nils-herrmann/clean_scirepeval huggingface dataset:
Inference of compressed Potts graphical models.

We consider the problem of inferring a graphical Potts model on a population of variables. This inverse Potts problem generally involves the inference of a large number of parameters, often larger than the number of available data, and, hence, requires the introduction of regularization. We study here a double regularization scheme, in which the number of Potts states (colors) available to each variable is reduced and interaction networks are made sparse. To achieve the color compression, only Potts states with large empirical frequency (exceeding some threshold) are explicitly modeled on each site, while the others are grouped into a single state. We benchmark the performances of this mixed regularization approach, with two inference algorithms, adaptive cluster expansion (ACE) and pseudolikelihood maximization (PLM), on synthetic data obtained by sampling disordered Potts models on Erdős-Rényi random graphs. We show in particular that color compression does not affect the quality of reconstruction of the parameters corresponding to high-frequency symbols, while drastically reducing the number of the other parameters and thus the computational time. Our procedure is also applied to multisequence alignments of protein families, with similar results.
Machine learning in APOGEE: Identification of stellar populations through chemical abundances

The vast volume of data generated by modern astronomical surveys offers test beds for the application of machine-learning. In these exploratory applications, it is important to evaluate potential existing tools and determine those that are optimal for extracting scientific knowledge from the available observations.
Aims. We explore the possibility of using unsupervised clustering algorithms to separate stellar populations with distinct chemical patterns.
Methods. Star clusters are likely the most chemically homogeneous populations in the Galaxy, and therefore any practical approach to identifying distinct stellar populations should at least be able to separate clusters from each other. We have applied eight clustering algorithms combined with four dimensionality reduction strategies to automatically distinguish stellar clusters using chemical abundances of 13 elements. Our test-bed sample includes 18 stellar clusters with a total of 453 stars.
Results. We have applied statistical tests showing that some pairs of clusters (e.g., NGC 2458–NGC 2420) are indistinguishable from each other when chemical abundances from the Apache Point Galactic Evolution Experiment (APOGEE) are used. However, for most clusters we are able to automatically assign membership with metric scores similar to previous works. The confusion level of the automatically selected clusters is consistent with statistical tests that demonstrate the impossibility of perfectly distinguishing all the clusters from each other. These statistical tests and confusion levels establish a limit for the prospect of blindly identifying stars born in the same cluster based solely on chemical abundances.
 We find that some of the algorithms we explored are capable of blindly identify stellar populations with similar ages and chemical distributions in the APOGEE data. Even though we are not able to fully separate the clusters from each other, the main confusion arises from clusters with similar ages. Because some stellar clusters are chemically indistinguishable, our study supports the notion of extending weak chemical tagging that involves families of clusters instead of individual clusters.
Characterisation of a CeBr3(LB) detector for space application

We describe the performance of a 23 × 23 × 30 mm3 low background cerium bromide, CeBr3(LB), scintillator crystal coupled to a Hamamatsu R11265U-200 photomultiplier. This detector will be the building block for a gamma-ray detector array designed to be the payload for a CubeSat to be launched in 2020. The aim of the mission is to study flashes of gamma rays of terrestrial origin. The design of the detector has been tuned for the detection of gamma rays in the 20 keV–3 MeV energy range.
Discovery and analysis of a ULX nebula in NGC 3521

We present Very Large Telescope/X-shooter and Chandra X-ray observatory/ACIS observations of the ULX [SST2011] J110545.62 + 000016.2 in the galaxy NGC 3521. The source identified as a candidate near-infrared counterpart to the ULX in our previous study shows an emission line spectrum of numerous recombination and forbidden lines in the visible and near-infrared spectral regime. The emission from the candidate counterpart is spatially extended (∼34 pc) and appears to be connected with an adjacent H ii region, located ∼138 pc to the NE. The measured velocities of the emission lines confirm that both the candidate counterpart and H ii region reside in NGC 3521. The intensity ratios of the emission lines from the ULX counterpart show that the line emission originates from the combined effect of shock and photoionization of low metallicity (12  + log  (O/H)  = 8.19 ± 0.11) gas. Unfortunately, there is no identifiable spectral signature directly related to the photosphere of the mass-donor star in our spectrum. From the archival Chandra data, we derive the X-ray luminosity of the source in the 0.3–7 keV range to be (1.9 ± 0.8) × 1040 er g cm−2 s−1, almost a factor of four higher than what is previously reported.
Multikernel Capsule Network for Schizophrenia Identification

Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., deep neural network) machine-learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. To overcome the aforementioned drawbacks, we proposed a multikernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match partition sizes of the brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of the widely used dropout strategy in deep learning, we developed capsule dropout in the capsule layer to prevent overfitting of the model. The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. MKCapsnet is promising for schizophrenia identification. Our study first utilized the capsule neural network for analyzing functional connectivity of magnetic resonance imaging (MRI) and proposed a novel multikernel capsule structure with the consideration of brain anatomical parcellation, which could be a new way to reveal brain mechanisms. In addition, we provided useful information in the parameter setting, which is informative for further studies using a capsule network for other neurophysiological signal classification.
Universality of power-law exponents by means of maximum-likelihood estimation.

Power-law-type distributions are extensively found when studying the behavior of many complex systems. However, due to limitations in data acquisition, empirical datasets often only cover a narrow range of observation, making it difficult to establish power-law behavior unambiguously. In this work we present a statistical procedure to merge different datasets, with two different aims. First, we obtain a broader fitting range for the statistics of different experiments or observations of the same system. Second, we establish whether two or more different systems may belong to the same universality class. By means of maximum likelihood estimation, this methodology provides rigorous statistical information to discern whether power-law exponents characterizing different datasets can be considered equal among them or not. This procedure is applied to the Gutenberg-Richter law for earthquakes and for synthetic earthquakes (acoustic emission events) generated in the laboratory: labquakes. Different earthquake catalogs have been merged finding a Gutenberg-Richter law holding for more than eight orders of magnitude in seismic moment. The value of the exponent of the energy distribution of labquakes depends on the material used in the compression experiments. By means of the procedure proposed in this manuscript, we find that the Gutenberg-Richter law for earthquakes and charcoal labquakes can be characterized by the same power-law exponent, whereas Vycor labquakes exhibit a significantly different exponent.
Sampling scheme for neuromorphic simulation of entangled quantum systems

Due to the complexity of the space of quantum many-body states the computation of expectation values by statistical sampling is, in general, a hard task. Neural network representations of such quantum states which can be physically implemented by neuromorphic hardware could enable efficient sampling. A scheme is proposed which leverages this capability to speed up sampling from so-called neural quantum states encoded by a restricted Boltzmann machine. Due to the complex network parameters a direct hardware implementation is not feasible. We overcome this problem by considering a phase reweighting scheme for sampling expectation values of observables. Applying our method to a set of paradigmatic entangled quantum states we find that, in general, the phase-reweighted sampling is subject to a form of sign problem, which renders the sampling computationally costly. The use of neuromorphic chips could allow reducing computation times and thereby extend the range of tractable system sizes.
Confirmatory Aspect-based Opinion Mining Processes

A new opinion extraction method is proposed to summarize unstructured, user-generated content (i.e., online customer reviews) in the fixed topic domains. To differentiate the current approach from other opinion extraction approaches, which are often exposed to a sparsity problem and lack of sentiment scores, a confirmatory aspect-based opinion mining framework is introduced along with its practical algorithm called DiSSBUS. In this procedure, 1) each customer review is disintegrated into a set of clauses; 2) each clause is summarized to bi-terms-a topic word and an evaluation word-using a part-of-speech (POS) tagger; and 3) each bi-term is matched to a pre-specified topic relevant to a specific domain. The proposed processes have two primary advantages over existing methods: 1) they can decompose a single review into a set of bi-terms related to pre-specified topics in the domain of interest and, therefore, 2) allow identification of the reviewer's opinions on the topics via evaluation words within the set of bi-terms. The proposed aspect-based opinion mining is applied to customer reviews of restaurants in Hawaii obtained from TripAdvisor, and the empirical findings validate the effectiveness of the method. 
 Clause-based sentiment analysis, Customer review, Opinion mining, Topic modeling, User-generate-contents.
Deep Retrieval-Based Dialogue Systems: A Short Review

