nils-herrmann/clean_scirepeval huggingface dataset:
A systematic review of human activity recognition using smartphones

Smartphones have become a global communication tool and more recently a technology for studying human behavior. Given their numerous built-in sensors, smartphones are able to capture detailed and continuous observations on activities of daily living. However, translation of measurements from these consumer-grade devices into research-grade physical activity patterns remains challenging. Over the years, researchers have proposed various human activity recognition (HAR) systems which vary in algorithmic details and statistical principles. In this paper, we summarize existing approaches to smartphone-based HAR. We systematically screened the literature on Scopus, PubMed, and Web of Science in the areas of data acquisition, data preprocessing, feature extraction, and activity classification. We ultimately identified 72 articles on smartphone-based HAR. To provide an understanding of the literature, we discuss each of these areas separately, identify the most common practices and their alternatives, and propose possible future research directions for this interesting and important field.
Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors

In essence, successful grasp boils down to correct responses to multiple contact events between fingertips and objects. In most scenarios, tactile sensing is adequate to distinguish contact events. Due to the nature of high dimensionality of tactile information, classifying spatiotemporal tactile signals using conventional model-based methods is difficult. In this work, we propose to predict and classify tactile signal using deep learning methods, seeking to enhance the adaptability of the robotic grasp system to external event changes that may lead to grasping failure. We develop a deep learning framework and collect 6650 tactile image sequences with a vision-based tactile sensor, and the neural network is integrated into a contact-event-based robotic grasping system. In grasping experiments, we achieved 52% increase in terms of object lifting success rate with contact detection, significantly higher robustness under unexpected loads with slip prediction compared with open-loop grasps, demonstrating that integration of the proposed framework into robotic grasping system substantially improves picking success rate and capability to withstand external disturbances.
Toward a Computational Theory of Evidence-Based Reasoning for Instructable Cognitive Agents

Evidence-based reasoning is at the core of many problem-solving and decision-making tasks in a wide variety of domains. Generalizing from the research and development of cognitive agents in several such domains, this paper presents progress toward a computational theory for the development of instructable cognitive agents for evidence-based reasoning tasks. The paper also illustrates the application of this theory to the development of four prototype cognitive agents in domains that are critical to the government and the public sector. Two agents function as cognitive assistants, one in intelligence analysis, and the other in science education. The other two agents operate autonomously, one in cybersecurity and the other in intelligence, surveillance, and reconnaissance. The paper concludes with the directions of future research on the proposed computational theory.
Semantic Understanding of Foggy Scenes with Purely Synthetic Data

This work addresses the problem of semantic scene understanding under foggy road conditions. Although marked progress has been made in semantic scene understanding over the recent years, it is mainly concentrated on clear weather outdoor scenes. Extending semantic segmentation methods to adverse weather conditions like fog is crucially important for outdoor applications such as self-driving cars. In this paper, we propose a novel method, which uses purely synthetic data to improve the performance on unseen real-world foggy scenes captured in the streets of Zurich and its surroundings. Our results highlight the potential and power of photo-realistic synthetic images for training and especially fine-tuning deep neural nets. Our contributions are threefold, 1) we created a purely synthetic, high-quality foggy dataset of 25,000 unique outdoor scenes, that we call Foggy Synscapes and plan to release publicly 2) we show that with this data we outperform previous approaches on real-world foggy test data 3) we show that a combination of our data and previously used data can even further improve the performance on real-world foggy data.
MIDV-2019: challenges of the modern mobile-based document OCR

Recognition of identity documents using mobile devices has become a topic of a wide range of computer vision research. The portfolio of methods and algorithms for solving such tasks as face detection, document detection and rectification, text field recognition, and other, is growing, and the scarcity of datasets has become an important issue. One of the openly accessible datasets for evaluating such methods is MIDV-500, containing video clips of 50 identity document types in various conditions. However, the variability of capturing conditions in MIDV-500 did not address some of the key issues, mainly significant projective distortions and different lighting conditions. In this paper we present a MIDV-2019 dataset, containing video clips shot with modern high-resolution mobile cameras, with strong projective distortions and with low lighting conditions. The description of the added data is presented, and experimental baselines for text field recognition in different conditions.
On the Possibility of Rewarding Structure Learning Agents: Mutual Information on Linguistic Random Sets

We present a first attempt to elucidate a theoretical and empirical approach to design the reward provided by a natural language environment to some structure learning agent. To this end, we revisit the Information Theory of unsupervised induction of phrase-structure grammars to characterize the behavior of simulated actions modeled as set-valued random variables (random sets of linguistic samples) constituting semantic structures. Our results showed empirical evidence of that simulated semantic structures (Open Information Extraction triplets) can be distinguished from randomly constructed ones by observing the Mutual Information among their constituents. This suggests the possibility of rewarding structure learning agents without using pretrained structural analyzers (oracle actors/experts).
Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning

