nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Match Anchors for Visual Object Detection

Modern CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Union (IoU). In this study, we propose a learning-to-match (LTM) method to break IoU restriction, allowing objects to match anchors in a flexible manner. LTM updates hand-crafted anchor assignment to “free” anchor matching by formulating detector training in the Maximum Likelihood Estimation (MLE) framework. During the training phase, LTM is implemented by converting the detection likelihood to anchor matching loss functions which are plug-and-play. Minimizing the matching loss functions drives learning and selecting features which best explain a class of objects with respect to both classification and localization. LTM is extended from anchor-based detectors to anchor-free detectors, validating the general applicability of learnable object-feature matching mechanism for visual object detection. Experiments on MS COCO dataset demonstrate that LTM detectors consistently outperform counterpart detectors with significant margins. The last but not the least, LTM requires negligible computational cost in both training and inference phases as it does not involve any additional architecture or parameter. Code has been made publicly available.
Free Flags Over Local Rings and Powering of High-dimensional Expanders

Powering the adjacency matrix of an expander graph results in a better expander of higher degree. In this paper we seek an analogue operation for high-dimensional expanders. We show that the naive approach to powering does not preserve high-dimensional expansion, and define a new powering operation, using geodesic walks on quotients of Bruhat-Tits buildings. When applying our geodesic powering operation to these complexes (and in particular to Ramanujan complexes), the resulting complexes are high-dimensional expanders of higher degrees. The crux of the proof is a combinatorial study of the spherical geometry of flags of free modules over finite local rings. This geometry, which is interesting in its own right, arises from the study of links of cells in the power complex. We show that these links are excellent expanders, thus establishing high dimensional expansion for the power-complex by the Garland technique. 
As an application, we use our powering operation to obtain new efficient double samplers, which were recently defined by Dinur and Kaufman. We achieve this by first showing that geodesic walks on Ramanujan complexes sample the vertices well, and then use the power-complex to construct a "large triangle walk", which samples well the geodesics.
An arbitrage-free conic martingale model with application to credit risk.

Conic martingales refer to Brownian martingales evolving between bounds. Among other potential applications, they have been suggested for the sake of modeling conditional survival probabilities under partial information, as usual in reduced-form models. Yet, conic martingale default models have a special feature; in contrast to the class of Cox models, they fail to satisfy the so-called \emph{immersion property}. Hence, it is not clear whether this setup is arbitrage-free or not. In this paper, we study the relevance of conic martingales-driven default models for practical applications in credit risk modeling. We first introduce an arbitrage-free conic martingale, namely the $\Phi$-martingale, by showing that it fits in the class of Dynamized Gaussian copula model of Crepey et al., thereby providing an explicit construction scheme for the default time. In particular, the $\Phi$-martingale features interesting properties inherent on its construction easing the practical implementation. Eventually, we apply this model to CVA pricing under wrong-way risk and CDS options, and compare our results with the JCIR++ (a.k.a. SSRJD) and TC-JCIR recently introduced as an alternative.
FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, non-autoregressive seq2seq models generate all tokens in one pass, which leads to increased efficiency through parallel processing on hardware such as GPUs. However, directly modeling the joint distribution of all tokens simultaneously is challenging, and even with increasingly complex model structures accuracy lags significantly behind autoregressive models. In this paper, we propose a simple, efficient, and effective model for non-autoregressive sequence generation using latent variable models. Specifically, we turn to generative flow, an elegant technique to model complex distributions using neural networks, and design several layers of flow tailored for modeling the conditional density of sequential latent variables. We evaluate this model on three neural machine translation (NMT) benchmark datasets, achieving comparable performance with state-of-the-art non-autoregressive NMT models and almost constant decoding time w.r.t the sequence length.
Exact results for scattering on ultrashort plane wave backgrounds

We give exact results for the emission spectra of both nonlinear Breit-Wheeler pair production and nonlinear Compton scattering in ultra-intense, ultra-short duration plane wave backgrounds, modelled as delta-function pulses. This includes closed form expressions for total scattering probabilities. We show explicitly that these probabilities do not exhibit the power-law scaling with intensity associated with the conjectured breakdown of (Furry picture) perturbation theory, instead scaling logarithmically in the high-intensity limit.
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks

