nils-herrmann/clean_scirepeval huggingface dataset:
AGNs at the cosmic dawn: predictions for future surveys from a ΛCDM cosmological model

Telescopes to be launched over the next decade and a half, such as JWST, EUCLID, ATHENA, and Lynx, promise to revolutionize the study of the high-redshift Universe and greatly advance our understanding of the early stages of galaxy formation. We use a model that follows the evolution of the masses and spins of supermassive black holes (SMBHs) within a semi-analytic model of galaxy formation to make predictions for the active galactic nucleus luminosity function at $z$ ≥ 7 in the broadband filters of JWST and EUCLID at near-infrared wavelengths, and ATHENA and Lynx at X-ray energies. The predictions of our model are relatively insensitive to the choice of seed black hole mass, except at the lowest luminosities (Lbol < 1043 erg s−1) and the highest redshifts ($z$ > 10). We predict that surveys with these different telescopes will select somewhat different samples of SMBHs, with EUCLID unveiling the most massive, highest accretion rate SMBHs, Lynx the least massive, lowest accretion rate SMBHs, and JWST and ATHENA covering objects inbetween. At $z$ = 7, we predict that typical detectable SMBHs will have masses, MBH ∼ 105–8 M⊙, and Eddington normalized mass accretion rates, $\dot{M}/\dot{M}_{\mathrm{Edd}}\sim 0.6{-}2$. The SMBHs will be hosted by galaxies of stellar mass M⋆ ∼ 108–10 M⊙, and dark matter haloes of mass Mhalo ∼ 1011–12 M⊙. We predict that the detectable SMBHs at $z$ = 10 will have slightly smaller black holes, accreting at slightly higher Eddington normalized mass accretion rates, in slightly lower mass host galaxies compared to those at $z$ = 7, and reside in haloes of mass Mhalo ∼ 1010–11 M⊙.
Invariant predictions of epidemic patterns from radically different forms of seasonal forcing

Seasonal variation in environmental variables, and in rates of contact among individuals, are fundamental drivers of infectious disease dynamics. Unlike most periodically forced physical systems, for which the precise pattern of forcing is typically known, underlying patterns of seasonal variation in transmission rates can be estimated approximately at best, and only the period of forcing is accurately known. Yet solutions of epidemic models depend strongly on the forcing function, so dynamical predictions—such as changes in epidemic patterns that can be induced by demographic transitions or mass vaccination—are always subject to the objection that the underlying patterns of seasonality are poorly specified. Here, we demonstrate that the key bifurcations of the standard epidemic model are invariant to the shape of seasonal forcing if the amplitude of forcing is appropriately adjusted. Consequently, analyses applicable to real disease dynamics can be conducted with a smooth, idealized sinusoidal forcing function, and qualitative changes in epidemic patterns can be predicted without precise knowledge of the underlying forcing pattern. We find similar invariance in a seasonally forced predator–prey model, and conjecture that this phenomenon—and the associated robustness of predictions—might be a feature of many other periodically forced dynamical systems.
Hyperspaces of countable compacta

. Hyperspaces H ( X ) of all countable compact subsets of a metric space X and A n ( X ) of inﬁnite compact subsets which have at most n ( n ∈ N ), or ﬁnitely many ( n = ω ) or countably many ( n = ω + 1) accumulation points are studied. By descriptive set-theoretical methods, we fully characterize them for 0-dimensional, dense-in-itself, Polish spaces and partially for σ -compact spaces X . Using the theory of absorbing sets, we get characterizations of H ( X ), A ω ( X ) and A ω +1 ( X ) for nondegenerate connected, locally connected Polish spaces X which are either locally compact or nowhere locally compact. For every n ∈ N , we show that if X is an interval or a simple closed curve, A n ( X ) is homeomorphic to the linear space c 0 = { ( x i ) ∈ R ω : lim x i = 0 } with the product topology; if X is a Peano continuum and a point p ∈ X is of order ≥ 2, then the hyperspace A 1 ( X, { p } ) of all compacta with exactly one accumulation point p also is homeomorphic to c 0 .
An Instantaneous Market Volatility Estimation

Working on different aspects of algorithmic trading we empirically discovered a new market invariant. It links together the volatility of the instrument with its traded volume, the average spread and the volume in the order book. The invariant has been tested on different markets and different asset classes. In all cases we did not find significant violation of the invariant. The formula for the invariant was used for the volatility estimation, which we called the instantaneous volatility. Quantitative comparison showed that it reproduces realised volatility better than one-day-ahead GARCH(1,1) prediction. Because of the short-term prediction nature, the instantaneous volatility could be used by algo developers, volatility traders and other market professionals.
Principal Cross-Border Flow Patterns in the European Electricity Markets

