nils-herrmann/clean_scirepeval huggingface dataset:
International crop trade networks: the impact of shocks and cascades

Analyzing available FAO data from 176 countries over 21 years, we observe an increase of complexity in the international trade of maize, rice, soy, and wheat. A larger number of countries play a role as producers or intermediaries, either for trade or food processing. In consequence, we find that the trade networks become more prone to failure cascades caused by exogenous shocks. In our model, countries compensate for demand deficits by imposing export restrictions. To capture these, we construct higher-order trade dependency networks for the different crops and years. These networks reveal hidden dependencies between countries and provide an estimate of necessary stock reserves to protect countries from cascading export restrictions. They differ substantially from first-order networks that do not take cascading effects into account. We find rice trade most prone to cascading export restrictions. A great number of Asian and African countries are most exposed to cascades. Noticeably, the main suppliers are similar for most of the crops: USA, Canada, Argentina, Brazil, and India. While shocks in the USA mainly affect South America and several Asian countries, the south of Africa is primarily dependent on American and Asian exporters. The north of Africa depends strongly on Europe, in particular via wheat imports.
Ultracompression: Framework For High Density Compression Of Ultrasound Volumes Using Physics Modeling Deep Neural Networks

Ultrasound image compression by preserving speckle-based key information is a challenging task. In this paper, we introduce an ultrasound image compression framework with the ability to retain realism of speckle appearance despite achieving very high-density compression factors. The compressor employs a tissue segmentation method, transmitting segments along with transducer frequency, number of samples and image size as essential information required for decompression. The decompressor is based on a convolutional network trained to generate patho-realistic ultrasound images which convey essential information pertinent to tissue pathology visible in the images. We demonstrate generaliz-ability of the building blocks using two variants to build the compressor. We have evaluated the quality of decompressed images using distortion losses as well as perception loss and compared it with other off the shelf solutions. The proposed method achieves a compression ratio of 725:1 while preserving the statistical distribution of speckles. This enables image segmentation on decompressed images to achieve dice score of 0.89 ± 0.11, which evidently is not so accurately achievable when images are compressed with current standards like JPEG, JPEG 2000, WebP and BPG. We envision this frame work to serve as a roadmap for speckle image compression standards.
Learning about Black Hole Binaries from their Ringdown Spectra.

The coalescence of two black holes generates gravitational waves that carry detailed information about the properties of those black holes and their binary configuration. The final coalescence cycles are in the form of a ringdown: a superposition of quasinormal modes of the merged remnant black hole. Each mode has an oscillation frequency and decay time that in general relativity is determined by the remnant's mass and spin. Measuring the frequency and decay time of multiple modes makes it possible to measure the remnant's mass and spin, and to test the waves against the predictions of gravity theories. In this Letter, we show that the relative amplitudes of these modes encode information about a binary's geometry. Focusing on the large mass-ratio limit, which provides a simple-to-use tool for effectively exploring parameter space, we demonstrate how a binary's geometry is encoded in the relative amplitudes of these modes, and how to parametrize the modes in this limit. Although more work is needed to assess how well this carries over to less extreme mass ratios, our results indicate that measuring multiple ringdown modes from coalescence may aid in measuring important source properties, such as the misalignment of its members' spins and orbit.
Exciting black hole modes via misaligned coalescences. II. The mode content of late-time coalescence waveforms

Using inspiral and plunge trajectories we construct with a generalized Ori-Thorne algorithm, we use a time-domain black hole perturbation theory code to compute the corresponding gravitational waves. The last cycles of these waveforms are a superposition of Kerr quasi-normal modes. In this paper, we examine how the modes' excitations vary as a function of source parameters, such as the larger black hole's spin and the geometry of the smaller body's inspiral and plunge. We find that the mixture of quasi-normal modes that characterize the final gravitational waves from a coalescence are entirely determined by the spin $a$ of the larger black hole, an angle $I$ which characterizes the misalignment of the orbital plane from the black hole's spin axis, a second angle $\theta_{\rm fin}$ which describes the location at which the small body crosses the black hole's event horizon, and the direction sgn($\dot{\theta}_{\rm fin}$) of the body's final motion. If these large mass-ratio results hold at less extreme mass ratios, then measuring multiple ringdown modes of binary black hole coalescence gravitational waves may provide important information about the source's binary properties, such as the misalignment of the orbit's angular momentum with black hole spin. This may be particularly useful for large mass binaries, for which the early inspiral waves are out of the detectors' most sensitive band.
Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference

A regression-based BNN model is proposed to predict spatiotemporal quantities like hourly rider demand with calibrated uncertainties. The main contributions of this paper are (i) A feed-forward deterministic neural network (DetNN) architecture that predicts cyclical time series data with sensitivity to anomalous forecasting events; (ii) A Bayesian framework applying SVGD to train large neural networks for such tasks, capable of producing time series predictions as well as measures of uncertainty surrounding the predictions. Experiments show that the proposed BNN reduces average estimation error by 10% across 8 U.S. cities compared to a fine-tuned multilayer perceptron (MLP), and 4% better than the same network architecture trained without SVGD.
High performance scheduling of mixed-mode DAGs on heterogeneous multicores

Many HPC applications can be expressed as mixed-mode computations, in which each node of a computational DAG is itself a parallel computation that can be molded at runtime to allocate different amounts of processing resources. At the same time, modern HPC systems are becoming increasingly heterogeneous to address the requirements of energy efficiency. Effectively using heterogeneous devices is complex, requiring the developer to be aware of each DAG nodes' criticality, and the relative performance of the underlying heterogeneous resources. 
This paper studies how irregular mixed-mode parallel computations can be mapped on a single-ISA heterogeneous architecture with the goals of performance and portability. To achieve high performance we analyze various schemes for heterogeneous scheduling, including both criticality-aware and performance-only schemes, and extend them with task molding to dynamically adjust the amount of resources used for each task. To achieve performance portability we track each DAG nodes' performance and construct an online model of the system and its performance. Using a HiKey960 big.LITTLE board as experimental system, the resulting scheduler implementations achieve large speed-ups when executing irregular DAGs compared to traditional random work stealing.
Axion production in unstable magnetized plasmas

Axions, the hypothetical particles restoring the charge-parity symmetry in the strong sector of the Standard Model and one of the most prone candidates for dark matter, are well known to interact with plasmas. In a recent publication , we show that if the plasma dynamically responds to the presence of axions, then a new quasiparticle (the axion-plasmon polariton) can be formed, being at the basis of a new generation of plasma-based detection techniques. In this work, we exploit the axion-plasmon hybridization to actively produce axions in streaming magnetized plasmas. The produced axions can then be detected by reconversion into photons in a scheme that is similar to the light-shining-through-a-wall experiments.
A Manifold Regularized Multi-Task Learning Model for IQ Prediction from Multiple fMRI Paradigms

Multi-modal brain functional connectivity (FC) data have shown great potential for providing insights into individual variations in behavioral and cognitive traits. The joint learning of multi-modal imaging data can utilize the intrinsic association, and thus can boost the learning performance. Although several multi-task based learning models have already been proposed by viewing the feature learning on each modality as one task, most of them ignore the geometric structure information inherent in the modalities, which may play an important role in extracting discriminative features. In this paper, we propose a new manifold regularized multi-task learning model by simultaneously considering between-subject and between-modality relationships. Besides employing a group-sparsity regularizer to jointly select a few common features across multiple tasks (modalities), we design a novel manifold regularizer to preserve the structure information both within and between modalities in our model. This will make our model more adaptive for realistic data analysis. Our model is then validated on the Philadelphia Neurodevelopmental Cohort dataset, where we regard our modalities as functional MRI (fMRI) data collected under two paradigms. Specifically, we conduct experimental studies on fMRI based FC network data in two task conditions for intelligence quotient (IQ) prediction. The results demonstrate that our proposed model can not only achieve improved prediction performance, but also yield a set of IQ-relevant biomarkers.
Definable V-topologies, Henselianity and NIP

We initiate the study of definable  see text]-topologies and show that there is at most one such  see text]-henselian NIP field. Equivalently, we show that if  see text] is a bi-valued NIP field with  see text] henselian (respectively, , then  see text] and  see text] are comparable (respectively, dependent). As a consequence, Shelah’s conjecture for NIP fields implies the henselianity conjecture for NIP fields. Furthermore, the latter conjecture is proved for any field admitting a henselian valuation with a dp-minimal residue field. We conclude by showing that Shelah’s conjecture is equivalent to the statement that any NIP field not contained in the algebraic closure of a finite field is
Asymptotic behavior of massless fields and the memory effect

