nils-herrmann/clean_scirepeval huggingface dataset:
Generative Image Inpainting with Submanifold Alignment

Image inpainting aims at restoring missing regions of corrupted images, which has many applications such as image restoration and object removal. However, current GAN-based generative inpainting models do not explicitly exploit the structural or textural consistency between restored contents and their surrounding contexts. To address this limitation, we propose to enforce the alignment (or closeness) between the local data submanifolds (subspaces) around restored images and those around the original (uncorrupted) images during the learning process of GAN-based inpainting models. We exploit Local Intrinsic Dimensionality (LID) to measure, in deep feature space, the alignment between data submanifolds learned by a GAN model and those of the original data, from a perspective of both images (denoted as iLID) and local patches (denoted as pLID) of images. We then apply iLID and pLID as regularizations for GAN-based inpainting models to encourage two different levels of submanifold alignments: 1) an image-level alignment to improve structural consistency, and 2) a patch-level alignment to improve textural details. Experimental results on four benchmark datasets show that our proposed model can generate more accurate results than state-of-the-art models.
Chainer: A Deep Learning Framework for Accelerating the Research Cycle

Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. Chainer provides acceleration using Graphics Processing Units with a familiar NumPy-like API through CuPy, supports general and dynamic models in Python through Define-by-Run, and also provides add-on packages for state-of-the-art computer vision models as well as distributed training.
Illusion of Causality in Visualized Data

Students who eat breakfast more frequently tend to have a higher grade point average. From this data, many people might confidently state that a before-school breakfast program would lead to higher grades. This is a reasoning error, because correlation does not necessarily indicate causation – X and Y can be correlated without one directly causing the other. While this error is pervasive, its prevalence might be amplified or mitigated by the way that the data is presented to a viewer. Across three crowdsourced experiments, we examined whether how simple data relations are presented would mitigate this reasoning error. The first experiment tested examples similar to the breakfast-GPA relation, varying in the plausibility of the causal link. We asked participants to rate their level of agreement that the relation was correlated, which they rated appropriately as high. However, participants also expressed high agreement with a causal interpretation of the data. Levels of support for the causal interpretation were not equally strong across visualization types: causality ratings were highest for text descriptions and bar graphs, but weaker for scatter plots. But is this effect driven by bar graphs aggregating data into two groups or by the visual encoding type? We isolated data aggregation versus visual encoding type and examined their individual effect on perceived causality. Overall, different visualization designs afford different cognitive reasoning affordances across the same data. High levels of data aggregation by graphs tend to be associated with higher perceived causality in data. Participants perceived line and dot visual encodings as more causal than bar encodings. Our results demonstrate how some visualization designs trigger stronger causal links while choosing others can help mitigate unwarranted perceptions of causality.
Hiring in the substance use disorder treatment related sector during the first five years of Medicaid expansion.

Effective treatment strategies exist for substance use disorder (SUD), however severe hurdles remain in ensuring adequacy of the SUD treatment (SUDT) workforce as well as improving SUDT affordability, access and stigma. Although evidence shows recent increases in SUD medication access from expanding Medicaid availability under the Affordable Care Act, it is yet unknown whether these policies also led to a growth in the changes in the nature of hiring in SUDT related workforce, partly due to poor data availability. Our study uses novel data to shed light on recent trends in a fast-evolving and policy-relevant labor market, and contributes to understanding the current SUDT related workforce and the effect of Medicaid expansion on hiring attempts in this sector. We examine attempts over 2010-2018 at hiring in the SUDT and related behavioral health sector as background for estimating the causal effect of the 2014-and-beyond state Medicaid expansion on these outcomes through "difference-in-difference" econometric models. We use Burning Glass Technologies (BGT) data covering virtually all U.S. job postings by employers. Nationally, we find little growth in the sector's hiring attempts in 2010-2018 relative to the rest of the economy or to health care as a whole. However, this masks diverging trends in subsectors, which saw reduction in hospital based hiring attempts, increases towards outpatient facilities, and changes in occupational hiring demand shifting from medical personnel towards counselors and social workers. Although Medicaid expansion did not lead to any statistically significant or meaningful change in overall hiring attempts, there was a shift in the hiring landscape.
Deep Kinematic Models for Physically Realistic Prediction of Vehicle Trajectories

Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to positively affect the quality of life of millions of people. To unlock this potential one of the critical aspects of the autonomous technology is understanding and predicting future movement of vehicles surrounding the SDV. This work presents a deep-learning-based method for kinematically feasible motion prediction of such traffic actors. Previous work did not explicitly encode vehicle kinematics and instead relied on the models to learn the constraints directly from the data, potentially resulting in kinematically infeasible, suboptimal trajectory predictions. To address this issue we propose a method that seamlessly combines ideas from the AI with physically grounded vehicle motion models. In this way we employ best of the both worlds, coupling powerful learning models with strong feasibility guarantees for their outputs. The proposed approach is general, being applicable to any type of learning method. Extensive experiments using deep convnets on real-world data strongly indicate its benefits, outperforming the existing state-of-the-art.
Cultural Association based on Machine Learning for Team Formation

Culture is core to human civilization. It evolved over time. Culture is about human intellectual achievements in social context. It is about how humans work together, perform particular task and overall lifestyle and dealing with other groups of civilization. Thus, it is concerned with establishing ideas. Does culture play key role in success? Does it impact on how two individuals can work together in achieving certain goals? Are some key questions. In this paper, we establish a means to derive cultural association and map it to culturally mediated success. Human interactions with the environment are typically in the form of expressions. Association between culture and behavior produce similar beliefs which lead to common principles and actions, while cultural similarity as a set of common expressions and responses. To measure cultural association among different candidates, we propose the use of a Graphical Association Method (GAM). The behaviors of candidates are captured through series of expressions and represented in the graphical form. Association among corresponding node and core nodes is used for the same. The proposed approach gives promising and interesting results.
Quantifying horizon dependence of asset prices: a cluster entropy approach

Market dynamic is quantified in terms of the entropy $S(\tau,n)$ of the clusters formed by the intersections between the series of the prices $p_t$ and the moving average $\widetilde{p}_{t,n}$. The entropy $S(\tau,n)$ is defined according to Shannon as $\sum P(\tau,n)\log P(\tau,n),$ with $P(\tau,n)$ the probability for the cluster to occur with duration $\tau$. \par The investigation is performed on high-frequency data of the Nasdaq Composite, Dow Jones Industrial Avg and Standard \& Poor 500 indexes downloaded from the Bloomberg terminal. The cluster entropy $S(\tau,n)$ is analysed in raw and sampled data over a broad range of temporal horizons $M$ varying from one to twelve months over the year 2018. The cluster entropy $S(\tau,n)$ is integrated over the cluster duration $\tau$ to yield the Market Dynamic Index $I(M,n)$, a synthetic figure of price dynamics. A systematic dependence of the cluster entropy $S(\tau,n)$ and the Market Dynamic Index $I(M,n)$ on the temporal horizon $M$ is evidenced. \par Finally, the Market Horizon Dependence}, defined as $H(M,n)=I(M,n)-I(1,n)$, is compared with the horizon dependence of the pricing kernel with different representative agents obtained via a Kullback-Leibler entropy approach. The Market Horizon Dependence $H(M,n)$ of the three assets is compared against the values obtained by implementing the cluster entropy $S(\tau,n)$ approach on artificially generated series (Fractional Brownian Motion).
Visual Place Recognition for Aerial Robotics: Exploring Accuracy-Computation Trade-off for Local Image Descriptors

Visual Place Recognition (VPR) is a fundamental yet challenging task for small Unmanned Aerial Vehicle (UAV). The core reasons are the extreme viewpoint changes, and limited computational power onboard a UAV which restricts the applicability of robust but computation intensive state-ofthe-art VPR methods. In this context, a viable approach is to use local image descriptors for performing VPR as these can be computed relatively efficiently without the need of any special hardware, such as a GPU. However, the choice of a local feature descriptor is not trivial and calls for a detailed investigation as there is a trade-off between VPR accuracy and the required computational effort. To fill this research gap, this paper examines the performance of several state-of-the-art local feature descriptors, both from accuracy and computational perspectives, specifically for VPR application utilizing standard aerial datasets. The presented results confirm that a trade-off between accuracy and computational effort is inevitable while executing VPR on resource-constrained hardware.
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes

