nils-herrmann/clean_scirepeval huggingface dataset:
Characterizing symplectic Grassmannians by varieties of minimal rational tangents

We show that if the variety of minimal rational tangents (VMRT) of a uniruled projective manifold at a general point is projectively equivalent to that of a symplectic or an odd-symplectic Grassmannian, the germ of a general minimal rational curve is biholomorphic to the germ of a general line in a presymplectic Grassmannian. As an application, we characterize symplectic and odd-symplectic Grassmannians, among Fano manifolds of Picard number 1, by their VMRT at a general point and prove their rigidity under global K\"ahler deformation. Analogous results for $G/P$ associated with a long root were obtained by Mok and Hong-Hwang a decade ago by using Tanaka theory for parabolic geometries. When $G/P$ is associated with a short root, for which symplectic Grassmannians are most prominent examples, the associated local differential geometric structure is no longer a parabolic geometry and standard machinery of Tanaka theory cannot be applied because of several degenerate features. To overcome the difficulty, we show that Tanaka's method can be generalized to a setting much broader than parabolic geometries, by assuming a pseudo-concavity type condition that certain vector bundles arising from Spencer complexes have no nonzero sections. The pseudo-concavity type condition is checked by exploiting geometry of minimal rational curves.
Contact Courant algebroids and $L_\infty$-algebras.

Let $L$ be a line bundle over $M$. In this paper we associate an $L_\infty$-algebra to any $L$-Courant algebroid (contact Courant algebroid in the sense of Grabowski). This construction is similar to the work of Roytenberg and Weinstein for Courant algebroids. Next we associate a $p$-term $L_\infty$-algebra to any isotropic involutive subbundle of $(\mathbb{D}L)^p := DL \oplus (\wedge^p (DL)^* \otimes L)$, where $DL$ is the gauge algebroid of $L$. In a particular case, we relate these $L_\infty$-algebras by a suitable morphism.
Learning Efficient Detector with Semi-supervised Adaptive Distillation

Knowledge Distillation (KD) has been used in image classification for model compression. However, rare studies apply this technology on single-stage object detectors. Focal loss shows that the accumulated errors of easily-classified samples dominate the overall loss in the training process. This problem is also encountered when applying KD in the detection task. For KD, the teacher-defined hard samples are far more important than any others. We propose ADL to address this issue by adaptively mimicking the teacher's logits, with more attention paid on two types of hard samples: hard-to-learn samples predicted by teacher with low certainty and hard-to-mimic samples with a large gap between the teacher's and the student's prediction. ADL enlarges the distillation loss for hard-to-learn and hard-to-mimic samples and reduces distillation loss for the dominant easy samples, enabling distillation to work on the single-stage detector first time, even if the student and the teacher are identical. Besides, ADL is effective in both the supervised setting and the semi-supervised setting, even when the labeled data and unlabeled data are from different distributions. For distillation on unlabeled data, ADL achieves better performance than existing data distillation which simply utilizes hard targets, making the student detector surpass its teacher. On the COCO database, semi-supervised adaptive distillation (SAD) makes a student detector with a backbone of ResNet-50 surpasses its teacher with a backbone of ResNet-101, while the student has half of the teacher's computation complexity. The code is avaiable at this https URL
Concave transforms of filtrations and rationality of Seshadri constants

We show that the subgraph of the concave transform of a multiplicative filtration on a section ring is the Newton--Okounkov body of a certain semigroup, and if the filtration is induced by a divisorial valuation, then the associated graded algebra is the algebra of sections of a concrete line bundle in higher dimension. We use this description to give a rationality criterion for certain Seshadri constants. Along the way we introduce Newton--Okounkov bodies of abstract graded semigroups and determine conditions for their slices to be Newton--Okounkov bodies of subsemigroups.
Overview of Information Theory, Computer Science Theory, and Stochastic Thermodynamics for Thermodynamics of Computation

I give a quick overview of some of the theoretical background necessary for using modern non-equilibrium statistical physics to investigate the thermodynamics of computation. I first present some of the necessary concepts from information theory, and then introduce some of the most important types of computational machine considered in computer science theory. 
After this I present a central result from modern non-equilibrium statistical physics: an exact expression for the entropy flow out of a system undergoing a given dynamics with a given initial distribution over states. This central expression is crucial for analyzing how the total entropy flow out of a computer depends on its global structure, since that global structure determines the initial distributions into all of the computer's subsystems, and therefore (via the central expression) the entropy flows generated by all of those subsystems. I illustrate these results by analyzing some of the subtleties concerning the benefits that are sometimes claimed for implementing an irreversible computation with a reversible circuit constructed out of Fredkin gates.
Tests of Pauli exclusion principle violations from noncommutative quantum gravity

