nils-herrmann/clean_scirepeval huggingface dataset:
The quenching and morphological evolution of central galaxies is facilitated by the feedback-driven expulsion of circumgalactic gas

We examine the connection between the properties of the circumgalactic medium (CGM) and the quenching and morphological evolution of central galaxies in the EAGLE and IllustrisTNG simulations. The simulations yield very different median CGM mass fractions, fCGM, as a function of halo mass, M200, with low-mass haloes being significantly more gas-rich in IllustrisTNG than in EAGLE. Nonetheless, in both cases scatter in fCGM at fixed M200 is strongly correlated with the specific star formation rate and the kinematic morphology of central galaxies. The correlations are strongest for ∼L⋆ galaxies, corresponding to the mass scale at which AGN feedback becomes efficient. This feedback elevates the CGM cooling time, preventing gas from accreting on to the galaxy to fuel star formation, and thus establishing a preference for quenched, spheroidal galaxies to be hosted by haloes with low fCGM for their mass. In both simulations, fCGM correlates negatively with the host halo’s intrinsic concentration, and hence with its binding energy and formation redshift, primarily because early halo formation fosters the rapid early growth of the central black hole (BH). This leads to a lower fCGM at fixed M200 in EAGLE because the BH reaches high accretion rates sooner, whilst in IllustrisTNG it occurs because the central BH reaches the mass threshold at which AGN feedback is assumed to switch from thermal to kinetic injection earlier. Despite these differences, there is consensus from these state-of-the-art simulations that the expulsion of efficiently cooling gas from the CGM is a crucial step in the quenching and morphological evolution of central galaxies.
Exploring the Mid-infrared SEDs of Six AGN Dusty Torus Models. I. Synthetic Spectra

At distances from the active galaxy nucleus where the ambient temperature falls below ∼1500–1800 K, dust is able to survive. It is thus possible to have a large dusty structure present that surrounds the active galaxy nucleus. This is the first of two papers aiming at comparing six dusty torus models with available spectral energy distributions, namely, Fritz et al., Nenkova et al., Hönig & Kishimoto, Siebenmorgen et al., Stalevski et al., and Hönig & Kishimoto. In this first paper we use synthetic spectra to explore the discrimination between these models and under which circumstances they allow us to restrict the torus parameters, while our second paper analyzes the best model to describe the mid-infrared spectroscopic data. We have produced synthetic spectra from current instruments GTC/CanariCam and Spitzer/IRS and future James Webb Space Telescope (JWST)/MIRI and JWST/NIRSpec instruments. We find that for a reasonable brightness (F12 μm > 100 mJy) we can actually distinguish among models except for the two pairs of parent models. We show that these models can be distinguished based on the continuum slopes and the strength of the silicate features. Moreover, their parameters can be constrained within 15% of error, irrespective of the instrument used, for all the models except Hönig & Kishimoto. However, the parameter estimates are ruined when more than 50% of circumnuclear contributors are included. Therefore, future high spatial resolution spectra such as those expected from JWST will provide enough coverage and spatial resolution to tackle this topic.
Tau lepton asymmetry by sterile neutrino emission—moving beyond one-zone supernova models

The mixing of active neutrinos with their sterile counterparts with keV mass is known to have a potentially major impact on the energy loss from the supernova core. By relying on a set of three static hydrodynamical backgrounds mimicking the early accretion phase and the Kelvin-Helmoltz cooling phase of a supernova, we develop the first self-consistent, radial- and time-dependent treatment of νs-ντ mixing in the dense stellar core. We follow the flavor evolution by including ordinary matter effects, collisional production of sterile neutrinos, as well as reconversions of sterile states into active ones. The dynamical feedback of the sterile neutrino production on the matter background leads to the development of a ντ-ν̄τ asymmetry (Yντ) that grows in time until it reaches a value larger than 0.15. Our results hint towards significant implications for the supernova physics, and call for a self-consistent modeling of the sterile neutrino transport in the supernova core to constrain the mixing parameters of sterile neutrinos.
Deep Learning for Estimating Synaptic Health of Primary Neuronal Cell Culture

Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery. Using the data from a single high-throughput imaging assay, a classification model for predicting the biological activity of candidate compounds was introduced. The image recognition model which is based on deep convolutional neural network (CNN) architecture with residual connections achieved accuracy of 99.6$\%$ on a binary classification task of distinguishing untreated and treated rodent primary neuronal cells with Amyloid-$\beta_{(25-35)}$.
Learning to Transfer Learn