We investigate the behavior of massless scalar, electromagnetic, and linearized gravitational perturbations near null infinity in d \geq 4 dimensional Minkowski spacetime (of both even and odd dimension) under the assumption that these fields admit a suitable expansion in 1/r. We also investigate the behavior of asymptotically flat, nonlinear gravitational perturbations near null infinity in all dimensions d\geq 4. We then consider the memory effect in fully nonlinear general relativity. We show that in even dimensions, the memory effect first arises at Coulombic order--i.e., order 1/r^{d-3}--and can naturally be decomposed into `null memory' and `ordinary memory.' Null memory is associated with an energy flux to null infinity. We show that ordinary memory is associated with the metric failing to be stationary at one order faster fall-off than Coulombic in the past and/or future. In odd dimensions, we show that the total memory effect at Coulombic order and slower fall-off always vanishes. Null memory is always of `scalar type', but the ordinary memory can be of any (i.e., scalar, vector, or tensor) type. In 4-spacetime dimensions, we give an explicit example in linearized gravity which gives rise to a nontrivial vector (i.e., magnetic parity) ordinary memory effect at order 1/r. We show that scalar memory is described by a diffeomorphism; vector and tensor memory cannot be. In d=4 dimensions, we show that there is a close relationship between memory and the charge and flux expressions associated with supertranslations. We analyze the behavior of solutions that are stationary at Coulombic order and show how these suggest `antipodal matching' between future and past null infinity, which gives rise to conservation laws. The relationship between memory and infrared divergences of the `out' state in quantum gravity is analyzed, and the nature of the `soft theorems' is explained.
Foreground-Aware Image Inpainting

Existing image inpainting methods typically fill holes by borrowing information from surrounding pixels. They often produce unsatisfactory results when the holes overlap with or touch foreground objects due to lack of information about the actual extent of foreground and background regions within the holes. These scenarios, however, are very important in practice, especially for applications such as distracting object removal. To address the problem, we propose a foreground-aware image inpainting system that explicitly disentangles structure inference and content completion. Specifically, our model learns to predict the foreground contour first, and then inpaints the missing region using the predicted contour as guidance. We show that by such disentanglement, the contour completion model predicts reasonable contours of objects, and further substantially improves the performance of image inpainting. Experiments show that our method significantly outperforms existing methods and achieves superior inpainting results on challenging cases with complex compositions.
Semantic Nighttime Image Segmentation with Synthetic Stylized Data, Gradual Adaptation and Uncertainty-Aware Evaluation

This work addresses the problem of semantic segmentation of nighttime images. The main direction of recent progress in semantic segmentation pertains to daytime scenes with favorable illumination conditions. We focus on improving the performance of state-of-the-art methods on the nighttime domain by adapting them to nighttime data without extra annotations, and designing a new evaluation framework to address the uncertainty of semantics in nighttime images. To this end, we make the following contributions: 1) a novel pipeline for dataset-scale guided style transfer to generate synthetic nighttime images from real daytime input; 2) a framework to gradually adapt semantic segmentation models from day to night via stylized and real images of progressively increasing darkness; 3) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation in adverse conditions; 4) the Dark Zurich dataset with 2416 nighttime and 2920 twilight unlabeled images plus 20 nighttime images with pixel-level annotations that conform to our newly-proposed evaluation. Our experiments evidence that both our stylized data per se and our gradual adaptation significantly boost performance at nighttime both for standard evaluation metrics and our metric. Moreover, our new evaluation reveals that state-of-the-art segmentation models output overly confident predictions at indiscernible regions compared to visible ones.
Stochastic Gradient Descent on a Tree: an Adaptive and Robust Approach to Stochastic Convex Optimization

Online minimization of an unknown convex function over the interval [0, 1] is considered under first-order stochastic bandit feedback, which returns a random realization of the gradient of the function at each query point. Without knowing the distribution of the random gradients, a learning algorithm sequentially chooses query points with the objective of minimizing regret defined as the expected cumulative loss of the function values at the query points in excess to the minimum value of the function. An approach based on devising a biased random walk on an infinite-depth binary tree constructed through successive partitioning of the domain of the function is developed. Each move of the random walk is guided by a sequential test based on confidence bounds on the empirical mean constructed using the law of the iterated logarithm. With no tuning parameters, this learning algorithm is robust to heavy-tailed noise with infinite variance and adaptive to unknown function characteristics (specifically, convex, strongly convex, and nonsmooth). It achieves the corresponding optimal regret orders (up to a $\sqrt{\log T}$ or a $\log \log T$ factor) in each class of functions and offers better or matching regret orders than the classical stochastic gradient descent approach which requires the knowledge of the function characteristics for tuning the sequence of step-sizes.
Machine Learning Techniques for Brand-Influencer Matchmaking on the Instagram Social Network

The social media revolution has changed the way that brands interact with consumers. Instead of spending their advertising budget on interstate billboards, more and more companies are choosing to partner with so-called Internet "influencers" --- individuals who have gained a loyal following on online platforms for the high quality of the content they post. Unfortunately, it's not always easy for small brands to find the right influencer: someone who aligns with their corporate image and has not yet grown in popularity to the point of unaffordability. In this paper we sought to develop a system for brand-influencer matchmaking, harnessing the power and flexibility of modern machine learning techniques. The result is an algorithm that can predict the most fruitful brand-influencer partnerships based on the similarity of the content they post.
Outline for a Quantum Theory of Gravity