Over its lifetime, a reinforcement learning agent is often tasked with different tasks. How to efficiently adapt a previously learned control policy from one task to another, remains an open research question. In this paper, we investigate how instructions formulated in natural language can enable faster and more effective task adaptation. This can serve as the basis for developing language instructed skills, which can be used in a lifelong learning setting. Our method is capable of assessing, given a set of developed base control policies, which policy will adapt best to a new unseen task.
Dark matter halo shapes in the Auriga simulations

We present shape measurements of Milky Way–sized dark matter haloes at redshift z = 0 in a suite of 30 zoom simulations from the Auriga project. We compare the results in full magnetohydrodynamics against dark matter–only simulations and find a strong influence of baryons in making dark matter haloes rounder at all radii compared to their dark matter–only counterparts. At distances ≲30 kpc, rounder dark matter distributions correlate with extended massive stellar discs and low-core gas densities. We measure the alignment between the halo and the disc shapes at different radii and find a high degree of alignment at all radii for most of the galaxies. In some cases, the alignment significantly changes as a function of radius implying that the halo shape twists; this effect correlates with recently formed bulges and is almost absent in the dark matter–only simulations. In a comparison against observational constraints, we find that $20{{\ \rm per\ cent}}$ of haloes in our sample are consistent with observational results derived from the Pal 5 stream that favours an almost spherical shape. Including baryons is a required element to achieve this level of agreement. In contrast, none of the simulations (neither dark matter only nor with baryons) match the constraints derived from the Sagittarius stream that favour an oblate dark matter halo.
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning

We propose a dual-hormone delivery strategy by exploiting deep reinforcement learning (RL) for people with Type 1 Diabetes (T1D). Specifically, double dilated recurrent neural networks (RNN) are used to learn the hormone delivery strategy, trained by a variant of Q-learning, whose inputs are raw data of glucose \& meal carbohydrate and outputs are dual-hormone (insulin and glucagon) delivery. Without prior knowledge of the glucose-insulin metabolism, we run the method on the UVA/Padova simulator. Hundreds days of self-play are performed to obtain a generalized model, then importance sampling is adopted to customize the model for personal use. \emph{In-silico} the proposed strategy achieves glucose time in target range (TIR) $93\%$ for adults and $83\%$ for adolescents given standard bolus, outperforming previous approaches significantly. The results indicate that deep RL is effective in building personalized hormone delivery strategy for people with T1D.
A note on optimal sampling strategy for structural variant detection using optical mapping

Structural variants compose the majority of human genetic variation, but are difficult to accurately assess using current genomic sequencing technologies. Optical mapping technologies, which measure the size of chromosomal fragments between labeled markers, offer an alternative approach. As these technologies mature toward becoming clinical tools, there is a need to develop an approach for determining the optimal strategy for sampling biological material in order to detect a structural variant at some threshold. Here we develop an optimization approach using a simple, yet realistic, model of the genomic mapping process using a hypergeometric distribution and probabilistic concentration inequalities. Our approach is both computationally and analytically tractable and includes a novel approach to getting tail bounds of hypergeometric distribution. We show that if a genomic mapping technology can sample most of the chromosomal fragments within a sample, comparatively little biological material is needed to detect a variant at high confidence.
BHAAV- A Text Corpus for Emotion Analysis from Hindi Stories

In this paper, we introduce the first and largest Hindi text corpus, named BHAAV, which means emotions in Hindi, for analyzing emotions that a writer expresses through his characters in a story, as perceived by a narrator/reader. The corpus consists of 20,304 sentences collected from 230 different short stories spanning across 18 genres such as Inspirational and Mystery. Each sentence has been annotated into one of the five emotion categories - anger, joy, suspense, sad, and neutral, by three native Hindi speakers with at least ten years of formal education in Hindi. We also discuss challenges in the annotation of low resource languages such as Hindi, and discuss the scope of the proposed corpus along with its possible uses. We also provide a detailed analysis of the dataset and train strong baseline classifiers reporting their performances.
Model-Based Reinforcement Learning Exploiting State-Action Equivalence

