nils-herrmann/clean_scirepeval huggingface dataset:
Robust and Adaptive Planning under Model Uncertainty

Planning under model uncertainty is a fundamental problem across many applications of decision making and learning. In this paper, we propose the Robust Adaptive Monte Carlo Planning (RAMCP) algorithm, which allows computation of risk-sensitive Bayes-adaptive policies that optimally trade off exploration, exploitation, and robustness. RAMCP formulates the risk-sensitive planning problem as a two-player zero-sum game, in which an adversary perturbs the agent's belief over the models. We introduce two versions of the RAMCP algorithm. The first, RAMCP-F, converges to an optimal risk-sensitive policy without having to rebuild the search tree as the underlying belief over models is perturbed. The second version, RAMCP-I, improves computational efficiency at the cost of losing theoretical guarantees, but is shown to yield empirical results comparable to RAMCP-F. RAMCP is demonstrated on an n-pull multi-armed bandit problem, as well as a patient treatment scenario.
Overview of initial results from the reconnaissance flyby of a Kuiper Belt planetesimal: 2014 MU69

The centerpiece objective of the NASA New Horizons first Kuiper Extended Mission (KEM-1) was the close flyby of the Kuiper Belt Object KBO) 2014 MU69, nicknamed Ultima Thule. On 1 Jan 2019 this flyby culminated, making the first close observations of a small KBO. Initial post flyby trajectory reconstruction indicated the spacecraft approached to within ~3500 km of MU69 at 5:33:19 UT. Here we summarize the earliest results obtained from that successful flyby. At the time of this submission, only 4 days of data down-link from the flyby were available; well over an order of magnitude more data will be down-linked by the time of this Lunar and Planetary Science Conference presentation in 2019 March. Therefore many additional results not available at the time of this abstract submission will be presented in this review talk.
Manipulation-Skill Assessment from Videos with Spatial Attention Network

Recent advances in computer vision have made it possible to automatically assess from videos the manipulation skills of humans in performing a task, which breeds many important applications in domains such as health rehabilitation and manufacturing. Previous methods of video-based skill assessment did not consider the spatial attention mechanism humans use in assessing videos, limiting their performance as only a small part of video regions is informative for skill assessment. Our motivation here is to estimate attention in videos that helps to focus on critically important video regions for better skill assessment. In particular, we propose a novel RNN-based spatial attention model that considers accumulated attention state from previous frames as well as high-level information about the progress of an undergoing task. We evaluate our approach on a newly collected dataset of infant grasping task and four existing datasets of hand manipulation tasks. Experiment results demonstrate that state-of-the-art performance can be achieved by considering attention in automatic skill assessment.
Evidence for modified Newtonian dynamics from Cavendish-type gravitational constant experiments

Recent experimental results for the gravitational constant G from Cavendish-type experiments were analysed in the framework of modified Newtonian dynamics (MOND). MOND corrections were applied to the equation of motion of a pendulum, under the assumption that the magnitude of the horizontal time dependent gravitational acceleration determines the amount of MOND corrections. The large vertical component of the local gravitational field of the earth is fully compensated by the alignment of the torsion pendulum in accordance with Newton’s third law and therefore not considered for MOND corrections. From the analysis of the MOND corrected equation of motion of a realistic torsion pendulum with mixed gravitational and electromagnetic restoring torque simple rules for meaningful MOND corrections of measured G values determined by different operational modes of Cavendish type G experiments were derived. Based on this analysis the reported discrepancies for G determined by ‘static deflection’ and ‘electrostatic servo’ methods of the ‘BIPM’ experiment by Quinn et al and between time-of-swing and angular acceleration feedback (AAF) methods for the ‘HUST’ experiment by Li et al could be fully resolved by MOND corrections using one common MOND interpolation function, determined by a one parameter fit. The MOND corrected ‘BIPM’ and ‘HUST’ results, along with other ‘single method’ results from G experiments by Gundlach and Merkovitz, Schlamminger et al and Newman et al lead to an average G value of 6.67 422 · 10−11 m3 kg−1 s−2 with a standard deviation of 12.5 ppm only. The applied MOND correction procedure and the fitted interpolation function employed for the G experiments were found to be consistent with the most viable MOND fits to galaxy rotation curves.
Sequential Attention-based Network for Noetic End-to-End Response Selection

