julilili42/papers huggingface dataset:
Four Pages Are Indeed Necessary for Planar Graphs
2022-02-25

An embedding of a graph in a book consists of a linear order of its vertices along the spine of the book and of an assignment of its edges to the pages of the book, so that no two edges on the same page cross. The book thickness of a graph is the minimum number of pages over all its book embeddings. Accordingly, the book thickness of a class of graphs is the maximum book thickness over all its members. In this paper, we address a long-standing open problem regarding the exact book thickness of the class of planar graphs, which previously was known to be either three or four. We settle this problem by demonstrating planar graphs that require four pages in any of their book embeddings, thus establishing that the book thickness of the class of planar graphs is four.
Overcoming the challenges in controlled thermal deposition of organic diradicals
2022-02-25

We have demonstrated that it is possible to evaporate diradicals in a controlled environment obtaining thin films in which the diradical character is preserved. However, evaporation represents a challenge. The presence of two radical sites makes the molecules more reactive, even in case of very stable single radicals. We have explored the parameters that play a role in this phenomenon. Bulk formation thermodynamics and delocalisation of the unpaired electrons play the major role. The higher the formation energies of the crystal, the more difficult is the evaporation of intact radicals. The larger the delocalization, the more stable is the film exposed to air. The evaporation of different radicals, also with a larger number of radical sites can be successfully addressed considering our findings.
Observational appearance of rapidly rotating neutron stars: X-ray bursts, cooling tail method, and radius determination
2022-02-25

Neutron stars (NSs) in low-mass X-ray binaries rotate at frequencies high enough to significantly deviate from sphericity ( 200--600 Hz). We investigate the effects of rapid rotation on the observational appearance of a NS. We propose analytical formulae relating gravitational mass and equatorial radius of the rapidly rotating NS to the mass and radius of a non-rotating NS of the same baryonic mass using accurate fully relativistic computations. We compute spectra from an oblate rotating NS observed at different inclination angles using the modified oblate Schwarzschild (MOS) approximation, where light bending is computed in Schwarzschild metric, but frame dragging and quadrupole moment of a NS are approximately accounted for in the photon redshift calculations. We generalize the cooling tail method to the case of a rapidly rotating NS to obtain the most probable values of and of the corresponding non-rotating NS with the same baryonic mass. We approximate the local spectra from the NS surface by a diluted blackbody using previously computed NS atmosphere models. We show that the NS radius could be overestimated by 3--3.5 km for face-on stars of km rotating at 700 Hz if the version of the cooling tail method for a non-rotating NS is used. We apply the method to an X-ray burst observed from the NS rotating at 532 Hz in SAX J1810.8 2609. The resulting radius of the non-rotating NS (assuming ) becomes km if it is viewed at inclination i=60 deg and km for a face-on view, which are smaller by 0.6 and 1.2 km than the radius obtained using standard cooling tail method ignoring rotation. The corresponding equatorial radii of these rapidly rotating NSs are 12.3 km (for i=60 deg) and 11.6 \\,km (for i=0 deg).
Spectral evolution of X-ray pulsar 4U 1901+03 during the 2019 outburst based on Insight-HXMT and NuSTAR observations
2022-02-25

We report on a detailed spectral analysis of emission from X-ray pulsar 4U 1901+03 using data obtained by the Insight-HXMT and NuSTAR observatories during the 2019 outburst of the source. Thanks to the extensive coverage of the outburst by Insight-HXMT, we were able to investigate the spectral evolution of the source as a function of flux, and compare these results to the previous reports, focusing on the properties of a putative absorption feature at around 10 keV. In particular, we demonstrate that the broadband X-ray continuum of 4U 1901+03 can be well described with a two-component continuum model without an absorption line at 10 keV, which casts doubt on its interpretation as a cyclotron line. The high quality of the data also allowed us to perform both phase-averaged and phase-resolved spectral analyses as a function of luminosity. Finally, we performed a detailed investigation of another absorption feature in the spectrum of the source around 30 keV recently reported in the NuSTAR data. We show that this feature appears to be significantly detected both in phase-averaged and phase-resolved spectra irrespective of the continuum model.
Collaborative Filtering under Model Uncertainty
2022-02-25

In their work, Dean, Rich, and Recht create a model to research recourse and availability of items in a recommender system. We used the definition of predictive multiplicity by Marx, Pin Calmon, and Ustun to examine different variations of this model, using different values for two model parameters. Pairwise comparison of their models show, that most of these models produce very similar results in terms of discrepancy and ambiguity for the availability and only in some cases the availability sets differ significantly.
The Isotopic Ecology of the Mammoth Steppe
2022-02-26