We review the main recent progresses in noncommutative space–time phenomenology in underground experiments. A popular model of noncommutative space–time is  This model predicts a violation of the spin-statistic theorem, in turn implying an energy and angular dependent violation of the Pauli exclusion principle. Pauli exclusion principle violating transitions in nuclear and atomic systems can be tested with very high accuracy in underground laboratory experiments such as DAMA/LIBRA and VIP(2). In this paper we derive that the  see text]-Poincaré model can be already ruled-out until the Planck scale, from nuclear transitions tests by DAMA/LIBRA experiment.
Attribute-Aware Attention Model for Fine-grained Representation Learning

How to learn a discriminative fine-grained representation is a key point in many computer vision applications, such as person re-identification, fine-grained classification, fine-grained image retrieval, etc. Most of the previous methods focus on learning metrics or ensemble to derive better global representation, which are usually lack of local information. Based on the considerations above, we propose a novel Attribute-Aware Attention Model ($A^3M$), which can learn local attribute representation and global category representation simultaneously in an end-to-end manner. The proposed model contains two attention models: attribute-guided attention module uses attribute information to help select category features in different regions, at the same time, category-guided attention module selects local features of different attributes with the help of category cues. Through this attribute-category reciprocal process, local and global features benefit from each other. Finally, the resulting feature contains more intrinsic information for image recognition instead of the noisy and irrelevant features. Extensive experiments conducted on Market-1501, CompCars, CUB-200-2011 and CARS196 demonstrate the effectiveness of our $A^3M$.
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification

Crowdsourcing has become widely used in supervised scenarios where training sets are scarce and difficult to obtain. Most crowdsourcing models in the literature assume labelers can provide answers to full questions. In classification contexts, full questions require a labeler to discern among all possible classes. Unfortunately, discernment is not always easy in realistic scenarios. Labelers may not be experts in differentiating all classes. In this work, we provide a full probabilistic model for a shorter type of queries. Our shorter queries only require "yes" or "no" responses. Our model estimates a joint posterior distribution of matrices related to labelers' confusions and the posterior probability of the class of every object. We developed an approximate inference approach, using Monte Carlo Sampling and Black Box Variational Inference, which provides the derivation of the necessary gradients. We built two realistic crowdsourcing scenarios to test our model. The first scenario queries for irregular astronomical time-series. The second scenario relies on the image classification of animals. We achieved results that are comparable with those of full query crowdsourcing. Furthermore, we show that modeling labelers' failures plays an important role in estimating true classes. Finally, we provide the community with two real datasets obtained from our crowdsourcing experiments. All our code is publicly available.
Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation

We conduct a large-scale, systematic study to evaluate the existing evaluation methods for natural language generation in the context of generating online product reviews. We compare human-based evaluators with a variety of automated evaluation procedures, including discriminative evaluators that measure how well machine-generated text can be distinguished from human-written text, as well as word overlap metrics that assess how similar the generated text compares to human-written references. We determine to what extent these different evaluators agree on the ranking of a dozen of state-of-the-art generators for online product reviews. We find that human evaluators do not correlate well with discriminative evaluators, leaving a bigger question of whether adversarial accuracy is the correct objective for natural language generation. In general, distinguishing machine-generated text is challenging even for human evaluators, and human decisions correlate better with lexical overlaps. We find lexical diversity an intriguing metric that is indicative of the assessments of different evaluators. A post-experiment survey of participants provides insights into how to evaluate and improve the quality of natural language generation systems.
Unary and Binary Classification Approaches and their Implications for Authorship Verification

Retrieving indexed documents, not by their topical content but their writing style opens the door for a number of applications in information retrieval (IR). One application is to retrieve textual content of a certain author X, where the queried IR system is provided beforehand with a set of reference texts of X. Authorship verification (AV), which is a research subject in the field of digital text forensics, is suitable for this purpose. The task of AV is to determine if two documents (i.e. an indexed and a reference document) have been written by the same author X. Even though AV represents a unary classification problem, a number of existing approaches consider it as a binary classification task. However, the underlying classification model of an AV method has a number of serious implications regarding its prerequisites, evaluability, and applicability. In our comprehensive literature review, we observed several misunderstandings regarding the differentiation of unary and binary AV approaches that require consideration. The objective of this paper is, therefore, to clarify these by proposing clear criteria and new properties that aim to improve the characterization of existing and future AV approaches. Given both, we investigate the applicability of eleven existing unary and binary AV methods as well as four generic unary classification algorithms on two self-compiled corpora. Furthermore, we highlight an important issue concerning the evaluation of AV methods based on fixed decision criterions, which has not been paid attention in previous AV studies.
Anomaly Detection in Networks with Application to Financial Transaction Networks

