nils-herrmann/clean_scirepeval huggingface dataset:
VARIATIONS ON 
$\Delta ^{1}_{1}$
 DETERMINACY AND ℵ ω 1

We consider a seemingly weaker form of 
$\Delta ^{1}_{1}$
 Turing determinacy. Let 
$2 \leqslant \rho < \omega _{1}^{\mathsf {CK}}$
 , 
$\textrm{Weak-Turing-Det}_{\rho }(\Delta ^{1}_{1})$
 is the statement: Every 
$\Delta ^{1}_{1}$
 set of reals cofinal in the Turing degrees contains two Turing distinct, 
$\Delta ^{0}_{\rho }$
 -equivalent reals. We show in 
$\mathsf {ZF}^-$
 : 
$\textrm{Weak-Turing-Det}_{\rho }(\Delta ^{1}_{1})$
 implies: for every 
$\nu < \omega _{1}^{\mathsf {CK}}$
 there is a transitive model 
${M \models \mathsf {ZF}^{-} + \textrm{``}\aleph _{\nu } \textrm{ exists''.}}$
 As a corollary: If every cofinal 
$\Delta ^{1}_{1}$
 set of Turing degrees contains both a degree and its jump, then for every 
$\nu < \omega_1^{\mathsf{CK}}$
 , there is atransitive model: 
$M \models \mathsf{ZF}^{-} + \textrm{``}\aleph_\nu \textrm{ exists''.}$
 • With a simple proof, this improves upon a well-known result of Harvey Friedman on the strength of Borel determinacy (though not assessed level-by-level). • Invoking Tony Martin’s proof of Borel determinacy, 
$\textrm{Weak-Turing-Det}_{\rho }(\Delta ^{1}_{1})$
 implies 
$\Delta ^{1}_{1}$
 determinacy. • We show further that, assuming 
$\Delta ^{1}_{1}$
 Turing determinacy, or Borel Turing determinacy, as needed: – Every cofinal 
$\Sigma ^{1}_{1}$
 set of Turing degrees contains a “hyp-Turing cone”: 
${\{x \in \mathcal {D} \mid d_{0} \leqslant _{T} x \leqslant _{h} d_{0} \}}$
 . – For a sequence 
$(A_{k})_{k < \omega }$
 of analytic sets of Turing degrees, cofinal in 
$\mathcal {D}$
 , 
$\bigcap _{k} A_{k}$
 is cofinal in 
$\mathcal {D}$
 .
Determination of jet calibration and energy resolution in proton-proton collisions at $\sqrt{s}$ = 8 TeV using the ATLAS detector

The jet energy scale, jet energy resolution, and their systematic uncertainties are measured for jets reconstructed with the ATLAS detector in 2012 using proton–proton data produced at a centre-of-mass energy of 8 TeV with an integrated luminosity of 20 fb−1. Jets are reconstructed from clusters of energy depositions in the ATLAS calorimeters using the anti-kt algorithm. A jet calibration scheme is applied in multiple steps, each addressing specific effects including mitigation of contributions from additional proton–proton collisions, loss of energy in dead material, calorimeter non-compensation, angular biases and other global jet effects. The final calibration step uses several in situ techniques and corrects for residual effects not captured by the initial calibration. These analyses measure both the jet energy scale and resolution by exploiting the transverse momentum balance in γ+jet, Z+jet, dijet, and multijet events. A statistical combination of these measurements is performed. In the central detector region, the derived calibration has a precision better than 1% for jets with transverse momentum 150 GeV
Optimal estimation with quantum optomechanical systems in the nonlinear regime

We study the fundamental bounds on precision measurements of parameters contained in a time-dependent nonlinear optomechanical Hamiltonian, which includes the nonlinear light-matter coupling, a mechanical displacement term, and a single-mode mechanical squeezing term. By using a recently developed method to solve the dynamics of this system, we derive a general expression for the quantum Fisher information and demonstrate its applicability through three concrete examples: estimation of the strength of a nonlinear light-matter coupling, the strength of a time-modulated mechanical displacement, and a single-mode mechanical squeezing parameter, all of which are modulated at resonance. Our results can be used to compute the sensitivity of a nonlinear optomechanical system to a number of external and internal effects, such as forces acting on the system or modulations of the light--matter coupling.
Risk as Challenge: A Dual System Stochastic Model for Binary Choice Behavior

