nils-herrmann/clean_scirepeval huggingface dataset:
Dual Residual Network for Accurate Human Activity Recognition

Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of automatic learning, we propose a novel asymmetric residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, while still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of the ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared the results with some conventional and state-of-the-art learning-based methods. We discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets.
Reinforcement Learning with Dynamic Boltzmann Softmax Updates

Value function estimation is an important task in reinforcement learning, i.e., prediction. The Boltzmann softmax operator is a natural value estimator and can provide several benefits. However, it does not satisfy the non-expansion property, and its direct use may fail to converge even in value iteration. In this paper, we propose to update the value function with dynamic Boltzmann softmax (DBS) operator, which has good convergence property in the setting of planning and learning. Experimental results on GridWorld show that the DBS operator enables better estimation of the value function, which rectifies the convergence issue of the softmax operator. Finally, we propose the DBS-DQN algorithm by applying the DBS operator, which outperforms DQN substantially in 40 out of 49 Atari games.
Dense Relational Captioning: Triple-Stream Networks for Relationship-Based Captioning

Our goal in this work is to train an image captioning model that generates more dense and informative captions. We introduce "relational captioning," a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in an image. Relational captioning is a framework that is advantageous in both diversity and amount of information, leading to image understanding based on relationships. Part-of-speech (POS, i.e. subject-object-predicate categories) tags can be assigned to every English word. We leverage the POS as a prior to guide the correct sequence of words in a caption. To this end, we propose a multi-task triple-stream network (MTTSNet) which consists of three recurrent units for the respective POS and jointly performs POS prediction and captioning. We demonstrate more diverse and richer representations generated by the proposed model against several baselines and competing methods.
Unpaired Image Denoising Using a Generative Adversarial Network in X-Ray CT

This paper proposes a deep learning-based denoising method for noisy low-dose computerized tomography (CT) images in the absence of paired training data. The proposed method uses a fidelity-embedded generative adversarial network (GAN) to learn a denoising function from unpaired training data of low-dose CT (LDCT) and standard-dose CT (SDCT) images, where the denoising function is the optimal generator in the GAN framework. This paper analyzes the f-GAN objective to derive a suitable generator that is optimized by minimizing a weighted sum of two losses: the Kullback-Leibler divergence between an SDCT data distribution and a generated distribution, and the $\ell _{2}$ loss between the LDCT image and the corresponding generated images (or denoised image). The computed generator reflects the prior belief about SDCT data distribution through training. We observed that the proposed method allows the preservation of fine anomalous features while eliminating noise. The experimental results show that the proposed deep-learning method with unpaired datasets performs comparably to a method using paired datasets. A clinical experiment was also performed to show the validity of the proposed method for noise arising in the low-dose X-ray CT.
Online Explanation Generation for Human-Robot Teaming

As AI becomes an integral part of our lives, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to justify its behavior is one of the key requirements of explainable agency. Prior work on explanation generation has been focused on supporting the rationale behind the robot's decision or behavior. These approaches, however, fail to consider the mental demand for understanding the received explanation. In other words, the human teammate is expected to understand an explanation no matter how much information is presented. In this work, we argue that explanations, especially those of a complex nature, should be made in an online fashion during the execution, which helps spread out the information to be explained and thus reduce the mental workload of humans in highly cognitive demanding tasks. However, a challenge here is that the different parts of an explanation may be dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented with three variations satisfying different "online" properties. The new explanation generation methods are based on a model reconciliation setting introduced in our prior work. We evaluated our methods both with human subjects in a simulated rover domain, using NASA Task Load Index (TLX), and synthetically with ten different problems across two standard IPC domains. Results strongly suggest that our methods generate explanations that are perceived as less cognitively demanding and much preferred over the baselines and are computationally efficient.
Robots that Sync and Swarm: A Proof of Concept in ROS 2

A unified mathematical model for synchronisation and swarming has recently been proposed. Each system entity, called a “swarmalator”, coordinates its internal phase and location with the other entities in a way that these two attributes are mutually coupled. This paper realises and studies, for the first time, the concept of swarmalators in a technical system. We adapt and extend the original model for its use with mobile robots and implement it in the Robot Operating System 2 (ROS 2). Simulations and experiments with small robots demonstrate the feasibility of the model and show its potential to be applied to real-world systems. All types of space-time patterns achieved in theory can be reproduced in practice. Applications can be found in monitoring, exploration, entertainment and art, among other domains.
A New Method to Constrain the Origins of Dark-matter-free Galaxies and Their Unusual Globular Clusters