This paper is motivated by the task of detecting anomalies in networks of financial transactions, with accounts as nodes and a directed weighted edge between two nodes denoting a money transfer. The weight of the edge is the transaction amount. Examples of anomalies in networks include long paths of large transaction amounts, rings of large payments, and cliques of accounts. There are many methods available which detect such specific structures in networks. Here we introduce a method which is able to detect previously unspecified anomalies in networks. The method is based on a combination of features from network comparison and spectral analysis as well as local statistics, yielding 140 main features. We then use a simple feature sum method, as well as a random forest method, in order to classify nodes as normal or anomalous. We test the method first on synthetic networks which we generated, and second on a set of synthetic networks which were generated without the methods team having access to the ground truth. The first set of synthetic networks was split in a training set of 70 percent of the networks, and a test set of 30 percent of the networks. The resulting classifier was then applied to the second set of synthetic networks. We compare our method with Oddball, a widely used method for anomaly detection in networks, as well as to random classification. While Oddball outperforms random classification, both our feature sum method and our random forest method outperform Oddball. On the test set, the random forest outperforms feature sum, whereas on the second synthetic data set, initially feature sum tends to pick up more anomalies than random forest, with this behaviour reversing for lower-scoring anomalies. In all cases, the top 2 percent of flagged anomalies contained on average over 90 percent of the planted anomalies.
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning

To use machine learning in high stakes applications (e.g. medicine), we need tools for building confidence in the system and evaluating whether it is reliable. Methods to improve model reliability often require new learning algorithms (e.g. using Bayesian inference to obtain uncertainty estimates). An alternative is to audit a model after it is trained. In this paper, we describe resampling uncertainty estimation (RUE), an algorithm to audit the pointwise reliability of predictions. Intuitively, RUE estimates the amount that a prediction would change if the model had been fit on different training data. The algorithm uses the gradient and Hessian of the model's loss function to create an ensemble of predictions. Experimentally, we show that RUE more effectively detects inaccurate predictions than existing tools for auditing reliability subsequent to training. We also show that RUE can create predictive distributions that are competitive with state-of-the-art methods like Monte Carlo dropout, probabilistic backpropagation, and deep ensembles, but does not depend on specific algorithms at train-time like these methods do.
Discrete Neural Processes

Many data generating processes involve latent random variables over discrete combinatorial spaces whose size grows factorially with the dataset. In these settings, existing posterior inference methods can be inaccurate and/or very slow. In this work we develop methods for efficient amortized approximate Bayesian inference over discrete combinatorial spaces, with applications to random permutations, probabilistic clustering (such as Dirichlet process mixture models) and random communities (such as stochastic block models). The approach is based on mapping distributed, symmetry-invariant representations of discrete arrangements into conditional probabilities. The resulting algorithms parallelize easily, yield iid samples from the approximate posteriors, and can easily be applied to both conjugate and nonconjugate models, as training only requires samples from the generative model.
Deep Representation Learning for Clustering of Health Tweets

Twitter has been a prominent social media platform for mining population-level health data and accurate clustering of health-related tweets into topics is important for extracting relevant health insights. In this work, we propose deep convolutional autoencoders for learning compact representations of health-related tweets, further to be employed in clustering. We compare our method to several conventional tweet representation methods including bag-of-words, term frequency-inverse document frequency, Latent Dirichlet Allocation and Non-negative Matrix Factorization with 3 different clustering algorithms. Our results show that the clustering performance using proposed representation learning scheme significantly outperforms that of conventional methods for all experiments of different number of clusters. In addition, we propose a constraint on the learned representations during the neural network training in order to further enhance the clustering performance. All in all, this study introduces utilization of deep neural network-based architectures, i.e., deep convolutional autoencoders, for learning informative representations of health-related tweets.
Stellar Flares from the First TESS Data Release: Exploring a New Sample of M Dwarfs