The interconnected European electricity markets see considerable cross-border trade between different countries. In conjunction with the structure and technical characteristics of the power grid and its operating rules, the corresponding commercial flows translate into actual physical flows on the interconnection lines. From the interplay of different physical, technical, and economic factors thus emerge complex spatio-temporal power flow patterns. Using Principal Component Analysis, in this contribution hourly time-series of cross-border physical flows between European countries in 2017 and 2018 are analyzed. The most important patterns in the time series of imports/exports and cross-border physical flows are identified. Their spatial and temporal structure, as well as their contribution to the overall variance is described. Additionally, we apply a tracing technique to the overall flow patterns, which allows identifying the physical power transfers between European countries through the common grid infrastructure.
Location Field Descriptors: Single Image 3D Model Retrieval in the Wild

We present Location Field Descriptors, a novel approach for single image 3D model retrieval in the wild. In contrast to previous methods that directly map 3D models and RGB images to an embedding space, we establish a common low-level representation in the form of location fields from which we compute pose invariant 3D shape descriptors. Location fields encode correspondences between 2D pixels and 3D surface coordinates and, thus, explicitly capture 3D shape and 3D pose information without appearance variations which are irrelevant for the task. This early fusion of 3D models and RGB images results in three main advantages: First, the bottleneck location field prediction acts as a regularizer during training. Second, major parts of the system benefit from training on a virtually infinite amount of synthetic data. Finally, the predicted location fields are visually interpretable and unblackbox the system. We evaluate our proposed approach on three challenging real-world datasets (Pix3D, Comp, and Stanford) with different object categories and significantly outperform the state-of-the-art by up to 20% absolute in multiple 3D retrieval metrics.
HyperStream: a Workflow Engine for Streaming Data

This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities. HyperStream overcomes the limitations of other computational engines and provides high-level interfaces to execute complex nesting, fusion, and prediction both in online and offline forms in streaming environments. HyperStream is a general purpose tool that is well-suited for the design, development, and deployment of Machine Learning algorithms and predictive models in a wide space of sequential predictive problems. 
Source code, installation instructions, examples, and documentation can be found at: this https URL.
Population Collapse in Elite-Dominated Societies: A Differential Equations Model without Differential Equations

The HANDY model of Motesharrei, Rivas, and Kalnay examines interactions with the environment by human populations, both between poor and rich people, i.e., "Commoners" and "Elites". The Elites control the society's wealth and consume it at a higher rate than Commoners, whose work produces the wealth. We say a model is "Elite-dominated" when the Elites' per capita population change rate is always at least as large as the Commoners'. We can show the HANDY model always exhibits population crashes for all choices of parameter values for which it is Elite-dominated. But any such model with explicit equations raises questions of how the resulting behaviors depend on the details of the models. How important are the particular design features codified in the differential equations? In this paper, we first replace the explicit equations of HANDY with differential equations that are only described conceptually or qualitatively - using only conditions that can be verified for explicit systems. Next, we discard the equations entirely, replacing them with qualitative conditions, and we prove these conditions imply population collapse must occur. In particular, one condition is that the model is Elite-dominated. We show that the HANDY model with Elite-dominated parameters satisfies our hypotheses and thus must undergo population collapse. Our approach of introducing qualitative mathematical hypotheses can better show the underlying features of the model that lead to collapse. We also ask how societies can avoid collapse.
Self-supervised Attention Model for Weakly Labeled Audio Event Classification

We describe a novel weakly labeled Audio Event Classification approach based on a self-supervised attention model. The weakly labeled framework is used to eliminate the need for expensive data labeling procedure and self-supervised attention is deployed to help a model distinguish between relevant and irrelevant parts of a weakly labeled audio clip in a more effective manner compared to prior attention models. We also propose a highly effective strongly supervised attention model when strong labels are available. This model also serves as an upper bound for the self-supervised model. The performances of the model with self-supervised attention training are comparable to the strongly supervised one which is trained using strong labels. We show that our self-supervised attention method is especially beneficial for short audio events. We achieve 8.8% and 17.6% relative mean average precision improvements over the current state-of-the-art systems for SL-DCASE-17and balanced AudioSet.
Unsupervised feature learning in remote sensing

The need for labeled data is among the most common and well-known practical obstacles to deploying deep learning algorithms to solve real-world problems. The current generation of learning algorithms requires a large volume of data labeled according to a static and pre-defined schema. Conversely, humans can quickly learn generalizations based on large quantities of unlabeled data, and turn these generalizations into classifications using spontaneous labels, often including labels not seen before. We apply a state-of-the-art unsupervised learning algorithm to the noisy and extremely imbalanced xView data set to train a feature extractor that adapts to several tasks: visual similarity search that performs well on both common and rare classes; identifying outliers within a labeled data set; and learning a natural class hierarchy automatically.
Improving Channel Charting with Representation -Constrained Autoencoders

Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captures spatial and radio geometries so that UEs close in space are close in the channel chart. In this paper, we demonstrate that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process. More specifically, we propose to include pairwise representation constraints into AEs with the goal of improving the quality of the learned channel charts. We show that such representation-constrained AEs recover the global geometry of the learned channel charts, which enables CC to perform approximate positioning without global navigation satellite systems or supervised learning methods that rely on extensive and expensive measurement campaigns.
Que ser\'a ser\'a? The uncertainty estimation of feature-based time series forecasts

Interval forecasts have significant advantages in providing uncertainty estimation to point forecasts, leading to the importance of providing prediction intervals (PIs) as well as point forecasts. In this paper, we propose a general feature-based time series forecasting framework, which is divided into "offline" and "online" parts. In the "offline" part, we explore how time series features connect with the prediction interval forecasting accuracy of different forecasting methods by exploring generalized additive models (GAMs), which makes our proposed framework interpretable in the effects of features on the interval forecasting accuracy. Our proposed framework is in essence a model averaging process and we introduce a threshold ratio for the selection of individual forecasting methods in this process. In the "online" part, we calculate the point forecasts and PIs of new series by pre-trained GAMs and the corresponding optimal threshold ratio. We illustrate that our feature-based forecasting framework outperforms all individual benchmark forecasting methods on M3 competition data, with an improved computational efficiency.
EdgeNet: Semantic Scene Completion from a Single RGB- D Image

Semantic scene completion is the task of predicting a complete 3D representation of volumetric occupancy with corresponding semantic labels for a scene from a single point of view. In this paper, we present EdgeNet, a new end-to-end neural network architecture that fuses information from depth and RGB, explicitly representing RGB edges in 3D space. Previous works on this task used either depth-only or depth with colour by projecting 2D semantic labels generated by a 2D segmentation network into the 3D volume, requiring a two step training process. Our EdgeNet representation encodes colour information in 3D space using edge detection and flipped truncated signed distance, which improves semantic completion scores especially in hard to detect classes. We achieved state-of-the-art scores on both synthetic and real datasets with a simpler and a more computationally efficient training pipeline than competing approaches.
How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design

Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit meaningful worst-case bounds, so the parameters are made available for the user to tune. Alternatively, parameters may be tuned implicitly within the proof of a worst-case guarantee. Worst-case instances, however, may be rare or nonexistent in practice. A growing body of research has demonstrated that data-driven algorithm design can lead to significant improvements in performance. This approach uses a training set of problem instances sampled from an unknown, application-specific distribution and returns a parameter setting with strong average performance on the training set. We provide a broadly applicable theory for deriving generalization guarantees that bound the difference between the algorithm’s average performance over the training set and its expected performance on the unknown distribution. Our results apply no matter how the parameters are tuned, be it via an automated or manual approach. The challenge is that for many types of algorithms, performance is a volatile function of the parameters: slightly perturbing the parameters can cause a large change in behavior. Prior research (e.g., Gupta and Roughgarden, SICOMP’17; Balcan et al., COLT’17, ICML’18, EC’18) has proved generalization bounds by employing case-by-case analyses of greedy algorithms, clustering algorithms, integer programming algorithms, and selling mechanisms. We uncover a unifying structure which we use to prove extremely general guarantees, yet we recover the bounds from prior research. Our guarantees, which are tight up to logarithmic factors in the worst case, apply whenever an algorithm’s performance is a piecewise-constant, -linear, or—more generally—piecewise-structured function of its parameters. Our theory also implies novel bounds for voting mechanisms and dynamic programming algorithms from computational biology.
Do Neural Language Representations Learn Physical Commonsense?

Humans understand language based on the rich background knowledge about how the physical world works, which in turn allows us to reason about the physical world through language. In addition to the properties of objects (e.g., boats require fuel) and their affordances, i.e., the actions that are applicable to them (e.g., boats can be driven), we can also reason about if-then inferences between what properties of objects imply the kind of actions that are applicable to them (e.g., that if we can drive something then it likely requires fuel). 
In this paper, we investigate the extent to which state-of-the-art neural language representations, trained on a vast amount of natural language text, demonstrate physical commonsense reasoning. While recent advancements of neural language models have demonstrated strong performance on various types of natural language inference tasks, our study based on a dataset of over 200k newly collected annotations suggests that neural language representations still only learn associations that are explicitly written down.
iCassava 2019Fine-Grained Visual Categorization Challenge