The noetic end-to-end response selection challenge as one track in Dialog System Technology Challenges 7 (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context. This paper describes our systems that are ranked the top on both datasets under this challenge, one focused and small (Advising) and the other more diverse and large (Ubuntu). Previous state-of-the-art models use hierarchy-based (utterance-level and token-level) neural networks to explicitly model the interactions among different turns' utterances for context modeling. In this paper, we investigate a sequential matching model based only on chain sequence for multi-turn response selection. Our results demonstrate that the potentials of sequential matching approaches have not yet been fully exploited in the past for multi-turn response selection. In addition to ranking the top in the challenge, the proposed model outperforms all previous models, including state-of-the-art hierarchy-based models, and achieves new state-of-the-art performances on two large-scale public multi-turn response selection benchmark datasets.
The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection

Aggregating extra features of novel modality brings great advantages for building robust pedestrian detector under adverse illumination conditions. However, misaligned imagery still persists in multispectral scenario and will depress the performance of detector in a non-trivial way. In this paper, we first present and explore the cross-modality disparity problem in multispectral pedestrian detection, providing insights into the utilization of multimodal inputs. Then, to further address this issue, we propose a novel framework including a region feature alignment module and the region of interest (RoI) jittering training strategy. Moreover, dense, high-quality, and modality-independent color-thermal annotation pairs are provided to scrub the large-scale KAIST dataset to benefit future multispectral detection research. Extensive experiments demonstrate that the proposed approach improves the robustness of detector with a large margin and achieves state-of-the-art performance with high efficiency. Code and data will be publicly available.
What Do Language Representations Really Represent?

A neural language model trained on a text corpus can be used to induce distributed representation of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just as it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations.We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, whereas genetic relationships—a convenient benchmark used for evaluation in previous work—appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another.
Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals.

Hashing has been widely applied to multimodal retrieval on large-scale multimedia data due to its efficiency in computation and storage. In this article, we propose a novel deep semantic multimodal hashing network (DSMHN) for scalable image-text and video-text retrieval. The proposed deep hashing framework leverages 2-D convolutional neural networks (CNN) as the backbone network to capture the spatial information for image-text retrieval, while the 3-D CNN as the backbone network to capture the spatial and temporal information for video-text retrieval. In the DSMHN, two sets of modality-specific hash functions are jointly learned by explicitly preserving both intermodality similarities and intramodality semantic labels. Specifically, with the assumption that the learned hash codes should be optimal for the classification task, two stream networks are jointly trained to learn the hash functions by embedding the semantic labels on the resultant hash codes. Moreover, a unified deep multimodal hashing framework is proposed to learn compact and high-quality hash codes by exploiting the feature representation learning, intermodality similarity-preserving learning, semantic label-preserving learning, and hash function learning with different types of loss functions simultaneously. The proposed DSMHN method is a generic and scalable deep hashing framework for both image-text and video-text retrievals, which can be flexibly integrated with different types of loss functions. We conduct extensive experiments for both single-modal- and cross-modal-retrieval tasks on four widely used multimodal-retrieval data sets. Experimental results on both image-text- and video-text-retrieval tasks demonstrate that the DSMHN significantly outperforms the state-of-the-art methods.
Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks

Activation functions play a crucial role in neural networks because they are the nonlinearities which have been attributed to the success story of deep learning. One of the currently most popular activation functions is ReLU, but several competitors have recently been proposed or ‘discovered’, including LReLU functions and swish. While most works compare newly proposed activation functions on few tasks (usually from image classification) and against few competitors (usually ReLU), we perform the first largescale comparison of 21 activation functions across eight different NLP tasks. We find that a largely unknown activation function performs most stably across all tasks, the so-called penalized tanh function. We also show that it can successfully replace the sigmoid and tanh gates in LSTM cells, leading to a 2 percentage point (pp) improvement over the standard choices on a challenging NLP task.
Low-Cost Transfer Learning of Face Tasks

Do we know what the different filters of a face network 
represent? Can we use this filter information to train other 
tasks without transfer learning? For instance, can age, head 
pose, emotion and other face related tasks be learned from 
face recognition network without transfer learning? Understanding the role of these filters allows us to transfer 
knowledge across tasks and take advantage of large data 
sets in related tasks. Given a pretrained network, we can 
infer which tasks the network generalizes for and the best 
way to transfer the information to a new task. 
We demonstrate a computationally inexpensive algorithm to reuse the filters of a face network for a task it was 
not trained for. Our analysis proves these attributes can be 
extracted with an accuracy comparable to what is obtained 
with transfer learning, but 10 times faster. We show that the 
information about other tasks is present in relatively small 
number of filters. We use these insights to do task specific 
pruning of a pretrained network. Our method gives significant compression ratios with reduction in size of 95% and 
computational reduction of 60%
Short life and abrupt death of PicSat, a small 3U CubeSat dreaming of exoplanet detection

