nils-herrmann/clean_scirepeval huggingface dataset:
Economically rational sample-size choice and irreproducibility

Several systematic studies have suggested that a large fraction of published research is not reproducible. One probable reason for low reproducibility is insufficient sample size, resulting in low power and low positive predictive value. It has been suggested that insufficient sample-size choice is driven by a combination of scientific competition and 'positive publication bias'. Here we formalize this intuition in a simple model, in which scientists choose economically rational sample sizes, balancing the cost of experimentation with income from publication. Specifically, assuming that a scientist's income derives only from 'positive' findings (positive publication bias) and that individual samples cost a fixed amount, allows to leverage basic statistical formulas into an economic optimality prediction. We find that if effects have i) low base probability, ii) small effect size or iii) low grant income per publication, then the rational (economically optimal) sample size is small. Furthermore, for plausible distributions of these parameters we find a robust emergence of a bimodal distribution of obtained statistical power and low overall reproducibility rates, matching empirical findings. Overall, the model describes a simple mechanism explaining both the prevalence and the persistence of small sample sizes. It suggests economic rationality, or economic pressures, as a principal driver of irreproducibility.
Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry

We propose a self-supervised learning framework for visual odometry (VO) that incorporates correlation of consecutive frames and takes advantage of adversarial learning. Previous methods tackle self-supervised VO as a local structure from motion (SfM) problem that recovers depth from single image and relative poses from image pairs by minimizing photometric loss between warped and captured images. As single-view depth estimation is an ill-posed problem, and photometric loss is incapable of discriminating distortion artifacts of warped images, the estimated depth is vague and pose is inaccurate. In contrast to previous methods, our framework learns a compact representation of frame-to-frame correlation, which is updated by incorporating sequential information. The updated representation is used for depth estimation. Besides, we tackle VO as a self-supervised image generation task and take advantage of Generative Adversarial Networks (GAN). The generator learns to estimate depth and pose to generate a warped target image. The discriminator evaluates the quality of generated image with high-level structural perception that overcomes the problem of pixel-wise loss in previous methods. Experiments on KITTI and Cityscapes datasets show that our method obtains more accurate depth with details preserved and predicted pose outperforms state-of-the-art self-supervised methods significantly.
AdvHat: Real-World Adversarial Attack on ArcFace Face ID System

In this paper we propose a novel easily reproducible technique to attack the best public Face ID system ArcFace in different shooting conditions. To create an attack, we print the rectangular paper sticker on a common color printer and put it on the hat. The adversarial sticker is prepared with a novel algorithm for off-plane transformations of the image which imitates sticker location on the hat. Such an approach confuses the state-of-the-art public Face ID model LResNet100E-IR, ArcFace@ms1m-refine-v2 and is transferable to other Face ID models.
QuicK-means: Acceleration of K-means by learning a fast transform

K-means -- and the celebrated Lloyd algorithm -- is more than the clustering method it was originally designed to be. 
It has indeed proven pivotal to help increase the speed of many machine learning and data analysis techniques such as indexing, nearest-neighbor search and prediction, data compression, Radial Basis Function networks; its beneficial use has been shown to carry over to the acceleration of kernel machines (when using the Nystrom method). 
Here, we propose a fast extension of K-means, dubbed QuicK-means, that rests on the idea of expressing the matrix of the $K$ centroids as a product of sparse matrices, a feat made possible by recent results devoted to find approximations of matrices as a product of sparse factors. Using such a decomposition squashes the complexity of the matrix-vector product between the factorized $K \times D$ centroid matrix $\mathbf{U}$ and any vector from $\mathcal{O}(K D)$ to $\mathcal{O}(A \log A+B)$, with $A=\min (K, D)$ and $B=\max (K, D)$, where $D$ is the dimension of the training data. This drastic computational saving has a direct impact in the assignment process of a point to a cluster, meaning that it is not only tangible at prediction time, but also at training time, provided the factorization procedure is performed during Lloyd's algorithm. We precisely show that resorting to a factorization step at each iteration does not impair the convergence of the optimization scheme and that, depending on the context, it may entail a reduction of the training time. Finally, we provide discussions and numerical simulations that show the versatility of our computationally-efficient QuicK-means algorithm.
Delivering Scientific Influence Analysis as a Service on Research Grants Repository

