nils-herrmann/clean_scirepeval huggingface dataset:
A note on BPS structures and Gopakumar–Vafa invariants

We regard the work of Maulik and Toda, proposing a sheaf-theoretic approach to Gopakumar-Vafa invariants, as defining a BPS structure, that is, a collection of BPS invariants together with a central charge. Assuming their conjectures, we show that a canonical flat section of the flat connection corresponding to this BPS structure, at the level of formal power series, reproduces the Gromov-Witten partition function for all genera, up to some error terms in genus 0 and 1. This generalises a result of Bridgeland and Iwaki for the contribution from genus 0 Gopakumar-Vafa invariants.
Approximation of Feynman path integrals with non-smooth potentials

We study the convergence in $L^2$ of the time slicing approximation of Feynman path integrals under low regularity assumptions on the potential. Inspired by the custom in Physics and Chemistry, the approximate propagators considered here arise from a series expansion of the action. The results are ultimately based on function spaces, tools and strategies which are typical of Harmonic and Time-frequency analysis.
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature

To determine the completeness of argumentative steps necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature. 
 Papers published in the Neural Information Processing Systems (NeurIPS, nee NIPS) journal where the official record showed a 2017 year of publication. 
 Studies reporting a (semi-)supervised model, or pre-processing fused with (semi-)supervised models for tabular data. 
Study Appraisal: Three reviewers applied the assessment criteria to determine argumentative completeness. The criteria were split into three groups, including: experiments (e.g real and/or synthetic data), baselines (e.g uninformed and/or state-of-art) and quantitative comparison (e.g. performance quantifiers with confidence intervals and formal comparison of the algorithm against baselines). 
 Of the 121 eligible manuscripts (from the sample of 679 abstracts), 99\% used real-world data and 29\% used synthetic data. 91\% of manuscripts did not report an uninformed baseline and 55\% reported a state-of-art baseline. 32\% reported confidence intervals for performance but none provided references or exposition for how these were calculated. 3\% reported formal comparisons. 
 The use of one journal as the primary information source may not be representative of all ML/AI literature. However, the NeurIPS conference is recognised to be amongst the top tier concerning ML/AI studies, so it is reasonable to consider its corpus to be representative of high-quality research. 
 Using the 2017 sample of the NeurIPS supervised learning corpus as an indicator for the quality and trustworthiness of current ML/AI research, it appears that complete argumentative chains in demonstrations of algorithmic effectiveness are rare.
On zero-free regions for the anti-ferromagnetic Potts model on bounded-degree graphs

For a graph $G=(V,E)$, $k\in \mathbb{N}$, and a complex number $w$ the partition function of the univariate Potts model is defined as \[ {\bf Z}(G;k,w):=\sum_{\phi:V\to [k]}\prod_{\substack{uv\in E \\ \phi(u)=\phi(v)}}w, \] where $[k]:=\{1,\ldots,k\}$. In this paper we give zero-free regions for the partition function of the anti-ferromagnetic Potts model on bounded degree graphs. In particular we show that for any $\Delta\in \mathbb{N}$ and any $k\geq e\Delta+1$, there exists an open set $U$ in the complex plane that contains the interval $[0,1)$ such that ${\bf Z}(G;k,w)\neq 0$ for any $w\in U$ and any graph $G$ of maximum degree at most $\Delta$. (Here $e$ denotes the base of the natural logarithm.) For small values of $\Delta$ we are able to give better results. 
As an application of our results we obtain improved bounds on $k$ for the existence of deterministic approximation algorithms for counting the number of proper $k$-colourings of graphs of small maximum degree.
Quantum interface of an electron and a nuclear ensemble

Coherent excitation of an ensemble of quantum objects underpins quantum many-body phenomena and offers the opportunity to realize a memory that stores quantum information. Thus far, a deterministic and coherent interface between a spin qubit and such an ensemble has remained elusive. In this study, we first used an electron to cool the mesoscopic nuclear spin ensemble of a semiconductor quantum dot to the nuclear sideband–resolved regime. We then implemented an all-optical approach to access individual quantized electronic-nuclear spin transitions. Lastly, we performed coherent optical rotations of a single collective nuclear spin excitation—a spin wave. These results constitute the building blocks of a dedicated local memory per quantum-dot spin qubit and promise a solid-state platform for quantum-state engineering of isolated many-body systems.
Information-Directed Exploration for Deep Reinforcement Learning

Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropriately account for heteroscedasticity, even in the bandit setting. Motivated by recent findings that address this issue in bandits, we propose to use Information-Directed Sampling (IDS) for exploration in reinforcement learning. As our main contribution, we build on recent advances in distributional reinforcement learning and propose a novel, tractable approximation of IDS for deep Q-learning. The resulting exploration strategy explicitly accounts for both parametric uncertainty and heteroscedastic observation noise. We evaluate our method on Atari games and demonstrate a significant improvement over alternative approaches.
Lecture notes on rectifiable Reifenberg for
 measures

These series of notes serve as an introduction to some of both the classical and modern techniques in Reifenberg theory. At its heart, Reifenberg theory is about studying general sets or measures which can be, in one sense or another, approximated on all scales by well behaved spaces, typically just Euclidean space itself. Such sets and measures turn out not to be arbitrary, and often times come with special structure inherited from what they are being approximated by. 
We will begin by recalling and proving the standard Reifenberg theorem, which says that sets in Euclidean space which are well approximated by affine subspaces on all scales must be homoemorphic to balls. These types of results have applications to studying the regular parts of solutions of nonlinear equations. The proof given is designed to move cleanly over to more complicated scenarios introduced later. 
The rest of the lecture notes are designed to introduce and prove the Rectifiable Reifenberg Theorem, including an introduction to the relevant concepts. The Rectifiable Reifenberg Theorem roughly says that if a measure $\mu$ is summably close on all scales to affine subspaces $L^k$, then $\mu=\mu^++\mu^k$ may be broken into pieces such that $\mu^k$ is $k$-rectifiable with uniform Hausdorff measure estimates, and $\mu^+$ has uniform bounds on its mass. These types of results have applications to studying the singular parts of solutions of nonlinear equations. The proof given is designed to give a baby introduction to ways of thinking in more modern PDE analysis, including an introduction to Neck regions and their Structure Theory.
Uniform Convergence Bounds for Codec Selection

We frame the problem of selecting an optimal audio encoding scheme as a supervised learning task. Through uniform convergence theory, we guarantee approximately optimal codec selection while controlling for selection bias. We present rigorous statistical guarantees for the codec selection problem that hold for arbitrary distributions over audio sequences and for arbitrary quality metrics. Our techniques can thus balance sound quality and compression ratio, and use audio samples from the distribution to select a codec that performs well on that particular type of data. The applications of our technique are immense, as it can be used to optimize for quality and bandwidth usage of streaming and other digital media, while significantly outperforming approaches that apply a fixed codec to all data sources.
Direct detection of bound states of asymmetric dark matter

We study the reach of direct detection experiments for large bound states (containing 10^4 or more dark nucleons) of Asymmetric Dark Matter. We consider ordinary nuclear recoils, excitation of collective modes (phonons), and electronic excitations, paying careful attention to the impact of the energy threshold of the experiment. Large exposure experiments with keV energy thresholds provide the best (future) limits when the Dark Matter is small enough to be treated as a point particle, but rapidly lose sensitivity for more extended dark bound states, or when the mediator is light. In those cases, low threshold, low exposure experiments (such as with a superfluid helium, polar material or superconducting target) are often more sensitive due to coherent enhancement over the dark nucleons. We also discuss indirect constraints on composite Asymmetric Dark Matter arising from self-interaction, formation history and the properties of the composite states themselves.
Probing Dark Matter Using Precision Measurements of Stellar Accelerations.

Dark matter comprises the bulk of the matter in the Universe, but its particle nature and cosmological origin remain mysterious. Knowledge of the dark matter density distribution in the Milky Way Galaxy is crucial both to our understanding of the standard cosmological model and for grounding direct and indirect searches for the particles comprising dark matter. Current measurements of Galactic dark matter content rely on model assumptions to infer the forces acting upon stars from the distribution of observed velocities. Here, we propose to apply the precision radial velocity method, optimized in recent years for exoplanet astronomy, to measure the change in the velocity of stars over time, thereby providing a direct probe of the local gravitational potential in the Galaxy. Using numerical simulations, we develop a realistic strategy to observe the differential accelerations of stars in our Galactic neighborhood with next-generation telescopes, at the level of 10^{-8}  cm/s^{2}. Our simulations show that detecting accelerations at this level with an ensemble of 10^{3} stars requires the effect of stellar noise on radial velocity measurements to be reduced to <10  cm/s. The measured stellar accelerations may then be used to extract the local dark matter density and morphological parameters of the density profile.
Vierbein interactions with antisymmetric components