Leveraging an equivalence property in the state-space of a Markov Decision Process (MDP) has been investigated in several studies. This paper studies equivalence structure in the reinforcement learning (RL) setup, where transition distributions are no longer assumed to be known. We present a notion of similarity between transition probabilities of various state-action pairs of an MDP, which naturally defines an equivalence structure in the state-action space. We present equivalence-aware confidence sets for the case where the learner knows the underlying structure in advance. These sets are provably smaller than their corresponding equivalence-oblivious counterparts. In the more challenging case of an unknown equivalence structure, we present an algorithm called ApproxEquivalence that seeks to find an (approximate) equivalence structure, and define confidence sets using the approximate equivalence. To illustrate the efficacy of the presented confidence sets, we present C-UCRL, as a natural modification of UCRL2 for RL in undiscounted MDPs. In the case of a known equivalence structure, we show that C-UCRL improves over UCRL2 in terms of regret by a factor of $\sqrt{SA/C}$, in any communicating MDP with $S$ states, $A$ actions, and $C$ classes, which corresponds to a massive improvement when $C \ll SA$. To the best of our knowledge, this is the first work providing regret bounds for RL when an equivalence structure in the MDP is efficiently exploited. In the case of an unknown equivalence structure, we show through numerical experiments that C-UCRL combined with ApproxEquivalence outperforms UCRL2 in ergodic MDPs.
Patch Refinement - Localized 3D Object Detection

We introduce Patch Refinement a two-stage model for accurate 3D object detection and localization from point cloud data. Patch Refinement is composed of two independently trained Voxelnet-based networks, a Region Proposal Network (RPN) and a Local Refinement Network (LRN). We decompose the detection task into a preliminary Bird's Eye View (BEV) detection step and a local 3D detection step. Based on the proposed BEV locations by the RPN, we extract small point cloud subsets ("patches"), which are then processed by the LRN, which is less limited by memory constraints due to the small area of each patch. Therefore, we can apply encoding with a higher voxel resolution locally. The independence of the LRN enables the use of additional augmentation techniques and allows for an efficient, regression focused training as it uses only a small fraction of each scene. Evaluated on the KITTI 3D object detection benchmark, our submission from January 28, 2019, outperformed all previous entries on all three difficulties of the class car, using only 50 % of the available training data and only LiDAR information.
Improving Generalization in Meta Reinforcement Learning using Learned Objectives

Biological evolution has distilled the experiences of many learners into the general learning algorithms of humans. Our novel meta reinforcement learning algorithm MetaGenRL is inspired by this process. MetaGenRL distills the experiences of many complex agents to meta-learn a low-complexity neural objective function that decides how future individuals will learn. Unlike recent meta-RL algorithms, MetaGenRL can generalize to new environments that are entirely different from those used for meta-training. In some cases, it even outperforms human-engineered RL algorithms. MetaGenRL uses off-policy second-order gradients during meta-training that greatly increase its sample efficiency.
3D Manhattan Room Layout Reconstruction from a Single 360 Image

Recent approaches for predicting layouts from 360 panoramas produce excellent results. These approaches build on a common framework consisting of three steps: a pre-processing step based on edge-based alignment, prediction of layout elements, and a post-processing step by fitting a 3D layout to the layout elements. Until now, it has been difficult to compare the methods due to multiple different design decisions, such as the encoding network (e.g. SegNet or ResNet), type of elements predicted (e.g. corners, wall/floor boundaries, or semantic segmentation), or method of fitting the 3D layout. To address this challenge, we summarize and describe the common framework, the variants, and the impact of the design decisions. For a complete evaluation, we also propose extended annotations for the Matterport3D dataset, and introduce two depth-based evaluation metrics.
Validated Variational Inference via Practical Posterior Error Bounds

Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to the widespread use of variational methods is the lack of post-hoc accuracy measures that are both theoretically justified and computationally efficient. In this paper, we provide rigorous bounds on the error of posterior mean and uncertainty estimates that arise from full-distribution approximations, as in variational inference. Our bounds are widely applicable, as they require only that the approximating and exact posteriors have polynomial moments. Our bounds are also computationally efficient for variational inference because they require only standard values from variational objectives, straightforward analytic calculations, and simple Monte Carlo estimates. We show that our analysis naturally leads to a new and improved workflow for validated variational inference. Finally, we demonstrate the utility of our proposed workflow and error bounds on a robust regression problem and on a real-data example with a widely used multilevel hierarchical model.
Superconformal algebras and holomorphic field theories