Research grants have played an important role in seeding and promoting fundamental research projects worldwide. There is a growing demand for developing and delivering scientific influence analysis as a service on research grant repositories. Such analysis can provide insight on how research grants help foster new research collaborations, encourage cross-organizational collaborations, influence new research trends, and identify technical leadership. This paper presents the design and development of a grants-based scientific influence analysis service, coined as GImpact. It takes a graph-theoretic approach to design and develop large scale scientific influence analysis over a large research-grant repository with three original contributions. First, we mine the grant database to identify and extract important features for grants influence analysis and represent such features using graph theoretic models. For example, we extract an institution graph and multiple associated aspect-based collaboration graphs, including a discipline graph and a keyword graph. Second, we introduce self-influence and co-influence algorithms to compute two types of collaboration relationship scores based on the number of grants and the types of grants for institutions. We compute the self-influence scores to reflect the grant based research collaborations among institutions and compute multiple co-influence scores to model the various types of cross-institution collaboration relationships in terms of disciplines and subject areas. Third, we compute the overall scientific influence score for every pair of institutions by introducing a weighted sum of the self-influence score and the multiple co-influence scores and conduct an influence-based clustering analysis. We evaluate GImpact using a real grant database, consisting of 2512 institutions and their grants received over a period of 14 years...
Visually constructing the chemical structure of a single molecule by scanning Raman picoscopy

The strong spatial confinement of a nanocavity plasmonic field has made it possible to visualize the inner structure of a single molecule and even to distinguish its vibrational modes in real space. With such ever-improved spatial resolution, it is anticipated that full vibrational imaging of a molecule could be achieved to reveal molecular structural details. Here we demonstrate full Raman images of individual vibrational modes at the ångström level for a single Mg-porphine molecule, revealing distinct characteristics of each vibrational mode in real space. Furthermore, by exploiting the underlying interference effect and Raman fingerprint database, we propose a new methodology for structural determination, which we have called ‘scanning Raman picoscopy’, to show how such ultrahigh-resolution spectromicroscopic vibrational images can be used to visually assemble the chemical structure of a single molecule through a simple Lego-like building process.
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

Many decision problems in science, engineering and economics are affected by uncertain parameters whose distribution is only indirectly observable through samples. The goal of data-driven decision-making is to learn a decision from finitely many training samples that will perform well on unseen test samples. This learning task is difficult even if all training and test samples are drawn from the same distribution---especially if the dimension of the uncertainty is large relative to the training sample size. Wasserstein distributionally robust optimization seeks data-driven decisions that perform well under the most adverse distribution within a certain Wasserstein distance from a nominal distribution constructed from the training samples. In this tutorial we will argue that this approach has many conceptual and computational benefits. Most prominently, the optimal decisions can often be computed by solving tractable convex optimization problems, and they enjoy rigorous out-of-sample and asymptotic consistency guarantees. We will also show that Wasserstein distributionally robust optimization has interesting ramifications for statistical learning and motivates new approaches for fundamental learning tasks such as classification, regression, maximum likelihood estimation or minimum mean square error estimation, among others.
Neural Cognitive Diagnosis for Intelligent Education Systems

Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts. Existing approaches usually mine linear interactions of student exercising process by manual-designed function (e.g., logistic function), which is not sufficient for capturing complex relations between students and exercises. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex exercising interactions, for getting both accurate and interpretable diagnosis results. Specifically, we project students and exercises to factor vectors and leverage multi neural layers for modeling their interactions, where the monotonicity assumption is applied to ensure the interpretability of both factors. Furthermore, we propose two implementations of NeuralCD by specializing the required concepts of each exercise, i.e., the NeuralCDM with traditional Q-matrix and the improved NeuralCDM+ exploring the rich text content. Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability.
Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground

Highly accurate potential energy surfaces are of key interest for the detailed understanding and predictive modeling of chemical systems. In recent years, several new types of force fields, which are based on machine learning algorithms and fitted to ab initio reference calculations, have been introduced to meet this requirement. Here we show how high-dimensional neural network potentials can be employed to automatically generate the potential energy surface of finite sized clusters at coupled cluster accuracy, namely CCSD(T*)-F12a/aug-cc-pVTZ. The developed automated procedure utilizes the established intrinsic properties of the model such that the configurations for the training set are selected in an unbiased and efficient way to minimize the computational effort of expensive reference calculations. These ideas are applied to protonated water clusters from the hydronium cation, H3O+, up to the tetramer, H9O4+, and lead to a common potential energy surface that describes all these systems at essentially converged coupled cluster accuracy with a fitting error of 0.06 kJ/mol per atom. The fit is validated in detail for all clusters up to the tetramer and yields reliable results not only for stationary points, but also for reaction pathways, intermediate configurations, as well as different sampling techniques. Per design the NNPs constructed in this fashion can handle very different conditions including the quantum nature of the nuclei and enhanced sampling techniques covering very low as well as high temperatures. This enables fast and exhaustive exploration of the targeted protonated water clusters with essentially converged interactions. In addition, the automated process will allow one to tackle finite systems much beyond the present case.
Design choices for productive, secure, data-intensive research at scale in the cloud

We present a policy and process framework for secure environments for productive data science research projects at scale, by combining prevailing data security threat and risk profiles into five sensitivity tiers, and, at each tier, specifying recommended policies for data classification, data ingress, software ingress, data egress, user access, user device control, and analysis environments. By presenting design patterns for security choices for each tier, and using software defined infrastructure so that a different, independent, secure research environment can be instantiated for each project appropriate to its classification, we hope to maximise researcher productivity and minimise risk, allowing research organisations to operate with confidence.
Interactive Collaborative Exploration using Incomplete Contexts

A well-known knowledge acquisition method in the field of Formal Concept Analysis (FCA) is attribute exploration. It is used to reveal dependencies in a set of attributes with help of a domain expert. In most applications no single expert is capable (time- and knowledge-wise) of exploring the knowledge domain alone. However, there is up to now no theory that models the interaction of multiple experts for the task of attribute exploration with incomplete knowledge. To this end, we to develop a theoretical framework that allows multiple experts to explore domains together. We use a representation of incomplete knowledge as three-valued contexts. We then adapt the corresponding version of attribute exploration to fit the setting of multiple experts. We suggest formalizations for key components like expert knowledge, interaction and collaboration strategy. In particular, we define an order that allows to compare the results of different exploration strategies on the same task with respect to their information completeness. Furthermore we discuss other ways of comparing collaboration strategies and suggest avenues for future research.
Water vapor detection in the transmission spectra of HD 209458 b with the CARMENES NIR channel

We aim at detecting water vapor in the atmosphere of the hot Jupiter HD 209458 b and perform a multi-band study in the near infrared with CARMENES.
Methods. The water vapor absorption lines from the atmosphere of the planet are Doppler-shifted due to the large change in its radial velocity during transit. This shift is of the order of tens of km s−1, whilst the Earth’s telluric and the stellar lines can be considered quasi-static. We took advantage of this shift to remove the telluric and stellar lines using SYSREM, which performs a principal component analysis including proper error propagation. The residual spectra contain the signal from thousands of planetary molecular lines well below the noise level. We retrieve the information from those lines by cross-correlating the residual spectra with models of the atmospheric absorption of the planet.
Results. We find a cross-correlation signal with a signal-to-noise ratio (S/N) of 6.4, revealing H2O in HD 209458 b. We obtain a net blueshift of the signal of –5.2 −1.3+2.6 km s−1 that, despite the large error bars, is a firm indication of day- to night-side winds at the terminator of this hot Jupiter. Additionally, we performed a multi-band study for the detection of H2O individually from the three near infrared bands covered by CARMENES. We detect H2O from its 0.96–1.06 μm band with a S/N of 5.8, and also find hints of a detection from the 1.06–1.26 μm band, with a low S/N of 2.8. No clear planetary signal is found from the 1.26–1.62 μm band.
Conclusions. Our significant H2O signal at 0.96–1.06 μm in HD 209458 b represents the first detection of H2O from this band individually, the bluest one to date. The unfavorable observational conditions might be the reason for the inconclusive detection from the stronger 1.15 and 1.4 μm bands. H2O is detected from the 0.96–1.06 μm band in HD 209458 b, but hardly in HD 189733 b, which supports a stronger aerosol extinction in the latter, in line with previous studies. Future data gathered at more stable conditions and with larger S/N at both optical and near-infrared wavelengths could help to characterize the presence of aerosols in HD 209458 b and other planets.
Lukthung Classification Using Neural Networks on Lyrics and Audios

