nils-herrmann/clean_scirepeval huggingface dataset:
Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses

Sentence position is a strong feature for news summarization, since the lead often (but not always) summarizes the key points of the article. In this paper, we show that recent neural systems excessively exploit this trend, which although powerful for many inputs, is also detrimental when summarizing documents where important content should be extracted from later parts of the article. We propose two techniques to make systems sensitive to the importance of content in different parts of the article. The first technique employs ‘unbiased’ data; i.e., randomly shuffled sentences of the source document, to pretrain the model. The second technique uses an auxiliary ROUGE-based loss that encourages the model to distribute importance scores throughout a document by mimicking sentence-level ROUGE scores on the training data. We show that these techniques significantly improve the performance of a competitive reinforcement learning based extractive system, with the auxiliary loss being more powerful than pretraining.
OCR4all—An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings

Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years, great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout analysis and segmentation, character recognition, and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper, we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. While a variety of materials can already be processed fully automatically, books with more complex layouts require manual intervention by the users. This is mostly due to the fact that the required ground truth for training stronger mixed models (for segmentation, as well as text recognition) is not available, yet, neither in the desired quantity nor quality. To deal with this issue in the short run, OCR4all offers a comfortable GUI that allows error corrections not only in the final output, but already in early stages to minimize error propagations. In the long run, this constant manual correction produces large quantities of valuable, high quality training material, which can be used to improve fully automatic approaches. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. During experiments, the fully automated application on 19th Century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. Furthermore, on very complex early printed books, even users with minimal or no experience were able to capture the text with manageable effort and great quality, achieving excellent Character Error Rates (CERs) below 0.5%. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings.
On the solutions of linear Volterra equations of the second kind with sum kernels

We consider a linear Volterra integral equation of the second kind with a sum kernel $K(t',t)=\sum_i K_i(t',t)$ and give the solution of the equation in terms of solutions of the separate equations with kernels $K_i$, provided these exist. As a corollary, we obtain a novel series representation for the solution with improved convergence properties. We illustrate our results with examples, including the first known Volterra equation solved by Heun's confluent functions. This solves a long-standing problem pertaining to the representation of such functions. The approach presented here has widespread applicability in physics via Volterra equations with degenerate kernels.
The little things matter: relating the abundance of ultrafaint satellites to the hosts’ assembly history

Ultrafaint dwarf galaxies ($M_\star \le 10^{5}\, {\rm M}_\odot$) are relics of an early phase of galaxy formation. They contain some of the oldest and most metal-poor stars in the Universe which likely formed before the epoch of hydrogen reionization. These galaxies are so faint that they can only be detected as satellites of the Milky Way. They are so small that they are not resolved in current cosmological hydrodynamic simulations. Here, we combine very high-resolution cosmological N-body simulations with a semi-analytic model of galaxy formation to study the demographics and spatial distribution of ultrafaint satellites in Milky Way-mass haloes. We show that the abundance of these galaxies is correlated with the assembly history of the host halo: at fixed mass, haloes assembled earlier contain, on average, more ultrafaint satellites today than haloes assembled later. We identify simulated galactic haloes that experience an ancient Gaia-Enceladus-Sausage-like and a recent LMC-like accretion event and find that the former occurs in 33 per cent of the sample and the latter in 9 per cent. Only 3 per cent experience both events and these are especially rich in ultrafaint satellites, most acquired during the ancient accretion event. Our models predict that the radial distribution of satellites is more centrally concentrated in early-forming haloes. Accounting for the depletion of satellites by tidal interactions with the central disc, we find a very good match to the observed radial distribution of satellites in the Milky Way over the entire radial range. This agreement is mainly due to the ability of our model to track ‘orphan’ galaxies after their subhaloes fall below the resolution limit of the simulation.
Star formation at the edge of the Local Group: a rising star formation history in the isolated galaxy WLM