Building dialogue systems that naturally converse with humans is being an attractive and an active research domain. Multiple systems are being designed everyday and several datasets are being available. For this reason, it is being hard to keep an up-to-date state-of-the-art. In this work, we present the latest and most relevant retrieval-based dialogue systems and the available datasets used to build and evaluate them. We discuss their limitations and provide insights and guidelines for future work.
Visual Entropy and the Visualization of Uncertainty

It is possible to find many different visual representations of data values in visualizations, it is less common to see visual representations that include uncertainty, especially in visualizations intended for non-technical audiences.  our aim is to rigorously define and evaluate the novel use of visual entropy as a measure of shape that allows us to construct an ordered scale of glyphs for use in representing both uncertainty and value in 2D and 3D environments.  We use sample entropy as a numerical measure of visual entropy to construct a set of glyphs using R and Blender which vary in their complexity.  A Bradley-Terry analysis of a pairwise comparison of the glyphs shows participants (n=19) ordered the glyphs as predicted by the visual entropy score (linear regression R2 >0.97, p<0.001). We also evaluate whether the glyphs can effectively represent uncertainty using a signal detection method, participants (n=15) were able to search for glyphs representing uncertainty with high sensitivity and low error rates.  visual entropy is a novel cue for representing ordered data and provides a channel that allows the uncertainty of a measure to be presented alongside its mean value.
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning

The majority of contemporary object-tracking approaches do not model interactions between objects. This contrasts with the fact that objects' paths are not independent: a cyclist might abruptly deviate from a previously planned trajectory in order to avoid colliding with a car. Building upon HART, a neural class-agnostic single-object tracker, we introduce a multi-object tracking method MOHART capable of relational reasoning. Importantly, the entire system, including the understanding of interactions and relations between objects, is class-agnostic and learned simultaneously in an end-to-end fashion. We explore a number of relational reasoning architectures and show that multi-headed self-attention outperforms the provided baselines and better accounts for complex physical interactions in a challenging toy experiment. We find that it leads to consistent performance gains in tracking as well as future trajectory prediction on three real-world datasets (MOTChallenge, UA-DETRAC, and Stanford Drone dataset), particularly in the presence of ego-motion, occlusions, crowded scenes, and faulty sensor inputs.
Increasing Shape Bias in ImageNet-Trained Networks Using Transfer Learning and Domain-Adversarial Methods

Convolutional Neural Networks (CNNs) have become the state-of-the-art method to learn from image data. However, recent research shows that they may include a texture and colour bias in their representation, contrary to the intuition that they learn the shapes of the image content and to human biological learning. Thus, recent works have attempted to increase the shape bias in CNNs in order to train more robust and accurate networks on tasks. One such approach uses style-transfer in order to remove texture clues from the data. This work reproduces this methodology on four image classification datasets, as well as extends the method to use domain-adversarial training in order to further increase the shape bias in the learned representation. The results show the proposed method increases the robustness and shape bias of the CNNs, while it does not provide a gain in accuracy.
MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible

The CMU Wilderness Multilingual Speech Dataset (Black, 2019) is a newly published multilingual speech dataset based on recorded readings of the New Testament. It provides data to build Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) models for potentially 700 languages. However, the fact that the source content (the Bible) is the same for all the languages is not exploited to date.Therefore, this article proposes to add multilingual links between speech segments in different languages, and shares a large and clean dataset of 8,130 parallel spoken utterances across 8 languages (56 language pairs). We name this corpus MaSS (Multilingual corpus of Sentence-aligned Spoken utterances). The covered languages (Basque, English, Finnish, French, Hungarian, Romanian, Russian and Spanish) allow researches on speech-to-speech alignment as well as on translation for typologically different language pairs. The quality of the final corpus is attested by human evaluation performed on a corpus subset (100 utterances, 8 language pairs). Lastly, we showcase the usefulness of the final product on a bilingual speech retrieval task.
A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.
A Physical Testbed for Intelligent Transportation Systems