By invoking an asymmetric metric tensor, and borrowing ideas from non-commutative geometry, string theory, and trace dynamics, we propose an action function for quantum gravity. The action is proportional to the four-dimensional non-commutative curvature scalar (which is torsion dependent), which is sourced by the Nambu–Goto world-sheet action for a string plus the Kalb–Ramond string action. This ‘quantum gravity’ is actually a non-commutative classical matrix dynamics, and the only two fundamental constants in the theory are the square of the Planck length and the speed of light. By treating the entity described by this action as a microstate, one constructs the statistical thermodynamics of a large number of such microstates, in the spirit of trace dynamics. Quantum field theory (and ℏ) and quantum general relativity (and G) emerge from the underlying matrix dynamics in the thermodynamic limit. Statistical fluctuations, which are inevitably present about equilibrium, are the source for spontaneous localisation, which drives macroscopic quantum gravitational systems to the classical general relativistic limit. While the mathematical formalism governing these ideas remains to be developed, we hope to highlight here the deep connection between quantum foundations and the sought-for quantum theory of gravity. In the sense described in this article, ongoing experimental tests of spontaneous collapse theories are in fact also tests of string theory!
Diverse mini-batch Active Learning

We study the problem of reducing the amount of labeled training data required to train supervised classification models. We approach it by leveraging Active Learning, through sequential selection of examples which benefit the model most. Selecting examples one by one is not practical for the amount of training examples required by the modern Deep Learning models. We consider the mini-batch Active Learning setting, where several examples are selected at once. We present an approach which takes into account both informativeness of the examples for the model, as well as the diversity of the examples in a mini-batch. By using the well studied K-means clustering algorithm, this approach scales better than the previously proposed approaches, and achieves comparable or better performance.
A Semi-Supervised Machine Learning Approach to Detecting Recurrent Metastatic Breast Cancer Cases Using Linked Cancer Registry and Electronic Medical Record Data

Most cancer data sources lack information on metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary information on cancer treatment and outcomes, yet are rarely used synergistically. To enable detection of metastatic breast cancer (MBC), we applied a semi-supervised machine learning framework to linked EMR-California Cancer Registry (CCR) data.  We studied 11,459 female patients treated at Stanford Health Care who received an incident breast cancer diagnosis from 2000-2014. The dataset consisted of structured data and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results (SEER) database. We extracted information on metastatic disease from patient notes to infer a class label and then trained a regularized logistic regression model for MBC classification. We evaluated model performance on a gold standard set of set of 146 patients.  There are 495 patients with de novo stage IV MBC, 1,374 patients initially diagnosed with Stage 0-III disease had recurrent MBC, and 9,590 had no evidence of metastatis. The median follow-up time is 96.3 months (mean 97.8, standard deviation 46.7). The best-performing model incorporated both EMR and CCR features. The area under the receiver-operating characteristic curve=0.925 [95% confidence interval: 0.880-0.969], sensitivity=0.861, specificity=0.878 and overall accuracy=0.870.  A framework for MBC case detection combining EMR and CCR data achieved good sensitivity, specificity and discrimination without requiring expert-labeled examples. This approach enables population-based research on how patients die from cancer and may identify novel predictors of cancer recurrence.
Spin of primordial black holes

Primordial black holes, formed from rare peaks in the primordial fluctuations ζ, are non-rotating if the RMS (root mean square) fluctuation ζrms is sent to zero. We show that the spin also vanishes at first order in ζrms, suggesting the dimensionless spin parameter arms ∼ ζrms2. We identify one quadratic contribution to the spin by calculating (and extrapolating to the formation time) the torque on a black hole due to ambient acoustic waves. For a reasonable density of primordial black holes this implies a percent level spin parameter.
A Second Galaxy Missing Dark Matter in the NGC 1052 Group

The ultra-diffuse galaxy NGC1052-DF2 has a very low velocity dispersion, indicating that it has little or no dark matter. Here we report the discovery of a second galaxy in this class, residing in the same group. NGC1052-DF4 closely resembles NGC1052-DF2 in terms of its size, surface brightness, and morphology; has a similar distance of ; and also has a population of luminous globular clusters extending out to from the center of the galaxy. Accurate radial velocities of the diffuse galaxy light and seven of the globular clusters were obtained with the Low Resolution Imaging Spectrograph on the Keck I telescope. The velocity of the diffuse light is identical to the median velocity of the clusters, , and close to the central velocity of the NGC 1052 group. The rms spread of the globular cluster velocities is very small at . Taking observational uncertainties into account we determine an intrinsic velocity dispersion of , consistent with the expected value from the stars alone ( ) and lower than expected from a standard NFW halo ( ). We conclude that NGC1052-DF2 is not an isolated case but that a class of such objects exists. The origin of these large, faint galaxies with an excess of luminous globular clusters and an apparent lack of dark matter is, at present, not understood.