PicSat was a three unit CubeSat (measuring 30 cm Χ 10 cm Χ 10 cm) which was developed to monitor the β Pictoris system. The main science objective was the detection of a possible transit of the giant planet β Pictoris b's Hill sphere. Secondary objectives included studying the circumstellar disk, and detecting exocomets in the visible band. The mission also had a technical objective: demonstrate our ability to inject starlight in a single mode fiber, on a small satellite platform. To answer all those objectives, a dedicated opto-mechanical payload was built, and integrated in a commercial 3U platform, along with a commercial ADCS (Attitude Determination and Control System). The satellite successfully reached Low Earth Orbit on the PSLV-C40 rocket, on January, 12, 2018. Unfortunately, on March, 20, 2018, after 10 weeks of operations, the satellite fell silent, and the mission came to an early end. Furthermore, due to a failure of the ADCS, the satellite never actually pointed toward its target star during the 10 weeks of operations. In this paper, we report on the PicSat mission development process, and on the reasons why it did not deliver any useful astronomical data
The Arrival of News and Return Jumps in Stock Markets: A Nonparametric Approach

This paper introduces a non-parametric framework to statistically examine how news events, such as company or macroeconomic announcements, contribute to the pre- and post-event jump dynamics of stock prices under the intraday seasonality of the news and jumps. We demonstrate our framework, which has several advantages over the existing methods, by using data for i) the S&P 500 index ETF, SPY, with macroeconomic announcements and ii) Nasdaq Nordic Large-Cap stocks with scheduled and non-scheduled company announcements. We provide strong evidence that non-scheduled company announcements and some macroeconomic announcements contribute jumps that follow the releases and also some evidence for pre-jumps that precede the scheduled arrivals of public information, which may indicate non-gradual information leakage. Especially interim reports of Nordic large-cap companies are found containing important information to yield jumps in stock prices. Additionally, our results show that releases of unexpected information are not reacted to uniformly across Nasdaq Nordic markets, even if they are jointly operated and are based on the same exchange rules.
Image Recognition of Tea Leaf Diseases Based on Convolutional Neural Network

In order to identify and prevent tea leaf diseases effectively, convolution neural network (CNN) was used to realize the image recognition of tea disease leaves. Firstly, image segmentation and data enhancement are used to preprocess the images, and then these images were input into the network for training. Secondly, to reach a higher recognition accuracy of CNN, the learning rate and iteration numbers were adjusted frequently and the dropout was added properly in the case of over-fitting. Finally, the experimental results show that the recognition accuracy of CNN is 93.75%, while the accuracy of SVM and BP neural network is 89.36% and 87.69% respectively. Therefore, the recognition algorithm based on CNN is better in classification and can improve the recognition efficiency of tea leaf diseases effectively.
Guess What's on my Screen? Clustering Smartphone Screenshots with Active Learning

A significant proportion of individuals' daily activities is experienced through digital devices. Smartphones in particular have become one of the preferred interfaces for content consumption and social interaction. Identifying the content embedded in frequently-captured smartphone screenshots is thus a crucial prerequisite to studies of media behavior and health intervention planning that analyze activity interplay and content switching over time. Screenshot images can depict heterogeneous contents and applications, making the a priori definition of adequate taxonomies a cumbersome task, even for humans. Privacy protection of the sensitive data captured on screens means the costs associated with manual annotation are large, as the effort cannot be crowd-sourced. Thus, there is need to examine utility of unsupervised and semi-supervised methods for digital screenshot classification. This work introduces the implications of applying clustering on large screenshot sets when only a limited amount of labels is available. In this paper we develop a framework for combining K-Means clustering with Active Learning for efficient leveraging of labeled and unlabeled samples, with the goal of discovering latent classes and describing a large collection of screenshot data. We tested whether SVM-embedded or XGBoost-embedded solutions for class probability propagation provide for more well-formed cluster configurations. Visual and textual vector representations of the screenshot images are derived and combined to assess the relative contribution of multi-modal features to the overall performance.
Exact solution of generalized cooperative SIR dynamics