Policy gradient methods are among the most effective methods in challenging reinforcement learning problems with large state and/or action spaces. However, little is known about even their most basic theoretical convergence properties, including: if and how fast they converge to a globally optimal solution (say with a sufficiently rich policy class); how they cope with approximation error due to using a restricted class of parametric policies; or their finite sample behavior. Such characterizations are important not only to compare these methods to their approximate value function counterparts (where such issues are relatively well understood, at least in the worst case), but also to help with more principled approaches to algorithm design. 
This work provides provable characterizations of computational, approximation, and sample size issues with regards to policy gradient methods in the context of discounted Markov Decision Processes (MDPs). We focus on both: 1) "tabular" policy parameterizations, where the optimal policy is contained in the class and where we show global convergence to the optimal policy, and 2) restricted policy classes, which may not contain the optimal policy and where we provide agnostic learning results. One insight of this work is in formalizing the importance how a favorable initial state distribution provides a means to circumvent worst-case exploration issues. Overall, these results place policy gradient methods under a solid theoretical footing, analogous to the global convergence guarantees of iterative value function based algorithms.
Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation

To learn target discriminative representations, using pseudo-labels is a simple yet effective approach for unsupervised domain adaptation. However, the existence of false pseudo-labels, which may have a detrimental influence on learning target representations, remains a major challenge. To overcome this issue, we propose a pseudo-labeling curriculum based on a density-based clustering algorithm. Since samples with high density values are more likely to have correct pseudo-labels, we leverage these subsets to train our target network at the early stage, and utilize data subsets with low density values at the later stage. We can progressively improve the capability of our network to generate pseudo-labels, and thus these target samples with pseudo-labels are effective for training our model. Moreover, we present a clustering constraint to enhance the discriminative power of the learned target features. Our approach achieves state-of-the-art performance on three benchmarks: Office-31, imageCLEF-DA, and Office-Home.
A Ricci-type flow on globally null manifolds and its gradient estimates

Locally, a screen integrable globally null manifold $M$ splits through a Riemannian leaf $M'$ of its screen distribution and a null curve $\mathcal{C}$ tangent to its radical distribution. The leaf $M'$ carries a lot of geometric information about $M$ and, in fact, forms a basis for the study of expanding and non-expanding horizons in black hole theory. In the present paper, we introduce a degenerate Ricci-type flow in $M'$ via the intrinsic Ricci tensor of $M$. Several new gradient estimates regarding the flow are proved.
New Techniques for Graph Edit Distance Computation

Due to their capacity to encode rich structural information, labeled graphs are often used for modeling various kinds of objects such as images, molecules, and chemical compounds. If pattern recognition problems such as clustering and classification are to be solved on these domains, a (dis-)similarity measure for labeled graphs has to be defined. A widely used measure is the graph edit distance (GED), which, intuitively, is defined as the minimum amount of distortion that has to be applied to a source graph in order to transform it into a target graph. The main advantage of GED is its flexibility and sensitivity to small differences between the input graphs. Its main drawback is that it is hard to compute. 
In this thesis, new results and techniques for several aspects of computing GED are presented. Firstly, theoretical aspects are discussed: competing definitions of GED are harmonized, the problem of computing GED is characterized in terms of complexity, and several reductions from GED to the quadratic assignment problem (QAP) are presented. Secondly, solvers for the linear sum assignment problem with error-correction (LSAPE) are discussed. LSAPE is a generalization of the well-known linear sum assignment problem (LSAP), and has to be solved as a subproblem by many GED algorithms. In particular, a new solver is presented that efficiently reduces LSAPE to LSAP. Thirdly, exact algorithms for computing GED are presented in a systematic way, and improvements of existing algorithms as well as a new mixed integer programming (MIP) based approach are introduced. Fourthly, a detailed overview of heuristic algorithms that approximate GED via upper and lower bounds is provided, and eight new heuristics are described. Finally, a new easily extensible C++ library for exactly or approximately computing GED is presented.
The water line emission and ortho-to-para ratio in the Orion Bar photon-dominated region