Music genre classification is a widely researched topic in music information retrieval (MIR). Being able to automatically tag genres will benefit music streaming service providers such as JOOX, Apple Music, and Spotify for their content-based recommendation. However, most studies on music classification have been done on western songs which differ from Thai songs. Lukthung, a distinctive and long-established type of Thai music, is one of the most popular music genres in Thailand and has a specific group of listeners. In this paper, we develop neural networks to classify such Lukthung genre from others using both lyrics and audios. Words used in Lukthung songs are particularly poetical, and their musical styles are uniquely composed of traditional Thai instruments. We leverage these two main characteristics by building a lyrics model based on bag-of-words (BoW), and an audio model using a convolutional neural network (CNN) architecture. We then aggregate the intermediate features learned from both models to build a final classifier. Our results show that the proposed three models outperform all of the standard classifiers where the combined model yields the best F1 score of 0.86, allowing Lukthung classification to be applicable to personalized recommendation for Thai audience.
Strategic Insider Trading Equilibrium With a Non-Fiduciary Market Maker

The continuous-time version of Kyle's (1985) model is studied, in which market makers are not fiduciaries. They have some market power which they utilize to set the price to their advantage, resulting in positive expected profits. This has several implications for the equilibrium, the most important being that by setting a modest fee conditional of the order flow, the market maker is able to obtain a profit of the order of magnitude, and even better than, a perfectly informed insider. Our model also indicates why speculative prices are more volatile than predicted by fundamentals.
Reinforcement Learning in Healthcare: A Survey

As a subfield of machine learning,
 reinforcement learning
 (RL) aims at optimizing decision making by using interaction samples of an agent with its environment and the potentially delayed feedbacks. In contrast to traditional supervised learning that typically relies on one-shot, exhaustive, and supervised reward signals, RL tackles sequential decision-making problems with sampled, evaluative, and delayed feedbacks simultaneously. Such a distinctive feature makes RL techniques a suitable candidate for developing powerful solutions in various healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged period with delayed feedbacks. By first briefly examining theoretical foundations and key methods in RL research, this survey provides an extensive overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis, and many other control or scheduling problems that have infiltrated every aspect of the healthcare system. In addition, we discuss the challenges and open issues in the current research and highlight some potential solutions and directions for future research.
Deep calibration of rough stochastic volatility models

Techniques from deep learning play a more and more important role for the important task of calibration of financial models. The pioneering paper by Hernandez [Risk, 2017] was a catalyst for resurfacing interest in research in this area. In this paper we advocate an alternative (two-step) approach using deep learning techniques solely to learn the pricing map -- from model parameters to prices or implied volatilities -- rather than directly the calibrated model parameters as a function of observed market data. Having a fast and accurate neural-network-based approximating pricing map (first step), we can then (second step) use traditional model calibration algorithms. In this work we showcase a direct comparison of different potential approaches to the learning stage and present algorithms that provide a suffcient accuracy for practical use. We provide a first neural network-based calibration method for rough volatility models for which calibration can be done on the y. We demonstrate the method via a hands-on calibration engine on the rough Bergomi model, for which classical calibration techniques are diffcult to apply due to the high cost of all known numerical pricing methods. Furthermore, we display and compare different types of sampling and training methods and elaborate on their advantages under different objectives. As a further application we use the fast pricing method for a Bayesian analysis of the calibrated model.
An Encoding Framework With Brain Inner State for Natural Image Identification

Neural encoding and decoding, which aim to characterize the relationship between stimuli and brain activities, have emerged as an important area in cognitive neuroscience. Traditional encoding models, which focus on feature extraction and mapping, consider the brain as an input–output mapper without inner states. In this article, inspired by the fact that the human brain acts like a state machine, we proposed a novel encoding framework that combines information from both the external world and the inner state to predict brain activity. The framework comprises two parts: 1) forward encoding model that deals with visual stimuli and 2) inner state model that captures influence from intrinsic connections in the brain. The forward model can be any traditional encoding model, making the framework flexible. The inner state model is a linear model to utilize information in the prediction residuals of the forward model. The proposed encoding framework achieved much better performance on natural image identification than forward-only models, with a maximum identification accuracy of 100%. The identification accuracy decreased slightly with the data set size increasing, but remained relatively stable with different identification methods. The results confirm that the new encoding framework is effective and robust when used for brain decoding.