We propose a novel framework, learning to transfer learn (L2TL), to improve transfer learning on a target dataset by judicious extraction of information from a source dataset. Our framework considers joint optimization of strongly-shared weights between models of source and target tasks, and employs adaptive weights for scaling of constituent loss terms. The adaptation of the weights is done using a reinforcement learning (RL)-based policy model, which is guided based on a performance metric on the target validation set. We demonstrate state-of-the-art performance of L2TL given fixed models, consistently outperforming fine-tuning baselines on various datasets. In addition, in the regimes of small-scale target datasets and significant label mismatch between source and target datasets, L2TL outperforms previous methods by a large margin.
Translating Mathematical Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level Training

In this paper we propose a deep neural network model with an encoder-decoder architecture that translates images of math formulas into their LaTeX markup sequences. The encoder is a convolutional neural network (CNN) that transforms images into a group of feature maps. To better capture the spatial relationships of math symbols, the feature maps are augmented with 2D positional encoding before being unfolded into a vector. The decoder is a stacked bidirectional long short-term memory (LSTM) model integrated with soft attention mechanism, which works as a language model to translate the encoder output into a sequence of LaTeX tokens. The neural network is trained in two steps. The first step is token-level training using the Maximum-Likelihood Estimation (MLE) as the objective function. Next, at completion of the tokenlevel training, we advance to a sequence-level training based on a new objective function inspired by the policy gradient algorithm from reinforcement learning. Our design also overcomes the exposure bias problem by closing the feedback loop in the decoder during sequence-level training, i.e., feeding in the predicted token instead of the ground truth token at every time step. The model is trained and evaluated on the IM2LATEX-100K dataset and shows state-of-the-art performance on both sequence-based and imagebased evaluation metrics. deep learning; encoder-decoder; seq2seq model; image to LaTeX; reinforcement learning; math formulas
A New Method for Employing Feedback to Improve Coding Performance

We introduce a novel mechanism, called timid/bold coding, by which feedback can be used to improve coding performance. For a certain class of DMCs, called compound-dispersion channels, we show that timid/bold coding allows for an improved second-order coding rate compared with coding without feedback. For DMCs that are not compound dispersion, we show that feedback does not improve the second-order coding rate. Thus we completely determine the class of DMCs for which feedback improves the second-order coding rate. An upper bound on the second-order coding rate is provided for compound-dispersion DMCs. We also show that feedback does not improve the second-order coding rate for very noisy DMCs. The main results are obtained by relating feedback codes to certain controlled diffusions.
Growth Dynamics of Value and Cost Trade-off in Competitive Temporal Networks

The question is: What does happen to the real-world networks which cause them not to grow permanently? The idea here is that real-world networks have to pay the cost of growth. We investigate the growth and trade-off between value and cost in the networks with cost and preferential attachment together. Since the preferential attachment in BA model does not consider any stop against the infinite growth of networks, we introduce a modified version of preferential attachment of BA model. This idea makes sense because the growth of real networks may be finite. In the present study, by combining preferential attachment in the science of temporal networks (interval graphs), and, the first-order differential equations of value and cost of making links, the future equilibrium of an evolving network is illustrated. During the process of achieving a winning position, the variables against growth such as the competition cost, besides with the internally structural cost may emerge. In the end, by applying this modified model, we found the circumstances which a trade-off between value and cost emerge.
Understanding model behavior using loops that matter

The link between structure and behavior is central to System Dynamics, but effective tools for understanding that relationship still elude us. The current state of the art in the field of loop dominance analysis relies on either practitioner intuition and experience or complex algorithmic manipulation in the form of eigenvalue analysis or pathway participation metrics. This paper presents a new and distinct method to find the 'loops that matter' in generating model behavior. This is a numeric method capable of determining the impact for every loop in a model and identifying which dominate behavior at each point in time. The method was inspired by observations on variable value changes during simulations and has been refined using empirical evaluation on a variety of different models. In addition to explaining behavior, the method shows promise for improving visualization and aggregation of simulation results.
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing

Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples. In this paper, we develop improved techniques for defending against adversarial examples. First, we propose an enhanced defense technique denoted Attention and Adversarial Logit Pairing (AT + ALP), which encourages both attention map and logit for the pairs of examples to be similar. When being applied to clean examples and their adversarial counterparts, AT + ALP improves accuracy on adversarial examples over adversarial training. We show that AT + ALP can effectively increase the average activations of adversarial examples in the key area and demonstrate that it focuses on discriminate features to improve the robustness of the model. Finally, we conduct extensive experiments using a wide range of datasets and the experiment results show that our AT + ALP achieves the state of the art defense performance. For example, on 17 Flower Category Database, under strong 200-iteration Projected Gradient Descent (PGD) gray-box and black-box attacks where prior art has 34 and 39% accuracy, our method achieves 50 and 51%. Compared with previous work, our work is evaluated under highly challenging PGD attack: the maximum perturbation ϵ ∈ {0.25, 0.5} i.e. L ∞ ∈ {0.25, 0.5} with 10–200 attack iterations. To the best of our knowledge, such a strong attack has not been previously explored on a wide range of datasets.
Feature2Vec: Distributional semantic modelling of human property knowledge

Feature norm datasets of human conceptual knowledge, collected in surveys of human volunteers, yield highly interpretable models of word meaning and play an important role in neurolinguistic research on semantic cognition. However, these datasets are limited in size due to practical obstacles associated with exhaustively listing properties for a large number of words. In contrast, the development of distributional modelling techniques and the availability of vast text corpora have allowed researchers to construct effective vector space models of word meaning over large lexicons. However, this comes at the cost of interpretable, human-like information about word meaning. We propose a method for mapping human property knowledge onto a distributional semantic space, which adapts the word2vec architecture to the task of modelling concept features. Our approach gives a measure of concept and feature affinity in a single semantic space, which makes for easy and efficient ranking of candidate human-derived semantic properties for arbitrary words. We compare our model with a previous approach, and show that it performs better on several evaluation tasks. Finally, we discuss how our method could be used to develop efficient sampling techniques to extend existing feature norm datasets in a reliable way.
NarrativeTime: Dense High-Speed Temporal Annotation on a Timeline

We present NarrativeTime, a new timeline-based annotation scheme for temporal order of events in text, and a new densely annotated fiction corpus comparable to TimeBank-Dense. NarrativeTime is considerably faster than schemes based on event pairs such as TimeML, and it produces more temporal links between events than TimeBank-Dense, while maintaining comparable agreement on temporal links. This is achieved through new strategies for encoding vagueness in temporal relations and an annotation workflow that takes into account the annotators' chunking and commonsense reasoning strategies. NarrativeTime comes with new specialized web-based tools for annotation and adjudication.
Testing the accuracy of halo occupation distribution modelling using hydrodynamic simulations

Halo models provide a simple and computationally inexpensive way to investigate the connection between galaxies and their dark matter haloes. However, these models rely on the assumption that the role of baryons can easily be parametrized in the modelling procedure. We aim to examine the ability of halo occupation distribution (HOD) modelling to reproduce the galaxy clustering found in two different hydrodynamic simulations, Illustris and EAGLE. For each simulation, we measure several galaxy clustering statistics on two different luminosity threshold samples. We then apply a simple five parameter HOD, which was fit to each simulation separately, to the corresponding dark matter-only simulations, and measure the same clustering statistics. We find that the halo mass function is shifted to lower masses in the hydrodynamic simulations, resulting in a galaxy number density that is too high when an HOD is applied to the dark matter-only simulation. However, the exact way in which baryons alter the mass function is remarkably different in the two simulations. After applying a correction to the halo mass function in each simulation, the HOD is able to accurately reproduce all clustering statistics for the high luminosity sample of galaxies. For the low luminosity sample, we find evidence that in addition to correcting the halo mass function, including spatial, velocity, and assembly bias parameters in the HOD is necessary to accurately reproduce clustering statistics.
Potential Flow Generator With L2 Optimal Transport Regularity for Generative Models

We propose a potential flow generator with <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> optimal transport regularity, which can be easily integrated into a wide range of generative models, including different versions of generative adversarial networks (GANs) and normalizing flow models. With only a slight augmentation to the original generator loss functions, our generator not only tries to transport the input distribution to the target one but also aims to find the one with minimum <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> transport cost. We show the effectiveness of our method in several 2-D problems and illustrate the concept of “proximity” due to the <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> optimal transport regularity. Subsequently, we demonstrate the effectiveness of the potential flow generator in image translation tasks with unpaired training data from the MNIST data set and the CelebA data set with a comparison against vanilla Wasserstein GAN with gradient penalty (WGAN-GP) and CycleGAN.
MultiLevel Composite Stochastic Optimization via Nested Variance Reduction