Given access to accurate solutions of the many-electron Schr\"odinger equation, nearly all chemistry could be derived from first principles. Exact wavefunctions of interesting chemical systems are out of reach because they are NP-hard to compute in general, but approximations can be found using polynomially-scaling algorithms. The key challenge for many of these algorithms is the choice of wavefunction approximation, or Ansatz, which must trade off between efficiency and accuracy. Neural networks have shown impressive power as accurate practical function approximators and promise as a compact wavefunction Ansatz for spin systems, but problems in electronic structure require wavefunctions that obey Fermi-Dirac statistics. Here we introduce a novel deep learning architecture, the Fermionic Neural Network, as a powerful wavefunction Ansatz for many-electron systems. The Fermionic Neural Network is able to achieve accuracy beyond other variational quantum Monte Carlo Ans\"atze on a variety of atoms and small molecules. Using no data other than atomic positions and charges, we predict the dissociation curves of the nitrogen molecule and hydrogen chain, two challenging strongly-correlated systems, to significantly higher accuracy than the coupled cluster method, widely considered the most accurate scalable method for quantum chemistry at equilibrium geometry. This demonstrates that deep neural networks can improve the accuracy of variational quantum Monte Carlo to the point where it outperforms other ab-initio quantum chemistry methods, opening the possibility of accurate direct optimisation of wavefunctions for previously intractable molecules and solids.
Stack-VS: Stacked Visual-Semantic Attention for Image Caption Generation

Recently, automatic image caption generation has been an important focus of the work on <italic>multimodal</italic> translation task. Existing approaches can be roughly categorized into two classes, <italic>top-down</italic> and <italic>bottom-up</italic>, the former transfers the image information (called as <italic>visual-level feature</italic>) directly into a caption, and the later uses the extracted words (called as <italic>semantic-level attribute</italic>) to generate a description. However, previous methods either are typically based one-stage decoder or partially utilize part of <italic>visual</italic>-level or <italic>semantic</italic>-level information for image caption generation. In this paper, we address the problem and propose an innovative multi-stage architecture (called as <italic>Stack-VS</italic>) for rich fine-grained image caption generation, via combining <italic>bottom-up</italic> and <italic>top-down</italic> attention models to effectively handle both <italic>visual</italic>-level and <italic>semantic</italic>-level information of an input image. Specifically, we also propose a novel well-designed stack decoder model, which is constituted by a sequence of decoder cells, each of which contains two LSTM-layers work interactively to re-optimize attention weights on both visual-level feature vectors and semantic-level attribute embeddings for generating a fine-grained image caption. Extensive experiments on the popular benchmark dataset <italic>MSCOCO</italic> show the significant improvements on different evaluation metrics, <italic>i.e.,</italic> the improvements on BLEU-4 / CIDEr / SPICE scores are 0.372, 1.226 and 0.216, respectively, as compared to the state-of-the-art.
Coherent optical communications enhanced by machine intelligence

Accuracy in discriminating between different received coherent signals is integral to the operation of many free-space communications protocols, and is often difficult when the receiver measures a weak signal. Here we design an optical communication scheme that uses balanced homodyne detection in combination with an unsupervised generative machine learning and convolutional neural network (CNN) system, and demonstrate its efficacy in a realistic simulated coherent quadrature phase shift keyed (QPSK) communications system. Additionally, we design the neural network system such that it autonomously learns to correct for the noise associated with a weak QPSK signal, which is distributed to the receiver prior to the implementation of the communications. We find that the scheme significantly reduces the overall error probability of the communications system, achieving the classical optimal limit. We anticipate that these results will allow for a significant enhancement of current classical and quantum coherent optical communications technologies.
Codimension bounds and rigidity of ancient mean curvature flows by the tangent flow at −∞

Motivated by the limiting behavior of an explicit class of compact ancient curve shortening flows, by adapting the work of Colding–Minicozzi [11], we prove codimension bounds for ancient mean curvature flows by their tangent flow at . In the case of the  see text]-covered circle, we apply this bound to prove a strong rigidity theorem. Furthermore, we extend this paradigm by showing that under the assumption of sufficiently rapid convergence, a compact ancient mean curvature flow is identical to its tangent flow at
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes

Dynamic Personalized Decision Making Beyond the Super-Extrapolatable and Super-Local Cases Contextual bandit problems model the inherent trade-off between exploration and exploitation in personalized decision making in marketing, healthcare, revenue management, and more. Specifically, the trade-off is characterized by the optimal growth rate of the regret. Intuitively, the optimal rate should depend on how complex the underlying supervised learning problem is, namely, how much can observing reward in one context tell us about mean rewards in another. To formalize this intuitive relationship, Hu, Kallus, and Mao study in “Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regimes” a nonparametric contextual bandit problem in which mean reward functions are β-times differentiable (more generally, Hölder β-smooth). This interpolates between two extremes previously studied in isolation: nondifferentiable bandits (β ≤ 1), with which running separated noncontextual bandits in different context regions achieves rate-optimal regret, and parametric-response bandits (β = ∞), with which rate-optimal regret can be achieved with minimal or no exploration because of infinite extrapolatability across contexts. The authors develop a rate-optimal algorithm that operates neither fully locally nor fully globally, revealing the optimal regret rate in this in-between smooth setting and shedding light on the crucial interplay of functional complexity and regret in dynamic personalized decision making.
Brans–Dicke Gravity with a Cosmological Constant Smoothes Out ΛCDM Tensions