Intelligent transportation systems (ITSs) and other smart-city technologies are increasingly advancing in capability and complexity. While simulation environments continue to improve, their fidelity and ease of use can quickly degrade as newer systems become increasingly complex. To remedy this, we propose a hardware- and software-based traffic management system testbed as part of a larger smart-city testbed. It comprises a network of connected vehicles, a network of intersection controllers, a variety of control services, and data analytics services. The main goal of our testbed is to provide researchers and students with the means to develop novel traffic and vehicle control algorithms with higher fidelity than what can be achieved with simulation alone. Specifically, we are using the testbed to develop an integrated management system that combines model-based control and data analytics to improve the system performance over time. In this paper, we give a detailed description of each component within the testbed and discuss its current developmental state. Additionally, we present initial results and propose future work.
CVA and Vulnerable Options in Stochastic Volatility Models

This work aims to provide an efficient method to evaluate the Credit Value Adjustment (CVA) for a vulnerable European option, which is an option subject to some default event concerning the issuer solvability. Financial options traded in OTC markets are of this type. In particular, we compute the CVA in some popular stochastic volatility models such as SABR, Hull et al., which have proven to fit quite well market derivatives prices, admitting correlation with the default event. This choice covers the relevant case of Wrong Way Risk (WWR) when a credit deterioration determines an increase in the claim value. Contrary to the structural modeling adopted in [G. Wang, X. Wang & K. Zhu (2017) Pricing vulnerable options with stochastic volatility,  A 485, 91–103; C. Ma, S. Yue & Y. Ma (2020) Pricing vulnerable options with Stochastic volatility and Stochastic interest rate, Computational Economics 56, 391–429], we use the reduced-form intensity-based approach to provide an explicit representation formula for the vulnerable option price and related CVA. Later, we specialize the evaluation formula and construct its approximation for the three models mentioned above. Assuming a CIR model for the default intensity process, we run a numerical study to test our approximation, comparing it with Monte Carlo simulations. The results show that for moderate values of the correlation and maturities not exceeding one year, the approximation is very satisfactory as of accuracy and computational time.
Weakly Supervised Localization using Min-Max Entropy: an Interpretable Framework

Weakly supervised object localization (WSOL) models aim to locate objects of interest in an image after being trained only on data with coarse image level labels. Deep learning models for WSOL rely typically on convolutional attention maps with no constraints on the regions of interest which allows these models to select any region, making them vulnerable to false positive regions and inconsistent predictions. This issue occurs in many application domains, e.g., medical image analysis, where interpretability is central to the prediction process. In order to improve the localization reliability, we propose a deep learning framework for WSOL with pixel level localization. Our framework is composed of two sequential sub-networks: a localizer that localizes regions of interest; followed by a classifier that classifies these regions. Within its end-to-end training, we incorporate the prior knowledge stating that, in an agnostic-class setup, an image is more likely to contain relevant –i.e., object of interest– and irrelevant regions –i.e., noise, background–. Based on the conditional entropy measured at the classifier level, the localizer is driven to spot relevant regions identified with low conditional entropy, and irrelevant regions identified with high conditional entropy. Our framework is able to recover large and even complete discriminative regions in an image using our recursive erasing algorithm that we incorporate within the backpropagation during training. Moreover, the framework handles intrinsically multi-instances. Experimental results on public datasets with medical images (GlaS colon cancer) and natural images (Caltech-UCSD Birds-200-2011, Oxford flower 102) show that, compared to state of the art WSOL methods, the proposed approach can provide significant improvements in terms of image-level classification and pixel-level localization. Our framework showed robustness to overfitting when dealing with few training samples. Performance improvements are due in large part to our framework effectiveness at disregarding irrelevant regions. A public reproducible PyTorch implementation is provided1.
Admissible restrictions of irreducible representations of reductive Lie groups: symplectic geometry and discrete decomposability

Let $G$ be a real reductive Lie group, $L$ a compact subgroup, and $\pi$ an irreducible admissible representation of $G$. This paper proves a necessary and sufficient condition for the finiteness of the multiplicities of $L$-types occurring in $\pi$ based on symplectic techniques. This leads us to a new proof of the criterion for the discrete decomposability of the restriction of unitary representations with respect to noncompact subgroups (the author, ). A number of examples are presented in connection with Kostant's convexity theorem and also with non-Riemannian locally symmetric spaces.