In this work we propose a new gravitational setup formulated in terms of two interacting vierbein fields. The theory is the fully diffeomorphism and local Lorentz invariant extension of a previous construction which involved a fixed reference vierbein. Certain vierbein components can be shifted by local Lorentz transformations and do not enter the associated metric tensors. We parameterize these components by an antisymmetric tensor field and give them a kinetic term in the action, thereby promoting them to dynamical variables. In addition, the action contains two Einstein-Hilbert terms and an interaction potential whose form is inspired by ghost-free massive gravity and bimetric theory. The resulting theory describes the interactions of a massless spin-2, a massive spin-2 and an antisymmetric tensor field. It can be generalized to the case of multiple massive spin-2 fields and multiple antisymmetric tensor fields. The absence of additional and potentially pathological degrees of freedom is verified in an ADM analysis. However, the antisymmetric tensor fluctuation around the maximally symmetric background solution has a tachyonic mass pole.
Stellar accelerations and the galactic gravitational field

Typical stars in the Milky Way galaxy have velocities of hundreds of kilometres per second and experience gravitational accelerations of $\sim\!10^{-10}~{\rm m\,s}^{-2}$, resulting in velocity changes of a few centimetres per second over a decade. Measurements of these accelerations would permit direct tests of the applicability of Newtonian dynamics on kiloparsec length scales and could reveal significant small-scale inhomogeneities within the galaxy, as well increasing the sensitivity of measurements of the overall mass distribution of the galaxy. Noting that a reasonable extrapolation of progress in exoplanet hunting spectrographs suggests that centimetre per second level precision will be attainable in the coming decade(s), we explore the possibilities such measurements would create. We consider possible confounding effects, including apparent accelerations induced by stellar motion and reflex velocities from planetary systems, along with possible strategies for their mitigation. If these issues can be satisfactorily addressed, it will be possible to use high-precision measurements of changing stellar velocities to perform a ‘blind search’ for dark matter, make direct tests of theories of non-Newtonian gravitational dynamics, detect local inhomogeneities in the dark matter density, and greatly improve measurements of the overall properties of the galaxy.
Cosmological beam dump: Constraints on dark scalars mixed with the Higgs boson

Precision cosmology provides a sensitive probe of extremely weakly coupled states due to thermal freeze-in production, with subsequent decays impacting physics during well-tested cosmological epochs. We explore the cosmological implications of the freeze-in production of a new scalar $S$ via the super-renormalizable Higgs portal. If the mass of $S$ is at or below the electroweak scale, peak freeze-in production occurs during the electroweak epoch. We improve the calculation of the freeze-in abundance by including all relevant QCD and electroweak production channels. The resulting abundance and subsequent decay of $S$ is constrained by a combination of X-ray data, cosmic microwave background anisotropies and spectral distortions, $N_{\rm eff}$, and the consistency of BBN with observations. These probes constrain technically natural couplings for such scalars from $m_S \sim$ keV all the way to $m_S \sim 100$ GeV. The ensuing constraints are similar in spirit to typical beam bump limits, but extend to much smaller couplings, down to mixing angles as small as $\theta_{Sh} \sim 10^{-16}$, and to masses all the way to the electroweak scale.
Quantum scrambling and state dependence of the butterfly velocity

Operator growth in spatially local quantum many-body systems defines a scrambling velocity. We prove that this scrambling velocity bounds the state dependence of the out-of-time-ordered correlator in local lattice models. We verify this bound in simulations of the thermal mixed-field Ising spin chain. For scrambling operators, the butterfly velocity shows a crossover from a microscopic high temperature value to a distinct value at temperatures below the energy gap.
Deep Transfer Learning for Static Malware Classification

We propose to apply deep transfer learning from computer vision to static malware classification. In the transfer learning scheme, we borrow knowledge from natural images or objects and apply to the target domain of static malware detection. As a result, training time of deep neural networks is accelerated while high classification performance is still maintained. We demonstrate the effectiveness of our approach on three experiments and show that our proposed method outperforms other classical machine learning methods measured in accuracy, false positive rate, true positive rate and $F_1$ score (in binary classification). We instrument an interpretation component to the algorithm and provide interpretable explanations to enhance security practitioners' trust to the model. We further discuss a convex combination scheme of transfer learning and training from scratch for enhanced malware detection, and provide insights of the algorithmic interpretation of vision-based malware classification techniques.
Differential Evolution with Nearest & Better Option for Function Optimization