The Mammoth Steppe was the dominant terrestrial biome of the Northern Hemisphere during the late Pleistocene. It encompassed a nonanalog community of animals living in a cold and treeless steppe-tundra landscape. The high diversity of species, including megafauna, could be supported by a productive environment. The carbon-13 and nitrogen-15 abundances in bone collagen confirmed that the coexistence of the large herbivores was facilitated by a pronounced dietary niche partitioning, with some species relatively flexible in the exploitation of browse and graze, while others were more specialized. The isotopic abundances of carbon and nitrogen in carnivores confirm a dietary partitioning, probably based on the size of prey, with an increasingly generalist behavior emerging after the Last Glacial Maximum with notable exceptions. Isotopic investigation reveals dynamic processes of ecological displacement and replacement, shedding new light on the potential niche spectrum of extant species that are now present as relic populations. ▪ The Mammoth Steppe is an extinct nonanalog ecosystem with high productivity and biodiversity despite the cold and dry conditions of the Last Glacial Period. ▪ Stable isotopes reveal that niche partitioning among herbivores and carnivores is a dominant trait of the Mammoth Steppe. ▪ Switches in preferred prey and ecological replacement are observed among carnivores over time, with the few highly specialized predators going extinct. ▪ Warmer and more humid conditions preceding the Holocene impacted large herbivores in most regions of the Mammoth Steppe, driving some of the largest ones to extinction.
The Unfolding Space Glove: A Wearable Spatio-Visual to Haptic Sensory Substitution Device for Blind People
2022-02-26

This paper documents the design, implementation and evaluation of the Unfolding Space Glove—an open source sensory substitution device. It transmits the relative position and distance of nearby objects as vibratory stimuli to the back of the hand and thus enables blind people to haptically explore the depth of their surrounding space, assisting with navigation tasks such as object recognition and wayfinding. The prototype requires no external hardware, is highly portable, operates in all lighting conditions, and provides continuous and immediate feedback—all while being visually unobtrusive. Both blind (n = 8) and blindfolded sighted participants (n = 6) completed structured training and obstacle courses with both the prototype and a white long cane to allow performance comparisons to be drawn between them. The subjects quickly learned how to use the glove and successfully completed all of the trials, though still being slower with it than with the cane. Qualitative interviews revealed a high level of usability and user experience. Overall, the results indicate the general processability of spatial information through sensory substitution using haptic, vibrotactile interfaces. Further research would be required to evaluate the prototype’s capabilities after extensive training and to derive a fully functional navigation aid from its features.
Chemical complexity in high-mass star formation: An observational and modeling case study of the AFGL 2591 VLA 3 hot core
2022-02-26

We present a detailed observational and modeling study of the hot core VLA 3 in the high-mass star-forming region AFGL 2591, which is a target region of the NOrthern Extended Millimeter Array (NOEMA) large program CORE. Using NOEMA observations at 1.37 mm with an angular resolution of ~0."42 (1 400 au at 3.33 kpc), we derived the physical and chemical structure of the source. We modeled the observed molecular abundances with the chemical evolution code MUSCLE (MUlti Stage ChemicaL codE). Results. With the kinetic temperature tracers CH3CN and H2CO we observe a temperature distribution with a power-law index of q = 0.41+-0.08. Using the visibilities of the continuum emission we derive a density structure with a power-law index of p = 1.7+-0.1. The hot core spectra reveal high molecular abundances and a rich diversity in complex molecules. The majority of the molecules have an asymmetric spatial distribution around the forming protostar(s), which indicates a complex physical structure on scales &lt; 1 400 au. Using MUSCLE, we are able to explain the observed molecular abundance of 10 out of 14 modeled species at an estimated hot core chemical age of ~21 100 years. In contrast to the observational analysis, our chemical modeling predicts a lower density power-law index of p &lt; 1.4. Reasons for this discrepancy are discussed. Conclusions. Combining high spatial resolution observations with detailed chemical modeling allows us to derive a concise picture of the physical and chemical structure of the famous AFGL 2591 hot core. The next steps are to conduct a similar analysis for the whole CORE sample, and then use this analysis to constrain the chemical diversity in high-mass star formation to a much greater depth.
Formal Total Synthesis of Salvinorin A
2022-02-26

Abstract The generation of the quaternary stereocenter at the C9 position of salvinorin A precursors by the Claisen rearrangement was investigated. The required allyl alcohol was prepared from a Wieland‐Miescher ketone using a known γ‐hydroxylation, reduction of the enone double bond, cyanohydrin formation, and elimination, yielding an unsaturated nitrile. A two‐step reduction led to the required allyl alcohol. The subsequent Johnson‐Claisen rearrangement provided a mixture of two diastereomeric 1,4‐unsaturated esters in a ratio of around 2.6 : 1. The major isomer could be converted to a key intermediate of the Hagiwara synthesis of salvinorin A.
Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions
2022-02-26