We present a novel method to constrain the past collisional evolution of observed globular cluster (GC) systems, in particular their mass functions. We apply our method to a pair of galaxies hypothesized to have recently undergone an episode of violent relaxation due to a strong galaxy–galaxy interaction, namely, NGC 1052-DF2 and NGC 1052-DF4. We begin by exploring the observational evidence for a collisional origin for these two recently discovered ultradiffuse galaxies observed in the NGC 1052 group, posited in the literature to be dark matter free. We compute the timescales for infall to the central nucleus due to dynamical friction for the GCs in these galaxies, using the shortest of these times to constrain how long ago a galaxy–galaxy interaction could have occurred. We go on to quantify the initial GC numbers and densities needed for significant collisional evolution to occur within the allotted times and show that, if the hypothesis of a previous galaxy–galaxy interaction is correct, a paucity of low-mass GCs should be revealed by deeper observational surveys. If any are found, they should be more spatially extended than the currently observed GC population. Finally, we apply our method to these galaxies, in order to illustrate its efficacy in constraining their dynamical evolution. Our results motivate more complete observations of the GC luminosity functions in these galaxies, in addition to future studies aimed at combining the method presented here with a suite of numerical simulations in order to further constrain the origins of the curious GC populations in these (and other) galaxies.
On the properties of the massive binary black hole merger GW170729

We present a detailed investigation into the properties of GW170729, the gravitational wave with the most massive and distant source confirmed to date. We employ an extensive set of waveform models, including new improved models that incorporate the effect of higher-order waveform modes which are particularly important for massive systems. We find no indication of spin-precession, but the inclusion of higher-order modes in the models results in an improved estimate for the mass ratio of $(0.3-0.8)$ at the 90\% credible level. Our updated measurement excludes equal masses at that level. We also find that models with higher-order modes lead to the data being more consistent with a smaller effective spin, with the probability that the effective spin is greater than zero being reduced from $99\%$ to $94\%$. The 90\% credible interval for the effective spin parameter is now $(-0.01-0.50)$. Additionally, the recovered signal-to-noise ratio increases by $\sim0.3$ units compared to analyses without higher-order modes. We study the effect of common spin priors on the derived spin and mass measurements, and observe small shifts in the spins, while the masses remain unaffected. We argue that our conclusions are robust against systematic errors in the waveform models. We also compare the above waveform-based analysis which employs compact-binary waveform models to a more flexible wavelet- and chirplet-based analysis. We find consistency between the two, with overlaps of $\sim 0.9$, typical of what is expected from simulations of signals similar to GW170729, confirming that the data are well-described by the existing waveform models. Finally, we study the possibility that the primary component of GW170729 was the remnant of a past merger of two black holes and find this scenario to be indistinguishable from the standard formation scenario.
Exploring a cosmic-ray origin of the multiwavelength emission in M31

Author(s): McDaniel, Alex; Jeltema, Tesla; Profumo,  A recent detection of spatially extended gamma-ray emission in the central region of the Andromeda galaxy (M31) has led to several possible explanations being put forth, including dark matter annihilation and millisecond pulsars. Another possibility is that the emission in M31 can be accounted for with a purely astrophysical cosmic-ray (CR) scenario. This scenario would lead to a rich multi-wavelength emission that can, in turn, be used to test it. Relativistic cosmic-ray electrons (CRe) in magnetic fields produce radio emission through synchrotron radiation, while X-rays and gamma rays are produced through inverse Compton scattering. Additionally, collisions of primary cosmic-ray protons (CRp) in the interstellar medium produce charged and neutral pions that then decay into secondary CRe (detectable through radiative processes) and gamma-rays. Here, we explore the viability of a CR origin for multi-wavelength emission in M31, taking into consideration three scenarios: a CR scenario dominated by primary CRe, one dominated by CRp and the resulting secondary CRe and gamma rays from neutral pion decay, and a final case in which both of these components exist simultaneously. We find that the multi-component model is the most promising, and is able to fit the multi-wavelength spectrum for a variety of astrophysical parameters consistent with previous studies of M31 and of cosmic-ray physics. However, the CR power injection implied by our models exceeds the estimated CR power injection from typical astrophysical cosmic-ray sources such as supernovae.
Leveling the playing field: fairness in AI versus human game benchmarks

From the beginning of the history of AI, there has been interest in games as a platform of research. As the field developed, human-level competence in complex games became a target researchers worked to reach. Only relatively recently has this target been finally met for traditional tabletop games such as Backgammon, Chess and Go. This prompted a shift in research focus towards electronic games, which provide unique new challenges. As is often the case with AI research, these results are liable to be exaggerated or mis-represented by either authors or third parties. The extent to which these game benchmarks constitute "fair" competition between human and AI is also a matter of debate. In this paper, we review statements made by reseachers and third parties in the general media and academic publications about these game benchmark results. We analyze what a fair competition would look like and suggest a taxonomy of dimensions to frame the debate of fairness in game contests between humans and machines. Eventually, we argue that there is no completely fair way to compare human and AI performance on a game.
Visualizing evidence for Alzheimer's disease in deep neural networks trained on structural MRI data

Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation ("Which change in voxels would change the outcome most?"), the LRP method is able to directly highlight positive contributions to the network classification in the input space. Thus, the highlighted areas can be interpreted as the 'positive evidence' used by the network for deciding whether an individual has AD. We find that this LRP-evidence indeed fulfills those expectations that one would have towards AD evidence: (1) it is very specific for individuals ("Why does this person have AD?") with high inter-patient variability, (2) there is very little evidence for AD in healthy controls and (3) areas that exhibit a lot of evidence correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed evidence per brain area, e.g. the 'evidence density' or 'evidence gain'. Although these metrics produce very individual 'fingerprints' of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including hippocampus and amygdala. We conclude that LRP provides a powerful tool for assisting clinicians in finding evidence for AD (and potentially other diseases) in structural MRI data.
Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration

Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration. Commonly these signals are added as bonus rewards, which results in a mixture policy that neither conducts exploration nor task fulfillment resolutely. In this paper, we instead learn separate intrinsic and extrinsic task policies and schedule between these different drives to accelerate exploration and stabilize learning. Moreover, we introduce a new type of intrinsic reward denoted as successor feature control (SFC), which is general and not task-specific. It takes into account statistics over complete trajectories and thus differs from previous methods that only use local information to evaluate intrinsic motivation. We evaluate our proposed scheduled intrinsic drive (SID) agent using three different environments with pure visual inputs: VizDoom, DeepMind Lab and DeepMind Control Suite. The results show a substantially improved exploration efficiency with SFC and the hierarchical usage of the intrinsic drives. A video of our experimental results can be found at this https URL.
Natural Language Generation at Scale: A Case Study for Open Domain Question Answering

Current approaches to Natural Language Generation (NLG) for dialog mainly focus on domain-specific, task-oriented applications (e.g. restaurant booking) using limited ontologies (up to 20 slot types), usually without considering the previous conversation context. Furthermore, these approaches require large amounts of data for each domain, and do not benefit from examples that may be available for other domains. This work explores the feasibility of applying statistical NLG to scenarios requiring larger ontologies, such as multi-domain dialog applications or open-domain question answering (QA) based on knowledge graphs. We model NLG through an Encoder-Decoder framework using a large dataset of interactions between real-world users and a conversational agent for open-domain QA. First, we investigate the impact of increasing the number of slot types on the generation quality and experiment with different partitions of the QA data with progressively larger ontologies (up to 369 slot types). Second, we perform multi-task learning experiments between open-domain QA and task-oriented dialog, and benchmark our model on a popular NLG dataset. Moreover, we experiment with using the conversational context as an additional input to improve response generation quality. Our experiments show the feasibility of learning statistical NLG models for open-domain QA with larger ontologies.
Resolved UV and [C ii] Structures of Luminous Galaxies within the Epoch of Reionization

We present new deep ALMA and Hubble Space Telescope (HST)/WFC3 observations of MASOSA and VR7, two luminous Lyα emitters (LAEs) at z = 6.5, for which the UV continuum levels differ by a factor of four. No IR dust continuum emission is detected in either, indicating little amounts of obscured star formation and/or high dust temperatures. MASOSA, with a UV luminosity M1500 = −20.9, compact size, and very high Lyα , is undetected in [C ii] to a limit of L[C ii] < 2.2 × 107 L⊙, implying a metallicity Z ≲ 0.07 Z⊙. Intriguingly, our HST data indicate a red UV slope β = −1.1 ± 0.7, at odds with the low dust content. VR7, which is a bright (M1500 = −22.4) galaxy with moderate color (β = −1.4 ± 0.3) and Lyα EW0 = 34 Å, is clearly detected in [C ii] emission (S/N = 15). VR7's rest-frame UV morphology can be described by two components separated by ≈1.5 kpc and is globally more compact than the [C ii] emission. The global [C ii]/UV ratio indicates Z ≈ 0.2 Z⊙, but there are large variations in the UV/[C ii] ratio on kiloparsec scales. We also identify diffuse, possibly outflowing, [C ii]-emitting gas at ≈100 km s−1 with respect to the peak. VR7 appears to be assembling its components at a slightly more evolved stage than other luminous LAEs, with outflows already shaping its direct environment at z ∼ 7. Our results further indicate that the global [C ii]−UV relation steepens at SFR < 30 M⊙ yr−1, naturally explaining why the [C ii]/UV ratio is anticorrelated with Lyα EW in many, but not all, observed LAEs.
Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems

With the advent of conversational assistants, like Amazon Alexa, Google Now, etc., dialogue systems are gaining a lot of traction, especially in industrial setting. These systems typically consist of Spoken Language understanding component which, in turn, consists of two tasks - Intent Classification (IC) and Slot Labeling (SL). Generally, these two tasks are modeled together jointly to achieve best performance. However, this joint modeling adds to model obfuscation. In this work, we first design framework for a modularization of joint IC-SL task to enhance architecture transparency. Then, we explore a number of self-attention, convolutional, and recurrent models, contributing a large-scale analysis of modeling paradigms for IC+SL across two datasets. Finally, using this framework, we propose a class of ‘label-recurrent’ models that otherwise non-recurrent, with a 10-dimensional representation of the label history, and show that our proposed systems are easy to interpret, highly accurate (achieving over 30% error reduction in SL over the state-of-the-art on the Snips dataset), as well as fast, at 2x the inference and 2/3 to 1/2 the training time of comparable recurrent models, thus giving an edge in critical real-world systems.
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening

We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. 1) Our network’s novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. 2) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. 3) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. 4) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network’s performance on different subpopulations of the screening population, the model’s design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breast_cancer_classifier.