We consider multi-level composite optimization problems where each mapping in the composition is the expectation over a family of random smooth mappings or the sum of some finite number of smooth mappings. We present a normalized proximal approximate gradient (NPAG) method where the approximate gradients are obtained via nested stochastic variance reduction. In order to find an approximate stationary point where the expected norm of its gradient mapping is less than $\epsilon$, the total sample complexity of our method is $O(\epsilon^{-3})$ in the expectation case, and $O(N+\sqrt{N}\epsilon^{-2})$ in the finite-sum case where $N$ is the total number of functions across all composition levels. In addition, the dependence of our total sample complexity on the number of composition levels is polynomial, rather than exponential as in previous work.
Inertial Single Vehicle Trajectory Prediction Baselines and Applications with the NGSIM Dataset

In the recent vehicle trajectory prediction literature, the most common baselines are briefly introduced without the necessary information to reproduce it. In this article we produce reproducible vehicle prediction results from simple models. For that purpose, the process is explicit, and the code is available. Those baseline models are a constant velocity model and a single-vehicle prediction model. They are applied on the NGSIM US-101 and I-80 datasets using only relative positions. Thus, the process can be reproduced with any database containing tracking of vehicle positions. The evaluation reports Root Mean Squared Error (RMSE), Final Displacement Error (FDE), Negative Log-Likelihood (NLL), and Miss Rate (MR). The NLL estimation needs a careful definition because several formulations that differ from the mathematical definition are used in other works. This article is meant to be used along with the published code to establish baselines for further work. An extension is proposed to replace the constant velocity assumption with a learned model using a recurrent neural network. This brings good improvements in accuracy and uncertainty estimation and opens possibilities for both complex and interpretable models.
Detecting and Reducing Bias in a High Stakes Domain

Gang-involved youth in cities such as Chicago sometimes post on social media to express their aggression towards rival gangs and previous research has demonstrated that a deep learning approach can predict aggression and loss in posts. To address the possibility of bias in this sensitive application, we developed an approach to systematically interpret the state of the art model. We found, surprisingly, that it frequently bases its predictions on stop words such as “a” or “on”, an approach that could harm social media users who have no aggressive intentions. To tackle this bias, domain experts annotated the rationales, highlighting words that explain why a tweet is labeled as “aggression”. These new annotations enable us to quantitatively measure how justified the model predictions are, and build models that drastically reduce bias. Our study shows that in high stake scenarios, accuracy alone cannot guarantee a good system and we need new evaluation methods.
Finding your feet: A Gaussian process model for estimating the abilities of batsmen in test cricket

In the sport of cricket, a player’s batting ability is traditionally measured using the batting average. However, the batting average fails to measure both short‐term changes in ability that occur during an innings and long‐term changes in ability that occur between innings due to factors such as age and experience in various match conditions. We derive and fit a Bayesian parametric model that employs a Gaussian process to measure and predict how the batting abilities of cricket players vary and fluctuate over the course of entire playing careers. The results allow us to better quantify and predict the batting ability of a player, compared with both traditional cricket statistics, such as the batting average, and more complex models, such as the official International Cricket Council ratings.
High efficiency rl agent

Now a day, model free algorithm achieve state of art performance on many RL problems, but the low efficiency of model free algorithm limited the usage. We combine model base RL, soft actor-critic framework, and curiosity. proposed an agent called RMC, giving a promise way to achieve good performance while maintain data efficiency. We suppress the performance of SAC and achieve state of the art performance, both on efficiency and stability. Meanwhile we can solving POMDP problem and achieve great generalization from MDP to POMDP.
Predicting Consumer Default: A Deep Learning Approach

We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation.
Learned reconstructions for practical mask-based lensless imaging

Mask-based lensless imagers are smaller and lighter than traditional lensed cameras. In these imagers, the sensor does not directly record an image of the scene; rather, a computational algorithm reconstructs it. Typically, mask-based lensless imagers use a model-based reconstruction approach that suffers from long compute times and a heavy reliance on both system calibration and heuristically chosen denoisers. In this work, we address these limitations using a bounded-compute, trainable neural network to reconstruct the image. We leverage our knowledge of the physical system by unrolling a traditional model-based optimization algorithm, whose parameters we optimize using experimentally gathered ground-truth data. Optionally, images produced by the unrolled network are then fed into a jointly-trained denoiser. As compared to traditional methods, our architecture achieves better perceptual image quality and runs 20× faster, enabling interactive previewing of the scene. We explore a spectrum between model-based and deep learning methods, showing the benefits of using an intermediate approach. Finally, we test our network on images taken in the wild with a prototype mask-based camera, demonstrating that our network generalizes to natural images.