We present the star formation history (SFH) of the isolated (D ∼ 970 kpc) Local Group dwarf galaxy Wolf–Lundmark–Melotte (WLM) measured from colour–magnitude diagrams (CMDs) constructed from deep Hubble Space Telescope imaging. Our observations include a central ($0.5 \, r_h$) and outer field ($0.7 \, r_h$) that reach below the oldest main-sequence turn-off. WLM has no early dominant episode of star formation: 20 per cent of its stellar mass formed by ∼12.5 Gyr ago ($z$ ∼ 5). It also has an SFR that rises to the present with 50 per cent of the stellar mass within the most recent 5 Gyr ($z$ < 0.7). There is evidence of a strong age gradient: the mean age of the outer field is 5 Gyr older than the inner field despite being only 0.4 kpc apart. Some models suggest such steep gradients are associated with strong stellar feedback and dark-matter core creation. The SFHs of real isolated dwarf galaxies and those from the Feedback in Realistic Environment suite are in good agreement for M⋆($z$ = 0) ∼ 107–109M⊙, but in worse agreement at lower masses ($M_{\star }(z=0) \sim 10^5\!-\!10^7 \, \mathrm{M}_{\odot }$). These differences may be explainable by systematics in the models (e.g. reionization model) and/or observations (HST field placement). We suggest that a coordinated effort to get deep CMDs between HST/JWST (crowded central fields) and WFIRST (wide-area halo coverage) is the optimal path for measuring global SFHs of isolated dwarf galaxies.
Tidal radii of main sequence stars -- III. Partial disruptions

In this paper, the third in this series, we continue our study of tidal disruption events of main-sequence stars by a non-spinning $10^{6}~\rm{M}_\odot$ supermassive black hole. Here we focus on the outcomes of partial disruptions. As the encounter becomes weaker, the debris mass is increasingly concentrated near the outer edges of the energy distribution. As a result, the mass fallback rate can deviate substantially from a $t^{-5/3}$ power-law, becoming more like a single peak with a tail declining as $t^{-p}$ with $p\simeq2-5$. Surviving remnants are spun-up in the prograde direction and are hotter than MS stars of the same mass. Their specific orbital energy is $\simeq10^{-3}\times$ that of the debris (but of either sign with respect to the black hole potential) while their specific angular momentum is close to that of the original star. Even for strong encounters, remnants have speeds at infinity relative to the black hole potential $\lesssim 300$ km s$^{-1}$, so they are unable to travel far out into the galactic bulge. Remnants bound to the black hole can possibly go through a second tidal disruption event.
SDSS-IV MaNGA: environmental dependence of gas metallicity gradients in local star-forming galaxies

Within the standard model of hierarchical galaxy formation in a Λ cold dark matter universe, the environment of galaxies is expected to play a key role in driving galaxy formation and evolution. In this paper, we investigate whether and how the gas metallicity and the star formation surface density (ΣSFR) depend on galaxy environment. To this end, we analyse a sample of 1162 local, star-forming galaxies from the galaxy survey Mapping Nearby Galaxies at APO (MaNGA). Generally, both parameters do not show any significant dependence on environment. However, in agreement with previous studies, we find that low-mass satellite galaxies are an exception to this rule. The gas metallicity in these objects increases while their ΣSFR decreases slightly with environmental density. The present analysis of MaNGA data allows us to extend this to spatially resolved properties. Our study reveals that the gas metallicity gradients of low-mass satellites flatten and their ΣSFR gradients steepen with increasing environmental density. By extensively exploring a chemical evolution model, we identify two scenarios that are able to explain this pattern: metal-enriched gas accretion or pristine gas inflow with varying accretion time-scales. The latter scenario better matches the observed ΣSFR gradients, and is therefore our preferred solution. In this model, a shorter gas accretion time-scale at larger radii is required. This suggests that ‘outside–in quenching’ governs the star formation processes of low-mass satellite galaxies in dense environments.
Clustering of primordial black holes formed in a matter-dominated epoch

In the presence of the local-type primordial non-Gaussianity, it is known that the clustering of primordial black holes (PBHs) emerges even on super-horizon scales at the formation time. This effect has been investigated in the high-peak limit of the PBH formation in the radiation-dominated epoch in the literature. There is another possibility that the PBH formation takes place in the early matter-dominated epoch. In this scenario, the high-peak limit is not applicable because even initially small perturbations grow and can become a PBH. We first derive a general formula to estimate the clustering of PBHs with primordial non-Gaussianity without assuming the high-peak limit, and then apply this formula to a model of PBH formation in a matter-dominated epoch. Clustering is less significant in the case of the PBH formation in the matter-dominated epoch than that in the radiation-dominated epoch. Nevertheless, it is much larger than the Poisson shot noise in many cases. Relations to the constraints of the isocurvature perturbations by the cosmic microwave background radiation are quantitatively discussed.
Pretrained Language Models for Sequential Sentence Classification

