion

Semantic scene understanding of unstructured environments is a highly challenging task for robots operating in the real world. Deep Convolutional Neural Network architectures define the state of the art in various segmentation tasks. So far, researchers have focused on segmentation with RGB data. In this paper, we study the use of multispectral and multimodal images for semantic segmentation and develop fusion architectures that learn from RGB, Near-InfraRed channels, and depth data. We introduce a first-of-its-kind multispectral segmentation benchmark that contains 15, 000 images and 366 pixel-wise ground truth annotations of unstructured forest environments. We identify new data augmentation strategies that enable training of very deep models using relatively small datasets. We show that our UpNet architecture exceeds the state of the art both qualitatively and quantitatively on our benchmark. In addition, we present experimental results for segmentation under challenging real-world conditions. Benchmark and demo are publicly available at http://deepscene.cs.uni-freiburg.de.
Cross-lingual Document Retrieval using Regularized Wasserstein Distance

Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare objects of different nature. Recently, a new promising metric called Word Mover’s Distance was proposed to measure the divergence between text passages. In this paper, we demonstrate that this metric can be extended to incorporate term-weighting schemes and provide more accurate and computationally efficient matching between documents using entropic regularization. We evaluate the benefits of both extensions in the task of cross-lingual document retrieval (CLDR). Our experimental results on eight CLDR problems suggest that the proposed methods achieve remarkable improvements in terms of Mean Reciprocal Rank compared to several baselines.
Wasserstein GAN: Technical Report

Even though the introduction of Generative Adversarial Nets lead to amazing applications and results, the training of these models are highly instable and always regarded as a black box. This is a technical report for the term project of the course COMP 541: Deep Learning. As the term project, a new approach on improving the training of Generative Adversarial Nets called Wasserstein GAN is implemented and the results are replicated for every model that is presented in the Wasserstein GAN paper. This report presents background on generative models, Wasserstein metric, models, training details and results.
NON-PARAMETRIC DISCRIMINATIVE REWARDS

Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptuallyspecified goals using only a stream of observations and actions. Our agent simultaneously learns a goal-conditioned policy and a goal achievement reward function that measures how similar a state is to the goal state. This dual optimization leads to a co-operative game, giving rise to a learned reward function that reflects similarity in controllable aspects of the environment instead of distance in the space of observations. We demonstrate the efficacy of our agent to learn, in an unsupervised manner, to reach a diverse set of goals on three domains – Atari, the DeepMind Control Suite and DeepMind Lab.
Modeling and Design of Multilevel Bang–Bang CDRs in the Presence of ISI and Noise

Multilevel clock-and-data recovery (CDR) systems are analyzed, modeled, and designed. A stochastic analysis provides probability density functions that are used to estimate the effect of intersymbol interference (ISI) and additive white noise on the characteristics of the phase detector (PD) in the CDR. A slope detector based novel multilevel bang-bang CDR architecture is proposed and modeled using the stochastic analysis and its performance compared with a typical multilevel Alexander PD-based CDR for equal-loop bandwidths. The rms jitter of the CDRs are predicted using a linear jitter model and a Markov chain and verified using behavioral simulations. Jitter tolerance simulations are also employed to compare the two CDRs. Both analytical calculations and behavioral simulations predict that at equal-loop bandwidths, the proposed architecture is superior to the Alexander type CDR at large ISI and low signal-to-noise ratios.
Hierarchical Adversarially Learned Inference

We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model. Both the generative and inference model are trained using the adversarial learning paradigm. We demonstrate that the hierarchical structure supports the learning of progressively more abstract representations as well as providing semantically meaningful reconstructions with different levels of fidelity. Furthermore, we show that minimizing the Jensen-Shanon divergence between the generative and inference network is enough to minimize the reconstruction error. The resulting semantically meaningful hierarchical latent structure discovery is exemplified on the CelebA dataset. There, we show that the features learned by our model in an unsupervised way outperform the best handcrafted features. Furthermore, the extracted features remain competitive when compared to several recent deep supervised approaches on an attribute prediction task on CelebA. Finally, we leverage the model’s inference network to achieve state-ofthe-art performance on a semi-supervised variant of the MNIST digit classification task.
On Generalized Bellman Equations and Temporal-Difference Learning

We consider o↵-policy temporal-di↵erence (TD) learning in discounted Markov decision processes, where the goal is to evaluate a policy in a model-free way by using observations of a state process generated without executing the policy. To curb the high variance issue in o↵-policy TD learning, we propose a new scheme of setting the parameters of TD, based on generalized Bellman equations. Our scheme is to set according to the eligibility trace iterates calculated in TD, thereby easily keeping these traces in a desired bounded range. Compared to prior works, this scheme is more direct and flexible, and allows much larger values for o↵-policy TD learning with bounded traces. Using Markov chain theory, we prove the ergodicity of the joint state-trace process under nonrestrictive conditions, and we show that associated with our scheme is a generalized Bellman equation (for the policy to be evaluated) that depends on both and the unique invariant probability measure of the state-trace process. These results not only lead immediately to a characterization of the convergence behavior of least-squares based implementation of our scheme, but also prepare the ground for further analysis of gradient-based implementations.