Viral diseases are major sources of poor yields for cassava, the 2nd largest provider of carbohydrates in this http URL least 80% of small-holder farmer households in Sub-Saharan Africa grow cassava. Since many of these farmers have smart phones, they can easily obtain photos of dis-eased and healthy cassava leaves in their farms, allowing the opportunity to use computer vision techniques to monitor the disease type and severity and increase yields. How-ever, annotating these images is extremely difficult as ex-perts who are able to distinguish between highly similar dis-eases need to be employed. We provide a dataset of labeled and unlabeled cassava leaves and formulate a Kaggle challenge to encourage participants to improve the performance of their algorithms using semi-supervised approaches. This paper describes our dataset and challenge which is part of the Fine-Grained Visual Categorization workshop at CVPR2019.
Mini-Batch Metropolis–Hastings With Reversible SGLD Proposal

Traditional Markov chain Monte Carlo (MCMC) algorithms are computationally intensive and do not scale well to large data. In particular, the Metropolis–Hastings (MH) algorithm requires passing over the entire dataset to evaluate the likelihood ratio in each iteration. We propose a general framework for performing MH-MCMC using mini-batches of the whole dataset and show that this gives rise to approximately a tempered stationary distribution. We prove that the algorithm preserves the modes of the original target distribution and derive an error bound on the approximation with mild assumptions on the likelihood. To further extend the utility of the algorithm to high-dimensional settings, we construct a proposal with forward and reverse moves using stochastic gradient and show that the construction leads to reasonable acceptance probabilities. We demonstrate the performance of our algorithm in both low dimensional models and high dimensional neural network applications. Particularly in the latter case, compared to popular optimization methods, our method is more robust to the choice of learning rate and improves testing accuracy.
The emission and scintillation properties of RRAT J2325−0530 at 154 MHz and 1.4 GHz

(RRATs) represent a relatively new class of pulsar, primarily characterised by their sporadic bursting emission of single pulses on time scales of minutes to hours. In addition to the difficulty involved in detecting these objects, low-frequency ( 
$ \lt 300\,\text{MHz}$
 ) observations of RRATs are sparse, which makes understanding their broadband emission properties in the context of the normal pulsar population problematic. Here, we present the simultaneous detection of RRAT J2325−0530 using the Murchison Widefield Array (154 MHz) and Parkes radio telescope ( 
$1.4\,\text{GHz}$
 ). On a single-pulse basis, we produce the first polarimetric profile of this pulsar, measure the spectral index ( 
$\alpha={-2.2\pm 0.1}$
 ), pulse energy distributions, and present the pulse rates in the context of detections in previous epochs. We find that the distribution of time between subsequent pulses is consistent with a Poisson process and find no evidence of clustering over the 
$\sim\!1.5\,\text{h}$
 observations. Finally, we are able to quantify the scintillation properties of RRAT J2325−0530 at 1.4 GHz, where the single pulses are modulated substantially across the observing bandwidth, and show that this characterisation is feasible even with irregular time sampling as a consequence of the sporadic emission behaviour.
Hospitalization in the transmission of dengue dynamics: The impact on public health policies.

Dengue virus has caused major problems for public health officials for decades in tropical and subtropical countries. We construct a compartmental model that includes the risk of hospitalization and its impact on public health policies. The basic reproductive number, $\mathcal{R}_0$, is computed, as well as a sensitivity analysis on $\mathcal{R}_0$ parameters and discuss the relevance in public health policies. The local and global stability of the disease-free equilibrium is established. Numerical simulations are performed to better determine future prevention/control strategies.
AxoNet: an AI-based tool to count retinal ganglion cell axons

In this work, we develop a robust, extensible tool to automatically and accurately count retinal ganglion cell axons in images of optic nerve tissue from various animal models of glaucoma.  The U-Net convolutional neural network architecture was adapted to learn pixelwise axon count density estimates, which were then integrated over the image area to determine axon counts. The tool, termed AxoNet, was trained and evaluated using a dataset containing images of optic nerve regions randomly selected from complete cross sections of intact rat optic nerves and manually annotated for axon count and location. Both control and damaged optic nerves were used. This rat-trained network was then applied to a separate dataset of non-human primate (NHP) optic nerve images. AxoNet was then compared to two existing automated axon counting tools, AxonMaster and AxonJ, using both datasets.  AxoNet outperformed the existing tools on both the rat and NHP optic nerve datasets as judged by mean absolute error, R2 values when regressing automated vs. manual counts, and Bland-Altman analysis.  The proposed tool allows for accurate quantification of axon numbers as a measure of glaucomatous damage. AxoNet is robust to variations in optic nerve tissue damage extent, image quality, and species of mammal.  The deep learning method does not rely on handcrafted image features for axon recognition. Therefore, this approach is not species-specific and can be extended to quantify additional optic nerve features. It will aid evaluation of optic nerve changes in glaucoma and potentially other neurodegenerative diseases.