As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. Recent successful models for this task have used hierarchical models to contextualize sentence representations, and Conditional Random Fields (CRFs) to incorporate dependencies between subsequent labels. In this work, we show that pretrained language models, BERT (Devlin et al., 2018) in particular, can be used for this task to capture contextual dependencies without the need for hierarchical encoding nor a CRF. Specifically, we construct a joint sentence representation that allows BERT Transformer layers to directly utilize contextual information from all words in all sentences. Our approach achieves state-of-the-art results on four datasets, including a new dataset of structured scientific abstracts.
A flexible framework for anomaly Detection via dimensionality reduction

Anomaly detection is challenging, especially for large datasets in high dimensions. Here we explore a gen-eral anomaly detection framework based on dimensionality reduction and unsupervised clustering. We release DRAMA, a general python package that implements the general framework with a wide range of built-in options. We test DRAMA on a wide variety of simulated and real datasets, in up to 3000 dimensions, and find it robust and highly competitive with commonly-used anomaly detection algorithms, especially in high dimensions. The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning and highly unbalanced datasets.
A comparison between pre-Newton and post-Newton approaches for solving a physical singular second-order boundary problem in the semi-infinite interval

In this paper, two numerical approaches based on the Newton iteration method with spectral algorithms are introduced to solve the Thomas-Fermi equation. That Thomas-Fermi equation is a nonlinear singular ordinary differential equation (ODE) with boundary condition in infinite. In these schemes, the Newton method is combined with a spectral method where in one of those, by Newton method we convert nonlinear ODE to a sequence of linear ODE then, solve them using the spectral method. In another one, by the spectral method the nonlinear ODE be converted to system of nonlinear algebraic equations, then, this system is solved by Newton method. In both approaches, the spectral method is based on fractional order of rational Gegenbauer functions. Finally, the obtained results of two introduced schemes are compared to each other in accuracy, runtime and iteration number. Numerical experiments are presented showing that our methods are as accurate as the best results which obtained until now.
Adversarial Robustness Against the Union of Multiple Perturbation Models

Owing to the susceptibility of deep learning systems to adversarial attacks, there has been a great deal of work in developing (both empirically and certifiably) robust classifiers, but the vast majority has defended against single types of attacks. Recent work has looked at defending against multiple attacks, specifically on the MNIST dataset, yet this approach used a relatively complex architecture, claiming that standard adversarial training can not apply because it "overfits" to a particular norm. In this work, we show that it is indeed possible to adversarially train a robust model against a union of norm-bounded attacks, by using a natural generalization of the standard PGD-based procedure for adversarial training to multiple threat models. With this approach, we are able to train standard architectures which are robust against $\ell_\infty$, $\ell_2$, and $\ell_1$ attacks, outperforming past approaches on the MNIST dataset and providing the first CIFAR10 network trained to be simultaneously robust against $(\ell_{\infty}, \ell_{2},\ell_{1})$ threat models, which achieves adversarial accuracy rates of $(47.6\%, 64.8\%, 53.4\%)$ for $(\ell_{\infty}, \ell_{2},\ell_{1})$ perturbations with radius $\epsilon = (0.03,0.5,12)$.
Counterfactual Story Reasoning and Generation

Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes. Despite being considered a necessary component of AI-complete systems, few resources have been developed for evaluating counterfactual reasoning in narratives. In this paper, we propose Counterfactual Story Rewriting: given an original story and an intervening counterfactual event, the task is to minimally revise the story to make it compatible with the given counterfactual event. Solving this task will require deep understanding of causal narrative chains and counterfactual invariance, and integration of such story reasoning capabilities into conditional language generation models. We present TIMETRAVEL, a new dataset of 29,849 counterfactual rewritings, each with the original story, a counterfactual event, and human-generated revision of the original story compatible with the counterfactual event. Additionally, we include 81,407 counterfactual “branches” without a rewritten storyline to support future work on semi- or un-supervised approaches to counterfactual story rewriting. Finally, we evaluate the counterfactual rewriting capacities of several competitive baselines based on pretrained language models, and assess whether common overlap and model-based automatic metrics for text generation correlate well with human scores for counterfactual rewriting.
A Classification Methodology Based on Subspace Graphs Learning