Challenge Theory (CT) is a new approach to decision under risk that departs significantly from expected utility and is based firmly on psychological, rather than economic, assumptions. The paper demonstrates that a purely cognitive-psychological paradigm for decision under risk can yield excellent predictions, comparable to those attained by more complex economic or psychological models that remain attached to conventional economic constructs and assumptions. The study presents a new model for predicting the popularity of choices made in binary risk problems.A CT-based regression model is tested on data gathered from 126 respondents who indicated their preferences with respect to 44 choice problems. Results support CT's central hypothesis, strongly associating between the Challenge Index (CI) attributable to every binary risk problem, and the observed popularity of the bold prospect in that problem (with r=-0.92 and r=-0.93 for gains and for losses, respectively). The novelty of the CT perspective as a new paradigm is illuminated by its simple, single-index (CI) representation of psychological effects proposed by Prospect Theory for describing choice behavior (certainty effect, reflection effect, overweighting small probabilities and loss aversion).
Strategic Coalitions in Stochastic Games

The article compares two different approaches of incorporating probability into coalition logics. One is based on the semantics of games with stochastic transitions and the other on games with the stochastic failures. The work gives an example of a non-trivial property of coalition power for the first approach and a complete axiomatization for the second approach. It turns out that the logical properties of the coalition power modality under the second approach depend on whether the modal language allows the empty coalition. The main technical results for the games with stochastic failures are a strong completeness theorem for the logical system without the empty coalition and an incompleteness theorem which shows that there is no strongly complete logical system in the language with the empty coalition.
An Anatomy of Graph Neural Networks Going Deep via the Lens of Mutual Information: Exponential Decay vs. Full Preservation

Graph Convolutional Network (GCN) has attracted intensive interests recently. One major limitation of GCN is that it often cannot benefit from using a deep architecture, while traditional CNN and an alternative Graph Neural Network architecture, namely GraphCNN, often achieve better quality with a deeper neural architecture. How can we explain this phenomenon? In this paper, we take the first step towards answering this question. We first conduct a systematic empirical study on the accuracy of GCN, GraphCNN, and ResNet-18 on 2D images and identified relative importance of different factors in architectural design. This inspired a novel theoretical analysis on the mutual information between the input and the output after $l$ GCN and GraphCNN layers. We identified regimes in which GCN suffers exponentially fast information lose and show that GraphCNN requires a much weaker condition for similar behavior to happen.
Language Transfer for Early Warning of Epidemics from Social Media

Statements on social media can be analysed to identify individuals who are experiencing red flag medical symptoms, allowing early detection of the spread of disease such as influenza. Since disease does not respect cultural borders and may spread between populations speaking different languages, we would like to build multilingual models. However, the data required to train models for every language may be difficult, expensive and time-consuming to obtain, particularly for low-resource languages. Taking Japanese as our target language, we explore methods by which data in one language might be used to build models for a different language. We evaluate strategies of training on machine translated data and of zero-shot transfer through the use of multilingual models. We find that the choice of source language impacts the performance, with Chinese-Japanese being a better language pair than English-Japanese. Training on machine translated data shows promise, especially when used in conjunction with a small amount of target language data.
The Quest for Interpretable and Responsible Artificial Intelligence

Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in computational biology, finance, law and robotics. However, such a highly positive impact is coupled with significant challenges: how do we understand the decisions suggested by these systems in order that we can trust them? How can they be held accountable for those decisions?
Learning protein conformational space by enforcing physics with convolutions and latent interpolations

Determining the different conformational states of a protein and the transition path between them is a central challenge in protein biochemistry, and is key to better understanding the relationship between biomolecular structure and function. This task is typically accomplished by sampling the protein conformational space with microseconds of molecular dynamics (MD) simulations. Despite advances in both computing hardware and enhanced sampling techniques, MD will always yield a discretized representation of this space, with transition states undersampled proportionally to their associated energy barrier. We design a convolutional neural network capable of learning a continuous and physically plausible conformational space representation, given example conformations generated by experiments and simulations. We show that this network, trained with MD simulations of two distinct protein states, can correctly predict a possible transition path between them, without any example on the transition state provided. We then show that our network, having a protein-independent architecture, can be trained in a transfer learning scenario, leading to performances superior to those of a network trained from scratch.
Lagrangian torus invariants using $ECH = SWF$

We construct distinguished elements in the embedded contact homology (and monopole Floer homology) of a 3-torus, associated with Lagrangian tori in symplectic 4-manifolds and their isotopy classes. They turn out not to be new invariants, instead they repackage the Gromov (and Seiberg-Witten) invariants of various torus surgeries. We then recover a result of Morgan-Mrowka-Szabo on product formulas for the Seiberg-Witten invariants along 3-tori.
Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects

This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data. We utilize structural T1-weighted brain MRI obtained from two different studies, Cam-CAN and UK Biobank. For the purpose of our investigation, we construct a dataset consisting of brain scans from 592 age- and sex-matched individuals, 296 subjects from each original study. Our results demonstrate that even after careful pre-processing with state-of-the-art neuroimaging pipelines a classifier can easily distinguish between the origin of the data with very high accuracy. Our analysis on the example application of sex classification suggests that current approaches to harmonize data are unable to remove scanner-specific bias leading to overly optimistic performance estimates and poor generalization. We conclude that multi-site data harmonization remains an open challenge and particular care needs to be taken when using such data with advanced machine learning methods for predictive modelling.
R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason

Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets. This prevents the community from reliably measuring the progress of RC systems. To address this issue, we introduce R4C, a new task for evaluating RC systems’ internal reasoning. R4C requires giving not only answers but also derivations: explanations that justify predicted answers. We present a reliable, crowdsourced framework for scalably annotating RC datasets with derivations. We create and publicly release the R4C dataset, the first, quality-assured dataset consisting of 4.6k questions, each of which is annotated with 3 reference derivations (i.e. 13.8k derivations). Experiments show that our automatic evaluation metrics using multiple reference derivations are reliable, and that R4C assesses different skills from an existing benchmark.
Multi-label Categorization of Accounts of Sexism using a Neural Framework

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in utilizing such data to study and counter sexism better. The existing work on sexism classification, which is different from sexism detection, has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization. We develop a neural solution for this multi-label classification that can combine sentence representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible, hierarchical architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. The best proposed method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.
Unlocking the synergy between CMB spectral distortions and anisotropies

Measurements of the cosmic microwave background (CMB) spectral distortions (SDs) will open a new window on the very early universe, providing new information complementary to that gathered from CMB temperature and polarization anisotropies. In this paper, we study their synergy as a function of the characteristics of the considered experiments. In particular, we examine a wide range of sensitivities for possible SD measurements, spanning from FIRAS up to noise levels 1000 times better than PIXIE, and study their constraining power when combined with current or future CMB anisotropy experiments such as Planck or LiteBIRD plus CMB-S4. We consider a number of different cosmological models such as the ΛCDM, as well as its extensions with the running of the scalar spectral index, the decay or the annihilation of dark matter (DM) particles. While upcoming CMB anisotropy experiments will be able to decrease the uncertainties on inflationary parameters such as As and ns by about a factor 2 in the ΛCDM case, we find that an SD experiment 100 times more sensitive than PIXIE (comparable to the proposed Super-PIXIE satellite) could potentially further contribute to constrain these parameters. This is even more significant in the case of the running of the scalar spectral index. Furthermore, as expected, constraints on DM particles decaying at redshifts probed by SDs will improve by orders of magnitude even with an experiment 10 times worse than PIXIE as compared to CMB anisotropies or Big Bang Nucleosynthesis bounds. On the contrary, DM annihilation constraints will not significantly improve over CMB anisotropy measurements. Finally, we forecast the constraints obtainable with sensitivities achievable either from the ground or from a balloon.
Small C1 actions of semidirect products on
compact manifolds

Let T be a compact fibered 3-manifold, presented as a mapping torus of a compact, ori-entable surface S with monodromy ψ, and let M be a compact Riemannian manifold. Our main result is that if the induced action ψ˚on H 1 pS, Rq has no eigenvalues on the unit circle, then there exists a neighborhood U of the trivial action in the space of C 1 actions of π 1 pT q on M such that any action in U is abelian. We will prove that the same result holds in the generality of an infinite cyclic extension of an arbitrary finitely generated group H, provided that the conjugation action of the cyclic group on H 1 pH, Rq ‰ 0 has no eigenvalues of modulus one. We thus generalize a result of A. McCarthy, which addressed the case of abelian-by-cyclic groups acting on compact manifolds.
The weak lensing bispectrum induced by gravity