Abstract This paper argues that machine learning (ML) and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a causal constraint on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection. We whittle down the issues to the question of whether causal knowledge is necessary for underwriting predictions about the outcomes of public health interventions. While there is great plausibility to the idea that it is, conviction that something is impossible does not by itself motivate a constraint to forbid trying. We disambiguate the possible motivations for such a constraint into definitional, metaphysical, epistemological, and pragmatic considerations and argue that “Proceed with caution” (rather than “Stop!”) is the outcome of each. We then argue that there are positive reasons to proceed, albeit cautiously. Causal inference enforces existing classification schema prior to the testing of associational claims (causal or otherwise), but associations and classification schema are more plausibly discovered (rather than tested or justified) in a back-and-forth process of gaining reflective equilibrium. ML instantiates this kind of process, we argue, and thus offers the welcome prospect of uncovering meaningful new concepts in epidemiology and public health—provided it is not causally constrained.
High Diversity of emm Types and Marked Tetracycline Resistance of Group A Streptococci and Other ß-Hemolytic Streptococci in Gabon, Central Africa
2022-02-26

Background: Group A ß-hemolytic streptococcus (GABHS) is a leading pathogen worldwide and post-streptococcal sequelae is a major cause of morbidity and mortality in resource-limited countries. The M protein (coded by the emm gene) is a key virulence factor and a component of GABHS vaccine candidates. As data on BHS in Central Africa are scarce, antibiotic resistance, emm diversity and potential vaccine coverage were investigated. Methods: In a prospective cross-sectional study, 1014 Gabonese were screened for streptococcal throat carriage, tonsillopharyngitis and pyoderma by throat and skin smear tests. All BHS were isolated, species were identified and analysis of antibiotic resistance, emm types and emm clusters was performed. Results: One hundred sixty-five BHS were detected, comprising 76 GABHS, 36 group C ß-hemolytic streptococcus (GCBHS) and 53 group G ß-hemolytic streptococcus (GGBHS) in 140 carrier, 9 tonsillopharyngitis and 16 pyoderma isolates. Eighty percentage of GABHS, 78% of GCBHS and 79% of GGBHS were tetracycline resistant. Forty-six emm types were identified. GABHS emm 58, emm 65 and emm 81 were most prevalent (26%). Emm diversity of GABHS was the highest, GCBHS and GGBHS were less divers. Every second GABHS, every third GCBHS and every tenth GGBHS carrier was colonized with emm types detected in tonsillopharyngitis or pyoderma isolates. Conclusions: Tetracycline resistance and emm type diversity was high among BHS carriers in Gabon with a potential coverage of 58% by the 30-valent GABHS vaccine. A relevant overlap of carrier emm types with emm types found in tonsillopharyngitis and pyoderma characterizes a shared pool of circulating BHS strains.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
2022-02-26

The field of defense strategies against adversarial attacks has significantly grown over the last years, but progress is hampered as the evaluation of adversarial defenses is often insufficient and thus gives a wrong impression of robustness. Many promising defenses could be broken later on, making it difficult to identify the state-of-the-art. Frequent pitfalls in the evaluation are improper tuning of hyperparameters of the attacks, gradient obfuscation or masking. In this paper we first propose two extensions of the PGD-attack overcoming failures due to suboptimal step size and problems of the objective function. We then combine our novel attacks with two complementary existing ones to form a parameter-free, computationally affordable and user-independent ensemble of attacks to test adversarial robustness. We apply our ensemble to over 50 models from papers published at recent top machine learning and computer vision venues. In all except one of the cases we achieve lower robust test accuracy than reported in these papers, often by more than , identifying several broken defenses.
Recurrent Neural Networks (RNNs): A gentle Introduction and Overview
2022-02-26

State-of-the-art solutions in the areas of "Language Modelling &amp; Generating Text", "Speech Recognition", "Generating Image Descriptions" or "Video Tagging" have been using Recurrent Neural Networks as the foundation for their approaches. Understanding the underlying concepts is therefore of tremendous importance if we want to keep up with recent or upcoming publications in those areas. In this work we give a short overview over some of the most important concepts in the realm of Recurrent Neural Networks which enables readers to easily understand the fundamentals such as but not limited to "Backpropagation through Time" or "Long Short-Term Memory Units" as well as some of the more recent advances like the "Attention Mechanism" or "Pointer Networks". We also give recommendations for further reading regarding more complex topics where it is necessary.
On Adaptive Attacks to Adversarial Example Defenses
2022-02-26

Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to adversarial examples. We find, however, that typical adaptive evaluations are incomplete. We demonstrate that thirteen defenses recently published at ICLR, ICML and NeurIPS---and chosen for illustrative and pedagogical purposes---can be circumvented despite attempting to perform evaluations using adaptive attacks. While prior evaluation papers focused mainly on the end result---showing that a defense was ineffective---this paper focuses on laying out the methodology and the approach necessary to perform an adaptive attack. We hope that these analyses will serve as guidance on how to properly perform adaptive attacks against defenses to adversarial examples, and thus will allow the community to make further progress in building more robust models.
Explaining the Explainer: A First Theoretical Analysis of LIME
2022-02-26