The ortho-to-para ratio (OPR) of water in the interstellar medium (ISM) is often assumed to be related to the formation temperature of water molecules, making it a potentially interesting tracer of the thermal history of interstellar gas.
Aims. A very low OPR of 0.1–0.5 was previously reported in the Orion Bar photon-dominated region (PDR), based on observations of two optically thin H218O lines which were analyzed by using a single-slab large velocity gradient (LVG) model. The corresponding spin temperature does not coincide with the kinetic temperature of the molecular gas in this UV-illuminated region. This was interpreted as an indication of water molecules being formed on cold icy grains which were subsequently released by UV photodesorption.
Methods. A more complete set of water observations in the Orion Bar, including seven H216O lines and one H218O line, carried out using Herschel/HIFI instrument, was reanalyzed using the Meudon PDR code to derive gas-phase water abundance and the OPR. The model takes into account the steep density and temperature gradients present in the region.
Results. The model line intensities are in good agreement with the observations assuming that water molecules formed with an OPR corresponding to thermal equilibrium conditions at the local kinetic temperature of the gas and when solely considering gas-phase chemistry and water gas-grain exchanges through adsorption and desorption. Gas-phase water is predicted to arise from a region deep into the cloud, corresponding to a visual extinction of AV ~ 9, with a H216O fractional abundance of ~2 × 10−7 and column density of (1.4 ± 0.8) × 1015 cm−2 for a total cloud depth of AV = 15. A line-of-sight average OPR of 2.8 ± 0.2 is derived.
Conclusions. The observational data are consistent with a nuclear spin isomer repartition corresponding to the thermal equilibrium at a temperature of 36 ± 2 K, much higher than the spin temperature previously reported for this region and close to the gas kinetic temperature in the water-emitting gas.
Pyramid Real Image Denoising Network

While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noise and denoising, they still perform poorly on real-world noisy images. The main reason is that the real-world noise is more sophisticated and diverse. To tackle the issue of blind denoising, in this paper, we propose a novel pyramid real image denoising network (PRIDNet), which contains three stages. First, the noise estimation stage uses channel attention mechanism to recalibrate the channel importance of input noise. Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features. Third, the stage of feature fusion adopts a kernel selecting operation to adaptively fuse multi-scale features. Experiments on two datasets of real noisy photographs demonstrate that our approach can achieve competitive performance in comparison with state-of-the-art denoisers in terms of both quantitative measure and visual perception quality.
Content and Colour Distillation for Learning Image Translations with the Spatial Profile Loss

Generative adversarial networks has emerged as a defacto standard for image translation problems. To successfully drive such models, one has to rely on additional networks e.g., discriminators and/or perceptual networks. Training these networks with pixel based losses alone are generally not sufficient to learn the target distribution. In this paper, we propose a novel method of computing the loss directly between the source and target images that enable proper distillation of shape/content and colour/style. We show that this is useful in typical image-to-image translations allowing us to successfully drive the generator without relying on additional networks. We demonstrate this on many difficult image translation problems such as image-to-image domain mapping, single image super-resolution and photo realistic makeup transfer. Our extensive evaluation shows the effectiveness of the proposed formulation and its ability to synthesize realistic images.
A Natural-language-based Visual Query Approach of Uncertain Human Trajectories

Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate geographic coordinates, e.g., positions of a mobile phone only refer to the regions (i.e., mobile cell stations) in which it resides, instead of accurate GPS coordinates. On the other hand, domain experts and general users prefer a natural way, such as using a natural language sentence, to access and analyze massive movement data. In this paper, we propose a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data. It is built up on encoding massive, spatially uncertain trajectories by the semantic information of the POls and regions covered by them, and then storing the trajectory documents in text database with an effective indexing scheme. The visual interface facilitates query condition specification, situation-aware visualization, and semantic exploration of large trajectory data. Usage scenarios on real-world human mobility datasets demonstrate the effectiveness of our approach.
New developments in conformal tracking for the CLIC detector

Conformal tracking is an innovative track finding strategy adopted for the detector at the Compact Linear Collider (CLIC), a proposed future electron-positron collider. It features a pattern recognition in a conformal-mapped plane using the cellular automaton algorithm to reconstruct the trajectory of charged particles in a magnetic field. The efficiency and robustness of the algorithm are validated using full-simulation studies in the challenging beam-induced background conditions expected for the 3 TeV stage of the CLIC collider. The tracking performance requirements, set by the ambitious CLIC physics programme, have been shown to be met. Moreover, thanks to its flexibility and geometry-agnostic nature, this algorithm was also shown to be easily adaptable to different detector designs and beam conditions.
Featuring the topology with the unsupervised machine learning

Images of line drawings are generally composed of primitive elements. One of the most fundamental elements to characterize images is the topology; line segments belong to a category different from closed circles, and closed circles with different winding degrees are nonequivalent. We investigate images with nontrivial winding using the unsupervised machine learning. We build an autoencoder model with a combination of convolutional and fully connected neural networks. We confirm that compressed data filtered from the trained model retain more than 90% of correct information on the topology, evidencing that image clustering from the unsupervised learning features the topology.