Recent studies have demonstrated that secondary non-Gaussianity induced by gravity will be detected with a high signal-to-noise ratio (S/N) by future and even by on-going weak lensing surveys. One way to characterize such non-Gaussianity is through the detection of a non-zero three-point correlation function of the lensing convergence field, or of its harmonic transform, the bispectrum. A recent study analysed the properties of the squeezed configuration of the bispectrum, when two wavenumbers are much larger than the third one. We extend this work by estimating the amplitude of the (reduced) bispectrum in four generic configurations, i.e. squeezed, equilateral, isosceles and folded, and for four different source redshifts zs = 0.5, 1.0, 1.5, 2.0, by using an ensemble of all-sky high-resolution simulations. We compare these results against theoretical predictions. We find that, while the theoretical expectations based on widely used fitting functions can predict the general trends of the reduced bispectra, a more accurate theoretical modelling will be required to analyse the next generation of all-sky weak lensing surveys. The disagreement is particularly pronounced in the squeezed limit.
Visual Indeterminacy in Generative Neural Art

Why are GANs such powerful tools for making art? This essay argues that GAN art often exhibits visual indeterminacy, a term coined by Pepperrell. GANs cause visual indeterminacy by creating plausible compositions and textures that nonetheless defy coherent explanation, and these are the GAN images often used in recent artworks. Because visual indeterminacy can be understood as a perceptual process, GANs provide a potential tool for both art and for neuroscience experiments based on perceptual uncertainty modeling.
Reflections on "Incremental Cardinality Constraints for MaxSAT"

To celebrate the first 25 years of the International Conference on Principles and Practice of Constraint Programming (CP) the editors invited the authors of the most cited paper of each year to write a commentary on their paper. This report describes our reflections on the CP 2014 paper "Incremental Cardinality Constraints for MaxSAT" and its impact on the Maximum Satisfiability community and beyond.
Learning to Remember from a Multi-Task Teacher

Recent studies on catastrophic forgetting during sequential learning typically focus on fixing the accuracy of the predictions for a previously learned task. In this paper we argue that the outputs of neural networks are subject to rapid changes when learning a new data distribution, and networks that appear to "forget" everything still contain useful representation towards previous tasks. Instead of enforcing the output accuracy to stay the same, we propose to reduce the effect of catastrophic forgetting on the representation level, as the output layer can be quickly recovered later with a small number of examples. Towards this goal, we propose an experimental setup that measures the amount of representational forgetting, and develop a novel meta-learning algorithm to overcome this issue. The proposed meta-learner produces weight updates of a sequential learning network, mimicking a multi-task teacher network's representation. We show that our meta-learner can improve its learned representations on new tasks, while maintaining a good representation for old tasks.
Multilingual Question Answering from Formatted Text applied to Conversational Agents

Recent advances in NLP with language models such as BERT, GPT-2, XLNet or XLM, have allowed surpassing human performance on Reading Comprehension tasks on large-scale datasets (e.g. SQuAD), and this opens up many perspectives for Conversational AI. However, task-specific datasets are mostly in English which makes it difficult to acknowledge progress in foreign languages. Fortunately, state-of-the-art models are now being pre-trained on multiple languages (e.g. BERT was released in a multilingual version managing a hundred languages) and are exhibiting ability for zero-shot transfer from English to others languages on XNLI. In this paper, we run experiments that show that multilingual BERT, trained to solve the complex Question Answering task defined in the English SQuAD dataset, is able to achieve the same task in Japanese and French. It even outperforms the best published results of a baseline which explicitly combines an English model for Reading Comprehension and a Machine Translation Model for transfer. We run further tests on crafted cross-lingual QA datasets (context in one language and question in another) to provide intuition on the mechanisms that allow BERT to transfer the task from one language to another. Finally, we introduce our application Kate. Kate is a conversational agent dedicated to HR support for employees that exploits multilingual models to accurately answer to questions, in several languages, directly from information web pages.
AlignNet-3D: Fast Point Cloud Registration of Partially Observed Objects

Methods tackling multi-object tracking need to estimate the number of targets in the sensing area as well as to estimate their continuous state. While the majority of existing methods focus on data association, precise state (3D pose) estimation is often only coarsely estimated by approximating targets with centroids or (3D) bounding boxes. However, in automotive scenarios, motion perception of surrounding agents is critical and inaccuracies in the vehicle close-range can have catastrophic consequences. In this work, we focus on precise 3D track state estimation and propose a learning-based approach for object-centric relative motion estimation of partially observed objects. Instead of approximating targets with their centroids, our approach is capable of utilizing noisy 3D point segments of objects to estimate their motion. To that end, we propose a simple, yet effective and efficient network, AlignNet-3D, that learns to align point clouds. Our evaluation on two different datasets demonstrates that our method outperforms computationally expensive, global 3D registration methods while being significantly more efficient. We make our data, code, and models available at https://www.vision.rwth-aachen.de/page/alignnet.