Machine learning is used more and more often for sensitive applications, sometimes replacing humans in critical decision-making processes. As such, interpretability of these algorithms is a pressing need. One popular algorithm to provide interpretability is LIME (Local Interpretable Model-Agnostic Explanation). In this paper, we provide the first theoretical analysis of LIME. We derive closed-form expressions for the coefficients of the interpretable model when the function to explain is linear. The good news is that these coefficients are proportional to the gradient of the function to explain: LIME indeed discovers meaningful features. However, our analysis also reveals that poor choices of parameters can lead LIME to miss important features.
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
2022-02-26

The point estimates of ReLU classification networks---arguably the most widely used neural network architecture---have been shown to yield arbitrarily high confidence far away from the training data. This architecture, in conjunction with a maximum a posteriori estimation scheme, is thus not calibrated nor robust. Approximate Bayesian inference has been empirically demonstrated to improve predictive uncertainty in neural networks, although the theoretical analysis of such Bayesian approximations is limited. We theoretically analyze approximate Gaussian distributions on the weights of ReLU networks and show that they fix the overconfidence problem. Furthermore, we show that even a simplistic, thus cheap, Bayesian approximation, also fixes these issues. This indicates that a sufficient condition for a calibrated uncertainty on a ReLU network is "to be a bit Bayesian". These theoretical results validate the usage of last-layer Bayesian approximation and motivate a range of a fidelity-cost trade-off. We further validate these findings empirically via various standard experiments using common deep ReLU networks and Laplace approximations.
Learning From Brains How to Regularize Machines
2022-02-26

Despite impressive performance on numerous visual tasks, Convolutional Neural Networks (CNNs) --- unlike brains --- are often highly sensitive to small perturbations of their input, e.g. adversarial noise leading to erroneous decisions. We propose to regularize CNNs using large-scale neuroscience data to learn more robust neural features in terms of representational similarity. We presented natural images to mice and measured the responses of thousands of neurons from cortical visual areas. Next, we denoised the notoriously variable neural activity using strong predictive models trained on this large corpus of responses from the mouse visual system, and calculated the representational similarity for millions of pairs of images from the model's predictions. We then used the neural representation similarity to regularize CNNs trained on image classification by penalizing intermediate representations that deviated from neural ones. This preserved performance of baseline models when classifying images under standard benchmarks, while maintaining substantially higher performance compared to baseline or control models when classifying noisy images. Moreover, the models regularized with cortical representations also improved model robustness in terms of adversarial attacks. This demonstrates that regularizing with neural data can be an effective tool to create an inductive bias towards more robust inference.
Automated Anonymisation of Visual and Audio Data in Classroom Studies
2022-02-26

Understanding students' and teachers' verbal and non-verbal behaviours during instruction may help infer valuable information regarding the quality of teaching. In education research, there have been many studies that aim to measure students' attentional focus on learning-related tasks: Based on audio-visual recordings and manual or automated ratings of behaviours of teachers and students. Student data is, however, highly sensitive. Therefore, ensuring high standards of data protection and privacy has the utmost importance in current practices. For example, in the context of teaching management studies, data collection is carried out with the consent of pupils, parents, teachers and school administrations. Nevertheless, there may often be students whose data cannot be used for research purposes. Excluding these students from the classroom is an unnatural intrusion into the organisation of the classroom. A possible solution would be to request permission to record the audio-visual recordings of all students (including those who do not voluntarily participate in the study) and to anonymise their data. Yet, the manual anonymisation of audio-visual data is very demanding. In this study, we examine the use of artificial intelligence methods to automatically anonymise the visual and audio data of a particular person.
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks
2022-02-26

In Bayesian Deep Learning, distributions over the output of classification neural networks are often approximated by first constructing a Gaussian distribution over the weights, then sampling from it to receive a distribution over the softmax outputs. This is costly. We reconsider old work (Laplace Bridge) to construct a Dirichlet approximation of this softmax output distribution, which yields an analytic map between Gaussian distributions in logit space and Dirichlet distributions (the conjugate prior to the Categorical distribution) in the output space. Importantly, the vanilla Laplace Bridge comes with certain limitations. We analyze those and suggest a simple solution that compares favorably to other commonly used estimates of the softmax-Gaussian integral. We demonstrate that the resulting Dirichlet distribution has multiple advantages, in particular, more efficient computation of the uncertainty estimate and scaling to large datasets and networks like ImageNet and DenseNet. We further demonstrate the usefulness of this Dirichlet approximation by using it to construct a lightweight uncertainty-aware output ranking for ImageNet.