We perform a study of stellar flares for the 24,809 stars observed with 2 minute cadence during the first two months of the TESS mission. Flares may erode exoplanets’ atmospheres and impact their habitability, but might also trigger the genesis of life around small stars. TESS provides a new sample of bright dwarf stars in our galactic neighborhood, collecting data for thousands of M dwarfs that might host habitable exoplanets. Here, we use an automated search for flares accompanied by visual inspection. Then, our public allesfitter code robustly selects the appropriate model for potentially complex flares via Bayesian evidence. We identify 1228 flaring stars, 673 of which are M dwarfs. Among 8695 flares in total, the largest superflare increased the stellar brightness by a factor of 16.1. Bolometric flare energies range from 1031.0 to 1036.9 erg, with a median of 1033.1 erg. Furthermore, we study the flare rate and energy as a function of stellar type and rotation period. We solidify past findings that fast rotating M dwarfs are the most likely to flare and that their flare amplitude is independent of the rotation period. Finally, we link our results to criteria for prebiotic chemistry, atmospheric loss through coronal mass ejections, and ozone sterilization. Four of our flaring M dwarfs host exoplanet candidates alerted on by TESS, for which we discuss how these effects can impact life. With upcoming TESS data releases, our flare analysis can be expanded to almost all bright small stars, aiding in defining criteria for exoplanet habitability.
BMF: Block matrix approach to factorization of large scale data

Matrix Factorization (MF) on large scale matrices is computationally as well as memory intensive task. Alternative convergence techniques are needed when the size of the input matrix is higher than the available memory on a Central Processing Unit (CPU) and Graphical Processing Unit (GPU). While alternating least squares (ALS) convergence on CPU could take forever, loading all the required matrices on to GPU memory may not be possible when the dimensions are significantly higher. Hence we introduce a novel technique that is based on considering the entire data into a block matrix and relies on factorization at a block level.
The Institutional Economics of Collective Waste Recovery Systems: an Empirical Investigation

The main purpose of the study is to develop the model for transaction costs measurement in the Collective Waste Recovery Systems. The methodology of New Institutional Economics is used in the research. The impact of the study is related both to the enlargement of the limits of the theory about the interaction between transaction costs and social costs and to the identification of institutional failures of the European concept for circular economy. A new model for social costs measurement is developed.  circular economy, transaction costs, extended producer responsibility JEL: A13, C51, D23, L22, Q53
HAZMAT. V. The Ultraviolet and X-Ray Evolution of K Stars

Knowing the high-energy radiation environment of a star over a planet’s formation and evolutionary period is critical in determining if that planet is potentially habitable and if any biosignatures could be detected, as UV radiation can severely change or destroy a planet’s atmosphere. Current efforts for finding a potentially habitable planet are focused on M stars, yet K stars may offer more habitable conditions due to decreased stellar activity and more distant and wider habitable zones (HZs). While M star activity evolution has been observed photometrically and spectroscopically, there has been no dedicated investigation of K star UV evolution. We present the first comprehensive study of the near-UV, far-UV, and X-ray evolution of K stars. We used members of young moving groups and clusters ranging in age from 10 to 625 Myr combined with field stars and their archived GALEX UV and ROSAT X-ray data to determine how the UV and X-ray radiation evolve. We find that the UV and X-ray flux incident on an HZ planet is 5–50 times lower than that of HZ planets around early-M stars and 50–1000 times lower than those around late-M stars, due to both an intrinsic decrease in K dwarf stellar activity occurring earlier than for M dwarfs and the more distant location of the K dwarf HZ.
Wobble: a data-driven method for precision radial velocities

Extreme-precision radial velocity (EPRV) exoplanet surveys face considerable data analysis challenges in extracting maximally precise RVs from spectra. Chief among these, particularly for the upcoming generation of red-optimized spectrographs targeting M dwarfs, is the presence of telluric absorption features which are not perfectly known. Another major limitation on the achievable RV precision is the need to adopt an imperfect stellar template against which to cross-correlate or otherwise match the observations. In both cases, precision-limiting reliance on external information can be sidestepped using the data directly. Here we propose a data-driven method to simultaneously extract precise RVs and infer the underlying stellar and telluric spectra using a linear model (in the log of flux). The model employs a convex objective and convex regularization to keep the optimization of the spectral components fast. We implement this method in wobble, an open-source python package which uses TensorFlow in one of its first non-neural-network applications to astronomical data. In this work, we demonstrate the performance of wobble on archival HARPS spectra. We recover the canonical exoplanet 51 Pegasi b, detect the secular RV evolution of the M dwarf Barnard's Star, and retrieve the Rossiter-McLaughlin effect for the Hot Jupiter HD 189733b. The method additionally produces extremely high-S/N composite stellar spectra and detailed time-variable telluric spectra, which we also present here.
Honey Authentication with Machine Learning Augmented Bright-Field Microscopy

Honey has been collected and used by humankind as both a food and medicine for thousands of years. However, in the modern economy, honey has become subject to mislabelling and adulteration making it the third most faked food product in the world. The international scale of fraudulent honey has had both economic and environmental ramifications. In this paper, we propose a novel method of identifying fraudulent honey using machine learning augmented microscopy.