Differential evolution is the conventional algorithm with the fastest convergence speed, but it may be trapped local optimal solution easily, so many researchers devote themselves into improve DE. Whale swarm algorithm (WSA) is a new algorithm with niching strategy we proposed previously, it is featured with simple mutation strategy and powerful global search capability, but for functions with high dimensions, it converges slower than conventional algorithms. Based on this fact, we proposed a new DE algorithm, called DE with nearest & better option (NbDE). In order to evaluate the performance of NbDE, we compare NbDE with several meta-heuristic algorithms in nine classical benchmark functions with different dimensions. The result have shown that NbDE outperforms other algorithms in convergence speed and accuracy.
Towards Deep Conversational Recommendations

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.
Wav2Letter++: A Fast Open-source Speech Recognition System

deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor library for maximum efficiency. We explain the architecture and design of the wav2letter++ system and compare it to other major open-source speech recognition systems. In some cases wav2letter++ is more than 2× faster than other optimized frameworks for training end-to-end neural networks for speech recognition. We also show that wav2letter++ training times scale linearly to 64 GPUs, the most we tested, for models with 100 million parameters. High-performance frameworks enable fast iteration, which is often a crucial factor in successful research and model tuning on new datasets and tasks.
Universal Successor Features Approximators

The ability of a reinforcement learning (RL) agent to learn about many reward functions at the same time has many potential benefits, such as the decomposition of complex tasks into simpler ones, the exchange of information between tasks, and the reuse of skills. We focus on one aspect in particular, namely the ability to generalise to unseen tasks. Parametric generalisation relies on the interpolation power of a function approximator that is given the task description as input; one of its most common form are universal value function approximators (UVFAs). Another way to generalise to new tasks is to exploit structure in the RL problem itself. Generalised policy improvement (GPI) combines solutions of previous tasks into a policy for the unseen task; this relies on instantaneous policy evaluation of old policies under the new reward function, which is made possible through successor features (SFs). Our proposed universal successor features approximators (USFAs) combine the advantages of all of these, namely the scalability of UVFAs, the instant inference of SFs, and the strong generalisation of GPI. We discuss the challenges involved in training a USFA, its generalisation properties and demonstrate its practical benefits and transfer abilities on a large-scale domain in which the agent has to navigate in a first-person perspective three-dimensional environment.
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss

The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective. In this work, we propose clustering-oriented representation learning (COREL) as an alternative to CCE in the context of a generalized attractive-repulsive loss framework. COREL has the consequence of building latent representations that collectively exhibit the quality of natural clustering within the latent space of the final hidden layer, according to a predefined similarity function. Despite being simple to implement, COREL variants outperform or perform equivalently to CCE in a variety of scenarios, including image and news article classification using both feed-forward and convolutional neural networks. Analysis of the latent spaces created with different similarity functions facilitates insights on the different use cases COREL variants can satisfy, where the Cosine-COREL variant makes a consistently clusterable latent space, while Gaussian-COREL consistently obtains better classification accuracy than CCE.
K2 Ultracool Dwarfs Survey – V. High superflare rates on rapidly rotating late-M dwarfs

We observed strong superflares (defined as flares with energy in excess of 1033 erg) on three late-M dwarfs: 2MASS J08315742+2042213 (hereafter 2M0831+2042; M7 V), 2MASS J08371832+2050349 (hereafter 2M0837+2050; M8 V), and 2MASS J08312608+2244586 (hereafter 2M0831+2244; M9 V). 2050 are members of the young (∼700 Myr) open cluster Praesepe. The strong superflare on 2M0831+2042 has an equivalent duration (ED) of 13.7 h and an estimated energy of 1.3 × 1035 erg. We observed five superflares on 2M0837+2050, on which the strongest superflare has an ED of 46.4 h and an estimated energy of 3.5 × 1035 erg. This energy is larger by 2.7 orders of magnitude than the largest flare observed on the older (7.6 Gyr) planet-hosting M8 dwarf TRAPPIST-1. Furthermore, we also observed five superflares on 2M0831+2244 which is probably a field star. The estimated energy of the strongest superflare on 2M0831+2244 is 6.1 × 1034 erg. 2050, and 2M0831+2244 have rotation periods of 0.556 ± 0.002, 0.193 ± 0.000, and 0.292 ± 0.001 d, respectively, which we measured by using K2 light curves. We compare the flares of younger targets with those of TRAPPIST-1 and discuss the possible impacts of such flares on planets in the habitable zone of late-M dwarfs.