We compute the holomorphic twists of four-dimensional superconformal algebras, and argue that the resulting algebras act naturally by holomorphic vector fields on holomorphically twisted superconformal theories. We demonstrate that this symmetry enhances to the action of an infinite-dimensional local Lie algebra, the Dolbeault resolution of all holomorphic vector fields on a punctured superspace. Global symmetries also enhance to the Dolbeault resolution of holomorphic functions valued in the Lie algebra; at the classical level, both of these higher symmetry algebras act naturally on the holomorphic twist of any Lagrangian theory, whether superconformal or not. We show that these algebras are related to two-dimensional chiral algebras extracted from four-dimensional superconformal theories in recent work; further deforming the differential induces the Koszul resolution of a plane in $\mathbb{C}^2$, and the cohomology of the higher symmetry algebras are the usual Virasoro and Kac-Moody chiral algebras. We show that the central extensions of those chiral algebras arise from recently studied central extensions of our higher symmetry algebras. However, the higher algebras admit many further deformations not originating in the global superconformal algebra; these localize to any smooth complex curve in $\mathbb{C}^2$, resolving the holomorphic vector fields there, and expect that they will lead to even more exotic behavior in the case of singular or nonreduced curves. We consider explicit examples of $\mathcal{N}=2$ gauge theories, and demonstrate that an anomaly to realizing the higher symmetry algebra at the quantum level vanishes precisely when the theory is, in fact, superconformal; for such theories, we also give an explicit description of the chiral algebras that result after further deformation.
Taxation and Social Justice

The link between taxation and justice is a classic debate issue, while also being very relevant at a time of changing environmental factors and conditions of the social and economic system. Technologically speaking, there are three types of taxes: progressive, proportional and regressive. Although justice, like freedom, is an element and manifestation of the imagined reality in citizens minds, the state must comply with it. In particular, the tax system has to adapt to the mass imagined reality in order for it to appear fairer and more acceptable.
Large-volume centimeter-wave cavities for axion searches

The scan rate of an axion haloscope is proportional to the square of the cavity volume. In this paper, a new class of thin-shell cavities are proposed to search for axionic dark matter. These cavities feature active volume much larger (>20×) than that of a conventional cylindrical haloscope, comparable quality factor Q, and a similar frequency tuning range. Full 3D numerical finite-element analyses have been used to show that the TM010 eigenmodes are singly polarized throughout the volume of the cavity and can facilitate axion-photon conversion in uniform magnetic field produced by a superconducting solenoid. To mitigate spurious mode crowding and volume losses due to localization, a pre-amplification binary summing network will be used for coupling. Because of the favorable frequency-scaling, the new cavities are most suitable for centimeter-wavelength (∼ 10–100 GHz), corresponding to the promising post-inflation axion production window. In this frequency range, the tight machining tolerances required for high-Q thin-shell cavities are achievable with standard machining techniques for near-infrared mirrors.
Phenomenological reconstruction of 
f(T)
 teleparallel gravity

We present a novel reconstruction method of $f(T)$ teleparallel gravity from phenomenological parameterizations of the deceleration parameter or other alternatives. This can be used as a toolkit to produce viable modified gravity scenarios directly related to cosmological observations. We test two parameterizations of the deceleration parameter considered in recent literatures in addition to one parametrization of the effective (total) equation of state (EoS) of the universe. We use the asymptotic behaviour of the matter density parameter as an extra constraint to identify the viable range of the model parameters. One of the tested models shows how tiny modification can produce viable cosmic scenarios quantitatively similar to $\Lambda$CDM but qualitatively different whereas the dark energy (DE) sector becomes dynamical and fully explained by modified gravity not by a cosmological constant.
X-ray Search for Axions from Nearby Isolated Neutron Stars

Axions may be produced thermally inside the cores of neutron stars (NSs), escape the stars due to their weak interactions with matter, and subsequently convert into X-rays in the magnetic fields surrounding the NSs. We perform a hard X-ray search from 2 - 8 keV arising from this emission mechanism from the nearby Magnificent Seven isolated NSs using archival XMM-Newton and Chandra data. These NSs are unique in that they have only been detected in the UV and soft X-ray bands; their observed fluxes are thought to arise from thermal emission at temperatures ~100 eV from the NS surfaces. We use the X-ray datasets to set the strongest limits to-date on the product of the axion-photon and axion-nucleon couplings for axion masses below ~$10^{-4}$ eV. Moreover, we interpret an observed excess of hard X-rays from the Magnificent Seven in the context of the axion model, and we discuss possible future measurements to help rule out or confirm this possibility.
A global view of the off-shell Higgs portal

We study for the first time the collider reach on the derivative Higgs portal, the leading effective interaction that couples a pseudo Nambu-Goldstone boson (pNGB) scalar Dark Matter to the Standard Model. We focus on Dark Matter pair production through an off-shell Higgs boson, which is analyzed in the vector boson fusion channel. A variety of future high-energy lepton colliders as well as hadron colliders are considered, including CLIC, a muon collider, the High-Luminosity and High-Energy versions of the LHC, and FCC-hh. Implications on the parameter space of pNGB Dark Matter are discussed. In addition, we give improved and extended results for the collider reach on the marginal Higgs portal, under the assumption that the new scalars escape the detector, as motivated by a variety of beyond the Standard Model scenarios.