In this paper, we propose a design methodology for one-class classifiers using an ensemble-of-classifiers approach. The objective is to select the best structures created during the training phase using an ensemble of spanning trees. It takes the best classifier, partitioning the area near a pattern into γγ−2 sub-spaces and combining all possible spanning trees that can be created starting from γ nodes. The proposed method leverages on a supervised classification methodology and the concept of minimum distance. We evaluate our approach on well-known benchmark datasets and results obtained demonstrate that it achieves comparable and, in many cases, state-of-the-art results. Moreover, it obtains good performance even with unbalanced datasets.
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors

With rapid adoption of deep learning in critical applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent uncertainties. While identifying all sources that account for the stochasticity of models is challenging, it is common to augment predictions with confidence intervals to convey the expected variations in a model's behavior. We require prediction intervals to be well-calibrated, reflect the true uncertainties, and to be sharp. However, existing techniques for obtaining prediction intervals are known to produce unsatisfactory results in at least one of these criteria. To address this challenge, we develop a novel approach for building calibrated estimators. More specifically, we use separate models for prediction and interval estimation, and pose a bi-level optimization problem that allows the former to leverage estimates from the latter through an \textit{uncertainty matching} strategy. Using experiments in regression, time-series forecasting, and object localization, we show that our approach achieves significant improvements over existing uncertainty quantification methods, both in terms of model fidelity and calibration error.
The Making Of A Theory Of The Vitreous Solid State: From 1 milli-K To 1 kilo-K

This is a short account of the basic principles of a comprehensive theory of the vitreous state, looking at glasses and their eventual > into a liquid state (the inverse glass >) from the perspective of their low-temperature properties, then working all the way up to the melting temperature (and back again). The theory is still in the making, so only essential guidelines will be provided. There appears to be no ideal-glass transition, no ideal-glass state, but a first theoretical justification of the Vogel-Fulcher-Tamman law can be provided, making glasses truly fascinating quantum as well as topological substances.
Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images

This paper presents a client/server privacy-preserving network in the context of multicentric medical image analysis. Our approach is based on adversarial learning which encodes images to obfuscate the patient identity while preserving enough information for a target task. Our novel architecture is composed of three components: 1) an encoder network which removes identity-specific features from input medical images, 2) a discriminator network that attempts to identify the subject from the encoded images, 3) a medical image analysis network which analyzes the content of the encoded images (segmentation in our case). By simultaneously fooling the discriminator and optimizing the medical analysis network, the encoder learns to remove privacy-specific features while keeping those essentials for the target task. Our approach is illustrated on the problem of segmenting brain MRI from the large-scale Parkinson Progression Marker Initiative (PPMI) dataset. Using longitudinal data from PPMI, we show that the discriminator learns to heavily distort input images while allowing for highly accurate segmentation results. Our results also demonstrate that an encoder trained on the PPMI dataset can be used for segmenting other datasets, without the need for retraining. The code is made available at: https://github.com/bachkimn/Privacy-Net-An-Adversarial-Approach-forIdentity-Obfuscated-Segmentation-of-MedicalImages
A proof of a conjecture of Shklyarov

We prove a conjecture of Shklyarov concerning the relationship between K. Saito's higher residue pairing and a certain pairing on the periodic cyclic homology of matrix factorization categories. Along the way, we give new proofs of a result of Shklyarov and Polishchuk-Vaintrob's Hirzebruch-Riemann-Roch formula for matrix factorizations.
FLAG: A Framework for FPGA-based LoAd Generation in Profinet Communication

Like other automated system technologies, PROFINET, a real-time Industrial Ethernet Standard has shown increasing level of 
integration into the present IT Infrastructure. Such vast use of PROFINET can expose the controllers and I/O devices to operate in 
critical failures when traffic goes unexpectedly higher than normal. Rigorous testing of the running devices then becomes essential 
and therefore, in this paper, we prototype and design an FPGA based load Generating solution called “FLAG (FPGA based LoAd 
Generator) for PROFINET based traffic at the desired load configurations such as, bits per second, the number and size of the 
packets with their Ethertypes and MAC addresses. We have employed, a Zynq-7000 FPGA as our implementation platform for 
the proposed FLAG framework. The system can easily be deployed and accessed via the web or command line interface for 
successful load generation. Extensive experiments have been conducted to verify the effectiveness of our proposed solution and 
the results confirm that the proposed framework is capable to generate precise load at Fast/Gigabit line rate with a defined number 
of packets.