In this paper, we introduce a general framework for co-infection as cooperative SIR dynamics. We first solve analytically CGCG model  and then the generalized model in symmetric scenarios. We calculate transition points, order parameter, i.e. total number of infected hosts. Also we show analytically there is a saddle-node bifurcation for two cooperative SIR dynamics and the transition is hybrid. Moreover, we investigate where symmetric solution is stable for initial fluctuations. Then we study asymmetric cases of parameters. The more asymmetry, for the primary and secondary infection rates of one pathogen in comparison to the other pathogen, can lead to the less infected hosts, the higher epidemic threshold and continuous transitions. Our model and results for coinfection in combination with super-infection  can open a road to model disease ecology.
Current and future measurements of semi-inclusivehadron+jet distributions with ALICE

The measurement of jets recoiling from a trigger hadron in heavy-ion collisions can be used to understand the properties of the Quark Gluon Plasma. Jet-medium interactions cause jets to lose energy in the medium and may modify the jet structure. Jet deflection towards large angles due to scattering off of quasi-particles in the Quark-Gluon Plasma may also occur, which can be studied through a measurement of the hadron-jet acoplanarity. These phenomena can be studied through the semi-inclusive distribution of track-based jets recoiling from a trigger hadron. This contribution presents the latest measurements and prospects of semi-inclusive hadron+jet distributions with ALICE. Constraints on energy loss in p-Pb collisions and future prospects to measure energy loss in smaller systems are shown. A jet shape measurement of N-subjettiness using recoil jets is outlined. Finally, prospects for hadron+jet acoplanarity measurements with ALICE are presented.
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

Artificial Neural Networks are connectionist systems that perform a given task by learning on examples without having prior knowledge about the task. This is done by finding an optimal point estimate for the weights in every node. Generally, the network using point estimates as weights perform well with large datasets, but they fail to express uncertainty in regions with little or no data, leading to overconfident decisions. 
In this paper, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights. Furthermore, the proposed BayesCNN architecture is applied to tasks like Image Classification, Image Super-Resolution and Generative Adversarial Networks. The results are compared to point-estimates based architectures on MNIST, CIFAR-10 and CIFAR-100 datasets for Image CLassification task, on BSD300 dataset for Image Super Resolution task and on CIFAR10 dataset again for Generative Adversarial Network task. 
BayesCNN is based on Bayes by Backprop which derives a variational approximation to the true posterior. We, therefore, introduce the idea of applying two convolutional operations, one for the mean and one for the variance. Our proposed method not only achieves performances equivalent to frequentist inference in identical architectures but also incorporate a measurement for uncertainties and regularisation. It further eliminates the use of dropout in the model. Moreover, we predict how certain the model prediction is based on the epistemic and aleatoric uncertainties and empirically show how the uncertainty can decrease, allowing the decisions made by the network to become more deterministic as the training accuracy increases. Finally, we propose ways to prune the Bayesian architecture and to make it more computational and time effective.
Diagrammatic Coupled Cluster Monte Carlo.

We propose a modified coupled cluster Monte Carlo algorithm that stochastically samples connected terms within the truncated Baker-Campbell-Hausdorff expansion of the similarity-transformed Hamiltonian by construction of coupled cluster diagrams on the fly. Our new approach-diagCCMC-allows propagation to be performed using only the connected components of the similarity-transformed Hamiltonian, greatly reducing the memory cost associated with the stochastic solution of the coupled cluster equations. We show that for perfectly local, noninteracting systems diagCCMC is able to represent the coupled cluster wavefunction with a memory cost that scales linearly with system size. The favorable memory cost is observed with the only assumption of fixed stochastic granularity and is valid for arbitrary levels of coupled cluster theory. Significant reduction in memory cost is also shown to smoothly appear with dissociation of a finite chain of helium atoms. This approach is also shown not to break down in the presence of strong correlation through the example of a stretched nitrogen molecule. Our novel methodology moves the theoretical basis of coupled cluster Monte Carlo closer to deterministic approaches.
Clock Time in Quantum Cosmology

We consider the conditioning of the timeless solution to the Wheeler–DeWitt equation by a predefined matter clock state in the simple scenario of an FLRW universe. The resulting evolution of the geometrodynamical degree of freedom with respect to clock time is characterized by the “Berry connection” of the reduced geometrodynamical space, which relies on the coupling of the clock with the geometry. When the connection vanishes, the standard Schrödinger equation is obtained for the geometry with respect to clock time. When one considers environment-induced decoherence in the semiclassical limit, this condition is satisfied, and clock time coincides with cosmic time. Explicit results for the conditioned wave functions for minimal clocks made up of two quantum harmonic oscillator eigenstates are shown.