We analyze Brans–Dicke gravity with a cosmological constant, Λ, and cold dark matter (BD-ΛCDM) in the light of the latest cosmological observations on distant supernovae, Hubble rate measurements at different redshifts, baryonic acoustic oscillations, large-scale structure formation data, gravitational weak-lensing, and the cosmic microwave background under full Planck 2015 cosmic microwave background likelihood. Our analysis includes both the background and perturbations equations. We find that BD-ΛCDM is observationally favored as compared to the concordance ΛCDM model, which is traditionally defined within General Relativity (GR). In particular, some well-known persisting tensions of the ΛCDM with the data, such as the excess in the mass fluctuation amplitude and especially the acute H0-tension with the local measurements, essentially disappear in this context. Furthermore, viewed from the GR standpoint, BD-ΛCDM cosmology mimics quintessence at c.l. near our time.
Number of Sign Changes: Segment of AR(1)

Let $X_{t}$ denote a stationary first-order autoregressive process. Consider $n$ contiguous observations (in time $t$) of the series (e.g., $X_{1}, ..., X_{n}$). Let its mean be zero and its lag-one serial correlation be $\rho$, which satisfies $|\rho| < 1$. Rice (1945) proved that $(n-1) \arccos(\rho)/\pi$ is the expected number of sign changes. A corresponding formula for higher-order moments was proposed by Nyberg, Lizana & Ambjornsson (2018), based on an independent interval approximation. We focus on the variance only, for small $n$, and see a promising fit between theory and model.
Adversarial Examples with Difficult Common Words for Paraphrase Identification

Despite the success of deep models for paraphrase identification on benchmark datasets, these models are still vulnerable to adversarial examples. In this paper, we propose a novel algorithm to generate a new type of adversarial examples to study the robustness of deep paraphrase identification models. We first sample an original sentence pair from the corpus and then adversarially replace some word pairs with difficult common words. We take multiple steps and use beam search to find a modification solution that makes the target model fail, and thereby obtain an adversarial example. The word replacement is also constrained by heuristic rules and a language model, to preserve the label and grammaticality of the example during modification. Experiments show that our algorithm can generate adversarial examples on which the performance of the target model drops dramatically. Meanwhile, human annotators are much less affected, and the generated sentences retain a good grammaticality. We also show that adversarial training with generated adversarial examples can improve model robustness.
Causal Effects of Brevity on Style and Success in Social Media

In online communities, where billions of people strive to propagate their messages, understanding how wording affects success is of primary importance. In this work, we are interested in one particularly salient aspect of wording: brevity. What is the causal effect of brevity on message success? What are the linguistic traits of brevity? When is brevity beneficial, and when is it not? Whereas most prior work has studied the effect of wording on style and success in observational setups, we conduct a controlled experiment, in which crowd workers shorten social media posts to prescribed target lengths and other crowd workers subsequently rate the original and shortened versions. This allows us to isolate the causal effect of brevity on the success of a message. We find that concise messages are on average more successful than the original messages up to a length reduction of 30--40%. The optimal reduction is on average between 10% and 20%. The observed effect is robust across different subpopulations of raters and is the strongest for raters who visit social media on a daily basis. Finally, we discover unique linguistic and content traits of brevity and correlate them with the measured probability of success in order to distinguish effective from ineffective shortening strategies. Overall, our findings are important for developing a better understanding of the effect of brevity on the success of messages in online social media.
Searching for a solar relaxion or scalar particle with XENON1T and LUX

We consider liquid xenon dark matter detectors for searching a light scalar particle produced in the solar core, specifically one that couples to electrons. Through its interaction with the electrons, the scalar particle can be produced in the Sun, mainly through Bremsstrahlung process, and subsequently it is absorbed by liquid xenon atoms, leaving prompt scintillation light and ionization events. Using the latest experimental results of XENON1T and Large Underground Xenon, we place bounds on the coupling between electrons and a light scalar as $g_{\phi ee} < 8 \times 10^{-15}$ from S1-only analysis, and as $g_{\phi ee} < 2 \times 10^{-15}$ from S2-only analysis. These can be interpreted as bounds on the mixing angle with the Higgs, $\sin \theta < 3 \times 10^{-9} \, \left(7 \times 10^{-10}\right)$, for the case of a relaxion that couples to the electrons via this mixing. The bounds are a factor few weaker than the strongest indirect bound inferred from stellar evolution considerations.
Stellar bars in counter-rotating dark matter haloes: the role of halo orbit reversals

Disc galaxies can exchange angular momentum and baryons with their host dark matter (DM) haloes. These haloes possess internal spin, λ, which is insignificant rotationally but does affect interactions between the baryonic and DM components. While statistics of prograde and retrograde spinning haloes in galaxies is not available at present, the existence of such haloes is important for galaxy evolution. In the previous works, we analysed dynamical and secular evolution of stellar bars in prograde spinning haloes and the DM response to the bar perturbation, and found that it is modified by the resonant interactions between the bar and the DM halo orbits. In this work, we follow the evolution of stellar bars in retrograde haloes. We find that this evolution differs substantially from evolution in rigid unresponsive haloes, discussed in the literature. First, we confirm that the bar instability is delayed progressively along the retrograde λ sequence. Secondly, the bar evolution in the retrograde haloes differs also from that in the prograde haloes, in that the bars continue to grow substantially over the simulation time of 10 Gyr. The DM response is also substantially weaker compared to this response in the prograde haloes. Thirdly, using orbital spectral analysis of the DM orbital structure, we find a phenomenon we call the orbit reversal – when retrograde DM orbits interact with the stellar bar, reverse their streaming and precession, and become prograde. This process dominates the inner halo region adjacent to the bar and allows these orbits to be trapped by the bar, thus increasing efficiency of angular momentum transfer by the inner Lindblad resonance. We demonstrate this reversal process explicitly in a number of examples.
The Complexity of Reachability in Affine Vector Addition Systems with States

Vector addition systems with states (VASS) are widely used for the formal verification of concurrent systems. Given their tremendous computational complexity, practical approaches have relied on techniques such as reachability relaxations, e.g., allowing for negative intermediate counter values. It is natural to question their feasibility for VASS enriched with primitives that typically translate into undecidability. Spurred by this concern, we pinpoint the complexity of integer relaxations w.r.t. arbitrary classes of affine operations. More specifically, we provide a trichotomy on the complexity of integer reachability in VASS extended with affine operations (affine VASS). Namely, we show that it is NP-complete for VASS with resets, PSPACE-complete for VASS with (pseudo-)transfers and VASS with (pseudo-)copies, and undecidable for any other class. We further present a dichotomy for standard reachability in affine VASS: it is decidable for VASS with permutations, and undecidable for any other class. This yields a complete and unified complexity landscape of reachability in affine VASS.
The CCAT-Prime Submillimeter Observatory

The Cerro Chajnantor Atacama Telescope-prime (CCAT-prime) is a new 6-m, off-axis, low-emissivity, large field-of-view submillimeter telescope scheduled for first light in the last quarter of 2021. In summary, (a) CCAT-prime uniquely combines a large field-of-view (up to 8-deg), low emissivity telescope (< 2%) and excellent atmospheric transmission (5600-m site) to achieve unprecedented survey capability in the submillimeter. (b) Over five years, CCAT-prime first generation science will address the physics of star formation, galaxy evolution, and galaxy cluster formation; probe the re-ionization of the Universe; improve constraints on new particle species; and provide for improved removal of dust foregrounds to aid the search for primordial gravitational waves.  In return, the community will be able to participate in survey planning and gain access to curated data sets. (d) For second generation science, CCAT-prime will be uniquely positioned to contribute high-frequency capabilities to the next generation of CMB surveys in partnership with the CMB-S4 and/or the Simons Observatory projects or revolutionize wide-field, sub-millimetter line intensity mapping surveys.
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs

Though state-of-the-art sentence representation models can perform tasks requiring significant knowledge of grammar, it is an open question how best to evaluate their grammatical knowledge. We explore five experimental methods inspired by prior work evaluating pretrained sentence representation models. We use a single linguistic phenomenon, negative polarity item (NPI) licensing, as a case study for our experiments. NPIs like any are grammatical only if they appear in a licensing environment like negation (Sue doesn’t have any cats vs. *Sue has any cats). This phenomenon is challenging because of the variety of NPI licensing environments that exist. We introduce an artificially generated dataset that manipulates key features of NPI licensing for the experiments. We find that BERT has significant knowledge of these features, but its success varies widely across different experimental methods. We conclude that a variety of methods is necessary to reveal all relevant aspects of a model’s grammatical knowledge in a given domain.